Merge branch 'develop' into fea/jit/rnn

.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 |

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/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} ${model_name})
@@ -109,6 +113,6 @@ if(WITH_GPU AND TENSORRT_FOUND)
        inference_download_and_uncompress(${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

@@ -222,7 +222,23 @@ 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;
+
+      // 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
+      }
+
+      LOG(INFO) << "Thread " << tid << " run " << num_times << " times...";
+      {
         Timer timer;
         timer.tic();
         for (int i = 0; i < num_times; i++) {
@@ -235,6 +251,7 @@ void TestMultiThreadPrediction(
         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/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

@@ -408,6 +408,42 @@ class LSTMJitCode : public VActJitCode {
   }
 };
 
+#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

@@ -89,6 +89,15 @@ 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

@@ -184,6 +184,44 @@ 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> {
@@ -349,6 +387,9 @@ 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/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/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):