Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into port_pybind11

cmake/configure.cmake

@@ -97,6 +97,14 @@ if(WITH_GPU)
         endif()
         include_directories(${TENSORRT_INCLUDE_DIR})
     endif()
+    if(WITH_ANAKIN)
+        if(${CUDA_VERSION_MAJOR} VERSION_LESS 8)
+            message(FATAL_ERROR "Anakin needs CUDA >= 8.0 to compile")
+        endif()
+        if(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
+            message(FATAL_ERROR "Anakin needs CUDNN >= 7.0 to compile")
+        endif()
+    endif()
 elseif(WITH_AMD_GPU)
     add_definitions(-DPADDLE_WITH_HIP)
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__")

cmake/external/anakin.cmake

@@ -2,10 +2,22 @@ if (NOT WITH_ANAKIN)
   return()
 endif()
 
-set(ANAKIN_INSTALL_DIR "${THIRD_PARTY_PATH}/install/anakin" CACHE PATH
-  "Anakin install path." FORCE)
-set(ANAKIN_INCLUDE "${ANAKIN_INSTALL_DIR}" CACHE STRING "root of Anakin header files")
-set(ANAKIN_LIBRARY "${ANAKIN_INSTALL_DIR}" CACHE STRING "path of Anakin library")
+INCLUDE(ExternalProject)
+set(ANAKIN_SOURCE_DIR  ${THIRD_PARTY_PATH}/anakin)
+# the anakin install dir is only default one now
+set(ANAKIN_INSTALL_DIR ${THIRD_PARTY_PATH}/anakin/src/extern_anakin/output)
+set(ANAKIN_INCLUDE     ${ANAKIN_INSTALL_DIR})
+set(ANAKIN_LIBRARY     ${ANAKIN_INSTALL_DIR})
+set(ANAKIN_SHARED_LIB  ${ANAKIN_LIBRARY}/libanakin.so)
+set(ANAKIN_SABER_LIB   ${ANAKIN_LIBRARY}/libanakin_saber_common.so)
+
+# TODO(luotao): ANAKIN_MODLE_URL will move to demo ci later.
+set(ANAKIN_MODLE_URL "http://paddle-inference-dist.bj.bcebos.com/mobilenet_v2.anakin.bin")
+execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_SOURCE_DIR}")
+execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL}")
+
+include_directories(${ANAKIN_INCLUDE})
+include_directories(${ANAKIN_INCLUDE}/saber/)
 
 set(ANAKIN_COMPILE_EXTRA_FLAGS 
     -Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
@@ -20,36 +32,33 @@ set(ANAKIN_COMPILE_EXTRA_FLAGS
     -Wno-reorder 
     -Wno-error=cpp)
 
-set(ANAKIN_LIBRARY_URL "https://github.com/pangge/Anakin/releases/download/Version0.1.0/anakin.tar.gz")
-
-# A helper function used in Anakin, currently, to use it, one need to recursively include
-# nearly all the header files.
-function(fetch_include_recursively root_dir)
-    if (IS_DIRECTORY ${root_dir})
-        include_directories(${root_dir})
-    endif()
-
-    file(GLOB ALL_SUB RELATIVE ${root_dir} ${root_dir}/*)
-    foreach(sub ${ALL_SUB})
-        if (IS_DIRECTORY ${root_dir}/${sub})
-            fetch_include_recursively(${root_dir}/${sub})
-        endif()
-    endforeach()
-endfunction()
-
-if (NOT EXISTS "${ANAKIN_INSTALL_DIR}")
-    # download library
-    message(STATUS "Download Anakin library from ${ANAKIN_LIBRARY_URL}")
-    execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
-    execute_process(COMMAND bash -c "rm -rf ${ANAKIN_INSTALL_DIR}/*")
-    execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; wget --no-check-certificate -q ${ANAKIN_LIBRARY_URL}")
-    execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
-    execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; tar xzf anakin.tar.gz")
-endif()
+ExternalProject_Add(
+    extern_anakin
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    # TODO(luotao): use PaddlePaddle/Anakin later
+    GIT_REPOSITORY      "https://github.com/luotao1/Anakin"
+    GIT_TAG             "3957ae9263eaa0b1986758dac60a88852afb09be"
+    PREFIX              ${ANAKIN_SOURCE_DIR}
+    UPDATE_COMMAND      ""
+    CMAKE_ARGS          -DUSE_GPU_PLACE=YES
+                        -DUSE_X86_PLACE=YES
+                        -DBUILD_WITH_UNIT_TEST=NO
+                        -DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
+                        -DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
+                        -DCUDNN_ROOT=${CUDNN_ROOT}
+                        ${EXTERNAL_OPTIONAL_ARGS}
+    CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
+)
 
