Update legacy places from nnvm to relay.

Jenkinsfile

@@ -310,17 +310,6 @@ stage('Integration Test') {
       }
     }
   },
-  'legacy: GPU': {
-    node('GPU') {
-      ws(per_exec_ws("tvm/legacy-python-gpu")) {
-        init_git()
-        unpack_lib('gpu', tvm_multilib)
-        timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_legacy.sh"
-        }
-      }
-    }
-  },
   'docs: GPU': {
     node('GPU') {
       ws(per_exec_ws("tvm/docs-python-gpu")) {

apps/benchmark/util.py

@@ -34,8 +34,8 @@ def get_network(name, batch_size, dtype='float32'):
 
     Returns
     -------
-    net: nnvm.symbol
-        The NNVM symbol of network definition
+    net: relay.Module
+        The relay function of network definition
     params: dict
         The random parameters for benchmark
     input_shape: tuple

apps/bundle_deploy/Makefile

@@ -16,15 +16,15 @@
 # under the License.
 
 # Makefile Example to bundle TVM modules.
+
 TVM_ROOT=$(shell cd ../..; pwd)
-NNVM_PATH=nnvm
 DMLC_CORE=${TVM_ROOT}/3rdparty/dmlc-core
-PKG_CFLAGS = -std=c++14 -Oz -fPIC\
+PKG_CFLAGS = -std=c++14 -O2 -fPIC\
 	-I${TVM_ROOT}/include\
 	-I${DMLC_CORE}/include\
-	-I${TVM_ROOT}/3rdparty/dlpack/include\
+	-I${TVM_ROOT}/3rdparty/dlpack/include
 
-PKG_LDFLAGS = -L${TVM_ROOT}/build
+PKG_LDFLAGS = -pthread
 
 build_dir := build
 
@@ -33,7 +33,7 @@ test: $(build_dir)/demo $(build_dir)/bundle.so
 
 $(build_dir)/demo: demo.cc
 	@mkdir -p $(@D)
-	$(CXX) $(PKG_CFLAGS) -o $@  $^
+	$(CXX) $(PKG_CFLAGS) -o $@  $^ -ldl
 
 # Serialize our graph.json file.
 $(build_dir)/graph.json.cc: $(build_dir)/graph.json
@@ -44,13 +44,13 @@ $(build_dir)/params.bin.cc: $(build_dir)/params.bin
 	xxd -i $^  > $@
 
 $(build_dir)/model.o $(build_dir)/graph.json $(build_dir)/params.bin: build_model.py
-	python $< -o $(build_dir)
+	python3 $< -o $(build_dir)
 
 # Build our bundle against the serialized bundle.cc API, the runtime.cc API, and
 # the serialized graph.json and params.bin
 $(build_dir)/bundle.so: bundle.cc runtime.cc $(build_dir)/model.o $(build_dir)/graph.json.cc $(build_dir)/params.bin.cc
 	@mkdir -p $(@D)
-	$(CXX) $(PKG_CFLAGS) -fvisibility=hidden -o $@  $^ $(PKG_LDFLAGS) -shared
+	$(CXX) -shared $(PKG_CFLAGS) -fvisibility=hidden -o $@  $^ $(PKG_LDFLAGS)
 
 clean:
 	rm -r $(build_dir)

apps/bundle_deploy/build_model.py

@@ -18,8 +18,7 @@
 
 import argparse
 import os
-import nnvm.compiler
-import nnvm.testing
+from tvm import relay
 import tvm
 import logging
 
@@ -34,22 +33,24 @@ def main():
     dshape = (1, 3, 224, 224)
     from mxnet.gluon.model_zoo.vision import get_model
     block = get_model('mobilenet0.25', pretrained=True)
-    net, params = nnvm.frontend.from_mxnet(block)
-    net = nnvm.sym.softmax(net)
+    shape_dict = {'data': dshape}
+    mod, params = relay.frontend.from_mxnet(block, shape_dict)
+    func = mod["main"]
+    func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs)
+
+    with relay.build_config(opt_level=3):
+        graph, lib, params = relay.build(
+            func, 'llvm --system-lib', params=params)
 
