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

CMakeLists.txt

@@ -47,33 +47,34 @@ find_package(Threads REQUIRED)
 
 include(simd)
 
-################################ Configurations #######################################
+################################ Exposed Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
-option(WITH_AMD_GPU     "Compile PaddlePaddle with AMD GPU"             OFF)
+option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
+option(WITH_PYTHON      "Compile PaddlePaddle with python interpreter"  ON)
+option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        OFF)
 option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
+option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
+option(WITH_DISTRIBUTE  "Compile with distributed support"              OFF)
+option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
+option(ON_INFER         "Turn on inference optimization."               OFF)
+option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
+################################ Internal Configurations #######################################
+option(WITH_AMD_GPU     "Compile PaddlePaddle with AMD GPU"             OFF)
 option(WITH_NGRAPH      "Compile PaddlePaddle with nGraph support."     OFF)
-option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
-option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        OFF)
-option(WITH_PYTHON      "Compile PaddlePaddle with python interpreter"  ON)
 option(WITH_PROFILER    "Compile PaddlePaddle with GPU profiler and gperftools"        OFF)
 option(WITH_JEMALLOC    "Compile PaddlePaddle with jemalloc"            OFF)
 option(WITH_COVERAGE    "Compile PaddlePaddle with code coverage"       OFF)
 option(COVERALLS_UPLOAD "Package code coverage data to coveralls"       OFF)
-option(WITH_DISTRIBUTE  "Compile with distributed support"              OFF)
 option(WITH_PSLIB       "Compile with pslib support"                    OFF)
 option(WITH_CONTRIB     "Compile the third-party contributation"        OFF)
 option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
 # TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
-option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
 option(ANAKIN_BUILD_FAT_BIN "Build anakin cuda fat-bin lib for all device plantform, ignored when WITH_ANAKIN=OFF" OFF)
 option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plantform. ignored when WITH_ANAKIN=OFF" ON)
 option(WITH_GRPC     "Use grpc as the default rpc framework"            ${WITH_DISTRIBUTE})
-option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
-option(ON_INFER         "Turn on inference optimization."               OFF)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference C++ high-level api interface"  OFF)
 option(WITH_HIGH_LEVEL_API_TEST   "Test fluid python high-level api interface"  OFF)
-option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
 option(WITH_FAST_MATH   "Make use of fast math library, might affect the precision to some extent" ON)
 

paddle/fluid/API.spec

@@ -241,6 +241,7 @@ paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output
 paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '46994d10276dd4cb803b4062b5d14329'))
 paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '731b21c62a4add60a33bd76d802ffc5c'))
 paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', 'b76ccca3735bea4a58a0dbf0d77c5393'))
+paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', 'a07a44c2bacdcd09c1f5f35a96a0514e'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139'))
 paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc'))
 paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'b0a1c2fc51c27a106da28f3308c41f5e'))

paddle/fluid/inference/api/analysis_predictor.cc

@@ -832,6 +832,45 @@ std::string AnalysisPredictor::GetSerializedProgram() const {
   return inference_program_->Proto()->SerializeAsString();
 }
 
+// Add SaveOptimModel
+void AnalysisPredictor::SaveOptimModel(const std::string &dir) {
+  // save model
+  std::string model_name = dir + "/model";
+  std::ofstream outfile;
+  outfile.open(model_name, std::ios::out | std::ios::binary);
+  std::string inference_prog_desc = GetSerializedProgram();
+  outfile << inference_prog_desc;
+  // save params
+  framework::ProgramDesc save_program;
+  auto *save_block = save_program.MutableBlock(0);
+
+  const framework::ProgramDesc &main_program = program();
+  const framework::BlockDesc &global_block = main_program.Block(0);
+  std::vector<std::string> save_var_list;
+  for (framework::VarDesc *var : global_block.AllVars()) {
+    if (IsPersistable(var)) {
+      framework::VarDesc *new_var = save_block->Var(var->Name());
+      new_var->SetShape(var->GetShape());
+      new_var->SetDataType(var->GetDataType());
+      new_var->SetType(var->GetType());
+      new_var->SetLoDLevel(var->GetLoDLevel());
+      new_var->SetPersistable(true);
+
+      save_var_list.push_back(new_var->Name());
+    }
+  }
+  std::sort(save_var_list.begin(), save_var_list.end());
+  auto *op = save_block->AppendOp();
+  op->SetType("save_combine");
+  op->SetInput("X", save_var_list);
+  op->SetAttr("file_path", dir + "/params");
+  op->CheckAttrs();
+
+  platform::CPUPlace place;
+  framework::Executor exe(place);
+  exe.Run(save_program, scope(), 0, true, true);
+}
+
 template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<AnalysisConfig>(
     const AnalysisConfig &config) {