-if (WITH_ANAKIN)
-    message(STATUS "Anakin for inference is enabled")
-    message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
-    fetch_include_recursively(${ANAKIN_INCLUDE})
-    link_directories(${ANAKIN_LIBRARY})
-endif()
+message(STATUS "Anakin for inference is enabled")
+message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
+
+add_library(anakin_shared SHARED IMPORTED GLOBAL)
+set_property(TARGET anakin_shared PROPERTY IMPORTED_LOCATION ${ANAKIN_SHARED_LIB})
+add_dependencies(anakin_shared extern_anakin protobuf mklml)
+
+add_library(anakin_saber SHARED IMPORTED GLOBAL)
+set_property(TARGET anakin_saber PROPERTY IMPORTED_LOCATION ${ANAKIN_SABER_LIB})
+add_dependencies(anakin_saber extern_anakin protobuf mklml)
+
+list(APPEND external_project_dependencies anakin_shared anakin_saber)

cmake/inference_lib.cmake

@@ -143,7 +143,7 @@ if (WITH_ANAKIN AND WITH_GPU)
     copy(anakin_inference_lib DEPS paddle_inference_api inference_anakin_api
         SRCS
         ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api
-        ${PADDLE_BINARY_DIR}/third_party/install/anakin/*.tar.gz # anakin release
+        ${ANAKIN_INSTALL_DIR} # anakin release
         DSTS ${dst_dir}/inference/anakin ${dst_dir}/inference/anakin)
      list(APPEND inference_deps anakin_inference_lib)
 endif()

doc/fluid/api/executor.rst

@@ -38,11 +38,3 @@ _switch_scope
 ..  autofunction:: paddle.fluid.executor._switch_scope
     :noindex:
 
-.. _api_fluid_executor_fetch_var:
-
-fetch_var
----------
-
-..  autofunction:: paddle.fluid.executor.fetch_var
-    :noindex:
-

doc/fluid/api/fluid.rst

@@ -106,22 +106,6 @@ _switch_scope
 ..  autofunction:: paddle.fluid._switch_scope
     :noindex:
 
-.. _api_fluid_fetch_var:
-
-fetch_var
----------
-
-..  autofunction:: paddle.fluid.fetch_var
-    :noindex:
-
-.. _api_fluid_Go:
-
-Go
---
-
-..  autoclass:: paddle.fluid.Go
-    :members:
-    :noindex:
 
 .. _api_fluid_make_channel:
 

paddle/fluid/API.spec

@@ -34,21 +34,10 @@ paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None,
 paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.get_var ArgSpec(args=['name', 'program'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Executor.as_lodtensor ArgSpec(args=['self', 'data'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
 paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
-paddle.fluid.fetch_var ArgSpec(args=['name', 'scope', 'return_numpy'], varargs=None, keywords=None, defaults=(None, True))
-paddle.fluid.Go.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.Go.construct_go_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.make_channel ArgSpec(args=['dtype', 'capacity'], varargs=None, keywords=None, defaults=(0,))
-paddle.fluid.channel_send ArgSpec(args=['channel', 'value', 'is_copy'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.channel_recv ArgSpec(args=['channel', 'return_value'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.channel_close ArgSpec(args=['channel'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Select.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.Select.case ArgSpec(args=['self', 'channel_action_fn', 'channel', 'value', 'is_copy'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.Select.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
 paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
@@ -62,20 +51,16 @@ paddle.fluid.CheckpointConfig.__init__ ArgSpec(args=['self', 'checkpoint_dir', '
 paddle.fluid.Inferencer.__init__ ArgSpec(args=['self', 'infer_func', 'param_path', 'place', 'parallel'], varargs=None, keywords=None, defaults=(None, False))
 paddle.fluid.Inferencer.infer ArgSpec(args=['self', 'inputs', 'return_numpy'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
 paddle.fluid.InferenceTranspiler.__init__ 
-paddle.fluid.InferenceTranspiler.fuse_batch_norm ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.InferenceTranspiler.fuse_relu_mkldnn ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
 paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.DistributeTranspilerConfig.__init__ 
 paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id'], varargs=None, keywords='kwargs', defaults=(None, None, None, None, None, 1, 0))
-paddle.fluid.ParallelExecutor.bcast_params ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.ParallelExecutor.run ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True))
 paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ExecutionStrategy) -> None
 paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.GradientScaleStrategy, arg0: int) -> None
@@ -338,14 +323,11 @@ paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init',
 paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.transpiler.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
 paddle.fluid.transpiler.InferenceTranspiler.__init__ 
-paddle.fluid.transpiler.InferenceTranspiler.fuse_batch_norm ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.InferenceTranspiler.fuse_relu_mkldnn ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
 paddle.fluid.transpiler.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/inference/analysis/analyzer.cc