-    with nnvm.compiler.build_config(opt_level=3):
-        graph, lib, params = nnvm.compiler.build(
-            net, 'llvm --system-lib', shape={'data': dshape}, params=params)
-    print(graph.symbol().debug_str())
     build_dir = os.path.abspath(opts.out_dir)
     if not os.path.isdir(build_dir):
         os.makedirs(build_dir)
 
     lib.save(os.path.join(build_dir, 'model.o'))
     with open(os.path.join(build_dir, 'graph.json'), 'w') as f_graph_json:
-        f_graph_json.write(graph.json())
+        f_graph_json.write(graph)
     with open(os.path.join(build_dir, 'params.bin'), 'wb') as f_params:
-        f_params.write(nnvm.compiler.save_param_dict(params))
+        f_params.write(relay.save_param_dict(params))
 
 
 if __name__ == '__main__':

apps/bundle_deploy/bundle.cc

@@ -6,9 +6,9 @@
  * to you 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
@@ -26,7 +26,9 @@ extern unsigned int build_graph_json_len;
 extern unsigned char build_params_bin[];
 extern unsigned int build_params_bin_len;
 
-#define TVM_BUNDLE_FUNCTION __attribute__((visibility("default"))) extern "C"
+#define TVM_BUNDLE_FUNCTION __attribute__((visibility("default")))
+
+extern "C" {
 
 TVM_BUNDLE_FUNCTION void *tvm_runtime_create() {
   const std::string json_data(&build_graph_json[0],
@@ -64,3 +66,4 @@ TVM_BUNDLE_FUNCTION void tvm_runtime_get_output(void *handle, int index,
   reinterpret_cast<tvm::runtime::Module *>(handle)->GetFunction("get_output")(
       index, reinterpret_cast<DLTensor *>(tensor));
 }
+}

apps/bundle_deploy/runtime.cc

@@ -25,13 +25,13 @@
 #include "../../src/runtime/c_runtime_api.cc"
 #include "../../src/runtime/cpu_device_api.cc"
 #include "../../src/runtime/workspace_pool.cc"
-#include "../../src/runtime/module_util.cc"
+#include "../../src/runtime/library_module.cc"
 #include "../../src/runtime/module.cc"
 #include "../../src/runtime/registry.cc"
 #include "../../src/runtime/file_util.cc"
 #include "../../src/runtime/threading_backend.cc"
 #include "../../src/runtime/thread_pool.cc"
 #include "../../src/runtime/ndarray.cc"
 #include "../../src/runtime/object.cc"
-#include "../../src/runtime/system_lib_module.cc"
+#include "../../src/runtime/system_library.cc"
 #include "../../src/runtime/graph/graph_runtime.cc"

apps/howto_deploy/Makefile

@@ -17,15 +17,14 @@
 
 # Makefile Example to deploy TVM modules.
 TVM_ROOT=$(shell cd ../..; pwd)
-NNVM_PATH=nnvm
 DMLC_CORE=${TVM_ROOT}/3rdparty/dmlc-core
 
 PKG_CFLAGS = -std=c++11 -O2 -fPIC\
 	-I${TVM_ROOT}/include\
 	-I${DMLC_CORE}/include\
 	-I${TVM_ROOT}/3rdparty/dlpack/include\
 
-PKG_LDFLAGS = -L${TVM_ROOT}/build -ldl -lpthread
+PKG_LDFLAGS = -L${TVM_ROOT}/build -ldl -pthread
 
 .PHONY: clean all
 
@@ -39,7 +38,7 @@ lib/libtvm_runtime_pack.o: tvm_runtime_pack.cc
 # The code library built by TVM
 lib/test_addone_sys.o: prepare_test_libs.py
 	@mkdir -p $(@D)
-	python prepare_test_libs.py
+	python3 prepare_test_libs.py
 