paddle/fluid/inference/api/analysis_predictor.h

@@ -86,6 +86,10 @@ class AnalysisPredictor : public PaddlePredictor {
 
   bool MkldnnQuantize();
 
+  // save program to  model
+  // save parameters to params
+  void SaveOptimModel(const std::string &dir);
+
  protected:
   // For memory optimization.
   bool need_collect_var_shapes_for_memory_optim();

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -196,6 +196,9 @@ TEST(AnalysisPredictor, Clone) {
   }
 }
 
+// This function is not released yet, will fail on some machine.
+// TODO(Superjomn) Turn on it latter.
+/*
 TEST(AnalysisPredictor, memory_optim) {
   AnalysisConfig config(FLAGS_dirname);
   config.DisableGpu();
@@ -246,6 +249,7 @@ TEST(AnalysisPredictor, memory_optim) {
 
   inference::CompareResult(output, output1);
 }
+*/
 
 #ifdef PADDLE_WITH_MKLDNN
 class MkldnnQuantizerTest : public testing::Test {

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

@@ -170,6 +170,15 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->SwitchIrOptim(true);
 }
 
+void SetOptimConfig(AnalysisConfig *cfg) {
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  cfg->SetModel(optimModelPath + "/model", optimModelPath + "/params");
+  cfg->SwitchIrOptim(true);
+  cfg->SwitchSpecifyInputNames();
+}
+
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
   DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
   std::vector<PaddleTensor> input_slots;
@@ -315,5 +324,44 @@ TEST(Analyzer_dam, compare_determine) {
                        input_slots_all);
 }
 
+// Save optim model
+TEST(Analyzer_dam, save_optim_model) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  mkdir(optimModelPath.c_str(), 0777);
+  auto predictor = CreateTestPredictor(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+      FLAGS_use_analysis);
+  (static_cast<AnalysisPredictor *>(predictor.get()))
+      ->SaveOptimModel(optimModelPath);
+}
+
+void CompareOptimAndOrig(const PaddlePredictor::Config *orig_config,
+                         const PaddlePredictor::Config *optim_config,
+                         const std::vector<std::vector<PaddleTensor>> &inputs) {
+  PrintConfig(orig_config, true);
+  PrintConfig(optim_config, true);
+  std::vector<std::vector<PaddleTensor>> orig_outputs, optim_outputs;
+  TestOneThreadPrediction(orig_config, inputs, &orig_outputs, false);
+  TestOneThreadPrediction(optim_config, inputs, &optim_outputs, false);
+  CompareResult(orig_outputs.back(), optim_outputs.back());
+}
+
+TEST(Analyzer_dam, compare_optim_orig) {
+  AnalysisConfig orig_cfg;
+  AnalysisConfig optim_cfg;
+  SetConfig(&orig_cfg);
+  SetOptimConfig(&optim_cfg);
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareOptimAndOrig(
+      reinterpret_cast<const PaddlePredictor::Config *>(&orig_cfg),
+      reinterpret_cast<const PaddlePredictor::Config *>(&optim_cfg),
+      input_slots_all);
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -32,6 +32,17 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
   SetFakeImageInput(inputs, FLAGS_infer_model);
 }
 