@@ -44,13 +44,13 @@ class DfgPassManagerImpl final : public DfgPassManager {
     if (FLAGS_inference_analysis_enable_tensorrt_subgraph_engine) {
       auto trt_teller = [&](const Node* node) {
         std::unordered_set<std::string> teller_set(
-            {"elementwise_add", "mul", "conv2d", "pool2d", "relu"});
+            {"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax"});
         if (!node->IsFunction()) return false;
 
         const auto* func = static_cast<const Function*>(node);
-        if (teller_set.count(func->func_type()))
+        if (teller_set.count(func->func_type())) {
           return true;
-        else {
+        } else {
           return false;
         }
       };

paddle/fluid/inference/api/CMakeLists.txt

@@ -45,7 +45,6 @@ endfunction(inference_api_test)
 
 cc_library(paddle_inference_api SRCS api.cc api_impl.cc DEPS lod_tensor)
 
-
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc
         DEPS paddle_inference_api)
@@ -62,22 +61,18 @@ inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_e
 endif()
 
 if (WITH_ANAKIN) # only needed in CI
-    # Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
-    # so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
-    # compile the libinference_anakin_api.a and compile with anakin.so.
-    fetch_include_recursively(${ANAKIN_INCLUDE})
     # compile the libinference_anakin_api.a and anakin.so.
-    nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc)
-    nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc)
-    target_compile_options(inference_anakin_api BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
-    target_compile_options(inference_anakin_api_shared BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
-    target_link_libraries(inference_anakin_api anakin anakin_saber_common)
-    target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
+    nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
+    #nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin)
+    function(anakin_target target_name)
+      target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+    endfunction()
+    anakin_target(inference_anakin_api)
+    #anakin_target(inference_anakin_api_shared)
     if (WITH_TESTING)
-        # this test is unstable, disable it first.
-        #cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
-                                  #ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
-                                  #DEPS inference_anakin_api_shared)
-        #target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
-     endif(WITH_TESTING)
+        cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
+                ARGS --model=${ANAKIN_SOURCE_DIR}/mobilenet_v2.anakin.bin
+                DEPS inference_anakin_api dynload_cuda SERIAL)
+        target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+    endif(WITH_TESTING)
 endif()

paddle/fluid/inference/api/paddle_inference_api.h

@@ -45,7 +45,7 @@ class PaddleBuf {
   PaddleBuf(void* data, size_t length)
       : data_(data), length_(length), memory_owned_{false} {}
   // Own memory.
-  PaddleBuf(size_t length)
+  explicit PaddleBuf(size_t length)
       : data_(new char[length]), length_(length), memory_owned_(true) {}
   // Resize to `length` bytes.
   void Resize(size_t length);

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

 # Add TRT tests
 nv_library(tensorrt_converter
   SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
-activation_op.cc
+activation_op.cc softmax_op.cc
   DEPS tensorrt_engine operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@@ -21,3 +21,6 @@ nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
 
 nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine elementwise_add_op SERIAL)
+
+nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
+        DEPS ${FLUID_CORE_MODULES} tensorrt_engine softmax_op SERIAL)

