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

test=develop

.gitignore

+python/paddle/fluid/tests/unittests/reader_reset_test.recordio
 paddle/operators/check_t.save
 paddle/operators/check_tensor.ls
 paddle/operators/tensor.save

AUTHORS.md

@@ -42,6 +42,7 @@
 | QiJune | Jun Qi |
 | qingqing01 | Qing-Qing Dang |
 | reyoung | Yang Yu |
+| Sand3r- | Michal Gallus |
 | Superjom | Chun-Wei Yan |
 | tensor-tang | Jian Tang |
 | tianbingsz | Tian-Bing Xu |

cmake/inference_lib.cmake

@@ -166,8 +166,8 @@ copy(framework_lib DEPS ${framework_lib_deps}
 
 set(module "memory")
 copy(memory_lib
-        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h
-        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail
+        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h ${src_dir}/${module}/allocation/*.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail ${dst_dir}/${module}/allocation
         )
 
 set(inference_deps paddle_fluid_shared paddle_fluid)

paddle/fluid/framework/operator.h

@@ -100,6 +100,7 @@ class OperatorBase {
 
   const std::string& Type() const { return type_; }
 
+  bool HasAttr(const std::string& name) const { return attrs_.count(name); }
   template <typename T>
   inline const T& Attr(const std::string& name) const {
     PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -7,16 +7,17 @@ set(analysis_deps # analysis_deps can be extended accross the project
 add_subdirectory(ir_passes)
 add_subdirectory(passes)
 
-cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass ${INFER_IR_PASSES})
+cc_library(analysis_helper SRCS helper.cc DEPS framework_proto proto_desc graph paddle_fluid_api)
+
+cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass ${INFER_IR_PASSES} analysis_helper)
 
 cc_library(argument SRCS argument.cc DEPS scope proto_desc)
 cc_library(analysis_pass SRCS analysis_pass.cc DEPS proto_desc)
 
 cc_library(analysis SRCS
   analyzer.cc
-  helper.cc
   analysis_pass
-  DEPS ${analysis_deps}
+  DEPS ${analysis_deps} analysis_helper
   )
 
 cc_test(test_dot SRCS dot_tester.cc DEPS analysis)

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -30,6 +30,7 @@ TEST(Analyzer, analysis_without_tensorrt) {
   Argument argument;
   argument.SetModelDir(FLAGS_inference_model_dir);
   argument.SetIrAnalysisPasses({"infer_clean_graph_pass"});
+  argument.SetUseGPU(false);
 
   Analyzer analyser;
   analyser.Run(&argument);
@@ -41,6 +42,7 @@ TEST(Analyzer, analysis_with_tensorrt) {
   argument.SetTensorRtWorkspaceSize(1 << 20);
   argument.SetModelDir(FLAGS_inference_model_dir);
   argument.SetIrAnalysisPasses({"infer_clean_graph_pass"});
+  argument.SetUseGPU(false);
 
   Analyzer analyser;
   analyser.Run(&argument);

paddle/fluid/inference/analysis/argument.h

@@ -116,6 +116,7 @@ struct Argument {
                       std::vector<std::string>);
 
   DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
+  DECL_ARGUMENT_FIELD(gpu_device_id, GPUDeviceId, int);
   DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
   DECL_ARGUMENT_FIELD(tensorrt_node_teller, TensorRtNodeTeller,
                       std::function<bool(const framework::ir::Node*)>);

paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt

@@ -4,4 +4,6 @@ set(analysis_deps ${analysis_deps}
         subgraph_detector tensorrt_subgraph_pass
         CACHE INTERNAL "")
 
+set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
+file(APPEND ${pass_file} "USE_PASS(tensorrt_subgraph_pass);\n")
 set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")

paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc

@@ -30,15 +30,28 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
   if (!argument->scope_valid()) {
     argument->SetScope(new framework::Scope);
   }
+  PADDLE_ENFORCE(argument->use_gpu_valid());
+
+  // The load program should run on the same device with the inference program,
+  // so that the parameters will on the same device, or they will keep copying
+  // between difference devices.
+  platform::Place place;
+  if (argument->use_gpu()) {
+    PADDLE_ENFORCE(argument->gpu_device_id_valid());
+    place = platform::CUDAPlace(argument->gpu_device_id());
+  } else {
+    place = platform::CPUPlace();
+  }
 
   if (argument->model_dir_valid()) {
-    auto program = LoadModel(argument->model_dir(), argument->scope_ptr());
+    auto program =
+        LoadModel(argument->model_dir(), argument->scope_ptr(), place);
     argument->SetMainProgram(program.release());
   } else if (argument->model_program_path_valid() &&
              argument->model_params_path_valid()) {
     auto program =
         LoadModel(argument->model_program_path(), argument->model_params_path(),
-                  argument->scope_ptr());
+                  argument->scope_ptr(), place);
     argument->SetMainProgram(program.release());
   } else {
     PADDLE_THROW(
@@ -52,16 +65,15 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
 }
 
 std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
-    const std::string &path, framework::Scope *scope) {
-  platform::CPUPlace place;
+    const std::string &path, framework::Scope *scope,
+    const platform::Place &place) {
   framework::Executor exe(place);
   return Load(&exe, scope, path);
 }
 
 std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
     const std::string &program_path, const std::string &params_path,
-    framework::Scope *scope) {
-  platform::CPUPlace place;
+    framework::Scope *scope, const platform::Place &place) {
   framework::Executor exe(place);
   return Load(&exe, scope, program_path, params_path);
 }

paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h

@@ -17,6 +17,7 @@
 #include <string>
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/analysis/analysis_pass.h"
+#include "paddle/fluid/platform/place.h"
 
 namespace paddle {
 namespace inference {
@@ -32,11 +33,12 @@ class IrGraphBuildPass : public AnalysisPass {
   std::string repr() const override;
 
  private:
-  std::unique_ptr<framework::ProgramDesc> LoadModel(const std::string &path,
-                                                    framework::Scope *scope);
+  std::unique_ptr<framework::ProgramDesc> LoadModel(
+      const std::string &path, framework::Scope *scope,
+      const platform::Place &place);
   std::unique_ptr<framework::ProgramDesc> LoadModel(
       const std::string &program_path, const std::string &params_path,
-      framework::Scope *scope);
+      framework::Scope *scope, const platform::Place &place);
 
   std::string model_binary_str_;
 };

paddle/fluid/inference/api/CMakeLists.txt

@@ -27,11 +27,10 @@ endif()
 cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
 cc_library(analysis_config SRCS analysis_config.cc DEPS lod_tensor paddle_pass_builder)
 cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc)
-cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder)
-cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder)
-cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
-cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
-
+cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager)
+cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS scope lod_tensor enforce)
+cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc)
+cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder DEPS zero_copy_tensor)
 
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc

paddle/fluid/inference/api/analysis_predictor.cc

@@ -285,6 +285,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
   status_program_optimized_ = true;
 
   argument_.SetUseGPU(config_.use_gpu);
+  argument_.SetGPUDeviceId(config_.device);
   // Analyze inference_program
   if (!config_.model_dir.empty()) {
     argument_.SetModelDir(config_.model_dir);
@@ -491,8 +492,7 @@ bool AnalysisPredictor::LoadParameters() {
   }
 
   // Use NaiveExecutor to Load parameters.
-  platform::CPUPlace place;
-  framework::NaiveExecutor e(place);
+  framework::NaiveExecutor e(place_);
   e.Prepare(scope_.get(), *load_program, 0, false);
   e.Run();
   VLOG(3) << "get " << scope_->LocalVarNames().size() << " vars after load";

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -116,8 +116,12 @@ class CpuPassStrategy : public PassStrategy {
 class GpuPassStrategy : public PassStrategy {
  public:
   GpuPassStrategy() : PassStrategy({}) {
+    // TODO(NHZlX) Problem with Data synchronization between GPU and CPU
+    // When running in GPU mode, the parameters are all on GPU. But the
+    // opearations of "conv_bn_fuse_pass" are on CPU.
     passes_.assign({
-        "infer_clean_graph_pass", "conv_bn_fuse_pass",
+        "infer_clean_graph_pass",
+        // "infer_clean_graph_pass", "conv_bn_fuse_pass",
     });
   }
 

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

@@ -18,7 +18,7 @@ nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
 nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
         DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine conv_op conv_transpose_op SERIAL)
 nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
-        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op SERIAL)
+        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op tensorrt_plugin SERIAL)
 nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
         DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin
              elementwise_add_op elementwise_mul_op SERIAL)

