Bugfix/beamsearch op

paddle/operators/CMakeLists.txt

@@ -178,14 +178,13 @@ foreach(src ${GENERAL_OPS})
 endforeach()
 file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
 
-
 set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
 
-
 cc_test(gather_test SRCS gather_test.cc DEPS tensor)
 cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
 cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
 cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor)
+cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op)
 cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory)
 if(WITH_GPU)
     cc_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)

paddle/operators/beam_search_op.cc

@@ -29,7 +29,7 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
   PruneEndidCandidates(pre_ids, &selected_items);
   // calculate the output tensor's height
   size_t num_instances = std::accumulate(
-      std::begin(items), std::end(items), 0,
+      std::begin(selected_items), std::end(selected_items), 0,
       [](size_t a, std::vector<Item> &b) { return a + b.size(); });
   // the output tensor shape should be [num_instances, 1]
   auto dims = framework::make_ddim(
@@ -48,12 +48,20 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
   size_t low_offset = 0;
   for (auto &items : selected_items) {
     low_level.push_back(low_offset);
+    sort(items.begin(), items.end(), [](const Item &a, const Item &b) {
+      if (a.offset < b.offset) {
+        return true;
+      }
+      return a.id < b.id;
+    });
     for (auto &item : items) {
       ids_data[low_offset] = item.id;
       scores_data[low_offset] = item.score;
       low_offset++;
     }
   }
+  low_level.push_back(low_offset);
+
   // fill lod
   auto abs_lod = framework::ToAbsOffset(ids_->lod());
   auto &high_level = abs_lod[lod_level_];
@@ -64,16 +72,21 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
   selected_scores->set_lod(lod);
 }
 
-void BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids,
-                                      std::vector<std::vector<Item>> *items) {
+int BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids,
+                                     std::vector<std::vector<Item>> *items) {
   auto *pre_ids_data = pre_ids.data<int64_t>();
 
+  int res = 0;
   for (size_t offset = 0; offset < items->size(); offset++) {
     auto prefix_id = pre_ids_data[offset];
     if (prefix_id == end_id_) {
       items->at(offset).clear();
+    } else {
+      res++;
     }
   }
+
+  return res;
 }
 
 std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
@@ -121,11 +134,7 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
   auto ids = *ids_;
   auto scores = *scores_;
 
-  auto source_abs_two_level_lod = framework::SliceInLevel(
-      ids.lod(), lod_level_, sent_offset_, sent_offset_ + 1);
-  source_abs_two_level_lod = framework::ToAbsOffset(source_abs_two_level_lod);
   auto abs_lod = framework::ToAbsOffset(ids.lod());
-  PADDLE_ENFORCE_GE(source_abs_two_level_lod.size(), 2UL);
 
   auto *ids_data = ids.data<int64_t>();
   auto *scores_data = scores.data<float>();

paddle/operators/beam_search_op.h

@@ -73,7 +73,15 @@ namespace operators {
  * second level:
  * [0, 2, 4]
  *
- * tensor's data
+ * id tensor's data
+ * [[
+ * 4,
+ * 1,
+ * 3,
+ * 8,
+ * ]]
+ *
+ * score tensor's data
  * [[
  * 0.5,
  * 0.3,
@@ -137,16 +145,21 @@ class BeamSearch {
     Item() {}
     Item(size_t offset, size_t id, float score)
         : offset(offset), id(id), score(score) {}
-    // offset in the lod_level_+1
+    // offset in the higher lod level.
     size_t offset;
+    // // prefix id in the lower lod level.
+    // size_t prefix;
     // the candidate id
     id_t id;
     // the corresponding score
     score_t score;
   };
 
-  void PruneEndidCandidates(const framework::LoDTensor& pre_ids,
-                            std::vector<std::vector<Item>>* items);
+  /*
+   * Delete all the records that follows the end token.
+   */
+  int PruneEndidCandidates(const framework::LoDTensor& pre_ids,
+                           std::vector<std::vector<Item>>* items);
 
   /*
    * Transform the items into a map whose key is offset, value is the items.

paddle/operators/beam_search_op_test.cc

@@ -0,0 +1,86 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/beam_search_op.h"
+
+#include <gtest/gtest.h>
+#include <vector>
+
+namespace paddle {
+namespace test {
+
+using std::vector;
+using framework::LoDTensor;
+using framework::LoD;
+using operators::BeamSearch;
+using paddle::platform::CPUPlace;
+using std::cout;
+using std::endl;
+
+void CreateInput(LoDTensor* ids, LoDTensor* scores) {
+  LoD lod;
+  vector<size_t> level0({0, 1, 4});
+  vector<size_t> level1({0, 1, 2, 3, 4});
+  lod.push_back(level0);
+  lod.push_back(level1);
+  ids->set_lod(lod);
+  scores->set_lod(lod);
+
+  auto dims = framework::make_ddim(vector<int64_t>({4, 3}));
+  ids->Resize(dims);
+  scores->Resize(dims);
+  CPUPlace place;
+
+  auto* ids_data = ids->mutable_data<int64_t>(place);
+  auto* scores_data = scores->mutable_data<float>(place);
+  vector<int64_t> _ids({4, 2, 5, 2, 1, 3, 3, 5, 2, 8, 2, 1});
+  vector<float> _scores(
+      {0.5, 0.3, 0.2, 0.6, 0.3, 0.1, 0.9, 0.5, 0.1, 0.7, 0.5, 0.1});
+
+  for (int i = 0; i < 12; i++) {
+    ids_data[i] = _ids[i];
+    scores_data[i] = _scores[i];
+  }
+}
+
+TEST(beam_search_op, run) {
+  CPUPlace place;
+  LoDTensor ids, scores;
+  CreateInput(&ids, &scores);
+
+  LoDTensor pre_ids;
+  pre_ids.Resize(framework::make_ddim(vector<int64_t>(4, 1)));
+  for (int i = 0; i < 4; i++) {
+    pre_ids.mutable_data<int64_t>(place)[i] = i + 1;
+  }
+
+  BeamSearch beamsearch(ids, scores, (int64_t)0, (int64_t)2, 0);
+  LoDTensor sids, sscores;
+  beamsearch(pre_ids, &sids, &sscores);
+
+  LOG(INFO) << "score: " << sscores << endl;
+
+  ASSERT_EQ(sids.lod(), sscores.lod());
+
+  vector<int> tids({2, 4, 3, 8});
+  vector<float> tscores({0.3, 0.5, 0.9, 0.7});
+
+  for (int i = 0; i < 4; i++) {
+    ASSERT_EQ(tids[i], sids.data<int64_t>()[i]);
+    ASSERT_EQ(tscores[i], sscores.data<float>()[i]);
+  }
+}
+
+}  // namespace test
+}  // namespace paddle