paddle/fluid/inference/tensorrt/convert/softmax_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+/*
+ * SoftMaxOp, ISoftMaxLayer in TRT. This Layer doesn't has weights.
+ */
+class SoftMaxOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(4)
+        << "convert a fluid softmax op to tensorrt softmax layer without bias";
+    framework::OpDesc op_desc(op, nullptr);
+    // Declare inputs
+    auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, SoftMax,
+                                       *const_cast<nvinfer1::ITensor*>(input1));
+
+    auto output_name = op_desc.Output("Out")[0];
+    engine_->SetITensor(output_name, layer->getOutput(0));
+    if (test_mode) {
+      engine_->DeclareOutput(output_name);
+    }
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(softmax);
+REGISTER_TRT_OP_CONVERTER(softmax, SoftMaxOpConverter);

paddle/fluid/inference/tensorrt/convert/test_softmax_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+   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 <gtest/gtest.h>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(SoftMaxOpConverter, main) {
+  framework::Scope scope;
+  std::unordered_set<std::string> parameters;
+  TRTConvertValidation validator(8, parameters, scope, 1000);
+
+  std::vector<int> tensor_shape{8, 10};
+  validator.DeclInputVar("softmax-X", tensor_shape,
+                         nvinfer1::DimsCHW(10, 1, 1));
+  validator.DeclOutputVar("softmax-Out", nvinfer1::DimsCHW(10, 1, 1));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("softmax");
+  desc.SetInput("X", {"softmax-X"});
+  desc.SetOutput("Out", {"softmax-Out"});
+
+  LOG(INFO) << "set OP";
+  validator.SetOp(*desc.Proto());
+  LOG(INFO) << "execute";
+
+  validator.Execute(3);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(softmax);

paddle/fluid/inference/tensorrt/convert/ut_helper.h

@@ -79,6 +79,12 @@ class TRTConvertValidation {
   }
 
   // Declare a Variable as input with random initialization.
+  void DeclInputVar(const std::string& name, const std::vector<int> tensor_dims,
+                    const nvinfer1::Dims& trt_dims) {
+    DeclVar(name, tensor_dims);
+    engine_->DeclareInput(name, nvinfer1::DataType::kFLOAT, trt_dims);
+  }
+
   void DeclInputVar(const std::string& name, const nvinfer1::Dims& dims) {
     DeclVar(name, dims);
     // Declare TRT inputs.
@@ -94,12 +100,18 @@ class TRTConvertValidation {
     DeclVar(name, dims);
   }
 
-  // Declare a variable in a fluid Scope.
-  void DeclVar(const std::string& name, const nvinfer1::Dims& dims,
-               bool is_param = false) {
+  void DeclVar(const std::string& name, const std::vector<int> dim_vec) {
     platform::CPUPlace place;
     platform::CPUDeviceContext ctx(place);
 
+    auto* x = scope_.Var(name);
+    auto* x_tensor = x->GetMutable<framework::LoDTensor>();
+    x_tensor->Resize(framework::make_ddim(dim_vec));
+    RandomizeTensor(x_tensor, place, ctx);
+  }
+  // Declare a variable in a fluid Scope.
+  void DeclVar(const std::string& name, const nvinfer1::Dims& dims,
+               bool is_param = false) {
     // Init Fluid tensor.
     std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
     // There is no batchsize in ITensor's shape, but We should add it to
@@ -107,10 +119,8 @@ class TRTConvertValidation {
     // if_add_batch_ flag is true, add the max batchsize to dim_vec.
     if (is_param != true && if_add_batch_ == true)
       dim_vec.insert(dim_vec.begin(), max_batch_size_);
-    auto* x = scope_.Var(name);
-    auto* x_tensor = x->GetMutable<framework::LoDTensor>();
-    x_tensor->Resize(framework::make_ddim(dim_vec));
-    RandomizeTensor(x_tensor, place, ctx);
+
+    DeclVar(name, dim_vec);
   }
 
   void SetOp(const framework::proto::OpDesc& desc) {

paddle/fluid/operators/elementwise_add_op.cu

@@ -16,60 +16,6 @@ limitations under the License. */
 #include "paddle/fluid/operators/elementwise_add_op.h"
 #include "paddle/fluid/platform/float16.h"
 