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -13,25 +13,57 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
 
+void DealCeilMode(const nvinfer1::Dims &input_shape, std::vector<int> ksize,
+                  std::vector<int> strides, std::vector<int> paddings,
+                  nvinfer1::DimsHW *pre_pad, nvinfer1::DimsHW *post_pad,
+                  int input_dims) {
+  int input_height = input_shape.d[input_dims - 2];
+  int input_width = input_shape.d[input_dims - 1];
+  int floor_h_output_size =
+      (input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+  int ceil_h_output_size =
+      (input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
+          strides[0] +
+      1;
+
+  int floor_w_output_size =
+      (input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
+  int ceil_w_output_size =
+      (input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) / strides[1] +
+      1;
+  if (floor_h_output_size != ceil_h_output_size) {
+    post_pad->h() = strides[0] - 1;
+  }
+
+  if (floor_w_output_size != ceil_w_output_size) {
+    post_pad->w() = strides[1] - 1;
+  }
+}
+
 /*
  * Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
  */
 class Pool2dOpConverter : public OpConverter {
  public:
-  void operator()(const framework::proto::OpDesc& op,
-                  const framework::Scope& scope, bool test_mode) override {
-    VLOG(3)
+  void operator()(const framework::proto::OpDesc &op,
+                  const framework::Scope &scope, bool test_mode) override {
+    VLOG(40)
         << "convert a fluid pool2d op to tensorrt pool2d layer without bias";
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
     PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
-    auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto *input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    nvinfer1::Dims input_shape = input1->getDimensions();
+    int input_dims = input_shape.nbDims;
+
+    PADDLE_ENFORCE_EQ(input_dims, 3UL);
 
     bool global_pooling = boost::get<bool>(op_desc.GetAttr("global_pooling"));
     std::string pool_type =
@@ -44,23 +76,6 @@ class Pool2dOpConverter : public OpConverter {
         boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
     bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
 
-    nvinfer1::Dims input_shape = input1->getDimensions();
-    int nbDims = input_shape.nbDims;
-    nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
-    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
-    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
-
-    if (global_pooling == true) {
-      nv_ksize.d[0] = input_shape.d[nbDims - 2];
-      nv_ksize.d[1] = input_shape.d[nbDims - 1];
-      nv_strides.h() = 1;
-      nv_strides.w() = 1;
-      nv_paddings.h() = 0;
-      nv_paddings.w() = 0;
-    }
-
-    PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
-
     nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
     if (pool_type == "max") {
       nv_pool_type = nvinfer1::PoolingType::kMAX;
@@ -70,42 +85,63 @@ class Pool2dOpConverter : public OpConverter {
       PADDLE_THROW("TensorRT unsupported pooling type!");
     }
 
-    if (ceil_mode) {
-      nvinfer1::DimsHW pre_pad(0, 0);
-      nvinfer1::DimsHW post_pad(0, 0);
-      int input_height = input_shape.d[nbDims - 2];
-      int input_width = input_shape.d[nbDims - 1];
-      int floor_h_output_size =
-          (input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
-      int ceil_h_output_size =
-          (input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
-              strides[0] +
-          1;
-
-      int floor_w_output_size =
-          (input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
-      int ceil_w_output_size =
-          (input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
-              strides[1] +
-          1;
-      if (floor_h_output_size != ceil_h_output_size) {
-        post_pad.h() = strides[0] - 1;
+    nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
+    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
+    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
+
+    nvinfer1::ILayer *layer = nullptr;
+
+    if (global_pooling == true) {
+      nv_ksize.d[0] = input_shape.d[input_dims - 2];
+      nv_ksize.d[1] = input_shape.d[input_dims - 1];
+      auto *layer = TRT_ENGINE_ADD_LAYER(
+          engine_, Pooling, *const_cast<nvinfer1::ITensor *>(input1),
+          nv_pool_type, nv_ksize);
+      PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
+      auto output_name = op_desc.Output("Out")[0];
+      layer->setName(("pool2d (Output: " + output_name + ")").c_str());
+      layer->getOutput(0)->setName(output_name.c_str());
+      engine_->SetITensor(output_name, layer->getOutput(0));
+      if (test_mode) {
+        engine_->DeclareOutput(output_name);
       }
+      return;
+    }
 
-      if (floor_w_output_size != ceil_w_output_size) {
-        post_pad.w() = strides[1] - 1;
+    if (pool_type == "max") {
+      nvinfer1::DimsHW pre_pad(paddings[0], paddings[1]);
+      nvinfer1::DimsHW post_pad(paddings[0], paddings[1]);
+      if (ceil_mode) {
+        // If ceil mode is true, we will pad the appropriate size to the input.
+        DealCeilMode(input_shape, ksize, strides, paddings, &pre_pad, &post_pad,
+                     input_dims);
+        auto *pad_layer = TRT_ENGINE_ADD_LAYER(
+            engine_, Padding, *const_cast<nvinfer1::ITensor *>(input1), pre_pad,
+            post_pad);
+        PADDLE_ENFORCE_NOT_NULL(
+            pad_layer, "pad layer in poolOp converter could not be created.");
+        input1 = pad_layer->getOutput(0);
+      }
+      auto *pool_layer = TRT_ENGINE_ADD_LAYER(
+          engine_, Pooling, *const_cast<nvinfer1::ITensor *>(input1),
+          nv_pool_type, nv_ksize);
+      PADDLE_ENFORCE_NOT_NULL(pool_layer, "pool layer could not be created.");
+      pool_layer->setStride(nv_strides);
+      pool_layer->setPadding(nv_paddings);
+      layer = pool_layer;
+    } else {
+      // Average pooling needs to exclude the padding pixels from the average
+      // mean.
+      // It is not supported well by TRT, we use a plugin here.
+      std::vector<int> input_shape_v;
+      for (int i = 0; i < input_dims; i++) {
+        input_shape_v.push_back(input_shape.d[i]);
       }
-      auto* layer = TRT_ENGINE_ADD_LAYER(
-          engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
-          post_pad);
-      input1 = layer->getOutput(0);
+      plugin::AvgPoolPlugin *plugin = new plugin::AvgPoolPlugin(
+          ceil_mode, ksize, strides, paddings, input_shape_v);
+      auto *avg_pool_layer = engine_->AddPlugin(&input1, 1, plugin);
+      layer = avg_pool_layer;
     }
-    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
-                                       *const_cast<nvinfer1::ITensor*>(input1),
-                                       nv_pool_type, nv_ksize);
-    PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
-    layer->setStride(nv_strides);
-    layer->setPadding(nv_paddings);
 
     auto output_name = op_desc.Output("Out")[0];
     layer->setName(("pool2d (Output: " + output_name + ")").c_str());

paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc

@@ -20,20 +20,21 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-void test_pool2d(bool global_pooling, bool ceil_mode) {
+void test_pool2d(bool global_pooling, bool ceil_mode,
+                 std::string pool_type = "max") {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
   TRTConvertValidation validator(5, parameters, scope, 1 << 15);
 
   // The ITensor's Dims should not contain the batch size.
   // So, the ITensor's Dims of input and output should be C * H * W.
-  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
+  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 6, 7));
   if (global_pooling)
     validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
   else if (ceil_mode)
-    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 3, 4));
   else
-    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 3, 3));
 
   // Prepare Op description
   framework::OpDesc desc;
@@ -41,10 +42,10 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
   desc.SetInput("X", {"pool2d-X"});
   desc.SetOutput("Out", {"pool2d-Out"});
 
-  std::vector<int> ksize({3, 3});
+  std::vector<int> ksize({2, 2});
   std::vector<int> strides({2, 2});
   std::vector<int> paddings({0, 0});
-  std::string pooling_t = "max";
+  std::string pooling_t = pool_type;
 
   desc.SetAttr("pooling_type", pooling_t);
   desc.SetAttr("ksize", ksize);
@@ -63,7 +64,8 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
 TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
 TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
 
-TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
+TEST(Pool2dOpConverter, max_ceil_test) { test_pool2d(false, true); }
+TEST(Pool2dOpConverter, avg_ceil_test) { test_pool2d(false, true, "avg"); }
 