 # Deploy using the all in one TVM package library
 lib/cpp_deploy_pack: cpp_deploy.cc lib/test_addone_sys.o lib/libtvm_runtime_pack.o

apps/rocm_rpc/Makefile

@@ -19,7 +19,6 @@
 ROCM_PATH=/opt/rocm
 
 TVM_ROOT=$(shell cd ../..; pwd)
-NNVM_PATH=nnvm
 DMLC_CORE=${TVM_ROOT}/3rdparty/dmlc-core
 
 PKG_CFLAGS = -std=c++11 -O2 -fPIC\

apps/sgx/README.md

@@ -49,7 +49,7 @@ mkdir build && cd build
 cmake .. -DUSE_LLVM=ON -DUSE_SGX=/opt/sgxsdk -DRUST_SGX_SDK=/opt/rust-sgx-sdk
 make -j4
 cd ..
-pip install -e python -e topi/python -e nnvm/python
+pip install -e python -e topi/python
 cd apps/sgx
 ```
 

apps/sgx/enclave/src/build_model.py

@@ -20,8 +20,8 @@
 import os
 from os import path as osp
 
-import nnvm.compiler
-import nnvm.testing
+from tvm import relay
+from tvm.relay import testing
 import tvm
 
 
@@ -30,24 +30,23 @@ def main():
     parser.add_argument('-o', '--out-dir', default='.')
     opts = parser.parse_args()
 
-    # from tutorials/nnvm_quick_start.py
     dshape = (1, 3, 224, 224)
-    net, params = nnvm.testing.resnet.get_workload(
+    net, params = relay.testing.resnet.get_workload(
         layers=18, batch_size=dshape[0], image_shape=dshape[1:])
 
-    with nnvm.compiler.build_config(opt_level=3):
-        graph, lib, params = nnvm.compiler.build(
-            net, 'llvm --system-lib', shape={'data': dshape}, params=params)
+    with relay.build_config(opt_level=3):
+        graph, lib, params = relay.build(
+            net, 'llvm --system-lib', params=params)
 
     build_dir = osp.abspath(opts.out_dir)
     if not osp.isdir(build_dir):
         os.makedirs(build_dir, exist_ok=True)
 
     lib.save(osp.join(build_dir, 'model.bc'))
     with open(osp.join(build_dir, 'graph.json'), 'w') as f_graph_json:
-        f_graph_json.write(graph.json())
+        f_graph_json.write(graph)
         with open(osp.join(build_dir, 'params.bin'), 'wb') as f_params:
-            f_params.write(nnvm.compiler.save_param_dict(params))
+            f_params.write(relay.save_param_dict(params))
 
 
 if __name__ == '__main__':

rust/Cargo.toml

@@ -22,7 +22,7 @@ members = [
 	"runtime",
 	"runtime/tests/test_tvm_basic",
 	"runtime/tests/test_tvm_dso",
-	"runtime/tests/test_nnvm",
+	"runtime/tests/test_nn",
 	"frontend",
 	"frontend/tests/basics",
 	"frontend/tests/callback",

rust/frontend/Cargo.toml

@@ -23,7 +23,7 @@ description = "Rust frontend support for TVM"
 repository = "https://github.com/apache/incubator-tvm"
 homepage = "https://github.com/apache/incubator-tvm"
 readme = "README.md"
-keywords = ["rust", "tvm", "nnvm"]
+keywords = ["rust", "tvm"]
 categories = ["api-bindings", "science"]
 authors = ["TVM Contributors"]
 edition = "2018"

rust/frontend/README.md

@@ -35,14 +35,12 @@ Here's a Python snippet for downloading and building a pretrained Resnet18 via A
 