-namespace paddle {
-namespace operators {
-
-template <typename T>
-__global__ void ElementwiseAddCUDAKernel(const T *x, const T *y, T *z, int n,
-                                         int post, int size) {
-  int idx_x = threadIdx.x + blockIdx.x * blockDim.x;
-  if (idx_x < size) {
-    int idx_y = idx_x / post - (idx_x / (n * post)) * n;
-    z[idx_x] = x[idx_x] + y[idx_y];
-  }
-}
-
-template <typename T>
-class ElementwiseAddKernel<platform::CUDADeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    using Tensor = framework::Tensor;
-
-    const auto x = ctx.Input<Tensor>("X");
-    const auto y = ctx.Input<Tensor>("Y");
-    auto z = ctx.Output<Tensor>("Out");
-    auto *z_data = z->mutable_data<T>(ctx.GetPlace());
-
-    auto &device = *(ctx.cuda_device_context().eigen_device());
-    const framework::DDim &x_dim = x->dims();
-    framework::DDim y_dim = y->dims();
-    int size = x->numel();
-    if (x_dim == y_dim) {
-      auto dim = framework::make_ddim({size});
-      auto z_eigen = framework::EigenTensor<T, 1>::From(*z, dim);
-      auto x_eigen = framework::EigenTensor<T, 1>::From(*x, dim);
-      auto y_eigen = framework::EigenTensor<T, 1>::From(*y, dim);
-      z_eigen.device(device) = x_eigen + y_eigen;
-    } else {
-      int axis = ctx.Attr<int>("axis");
-      axis = (axis == -1 ? x_dim.size() - y_dim.size() : axis);
-      y_dim = trim_trailing_singular_dims(y_dim);
-      axis = (y_dim.size() == 0) ? x_dim.size() : axis;
-      int pre, n, post;
-      get_mid_dims(x_dim, y_dim, axis, &pre, &n, &post);
-      int threads = 512;
-      int grids = (size + threads - 1) / threads;
-      auto stream = ctx.cuda_device_context().stream();
-      ElementwiseAddCUDAKernel<T><<<grids, threads, 0, stream>>>(
-          x->data<T>(), y->data<T>(), z_data, n, post, size);
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 

paddle/fluid/operators/elementwise_add_op.h

@@ -144,41 +144,16 @@ class ElementwiseAddGradKernel : public framework::OpKernel<T> {
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    // skip out, x, y
+    auto* out = dout;
+    auto *x = dout, *y = dout;
 
-    if (dx != nullptr) {
-      // In fact, we can just share memory, but it may cause a bug of memory
-      // optimizer
-      // dx->ShareDataWith(*dout);
-      framework::TensorCopy(*dout, ctx.GetPlace(),
-                            ctx.template device_context<DeviceContext>(), dx);
-    }
-
-    if (dy == nullptr) return;
-
-    const framework::DDim& x_dim = dout->dims();
-    framework::DDim y_dim = dy->dims();
-    if (x_dim == y_dim) {
-      // dy->ShareDataWith(*dout);
-      framework::TensorCopy(*dout, ctx.GetPlace(),
-                            ctx.template device_context<DeviceContext>(), dy);
+    if (platform::is_cpu_place(ctx.GetPlace()) && dx != nullptr &&
+        dy != nullptr && (dx->dims() == dy->dims())) {
+      elementwise_add_grad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
     } else {
-      dy->mutable_data<T>(ctx.GetPlace());
-      // Perform reduction to dout to calculate dy
-      int axis = ctx.Attr<int>("axis");
-      axis = (axis == -1 ? x_dim.size() - y_dim.size() : axis);
-      y_dim = trim_trailing_singular_dims(y_dim);
-      axis = (y_dim.size() == 0) ? x_dim.size() : axis;
-
-      auto& device =
-          *(ctx.template device_context<DeviceContext>().eigen_device());
-      int pre, n, post;
-      get_mid_dims(x_dim, y_dim, axis, &pre, &n, &post);
-      auto eigen_dout = framework::EigenTensor<T, 3>::From(
-          *dout, framework::make_ddim({pre, n, post}));
-      auto eigen_dy =
-          framework::EigenTensor<T, 1>::From(*dy, framework::make_ddim({n}));
-      eigen_dy.device(device) = eigen_dout.sum(
-          framework::EigenDim<2>::From(framework::make_ddim({0, 2})));
+      default_elementwise_add_grad<DeviceContext, T>(ctx, x, y, out, dout, dx,
+                                                     dy);
     }
   }
 };