 }  // namespace tensorrt
 }  // namespace inference

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

 nv_library(tensorrt_plugin
            SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu
+           avg_pool_op_plugin.cu
            DEPS enforce tensorrt_engine)

paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu

+// 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/plugin/avg_pool_op_plugin.h"
+#include "paddle/fluid/operators/math/pooling.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+nvinfer1::Dims AvgPoolPlugin::getOutputDimensions(
+    int index, const nvinfer1::Dims* inputDims, int nbInputs) {
+  assert(nbInputs == 1);
+  assert(index == 0);
+  assert(inputDims[0].nbDims == 3);
+  nvinfer1::Dims const& input_dims = inputDims[0];
+
+  nvinfer1::Dims output_dims = input_dims;
+
+  output_dims.d[1] = output_shape_[1];
+  output_dims.d[2] = output_shape_[2];
+  return output_dims;
+}
+
+int AvgPoolPlugin::enqueue(int batchSize, const void* const* inputs,
+                           void** outputs, void* workspace,
+                           cudaStream_t stream) {
+  auto const& input_dims = this->getInputDims(0);
+  int input_size = 0;
+  float const* idata = reinterpret_cast<float const*>(inputs[0]);
+  float** odatas = reinterpret_cast<float**>(outputs);
+
+  paddle::operators::math::AvgPool<float> pool_process;
+  paddle::operators::math::Pool2dDirectCUDAFunctor<
+      paddle::operators::math::AvgPool<float>, float>
+      pool2d_forward;
+
+  std::vector<int> input_shape = input_shape_;
+  std::vector<int> output_shape = output_shape_;
+  input_shape.insert(input_shape.begin(), batchSize);
+  output_shape.insert(output_shape.begin(), batchSize);
+
+  pool2d_forward(idata, input_shape, output_shape, ksize_, strides_, paddings_,
+                 pool_process, true, odatas[0], stream);
+
+  return cudaGetLastError() != cudaSuccess;
+}
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h

+// 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.
+
+#pragma once
+#include <cassert>
+#include <vector>
+#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+class AvgPoolPlugin : public PluginTensorRT {
+ private:
+  bool ceil_mode_;
+  std::vector<int> ksize_;
+  std::vector<int> strides_;
+  std::vector<int> paddings_;
+  std::vector<int> input_shape_;
+  std::vector<int> output_shape_;
+
+ protected:
+  size_t getSerializationSize() override {
+    return SerializedSize(ceil_mode_) + SerializedSize(ksize_) +
+           SerializedSize(strides_) + SerializedSize(paddings_) +
+           SerializedSize(input_shape_) + getBaseSerializationSize();
+  }
+
+  // TRT will call this func when we need to serialize the configuration of
+  // tensorrt.
+  // It should not be called by users.
+  void serialize(void *buffer) override {
+    serializeBase(buffer);
+    SerializeValue(&buffer, ceil_mode_);
+    SerializeValue(&buffer, ksize_);
+    SerializeValue(&buffer, strides_);
+    SerializeValue(&buffer, paddings_);
+    SerializeValue(&buffer, input_shape_);
+  }
+
+ public:
+  AvgPoolPlugin(bool ceil_mode, std::vector<int> ksize,
+                std::vector<int> strides, std::vector<int> paddings,
+                std::vector<int> input_shape)
+      : ceil_mode_(ceil_mode),
+        ksize_(ksize),
+        strides_(strides),
+        paddings_(paddings),
+        input_shape_(input_shape) {
+    int output_h, output_w;
+    output_shape_ = input_shape_;
+    if (!ceil_mode_) {
+      output_h =
+          (input_shape[1] - ksize_[0] + 2 * paddings_[0]) / strides_[0] + 1;
+      output_w =
+          (input_shape[2] - ksize_[1] + 2 * paddings_[1]) / strides_[1] + 1;
+    } else {
+      output_h =
+          (input_shape[1] - ksize_[0] + 2 * paddings_[0] + strides_[0] - 1) /
+              strides_[0] +
+          1;
+      output_w =
+          (input_shape[2] - ksize_[1] + 2 * paddings_[1] + strides_[1] - 1) /
+              strides_[1] +
+          1;
+    }
+    output_shape_[1] = output_h;
+    output_shape_[2] = output_w;
+  }
+
+  // It was used for tensorrt deserialization.
+  // It should not be called by users.
+  AvgPoolPlugin(void const *serialData, size_t serialLength) {
+    deserializeBase(serialData, serialLength);
+    DeserializeValue(&serialData, &serialLength, &ceil_mode_);
+    DeserializeValue(&serialData, &serialLength, &ksize_);
+    DeserializeValue(&serialData, &serialLength, &strides_);
+    DeserializeValue(&serialData, &serialLength, &paddings_);
+    DeserializeValue(&serialData, &serialLength, &input_shape_);
+  }
+
+  AvgPoolPlugin *clone() const override {
+    return new AvgPoolPlugin(ceil_mode_, ksize_, strides_, paddings_,
+                             input_shape_);
+  }
+
+  const char *getPluginType() const override { return "avg_pool"; }
+  int getNbOutputs() const override { return 1; }
+  nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims *inputs,
+                                     int nbInputDims) override;
+  int initialize() override { return 0; }
+  int enqueue(int batchSize, const void *const *inputs, void **outputs,
+              void *workspace, cudaStream_t stream) override;
+};
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tests/api/CMakeLists.txt

 set(INFERENCE_EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor)
 
+if(WITH_GPU AND TENSORRT_FOUND)
+    set(INFERENCE_EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} analysis ${analysis_deps} ir_pass_manager analysis_predictor)
+endif()
+
 function(download_model install_dir model_name)
     if (NOT EXISTS ${install_dir})
         inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${model_name})
@@ -27,14 +31,14 @@ function(inference_analysis_api_test_with_fake_data target install_dir filename
 endfunction()
 
 # RNN1
-if(NOT APPLE)
+if(NOT APPLE AND WITH_MKLML)
     set(RNN1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn1")
     download_model_and_data(${RNN1_INSTALL_DIR} "rnn1%2Fmodel.tar.gz" "rnn1%2Fdata.txt.tar.gz")
     inference_analysis_api_test(test_analyzer_rnn1 ${RNN1_INSTALL_DIR} analyzer_rnn1_tester.cc)
 else()
-    # TODO: fix this test on MACOS, the reason is that
-    # fusion_seqexpand_concat_fc_op is not supported on MACOS
-    message(WARNING "These tests has been disabled in OSX before being fixed: \n test_analyzer_rnn1")
+    # TODO: fix this test on MACOS and OPENBLAS, the reason is that
+    # fusion_seqexpand_concat_fc_op is not supported on MACOS and OPENBLAS
+    message(WARNING "These tests has been disabled in OSX or WITH_MKL=OFF before being fixed: \n test_analyzer_rnn1")
 endif()
 
 # RNN2
@@ -75,11 +79,11 @@ endif()
 inference_analysis_api_test(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc)
 
 # resnet50
-inference_analysis_api_test_with_fake_data(test_analyzer_resnet50 
+inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
   "${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz")
 
 # mobilenet with depthwise_conv op
-inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet 
+inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet
   "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz")
 
 # anakin
@@ -89,15 +93,15 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
    set(ANAKIN_RNN1_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/rnn1")
    inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn.anakin2.model.bin")
    inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn_data.txt")
-   cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc 
-           ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin 
+   cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
+           ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin
                 --datapath=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn_data.txt
            DEPS inference_anakin_api_shared SERIAL)
    # anakin mobilenet
    if(WITH_GPU)
        set(ANAKIN_MOBILENET_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/mobilenet")
        inference_download(${ANAKIN_MOBILENET_INSTALL_DIR} ${INFERENCE_URL} "mobilenet_v2.anakin.bin")
-       cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc 
+       cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
                ARGS --model=${ANAKIN_MOBILENET_INSTALL_DIR}/mobilenet_v2.anakin.bin
                DEPS inference_anakin_api_shared dynload_cuda SERIAL)
    endif()
@@ -109,6 +113,6 @@ if(WITH_GPU AND TENSORRT_FOUND)
        inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
    endif()
    inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
-      EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} analysis ${analysis_deps} ir_pass_manager analysis_predictor
+      EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
         ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
 endif()

paddle/fluid/inference/tests/api/tester_helper.h

@@ -222,19 +222,36 @@ void TestMultiThreadPrediction(
       // The inputs of each thread are all the same.
       std::vector<PaddleTensor> outputs_tid;
       auto &predictor = predictors[tid];
-      LOG(INFO) << "running thread " << tid;
-      Timer timer;
-      timer.tic();
-      for (int i = 0; i < num_times; i++) {
-        for (const auto &input : inputs) {
-          ASSERT_TRUE(predictor->Run(input, &outputs_tid));
+
+      // warmup run
+      LOG(INFO) << "Running thread " << tid << ", warm up run...";
+      {
+        Timer warmup_timer;
+        warmup_timer.tic();
+        predictor->Run(inputs[0], outputs, batch_size);
+        PrintTime(batch_size, 1, num_threads, tid, warmup_timer.toc(), 1);
+#if !defined(_WIN32)
+        if (FLAGS_profile) {
+          paddle::platform::ResetProfiler();
         }
+#endif
       }
 