 ```python
 block = get_model('resnet18_v1', pretrained=True)
-
-sym, params = nnvm.frontend.from_mxnet(block)
-# add the softmax layer for prediction
-net = nnvm.sym.softmax(sym)
+
+sym, params = relay.frontend.from_mxnet(block, shape_dict)
 # compile the model
-with nnvm.compiler.build_config(opt_level=opt_level):
-    graph, lib, params = nnvm.compiler.build(
-        net, target, shape={"data": data_shape}, params=params)
+with relay.build_config(opt_level=opt_level):
+    graph, lib, params = relay.build(
+        net, target, params=params)
 # same the model artifacts
 lib.save(os.path.join(target_dir, "deploy_lib.o"))
 cc.create_shared(os.path.join(target_dir, "deploy_lib.so"),
@@ -51,7 +49,7 @@ cc.create_shared(os.path.join(target_dir, "deploy_lib.so"),
 with open(os.path.join(target_dir, "deploy_graph.json"), "w") as fo:
     fo.write(graph.json())
 with open(os.path.join(target_dir,"deploy_param.params"), "wb") as fo:
-    fo.write(nnvm.compiler.save_param_dict(params))
+    fo.write(relay.save_param_dict(params))
 ```
 
 Now, we need to input the artifacts to create and run the *Graph Runtime* to detect our input cat image
@@ -113,7 +111,7 @@ and the model correctly predicts the input image as **tiger cat**.
 
 Please follow TVM [installations](https://docs.tvm.ai/install/index.html), `export TVM_HOME=/path/to/tvm` and add `libtvm_runtime` to your `LD_LIBRARY_PATH`.
 
-*Note:* To run the end-to-end examples and tests, `tvm`, `nnvm` and `topi` need to be added to your `PYTHONPATH` or it's automatic via an Anaconda environment when it is installed individually.
+*Note:* To run the end-to-end examples and tests, `tvm` and `topi` need to be added to your `PYTHONPATH` or it's automatic via an Anaconda environment when it is installed individually.
 
 ## Supported TVM Functionalities
 

rust/frontend/examples/resnet/README.md

@@ -18,11 +18,11 @@
 ## Resnet example
 
 This end-to-end example shows how to:
-* build `Resnet 18` with `tvm` and `nnvm` from Python
+* build `Resnet 18` with `tvm` from Python
 * use the provided Rust frontend API to test for an input image
 
-To run the example with pretrained resnet weights, first `tvm`, `nnvm` and `mxnet` must be installed for the python build. To install mxnet for cpu, run `pip install mxnet`
-and to install `tvm` and `nnvm` with `llvm` follow the [TVM installation guide](https://docs.tvm.ai/install/index.html).
+To run the example with pretrained resnet weights, first `tvm`  and `mxnet` must be installed for the python build. To install mxnet for cpu, run `pip install mxnet`
+and to install `tvm` with `llvm` follow the [TVM installation guide](https://docs.tvm.ai/install/index.html).
 
 * **Build the example**: `cargo build
 

rust/runtime/Cargo.toml

@@ -22,7 +22,7 @@ license = "Apache-2.0"
 description = "A static TVM runtime"
 repository = "https://github.com/apache/incubator-tvm"
 readme = "README.md"
-keywords = ["tvm", "nnvm"]
+keywords = ["tvm"]
 categories = ["api-bindings", "science"]
 authors = ["TVM Contributors"]
 edition = "2018"

rust/runtime/src/graph.rs

@@ -440,7 +440,7 @@ named!(
     )
 );
 
-/// Loads a param dict saved using `nnvm.compiler.save_param_dict`.
+/// Loads a param dict saved using `relay.save_param_dict`.
 pub fn load_param_dict(bytes: &[u8]) -> Result<HashMap<String, Tensor>, GraphFormatError> {
     if let Ok((remaining_bytes, param_dict)) = parse_param_dict(bytes) {
         if remaining_bytes.len() == 0 {

rust/runtime/src/threading.rs

@@ -296,7 +296,7 @@ pub(crate) fn sgx_join_threads() {
     ocall_packed!("__sgx_thread_group_join__", 0);
 }
 
-// @see https://github.com/apache/incubator-tvm/issues/988 for information on why this function is used.
+// @see issue 988 for information on why this function is used.
 #[no_mangle]
 pub extern "C" fn TVMBackendParallelBarrier(_task_id: usize, penv: *const TVMParallelGroupEnv) {
     let barrier: &Arc<Barrier> = unsafe { &*((*penv).sync_handle as *const Arc<Barrier>) };