paddle/fluid/pybind/pybind.cc

@@ -666,7 +666,7 @@ All parameter, weight, gradient are variables in Paddle.
                   const std::string &, Scope *, std::vector<Scope *> &,
                   const ExecutionStrategy &, const BuildStrategy &, size_t,
                   size_t>())
-      .def("bcast_params", &ParallelExecutor::BCastParamsToDevices)
+      .def("_bcast_params", &ParallelExecutor::BCastParamsToDevices)
       // NOTE: even we return a vec<Scope*>* to Python use reference policy.
       // We still cannot get local_scope from this vector, since the element
       // of vec<Scope*> will be freed by Python GC. We can only return Scope*

python/paddle/fluid/__init__.py

@@ -48,8 +48,6 @@ from .data_feeder import DataFeeder
 from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
 from .transpiler import DistributeTranspiler, InferenceTranspiler, \
     memory_optimize, release_memory, DistributeTranspilerConfig
-from .concurrency import (Go, make_channel, channel_send, channel_recv,
-                          channel_close, Select)
 from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
 from . import clip
 from . import profiler
@@ -61,7 +59,7 @@ from paddle.fluid.layers.math_op_patch import monkey_patch_variable
 
 Tensor = LoDTensor
 
-__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
+__all__ = framework.__all__ + executor.__all__ + \
     trainer.__all__ + inferencer.__all__ + transpiler.__all__ + \
     parallel_executor.__all__ + lod_tensor.__all__ + [
         'io',

python/paddle/fluid/concurrency.py

@@ -19,8 +19,7 @@ from .layers import fill_constant
 from . import core
 
 __all__ = [
-    'Go', 'make_channel', 'channel_send', 'channel_recv', 'channel_close',
-    'Select'
+    'make_channel', 'channel_send', 'channel_recv', 'channel_close', 'Select'
 ]
 
 
@@ -35,10 +34,10 @@ class Go(BlockGuard):
     def __exit__(self, exc_type, exc_val, exc_tb):
         if exc_type is not None:
             return False
-        self.construct_go_op()
+        self._construct_go_op()
         return super(Go, self).__exit__(exc_type, exc_val, exc_tb)
 
-    def construct_go_op(self):
+    def _construct_go_op(self):
         main_program = self.helper.main_program
         go_block = main_program.current_block()
         parent_block = main_program.block(main_program.current_block()

python/paddle/fluid/executor.py

@@ -18,9 +18,7 @@ import six
 from .framework import Program, default_main_program, Variable
 from . import core
 
-__all__ = [
-    'Executor', 'global_scope', 'scope_guard', '_switch_scope', 'fetch_var'
-]
+__all__ = ['Executor', 'global_scope', 'scope_guard', '_switch_scope']
 
 g_scope = core.Scope()
 
@@ -171,7 +169,7 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     return fetch_count > 0
 
 
-def fetch_var(name, scope=None, return_numpy=True):
+def _fetch_var(name, scope=None, return_numpy=True):
     """
     Fetch the value of the variable with the given name from the
     given scope.
@@ -222,6 +220,37 @@ def _get_program_cache_key(feed, fetch_list):
     return str(feed_var_names + fetch_var_names)
 
 
+def _as_lodtensor(data, place):
+    """
+        Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
+        For higher dimensional sequence data, please use LoDTensor directly.
+
+        Examples:
+            >>> import paddle.fluid as fluid
+            >>> place = fluid.CPUPlace()
+            >>> exe = fluid.executor(place)
+            >>> data = np.array(size=(100, 200, 300))
+            >>> np_outs = map(lambda x: fluid.executor._as_lodtensor(x, place), data)
+            >>>     ...
+
+        Args:
+            data(numpy.ndarray): a instance of array
+
+        Returns:
+            LoDTensor
+        """
+    if isinstance(data, list):
+        raise RuntimeError("Some of your feed data hold LoD information. \
+                They can not be completely cast from a list of Python \
+                ndarray to LoDTensor. Please convert data to LoDTensor \
+                directly before feeding the data.\
+                ")
+    # single tensor case
+    tensor = core.LoDTensor()
+    tensor.set(data, place)
+    return tensor
+
+
 class Executor(object):
     """
     An Executor in Python, only support the single-GPU running. For multi-cards, please refer to
@@ -250,35 +279,6 @@ class Executor(object):
         self.program_caches = dict()
         self._closed = False
 