-      auto time = timer.toc();
-      total_time += time;
-      PrintTime(batch_size, num_times, num_threads, tid, time / num_times,
-                inputs.size());
+      LOG(INFO) << "Thread " << tid << " run " << num_times << " times...";
+      {
+        Timer timer;
+        timer.tic();
+        for (int i = 0; i < num_times; i++) {
+          for (const auto &input : inputs) {
+            ASSERT_TRUE(predictor->Run(input, &outputs_tid));
+          }
+        }
+
+        auto time = timer.toc();
+        total_time += time;
+        PrintTime(batch_size, num_times, num_threads, tid, time / num_times,
+                  inputs.size());
+      }
     });
   }
   for (int i = 0; i < num_threads; ++i) {

paddle/fluid/inference/tests/api/trt_models_tester.cc

@@ -145,5 +145,3 @@ TEST(TensorRT_mobilenet, analysis) {
 
 }  // namespace inference
 }  // namespace paddle
-
-USE_PASS(tensorrt_subgraph_pass);

paddle/fluid/memory/allocation/best_fit_allocator_test.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/memory/allocation/best_fit_allocator.h"
+#include <random>
 #include <thread>  // NOLINT
 #include <vector>
 #include "gtest/gtest.h"

paddle/fluid/memory/allocation/best_fit_allocator_test.cu

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <random>
 #include <thread>  // NOLINT
 #include <vector>
 #include "gtest/gtest.h"

paddle/fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc

+/* Copyright (c) 2016 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 <mkldnn/include/mkldnn.hpp>
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+#include "paddle/fluid/operators/math/jit_kernel.h"
+#include "xbyak.h"
+#include "xbyak_util.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::DataLayout;
+using mkldnn::memory;
+
+static mkldnn::memory::format StringToMKLDNNFormat(std::string& format) {
+  std::transform(format.begin(), format.end(), format.begin(), ::tolower);
+
+  if (!format.compare("nchw")) {
+    return memory::format::nchw;
+  } else if (!format.compare("nchw16c")) {
+    return memory::format::nChw16c;
+  } else if (!format.compare("nchw8c")) {
+    return memory::format::nChw8c;
+  } else if (!format.compare("nhwc")) {
+    return memory::format::nhwc;
+  } else {
+    return memory::format::any;
+  }
+}
+
+static void UpdateDataFormat(const framework::ExecutionContext& ctx,
+                             framework::Tensor* tensor, const char* attribute) {
+  if (ctx.op().HasAttr(attribute)) {
+    auto format_as_string = ctx.Attr<std::string>(attribute);
+    auto format = StringToMKLDNNFormat(format_as_string);
+    if (format != memory::format::any) {
+      tensor->set_format(format);
+    }
+  }
+}
+
+template <typename T>
+static void ReorderInput(framework::Tensor* tensor,
+                         const platform::Place& place,
+                         const mkldnn::engine& engine, bool isFourDim) {
+  using platform::to_void_cast;
+  auto dims = paddle::framework::vectorize2int(tensor->dims());
+  framework::Tensor out_tensor;
+  out_tensor.Resize(tensor->dims());
+  out_tensor.set_format(isFourDim ? memory::format::nchw : memory::format::nc);
+  out_tensor.set_layout(tensor->layout());
+  mkldnn::memory input_memory = {
+      {{dims, platform::MKLDNNGetDataType<T>(), tensor->format()}, engine},
+      to_void_cast<T>(tensor->data<T>())};
+  mkldnn::memory output_memory = {
+      {{dims, platform::MKLDNNGetDataType<T>(), out_tensor.format()}, engine},
+      to_void_cast<T>(out_tensor.mutable_data<T>(place))};
+  platform::Reorder(input_memory, output_memory);
+  tensor->ShareDataWith(out_tensor);
+}
+
+template <typename T>
+class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    int axis = ctx.Attr<int>("axis");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    const T* x_data = x->data<T>();
+    const T* y_data = y->data<T>();
+    T* z_data = z->mutable_data<T>(ctx.GetPlace());
+
+    auto x_dims = x->dims();
+    auto y_dims_untrimmed = y->dims();
+    auto x_int_dims = paddle::framework::vectorize2int(x_dims);
+
+    UpdateDataFormat(ctx, (Tensor*)x, "x_data_format");
+    UpdateDataFormat(ctx, (Tensor*)y, "y_data_format");
+
+    Xbyak::util::Cpu cpu;
+    const bool is_avx512_enabled = cpu.has(Xbyak::util::Cpu::tAVX512F);
+    const bool are_dims_divisable = !(x_int_dims[1] % 16);
+    const bool is_x_format_correct = x->format() == memory::format::nChw16c;
+    const bool is_y_format_correct = y->format() == memory::format::nc;
+    if (is_x_format_correct && is_y_format_correct && are_dims_divisable &&
+        is_avx512_enabled) {
+      int pre, n, post;
+      get_mid_dims(x_dims, y_dims_untrimmed, axis, &pre, &n, &post);
+
+      if (post == 1) {
+        PADDLE_THROW("Not implemented when post is 1");
+      } else {
+        // Just check whether it works for RE-Resnext.
+        PADDLE_ENFORCE_EQ(x_dims.size(), 4, "X should have 4 dimensions");
+
+        int n = x_dims[0];
+        int c = x_dims[1];
+        int h = x_dims[2];
+        int w = x_dims[3];
+
+        PADDLE_ENFORCE(y_dims_untrimmed[0] == n && y_dims_untrimmed[1] == c,
+                       "Y should be in nc format");
+
+        constexpr int simd_width = 16;
+        int C = c / simd_width;
+
+        const auto& multiply =
+            math::jitkernel::KernelPool::Instance()
+                .template Get<math::jitkernel::EltwiseMulnChw16cNCKernel<T>>(n);
+
+#pragma omp parallel for collapse(2)
+        for (int ni = 0; ni < n; ni++) {
+          for (int ci = 0; ci < C; ci++) {
+            auto ptr_x =
+                x_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
+
+            auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
+            auto ptr_z =
+                z_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
+
+            multiply->Compute(ptr_x, ptr_y, ptr_z, h, w);
+          }
+        }
+      }
+
+      z->set_layout(DataLayout::kMKLDNN);
+      z->set_format(x->format());
+    } else {
+      // Fallback to naive version:
+      const bool are_inputs_in_same_format = x->format() == y->format();
+      const bool is_x_nchw = x->format() == memory::format::nchw;
+      const bool is_x_nc = x->format() == memory::format::nc;
+      const bool is_y_nchw = y->format() == memory::format::nchw;
+      const bool is_y_nc = y->format() == memory::format::nc;
+      if (!are_inputs_in_same_format) {
+        using platform::MKLDNNDeviceContext;
+        auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+        const auto& mkldnn_engine = dev_ctx.GetEngine();
+        if (!(is_x_nchw || is_x_nc))
+          ReorderInput<T>((Tensor*)x, ctx.GetPlace(), mkldnn_engine,
+                          x->dims().size() == 4);
+        if (!(is_y_nchw || is_y_nc))
+          ReorderInput<T>((Tensor*)y, ctx.GetPlace(), mkldnn_engine,
+                          y->dims().size() == 4);
+      }
+
+      auto mul_func = [](T a, T b) -> T { return a * b; };
+
+      TransformFunctor<decltype(mul_func), T,
+                       paddle::platform::CPUDeviceContext, T>
+          functor(
+              x, y, z,
+              ctx.template device_context<paddle::platform::CPUDeviceContext>(),
+              mul_func);
+
+      axis = (axis == -1 ? x_dims.size() - y_dims_untrimmed.size() : axis);
+      PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+                     "Axis should be in range [0, x_dims)");
+
+      auto y_dims = trim_trailing_singular_dims(y_dims_untrimmed);
+      axis = (y_dims.size() == 0) ? x_dims.size() : axis;
+
+      int pre, n, post;
+      get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
+
+      if (post == 1) {
+        functor.RunRowWise(n, pre);
+      } else {
+        functor.RunMidWise(n, pre, post);
+      }
+      z->set_layout(DataLayout::kMKLDNN);
+      z->set_format(x->format());
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(elementwise_mul, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::ElementwiseMulMKLDNNKernel<float>)