+void SetOptimConfig(AnalysisConfig *cfg) {
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  cfg->SetModel(optimModelPath + "/model", optimModelPath + "/params");
+  cfg->DisableGpu();
+  cfg->SwitchIrOptim();
+  cfg->SwitchSpecifyInputNames();
+  cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
+}
+
 // Easy for profiling independently.
 void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
@@ -87,13 +98,51 @@ TEST(Analyzer_resnet50, compare_mkldnn) { compare(true /* use_mkldnn */); }
 TEST(Analyzer_resnet50, compare_determine) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
   CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
                        input_slots_all);
 }
 
+// Save optim model
+TEST(Analyzer_resnet50, save_optim_model) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  mkdir(optimModelPath.c_str(), 0777);
+  auto predictor = CreateTestPredictor(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+      FLAGS_use_analysis);
+  (static_cast<AnalysisPredictor *>(predictor.get()))
+      ->SaveOptimModel(optimModelPath);
+}
+
+void CompareOptimAndOrig(const PaddlePredictor::Config *orig_config,
+                         const PaddlePredictor::Config *optim_config,
+                         const std::vector<std::vector<PaddleTensor>> &inputs) {
+  PrintConfig(orig_config, true);
+  PrintConfig(optim_config, true);
+  std::vector<std::vector<PaddleTensor>> orig_outputs, optim_outputs;
+  TestOneThreadPrediction(orig_config, inputs, &orig_outputs, false);
+  TestOneThreadPrediction(optim_config, inputs, &optim_outputs, false);
+  CompareResult(orig_outputs.back(), optim_outputs.back());
+}
+
+TEST(Analyzer_resnet50, compare_optim_orig) {
+  AnalysisConfig orig_cfg;
+  AnalysisConfig optim_cfg;
+  SetConfig(&orig_cfg);
+  SetOptimConfig(&optim_cfg);
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareOptimAndOrig(
+      reinterpret_cast<const PaddlePredictor::Config *>(&orig_cfg),
+      reinterpret_cast<const PaddlePredictor::Config *>(&optim_cfg),
+      input_slots_all);
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/op_use_default_grad_op_maker.spec

@@ -29,8 +29,6 @@ pool3d
 prelu
 quantize
 rank_loss
-reduce_all
-reduce_any
 reduce_max
 reduce_mean
 reduce_min