-    def as_lodtensor(self, data):
-        """
-        Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
-        For higher dimensional sequence data, please use LoDTensor directly.
-
-        Examples:
-            >>> import paddle.fluid as fluid
-            >>> exe = fluid.executor(fluid.CPUPlace())
-            >>> data = np.array(size=(100, 200, 300))
-            >>> np_outs = map(lambda x: exe.as_lodtensor(x), data)
-            >>>     ...
-
-        Args:
-            data(numpy.ndarray): a instance of array
-
-        Returns:
-            LoDTensor
-        """
-        if isinstance(data, list):
-            raise RuntimeError("Some of your feed data hold LoD information. \
-                They can not be completely cast from a list of Python \
-                ndarray to LoDTensor. Please convert data to LoDTensor \
-                directly before feeding the data.\
-                ")
-        # single tensor case
-        tensor = core.LoDTensor()
-        tensor.set(data, self.place)
-        return tensor
-
     def _get_program_cache(self, program_cache_key):
         return self.program_caches.get(program_cache_key, None)
 
@@ -338,7 +338,7 @@ class Executor(object):
                 feed_target_name = op.desc.output('Out')[0]
                 cur_feed = feed[feed_target_name]
                 if not isinstance(cur_feed, core.LoDTensor):
-                    cur_feed = self.as_lodtensor(cur_feed)
+                    cur_feed = _as_lodtensor(cur_feed, self.place)
                 idx = op.desc.attr('col')
                 core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
             else:

python/paddle/fluid/parallel_executor.py

@@ -279,19 +279,19 @@ class ParallelExecutor(object):
         arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
 
         if self.is_dist:
-            self.bcast_params()
+            self._bcast_params()
 
         if return_numpy:
             return executor.as_numpy(arr)
 
         return [arr[i] for i in range(len(arr))]
 
-    def bcast_params(self):
+    def _bcast_params(self):
         """
         Broadcast the parameters to other devices. It is used during
         distributed training.
         """
-        self.executor.bcast_params(set(self.persistable_vars))
+        self.executor._bcast_params(set(self.persistable_vars))
 
     @property
     def device_count(self):

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -537,5 +537,35 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 
 
+class TestRMSPropOptimizer(TranspilerTest):
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'),
+                                    bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        optimizer = fluid.optimizer.RMSProp(learning_rate=0.1)
+        optimizer.minimize(avg_cost)
+        return
+
+    def transpiler_test_impl(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep)
+        pserver2, startup2 = self.get_pserver(self.pserver2_ep)
+
+        self.assertEqual(len(pserver.blocks), 3)
+        # block1~2: optimize pass
+        self.assertEqual([op.type for op in pserver.blocks[1].ops],
+                         ["sum", "scale", "rmsprop"])
+        # the variable #fc_w will be split into two blocks
+        fc_w_var = startup.global_block().var("fc_w.block1")
+        self.assertEqual(fc_w_var.shape, (500, 1000))
+        moment_var = startup.global_block().var("momentum_1")
+        self.assertEqual(moment_var.shape, (500, 1000))
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_fetch_var.py

@@ -26,7 +26,7 @@ class TestFetchVar(op_test.OpTest):
         layers.assign(input=val, output=x)
         exe = fluid.Executor(fluid.CPUPlace())
         exe.run(fluid.default_main_program(), feed={}, fetch_list=[])
-        fetched_x = fluid.fetch_var("x")
+        fetched_x = fluid.executor._fetch_var("x")
         self.assertTrue(
             numpy.array_equal(fetched_x, val),
             "fetch_x=%s val=%s" % (fetched_x, val))

python/paddle/fluid/tests/unittests/test_py_reader_push_pop.py

@@ -62,7 +62,8 @@ class TestPyReader(unittest.TestCase):
                     next_data = np.random.uniform(
                         low=0, high=1000,
                         size=(batch_size, ) + shape[1:]).astype(dtype)
-                    in_data.append(executor.as_lodtensor(next_data))
+                    in_data.append(
+                        fluid.executor._as_lodtensor(next_data, place))
 
                 self.inputs.append(in_data)
 

python/paddle/fluid/tests/unittests/transformer_model.py

@@ -22,7 +22,7 @@ pos_enc_param_names = (
     "src_pos_enc_table",
     "trg_pos_enc_table", )
 
-batch_size = 64
+batch_size = 2
 
 
 def position_encoding_init(n_position, d_pos_vec):