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -97,6 +97,20 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
         .EqualGreaterThan(-1);
     AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "x_data_format",
+        "(string, default NCHW) Only used in mkldnn"
+        "An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
+        "Defaults to \"\". Specify the data format of the output data, "
+        "the input will be transformed automatically. ")
+        .SetDefault("");
+    AddAttr<std::string>(
+        "y_data_format",
+        "(string, default \"\") Only used in mkldnn"
+        "An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
+        "Defaults to \"\". Specify the data format of the output data, "
+        "the input will be transformed automatically. ")
+        .SetDefault("");
     AddComment(string::Sprintf(R"DOC(
 Elementwise %s Operator
 

paddle/fluid/operators/math/jit_code.h

@@ -322,6 +322,42 @@ class VActJitCode : public JitCode {
   ymm_t ymm_dst = ymm_t(1);
 };
 
+#ifdef PADDLE_WITH_MKLDNN
+struct EltwiseMulnChw16cNC : public Xbyak::CodeGenerator {
+  explicit EltwiseMulnChw16cNC(size_t code_size = 256 * 1024)
+      : Xbyak::CodeGenerator(code_size) {
+    // RDI is ptr x_input
+    // RSI is ptr y_input
+    // RDX is ptr output
+    // RCX is height
+    // r8 is width
+
+    push(rbx);
+
+    xor_(rax, rax);
+    xor_(r10, r10);
+    vmovups(zmm3, ptr[rsi]);
+
+    L("h_loop");
+    xor_(rbx, rbx);
+    L("w_loop");
+    vmovups(zmm2, ptr[rdi + rax]);
+    vmulps(zmm1, zmm2, zmm3);
+    vmovups(ptr[rdx + rax], zmm1);
+    add(rax, 64);
+    inc(rbx);
+    cmp(r8, rbx);
+    jnz("w_loop");
+    inc(r10);
+    cmp(r10, rcx);
+    jnz("h_loop");
+
+    pop(rbx);
+    ret();
+  }
+};
+#endif
+
 }  // namespace gen
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_kernel.h

@@ -95,6 +95,15 @@ class VAddBiasKernel : public Kernel {
   void (*Compute)(const T *, const T *, T *, int);
 };
 
+#ifdef PADDLE_WITH_MKLDNN
+template <typename T>
+class EltwiseMulnChw16cNCKernel : public Kernel {
+ public:
+  // nChw16c = nChw16c .* NC
+  void (*Compute)(const float *, const float *, float *, int, int);
+};
+#endif
+
 template <typename T>
 class VActKernel : public Kernel {
  public:

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -226,6 +226,44 @@ bool VAddKernelImpl<double>::useMKL(int d) {
 }
 #endif
 
+#ifdef PADDLE_WITH_MKLDNN
+/* EltwiseMul for nChw16c & NC inputs JitKernel */
+template <typename T>
+class EltwiseMulnChw16cNCKernelImpl
+    : public math::jitkernel::EltwiseMulnChw16cNCKernel<T> {
+ public:
+  JITKERNEL_DECLARE_STATIC_FUNC;
+  explicit EltwiseMulnChw16cNCKernelImpl(int d)
+      : EltwiseMulnChw16cNCKernel<T>() {
+    using mul_func_t = void (*)(const float*, const float*, float*, int, int);
+#ifdef PADDLE_WITH_XBYAK
+    if (useJIT(d)) {
+      // roughly estimate the size of code
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
+      sz = sz > 4096 ? sz : 4096;
+      jitcode_.reset(new gen::EltwiseMulnChw16cNC(sz));
+      this->Compute = (mul_func_t)jitcode_->getCode();
+      return;
+    }
+#endif
+    PADDLE_THROW(
+        "This kernel shouldn't be used in Non-Xbyak, Non-MKL-DNN "
+        "environemnt");
+  }
+
+#ifdef PADDLE_WITH_XBYAK
+
+ private:
+  std::unique_ptr<gen::EltwiseMulnChw16cNC> jitcode_{nullptr};
+};
+
+template <>
+bool EltwiseMulnChw16cNCKernelImpl<float>::useJIT(int d) {
+  return true;
+}
+#endif
+#endif
+
 /* VAddRelu JitKernel */
 template <typename T>
 class VAddReluKernelImpl : public VAddReluKernel<T> {
@@ -394,6 +432,9 @@ REGISTER_JITKERNEL(vscal, VScalKernel);
 REGISTER_JITKERNEL(vaddbias, VAddBiasKernel);
 REGISTER_JITKERNEL(vrelu, VReluKernel);
 REGISTER_JITKERNEL(videntity, VIdentityKernel);
+#ifdef PADDLE_WITH_MKLDNN
+REGISTER_JITKERNEL(eltwise_mul_nchw16c, EltwiseMulnChw16cNCKernel);
+#endif
 
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/pooling.cu

@@ -153,6 +153,37 @@ __global__ void KernelMaxPool2DGrad(
   }
 }
 
+template <typename PoolProcess, typename T>
+void Pool2dDirectCUDAFunctor<PoolProcess, T>::operator()(
+    const T* input, const std::vector<int>& input_shape,
+    const std::vector<int>& output_shape, const std::vector<int>& ksize,
+    const std::vector<int>& strides, const std::vector<int>& paddings,
+    PoolProcess pool_compute, bool exclusive, T* output, cudaStream_t stream) {
+  const int batch_size = input_shape[0];
+  const int input_channels = input_shape[1];
+  const int input_height = input_shape[2];
+  const int input_width = input_shape[3];
+  const int output_channels = output_shape[1];
+  const int output_height = output_shape[2];
+  const int output_width = output_shape[3];
+  const int ksize_height = ksize[0];
+  const int ksize_width = ksize[1];
+  const int stride_height = strides[0];
+  const int stride_width = strides[1];
+  const int padding_height = paddings[0];
+  const int padding_width = paddings[1];
+
+  int nthreads = batch_size * output_channels * output_height * output_width;
+  int blocks = (nthreads + 1024 - 1) / 1024;
+  dim3 threads(1024, 1);
+  dim3 grid(blocks, 1);
+
+  KernelPool2D<PoolProcess, T><<<grid, threads, 0, stream>>>(
+      nthreads, input, input_channels, input_height, input_width, output_height,
+      output_width, ksize_height, ksize_width, stride_height, stride_width,
+      padding_height, padding_width, pool_compute, exclusive, output);
+}
+
 /*
  * All tensors are in NCHW format.
  * Ksize, strides, paddings are two elements. These two elements represent
@@ -291,6 +322,11 @@ class MaxPool2dGradFunctor<platform::CUDADeviceContext, T> {
   }
 };
 
+template class Pool2dDirectCUDAFunctor<paddle::operators::math::MaxPool<float>,
+                                       float>;
+template class Pool2dDirectCUDAFunctor<paddle::operators::math::AvgPool<float>,
+                                       float>;
+
 template class MaxPool2dGradFunctor<platform::CUDADeviceContext, float>;
 template class MaxPool2dGradFunctor<platform::CUDADeviceContext, double>;
 

paddle/fluid/operators/math/pooling.h

@@ -82,6 +82,19 @@ class AvgPoolGrad {
  * This is different from average pooling. So we rewrite the max_pool_grad:
  * MaxPool2dGradFunctor, MaxPool3dGradFunctor.
  */
+#ifdef PADDLE_WITH_CUDA
+template <typename PoolProcess, typename T>
+class Pool2dDirectCUDAFunctor {
+ public:
+  void operator()(const T* input, const std::vector<int>& input_shape,
+                  const std::vector<int>& output_shape,
+                  const std::vector<int>& ksize,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings, PoolProcess pool_compute,
+                  bool exclusive, T* output, cudaStream_t stream);
+};
+#endif
+
 template <typename DeviceContext, typename PoolProcess, typename T>
 class Pool2dFunctor {
  public:

paddle/fluid/operators/stack_op.h

@@ -147,20 +147,32 @@ class StackKernel : public framework::OpKernel<T> {
     auto &dim = x[0]->dims();
     for (auto i = 0; i < axis; ++i) pre *= dim[i];
     for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
-    int total_num = pre * n * post;
 
-    auto &dev_ctx = ctx.template device_context<DeviceContext>();
 #ifdef __NVCC__
+    int total_num = pre * n * post;
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+
     thrust::device_vector<const T *> device_x_vec(x_datas);
     auto x_data_arr = device_x_vec.data().get();
-#else
-    auto x_data_arr = x_datas.data();
-#endif
+
     StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
-#ifdef __NVCC__
+
     // Wait() must be called because device_x_vec may be destructed before
     // kernel ends
     dev_ctx.Wait();
+#else
+    auto x_data_arr = x_datas.data();
+
+    size_t x_offset = 0;
+    size_t y_offset = 0;
+    for (int i = 0; i < pre; i++) {
+      for (int j = 0; j < n; j++) {
+        std::memcpy(y_data + y_offset, x_data_arr[j] + x_offset,
+                    post * sizeof(T));
+        y_offset += post;
+      }
+      x_offset += post;
+    }
 #endif
   }
 };

paddle/fluid/platform/init.cc

@@ -38,6 +38,7 @@ std::once_flag p2p_init_flag;
 
 void InitGflags(std::vector<std::string> argv) {
   std::call_once(gflags_init_flag, [&]() {
+    FLAGS_logtostderr = true;
     argv.insert(argv.begin(), "dummy");
     int argc = argv.size();
     char **arr = new char *[argv.size()];

paddle/fluid/pybind/pybind.cc

@@ -359,6 +359,9 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<platform::Communicator>();
            },
            py::return_value_policy::reference)
+
+#endif
+#ifndef _WIN32
       .def("get_reader",
            [](Variable &self) -> framework::ReaderHolder * {
              PADDLE_ENFORCE(self.IsType<framework::ReaderHolder>());
@@ -366,7 +369,7 @@ All parameter, weight, gradient are variables in Paddle.
            },
            py::return_value_policy::reference)
 #endif
-      ;
+      ;  // NOLINT
 
 #if !defined(_WIN32)
   py::class_<framework::ReaderHolder>(m, "Reader", "")

python/paddle/fluid/layers/nn.py

@@ -726,11 +726,11 @@ def dynamic_gru(input,
             create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
         bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
-            of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates 
+            of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
             the bias in the update gate, reset gate and candidate calculations.
-            If it is set to False, no bias will be applied to the update gate, 
-            reset gate and candidate calculations. If it is set to None or one 
-            attribute of ParamAttr, dynamic_gru will create ParamAttr as 
+            If it is set to False, no bias will be applied to the update gate,
+            reset gate and candidate calculations. If it is set to None or one
+            attribute of ParamAttr, dynamic_gru will create ParamAttr as
             bias_attr. If the Initializer of the bias_attr is not set, the bias
             is initialized zero. Default: None.
         is_reverse(bool): Whether to compute reversed GRU, default
@@ -847,11 +847,11 @@ def gru_unit(input,
             create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
         bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
-            of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates 
+            of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
             the bias in the update gate, reset gate and candidate calculations.
-            If it is set to False, no bias will be applied to the update gate, 
-            reset gate and candidate calculations. If it is set to None or one 
-            attribute of ParamAttr, gru_unit will create ParamAttr as 
+            If it is set to False, no bias will be applied to the update gate,
+            reset gate and candidate calculations. If it is set to None or one
+            attribute of ParamAttr, gru_unit will create ParamAttr as
             bias_attr. If the Initializer of the bias_attr is not set, the bias
             is initialized zero. Default: None.
         activation (string): The activation type for cell (actNode).
@@ -1064,9 +1064,9 @@ def dropout(x,
                                            inference: out = input
                                            (make is a tensor same shape with input, value is 0 or 1
                                             ratio of 0 is dropout_prob)
-                                           dropout op can be removed from the program. 
+                                           dropout op can be removed from the program.
                                            the program will be efficient
-                                        
+
 
 
     Returns:
@@ -2149,7 +2149,7 @@ def pool2d(input,
         ceil_mode (bool): ${ceil_mode_comment}
         name (str|None): A name for this layer(optional). If set None, the
                         layer will be named automatically.
-        exclusive (bool): Whether to exclude padding points in average pooling 
+        exclusive (bool): Whether to exclude padding points in average pooling
                           mode, default is true
 
     Returns:
@@ -2240,7 +2240,7 @@ def pool3d(input,
         ceil_mode (bool): ${ceil_mode_comment}
         name (str): A name for this layer(optional). If set None, the layer
             will be named automatically.
-        exclusive (bool): Whether to exclude padding points in average pooling 
+        exclusive (bool): Whether to exclude padding points in average pooling
                           mode, default is true
 
     Returns:
@@ -4342,7 +4342,7 @@ def nce(input,
         sampler (str): The sampler used to sample class from negtive classes.
                        It can be 'uniform', 'log_uniform' or 'custom_dist'.
                        default: 'uniform'.
-        custom_dist (Variable): A tensor with shape [num_total_classes]. 
+        custom_dist (Variable): A tensor with shape [num_total_classes].
                        It is used when sampler is set to 'custom_dist'.
                        custom_dist[i] is the probsbility of i-th class to be sampled.
                        default: None.
@@ -4385,7 +4385,7 @@ def nce(input,
                           num_neg_samples=3,
                           sampler="custom_dist",
                           custom_dist=dist)
-            
+
     """
     helper = LayerHelper('nce', **locals())
     assert isinstance(input, Variable)
@@ -4556,9 +4556,9 @@ def transpose(x, perm, name=None):
     Examples:
         .. code-block:: python
 
-            # use append_batch_size=False to avoid prepending extra 
+            # use append_batch_size=False to avoid prepending extra
             # batch size in shape
-            x = fluid.layers.data(name='x', shape=[5, 10, 15], 
+            x = fluid.layers.data(name='x', shape=[5, 10, 15],
                             dtype='float32', append_batch_size=False)
             x_transposed = layers.transpose(x, perm=[1, 0, 2])
     """
@@ -4835,7 +4835,7 @@ def softmax_with_cross_entropy(logits,
     3) If numeric_stable_mode is True, softmax is calculated first by:
 
     .. math::
-        
+
         max_j = \\max_{i=0}^{K}{\\text{logit}_i}
 
         log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
@@ -4858,18 +4858,18 @@ def softmax_with_cross_entropy(logits,
         numeric_stable_mode (bool): A flag to indicate whether to use a more
                                     numerically stable algorithm. Only valid
                                     when soft_label is False and GPU is used.
-                                    When soft_label is True or CPU is used, 
-                                    the algorithm is always numerically stable. 
-                                    Note that the speed may be slower when use 
+                                    When soft_label is True or CPU is used,
+                                    the algorithm is always numerically stable.
+                                    Note that the speed may be slower when use
                                     stable algorithm. Default: False
-        return_softmax (bool): A flag indicating whether to return the softmax 
+        return_softmax (bool): A flag indicating whether to return the softmax
                                along with the cross entropy loss. Default: False
 
     Returns:
-        Variable or Tuple of two Variables: Return the cross entropy loss if 
-                              `return_softmax` is False, otherwise the tuple 
-                              (loss, softmax), where the cross entropy loss is 
-                              a 2-D tensor with shape [N x 1], and softmax is a 
+        Variable or Tuple of two Variables: Return the cross entropy loss if
+                              `return_softmax` is False, otherwise the tuple
+                              (loss, softmax), where the cross entropy loss is
+                              a 2-D tensor with shape [N x 1], and softmax is a
                               2-D tensor with shape [N x K].
 