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -751,14 +751,14 @@ class DistributeTranspiler(object):
                     out_name = op.output("Out")
 
                     ids_var = program.global_block().vars[ids_name[0]]
-                    prefetch_input_vars = self.create_splited_vars(
+                    prefetch_input_vars = self._create_splited_vars(
                         source_var=ids_var,
                         block=program.global_block(),
                         tag="_prefetch_in_")
                     self.all_prefetch_input_vars.append(prefetch_input_vars)
 
                     out_var = program.global_block().vars[out_name[0]]
-                    prefetch_output_vars = self.create_splited_vars(
+                    prefetch_output_vars = self._create_splited_vars(
                         source_var=out_var,
                         block=program.global_block(),
                         tag="_prefetch_out_")
@@ -1040,7 +1040,7 @@ class DistributeTranspiler(object):
             program.global_block()._sync_with_cpp()
         return var_mapping
 
-    def create_splited_vars(self, source_var, block, tag):
+    def _create_splited_vars(self, source_var, block, tag):
         return [
             block.create_var(
                 name=str(source_var.name + tag + str(index)),
@@ -1184,18 +1184,39 @@ class DistributeTranspiler(object):
         program = optimize_block.program
         pserver_block = program.global_block()
         new_inputs = collections.OrderedDict()
+
         # update param/grad shape first, then other inputs like
         # moment can use the updated shape
+        def _get_param_block(opt_op):
+            # param is already created on global program
+            param_block = None
+            for p in self.param_grad_ep_mapping[endpoint]["params"]:
+                if same_or_split_var(p.name, opt_op.input("Param")[0]):
+                    param_block = p
+                    break
+            return param_block
+
         for key in opt_op.input_names:
             if key == "Grad":
                 new_inputs[key] = merged_var
+            # For RMSProp optimizer
+            elif key == "Moment" or key == "MeanSquare":
+                param_block = _get_param_block(opt_op)
+                if not param_block:
+                    return
+                moment_var = origin_program.global_block().vars[opt_op.input(
+                    key)[0]]
+                tmpvar = pserver_block.create_var(
+                    name=moment_var.name,
+                    persistable=moment_var.persistable,
+                    dtype=moment_var.dtype,
+                    # change to use same shape as param
+                    # TODO(typhoonzero): didn't append .block in the var name,
+                    # may affect checkpoint saving? Need to verify.
+                    shape=param_block.shape)
+                new_inputs[key] = tmpvar
             elif key == "Param":
-                # param is already created on global program
-                param_block = None
-                for p in self.param_grad_ep_mapping[endpoint]["params"]:
-                    if same_or_split_var(p.name, opt_op.input(key)[0]):
-                        param_block = p
-                        break
+                param_block = _get_param_block(opt_op)
                 if not param_block:
                     return
                 tmpvar = pserver_block.create_var(
@@ -1221,7 +1242,7 @@ class DistributeTranspiler(object):
 
         for key in opt_op.input_names:
             new_shape = None
-            if key in ["Param", "Grad", "LearningRate"]:
+            if key in ["Param", "Grad", "LearningRate", "Moment", "MeanSquare"]:
                 continue
             var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
             # update accumulator variable shape

python/paddle/fluid/transpiler/inference_transpiler.py

@@ -57,10 +57,10 @@ class InferenceTranspiler(object):
             scope = global_scope()
         if not isinstance(scope, core.Scope):
             raise TypeError("scope should be as Scope type or None")
-        self.fuse_batch_norm(program, place, scope)
-        self.fuse_relu_mkldnn(program)
+        self._fuse_batch_norm(program, place, scope)
+        self._fuse_relu_mkldnn(program)
 
-    def fuse_relu_mkldnn(self, program):
+    def _fuse_relu_mkldnn(self, program):
         '''
         Transpile the program by fused relu activation for MKLDNN program.
 
@@ -104,7 +104,7 @@ class InferenceTranspiler(object):
         # And a better solution will be considered later.
         program = program.clone()
 
-    def fuse_batch_norm(self, program, place, scope):
+    def _fuse_batch_norm(self, program, place, scope):
         '''
         Transpile the program by fused batch normalization.
 

python/requirements.txt

 requests==2.9.2
-numpy>=1.12
+numpy>=1.12,<=1.14 #TODO:change to ">=1.12" when numpy fix bug in 1.15 and higher version
 protobuf==3.1
 recordio>=0.1.0
 matplotlib