     Examples:
@@ -5756,20 +5756,20 @@ def image_resize(input,
                          Default: None
         name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
-        resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST' 
+        resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST'
                        currently.
                        Default: 'BILINEAR'
-        actual_shape(Variable): An optional input to specify output shape 
-                                dynamically. If provided, image resize  
-                                according to this given shape rather than 
+        actual_shape(Variable): An optional input to specify output shape
+                                dynamically. If provided, image resize
+                                according to this given shape rather than
                                 :attr:`out_shape` and :attr:`scale` specifying
-                                shape. That is to say actual_shape has the 
-                                highest priority. It is recommended to use 
-                                actual_shape instead of :attr:`out_shape` if you 
-                                want to specify output shape dynamically. When 
-                                using actual_shape to specify output shape, one of 
-                                :attr:`out_shape` and :attr:`scale` should also be 
-                                set, otherwise errors would be occured in graph 
+                                shape. That is to say actual_shape has the
+                                highest priority. It is recommended to use
+                                actual_shape instead of :attr:`out_shape` if you
+                                want to specify output shape dynamically. When
+                                using actual_shape to specify output shape, one of
+                                :attr:`out_shape` and :attr:`scale` should also be
+                                set, otherwise errors would be occured in graph
                                 constructing stage.
                                 Default: None
 
@@ -5780,7 +5780,7 @@ def image_resize(input,
     Raises:
         TypeError: out_shape should be a list or tuple or Variable.
         TypeError: actual_shape should either be Variable or None.
-        ValueError: The 'resample' of image_resize can only be 'BILINEAR' 
+        ValueError: The 'resample' of image_resize can only be 'BILINEAR'
                     or 'NEAREST' currently.
         ValueError: One of out_shape and scale must not be None.
         ValueError: out_shape length should be 2.
@@ -5788,7 +5788,7 @@ def image_resize(input,
     Examples:
         .. code-block:: python
 
-            out = fluid.layers.image_resize(input, out_shape=[12, 12])
+            out = fluid.layers.image_resize(input, out_shape=[12, 12], resample="NEAREST")
     """
     resample_methods = {
         'BILINEAR': 'bilinear',
@@ -5852,17 +5852,17 @@ def resize_bilinear(input,
                     name=None,
                     actual_shape=None):
     """
-    Resize input by performing bilinear interpolation based on given 
-    output shape which specified by actual_shape, out_shape and scale 
+    Resize input by performing bilinear interpolation based on given
+    output shape which specified by actual_shape, out_shape and scale
     in priority order.
 
-    Bilinear interpolation is an extension of linear interpolation for 
-    interpolating functions of two variables (e.g. H-direction and 
-    W-direction in this op) on a rectilinear 2D grid. The key idea is 
-    to perform linear interpolation first in one direction, and then 
+    Bilinear interpolation is an extension of linear interpolation for
+    interpolating functions of two variables (e.g. H-direction and
+    W-direction in this op) on a rectilinear 2D grid. The key idea is
+    to perform linear interpolation first in one direction, and then
     again in the other direction.
 
-    For details of bilinear interpolation, please refer to Wikipedia: 
+    For details of bilinear interpolation, please refer to Wikipedia:
     https://en.wikipedia.org/wiki/Bilinear_interpolation
 
     Args:
@@ -5875,22 +5875,27 @@ def resize_bilinear(input,
              a higher priority than scale. Default: None.
 
         name(str|None): The output variable name.
-        actual_shape(Variable): An optional input to specify output shape 
-                                dynamically. If provided, image resize  
-                                according to this given shape rather than 
+        actual_shape(Variable): An optional input to specify output shape
+                                dynamically. If provided, image resize
+                                according to this given shape rather than
                                 :attr:`out_shape` and :attr:`scale` specifying
-                                shape. That is to say actual_shape has the 
-                                highest priority. It is recommended to use 
-                                actual_shape instead of :attr:`out_shape` if you 
-                                want to specify output shape dynamically. When 
-                                using actual_shape to specify output shape, one of 
-                                :attr:`out_shape` and :attr:`scale` should also be 
-                                set, otherwise errors would be occured in graph 
+                                shape. That is to say actual_shape has the
+                                highest priority. It is recommended to use
+                                actual_shape instead of :attr:`out_shape` if you
+                                want to specify output shape dynamically. When
+                                using actual_shape to specify output shape, one of
+                                :attr:`out_shape` and :attr:`scale` should also be
+                                set, otherwise errors would be occured in graph
                                 constructing stage.
                                 Default: None
 
     Returns:
         ${out_comment}.
+
+    Examples:
+        .. code-block:: python
+
+            out = fluid.layers.resize_bilinear(input, out_shape=[12, 12])
     """
 
     return image_resize(input, out_shape, scale, name, 'BILINEAR', actual_shape)
@@ -5904,11 +5909,11 @@ def resize_nearest(input,
                    actual_shape=None):
     """
     Resize input by performing nearest neighbor interpolation in both the
-    3rd dimention(in height direction) and the 4th dimention(in width 
-    direction) based on given output shape which specified by actual_shape, 
+    3rd dimention(in height direction) and the 4th dimention(in width
+    direction) based on given output shape which specified by actual_shape,
     out_shape and scale in priority order.
 
-    For details of nearest neighbor interpolation, please refer to Wikipedia: 
+    For details of nearest neighbor interpolation, please refer to Wikipedia:
     https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
 
     Args:
@@ -5921,22 +5926,27 @@ def resize_nearest(input,
              a higher priority than scale. Default: None.
 
         name(str|None): The output variable name.
-        actual_shape(Variable): An optional input to specify output shape 
-                                dynamically. If provided, image resize  
-                                according to this given shape rather than 
+        actual_shape(Variable): An optional input to specify output shape
+                                dynamically. If provided, image resize
+                                according to this given shape rather than
                                 :attr:`out_shape` and :attr:`scale` specifying
-                                shape. That is to say actual_shape has the 
-                                highest priority. It is recommended to use 
-                                actual_shape instead of :attr:`out_shape` if you 
-                                want to specify output shape dynamically. When 
-                                using actual_shape to specify output shape, one of 
-                                :attr:`out_shape` and :attr:`scale` should also be 
-                                set, otherwise errors would be occured in graph 
+                                shape. That is to say actual_shape has the
+                                highest priority. It is recommended to use
+                                actual_shape instead of :attr:`out_shape` if you
+                                want to specify output shape dynamically. When
+                                using actual_shape to specify output shape, one of
+                                :attr:`out_shape` and :attr:`scale` should also be
+                                set, otherwise errors would be occured in graph
                                 constructing stage.
                                 Default: None
 
     Returns:
         ${out_comment}.
+
+    Examples:
+        .. code-block:: python
+
+            out = fluid.layers.resize_nearest(input, out_shape=[12, 12])
     """
 
     return image_resize(input, out_shape, scale, name, 'NEAREST', actual_shape)
@@ -6436,15 +6446,15 @@ def affine_grid(theta, out_shape, name=None):
                         [x_14, x_15, x_16]]
                        [[x_21, x_22, x_23]
                         [x_24, x_25, x_26]]]
-      
+
               out_shape = [2, 3, 5, 5]
-      
+
           Step 1:
-      
+
               Generate normalized coordinates according to out_shape.
               The values of the normalized coordinates are in the interval between -1 and 1.
               The shape of the normalized coordinates is [2, H, W] as below:
-      
+
               C = [[[-1.  -1.  -1.  -1.  -1. ]
                     [-0.5 -0.5 -0.5 -0.5 -0.5]
                     [ 0.   0.   0.   0.   0. ]
@@ -7692,6 +7702,15 @@ def logical_and(x, y, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            right = fluid.layers.data(
+                name='right', shape=[1], dtype='int32')
+            result = fluid.layers.logical_and(x=left, y=right)
     """
 
     return _logical_op(
@@ -7711,6 +7730,15 @@ def logical_or(x, y, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            right = fluid.layers.data(
+                name='right', shape=[1], dtype='int32')
+            result = fluid.layers.logical_or(x=left, y=right)
     """
 
     return _logical_op(
@@ -7730,6 +7758,15 @@ def logical_xor(x, y, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            right = fluid.layers.data(
+                name='right', shape=[1], dtype='int32')
+            result = fluid.layers.logical_xor(x=left, y=right)
     """
 
     return _logical_op(
@@ -7748,6 +7785,13 @@ def logical_not(x, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            result = fluid.layers.logical_not(x=left)
     """
 
     return _logical_op(
@@ -7767,6 +7811,13 @@ def clip(x, min, max, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            input = fluid.layers.data(
+                name='data', shape=[1], dtype='float32')
+            reward = fluid.layers.clip(x=input, min=-1.0, max=1.0)
     """
 
     helper = LayerHelper("clip", **locals())
@@ -7799,6 +7850,13 @@ def clip_by_norm(x, max_norm, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            input = fluid.layers.data(
+                name='data', shape=[1], dtype='float32')
+            reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
     """
 
     helper = LayerHelper("clip_by_norm", **locals())
@@ -7944,19 +8002,19 @@ def maxout(x, groups, name=None):
 def space_to_depth(x, blocksize, name=None):
     """
     Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width]
-    
-    This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the 
-    input LoDtensor where values from the height and width dimensions are moved to the channel dimension. 
+
+    This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the
+    input LoDtensor where values from the height and width dimensions are moved to the channel dimension.
     The attr blocksize indicates the input block size.
-    
-    space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according 
+
+    space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according
     to blocksize to construct output with shape [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]:
-    
-    space_to_depth is used to This operation is useful for resizing the activations between convolutions 
+
+    space_to_depth is used to This operation is useful for resizing the activations between convolutions
     (but keeping all data)
 
     - Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
-    - The depth of the output tensor is block_size * block_size * input channel 
+    - The depth of the output tensor is block_size * block_size * input channel
     - The Y, X coordinates within each block of the input become the high order component of the output channel index
     - channel should be divisible by square of blocksize
     - height, width should be divsible by blocksize
@@ -8003,7 +8061,7 @@ def space_to_depth(x, blocksize, name=None):
 
 @templatedoc()
 def sequence_reverse(x, name=None):
-    """ 
+    """
     ${comment}
 
     Args:
@@ -8070,21 +8128,21 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
 
 
 def similarity_focus(input, axis, indexes, name=None):
-    """  
+    """
     SimilarityFocus Operator
 
     Generate a similarity focus mask with the same shape of input using the following method:
-    1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding 
-       to the axis according to the indexes. For example, if axis=1 and indexes=[a], 
-       it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X 
+    1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
+       to the axis according to the indexes. For example, if axis=1 and indexes=[a],
+       it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
        is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
-    2. For each index, find the largest numbers in the tensor T, so that the same 
-       row and same column has at most one number(what it means is that if the 
-       largest number has been found in the i-th row and the j-th column, then 
-       the numbers in the i-th row or j-th column will be skipped. And then the 
-       next largest number will be selected from the remaining numbers. Obviously 
-       there will be min(B, C) numbers), and mark the corresponding position of the 
-       3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for 
+    2. For each index, find the largest numbers in the tensor T, so that the same
+       row and same column has at most one number(what it means is that if the
+       largest number has been found in the i-th row and the j-th column, then
+       the numbers in the i-th row or j-th column will be skipped. And then the
+       next largest number will be selected from the remaining numbers. Obviously
+       there will be min(B, C) numbers), and mark the corresponding position of the
+       3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
        each index.
     3. Broadcast the 3-D similarity focus mask to the same shape of input X.
 
@@ -8140,16 +8198,16 @@ def similarity_focus(input, axis, indexes, name=None):
                               [1.0, 0.0]]]]
 
     Args:
-        input(Variable): The input tensor variable(default float). It should 
+        input(Variable): The input tensor variable(default float). It should
             be a 4-D tensor with shape [BatchSize, A, B, C].
         axis(int): Indicating the dimension to be selected. It can only be
             1, 2 or 3.
         indexes(list): Indicating the indexes of the selected dimension.
 
     Returns:
-        Variable: A tensor variable with the same shape and same type 
+        Variable: A tensor variable with the same shape and same type
             as the input.
-        
+
     Examples:
         .. code-block:: python
             data = fluid.layers.data(
@@ -8252,12 +8310,12 @@ def hash(input, hash_size, num_hash=1, name=None):
 @templatedoc()
 def grid_sampler(x, grid, name=None):
     """
-    This operation samples input X by using bilinear interpolation based on 
+    This operation samples input X by using bilinear interpolation based on
     flow field grid, which is usually gennerated by affine_grid. The grid of
-    shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates 
-    with shape [N, H, W] each, where grid_x is indexing the 4th dimension 
-    (in width dimension) of input data x and grid_y is indexng the 3rd 
-    dimention (in height dimension), finally results is the bilinear 
+    shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
+    with shape [N, H, W] each, where grid_x is indexing the 4th dimension
+    (in width dimension) of input data x and grid_y is indexng the 3rd
+    dimention (in height dimension), finally results is the bilinear
     interpolation value of 4 nearest corner points.
 
     Step 1:
@@ -8267,7 +8325,7 @@ def grid_sampler(x, grid, name=None):
     grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
 
     Step 2:
-    Indices input data X with grid (x, y) in each [H, W] area, and bilinear 
+    Indices input data X with grid (x, y) in each [H, W] area, and bilinear
     interpolate point value by 4 nearest points.
 
       wn ------- y_n ------- en
@@ -8304,7 +8362,7 @@ def grid_sampler(x, grid, name=None):
         name (str, default None): The name of this layer.
 
     Returns:
-        out(Variable): Output of shape [N, C, H, W] data samples input X 
+        out(Variable): Output of shape [N, C, H, W] data samples input X
         using bilnear interpolation based on input grid.
 
     Exmples:

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -45,6 +45,10 @@ if(APPLE)
     list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
     list(REMOVE_ITEM TEST_OPS test_fuse_elewise_add_act_pass)
 endif()
+if(NOT WITH_MKLML)
+    # this op is not support on openblas
+    list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op)
+endif()
 
 function(py_test_modules TARGET_NAME)
   if(WITH_TESTING)

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

@@ -362,7 +362,9 @@ class OpTest(unittest.TestCase):
             else:
                 return []
         places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
+        cpu_only = self._cpu_only if hasattr(self, '_cpu_only') else False
+        if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type)\
+           and not cpu_only:
             places.append(core.CUDAPlace(0))
         return places
 

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

+#  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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+from test_elementwise_mul_op import *
+
+
+class TestElementwiseMulMKLDNNOp_BroadcastNCHW16c(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+        self.y = np.random.rand(1, 16).astype(self.dtype)
+
+        self.out = x * self.y.reshape(1, 16, 1, 1)
+        self.out = self.out.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_BroadcastNCHW16c, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nc"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+@unittest.skip(
+    "Not implemented yet.")  # TODO(mgallus): enable when implemented.
+class TestElementwiseMulMKLDNNOp_BroadcastNCHW8c(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 8, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 8, 2, 2)
+        self.y = np.random.rand(1, 8).astype(self.dtype)
+
+        self.out = x * self.y.reshape(1, 8, 1, 1)
+        self.out = self.out.transpose(0, 2, 3, 1).reshape(1, 8, 2, 2)
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_BroadcastNCHW8c, self).setUp()
+        self.attrs["x_data_format"] = "nchw8c"
+        self.attrs["y_data_format"] = "nc"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNCHW(ElementwiseMulOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = np.random.rand(1, 16).astype(self.dtype)
+
+        self.out = self.x * self.y.reshape(1, 16, 1, 1)
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNCHW16C(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = self.x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackNCHW16C, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nchw16c"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNoReorders(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = self.x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackNoReorders, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nchw16c"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackWithReorder1(ElementwiseMulOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = self.x * y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackWithReorder1, self).setUp()
+        self.attrs["x_data_format"] = "nchw"
+        self.attrs["y_data_format"] = "nchw16c"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackWithReorder2(ElementwiseMulOp):
+    def init_input_output(self):
+        self.y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackWithReorder2, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nchw"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNoReorders2(ElementwiseMulOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 16).astype(self.dtype)
+        self.y = np.random.rand(1, 16).astype(self.dtype)
+
+        self.out = self.x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackNoReorders2, self).setUp()
+        self.attrs["x_data_format"] = "nc"
+        self.attrs["y_data_format"] = "nc"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -21,13 +21,24 @@ from paddle.fluid.op import Operator
 
 
 class ElementwiseMulOp(OpTest):
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
     def setUp(self):
         self.op_type = "elementwise_mul"
+        self.dtype = np.float32
+        self.axis = -1
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+
         self.inputs = {
-            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float64"),
-            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float64")
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
         }
-        self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
+        self.outputs = {'Out': self.out}
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
 
     def test_check_output(self):
         self.check_output()
@@ -41,6 +52,17 @@ class ElementwiseMulOp(OpTest):
     def test_check_grad_ingore_y(self):
         self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
 
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.out = np.multiply(self.x, self.y)
+
+    def init_dtype(self):
+        pass
+
+    def init_axis(self):
+        pass
+
 
 class TestElementwiseMulOp_scalar(ElementwiseMulOp):
     def setUp(self):
@@ -63,17 +85,13 @@ class TestElementwiseMulOp_Vector(ElementwiseMulOp):
 
 
 class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp):
-    def setUp(self):
-        self.op_type = "elementwise_mul"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float64),
-            'Y': np.random.rand(2).astype(np.float64)
-        }
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(2).astype(self.dtype)
+        self.out = self.x * self.y.reshape(2, 1, 1)
 
-        self.attrs = {'axis': 0}
-        self.outputs = {
-            'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1)
-        }
+    def init_axis(self):
+        self.axis = 0
 
 
 class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp):

python/requirements.txt

 requests==2.9.2
-numpy>=1.12,<=1.14 #TODO:change to ">=1.12" when numpy fix bug in 1.15 and higher version
+numpy>=1.12
 protobuf==3.1
 recordio>=0.1.0
 matplotlib==2.2.3 # TODO: let python3 paddlepaddle package use latest matplotlib