paddle/fluid/operators/cvm_op.cc

+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/cvm_op.h"
+#include <memory>
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class CVMOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto cvm_dims = ctx->GetInputDim("CVM");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(cvm_dims.size(), 2UL, "Input(CVM)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(cvm_dims[1], 2UL,
+                      "The 2nd dimension of "
+                      "Input(CVM) should be 2.");
+
+    if (ctx->Attrs().Get<bool>("use_cvm")) {
+      ctx->SetOutputDim("Y", {x_dims[0], x_dims[1]});
+    } else {
+      ctx->SetOutputDim("Y", {x_dims[0], x_dims[1] - 2});
+    }
+    ctx->ShareLoD("X", /*->*/ "Y");
+  }
+
+ protected:
+  // Explicitly set that the data type of computation kernel of
+  // cvm
+  // is determined by its input "X".
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   platform::CPUPlace());
+  }
+};
+
+class CVMGradientOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
+                   "Input(Y@GRAD) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                   "Output(X@GRAD) should be not null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto cvm_dims = ctx->GetInputDim("CVM");
+    auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(cvm_dims.size(), 2, "Input(CVM)'s rank should be 2.");
+
+    PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
+                      "The 1st dimension of Input(X) and Input(Y@Grad) should "
+                      "be equal.");
+
+    PADDLE_ENFORCE_EQ(cvm_dims[1], 2,
+                      "When Attr(soft_label) == false, the 2nd dimension of "
+                      "Input(CVM) should be 2.");
+    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+    ctx->ShareLoD("X", framework::GradVarName("X"));
+  }
+
+ protected:
+  // Explicitly set that the data type of computation kernel of
+  // cvm
+  // is determined by its input "X".
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   platform::CPUPlace());
+  }
+};
+
+class CVMOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
+             "[N x D],"
+             " where N is the batch size and D is the emebdding dim. ");
+    AddInput("CVM",
+             "(Tensor),  a 2-D Tensor with shape [N x 2], where N is the batch "
+             "size, 2 is show and click.");
+    AddOutput("Y",
+              "(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
+              "[N x K].");
+    AddAttr<bool>("use_cvm", "bool, use cvm or not").SetDefault(true);
+    AddComment(R"DOC(
+CVM Operator.
+
+      We assume that input X is a embedding vector with cvm_feature(show and click), which shape is [N * D] (D is 2(cvm_feature) + embedding dim, N is batch_size)
+      if use_cvm is True, we will log(cvm_feature), and output shape is [N * D].
+      if use_cvm is False, we will remove cvm_feature from input, and output shape is [N * (D - 2)].
+
+)DOC");
+  }
+};
+
+class CVMGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("cvm_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("CVM", Input("CVM"));
+    op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(cvm, ops::CVMOp, ops::CVMOpMaker, ops::CVMGradOpDescMaker);
+
+REGISTER_OPERATOR(cvm_grad, ops::CVMGradientOp);
+
+REGISTER_OP_CPU_KERNEL(cvm, ops::CVMOpKernel<float>, ops::CVMOpKernel<double>);
+
+REGISTER_OP_CPU_KERNEL(cvm_grad, ops::CVMGradOpKernel<float>,
+                       ops::CVMGradOpKernel<double>);

paddle/fluid/operators/cvm_op.h

+/* Copyright (c) 2019 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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+class CVMOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const LoDTensor* x = context.Input<LoDTensor>("X");
+    const T* x_data = x->data<T>();
+    auto lod = x->lod()[0];
+    int64_t item_size = x->numel() / x->dims()[0];
+    int offset = 2;
+    if (!context.Attr<bool>("use_cvm")) {
+      item_size -= offset;
+    }
+    LoDTensor* y = context.Output<LoDTensor>("Y");
+    T* y_data = y->mutable_data<T>(context.GetPlace());
+
+    int seq_num = static_cast<int>(lod.size()) - 1;
+    for (int i = 0; i < seq_num; ++i) {
+      int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
+
+      for (int j = 0; j < seq_len; ++j) {
+        if (context.Attr<bool>("use_cvm")) {
+          std::memcpy(y_data, x_data, item_size * sizeof(T));
+          y_data[0] = log(y_data[0] + 1);
+          y_data[1] = log(y_data[1] + 1) - y_data[0];
+          x_data += item_size;
+          y_data += item_size;
+        } else {
+          std::memcpy(y_data, x_data + offset, item_size * sizeof(T));
+          x_data += item_size + offset;
+          y_data += item_size;
+        }
+      }
+    }
+  }
+};
+
+template <typename T>
+class CVMGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    LoDTensor* dx = context.Output<LoDTensor>(framework::GradVarName("X"));
+    T* dx_data = dx->mutable_data<T>(context.GetPlace());
+
+    const Tensor* cvm = context.Input<Tensor>("CVM");
+    const T* cvm_data = cvm->data<T>();
+    int offset = 2;
+    const framework::LoDTensor* dOut =
+        context.Input<framework::LoDTensor>(framework::GradVarName("Y"));
+    const T* dout_data = dOut->data<T>();
+
+    auto lod = dx->lod()[0];
+    int64_t item_size = dx->numel() / dx->dims()[0];
+    if (!context.Attr<bool>("use_cvm")) {
+      item_size -= offset;
+    }
+
+    int seq_num = static_cast<int>(lod.size()) - 1;
+    for (int i = 0; i < seq_num; ++i) {
+      int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
+
+      for (int j = 0; j < seq_len; ++j) {
+        if (context.Attr<bool>("use_cvm")) {
+          std::memcpy(dx_data, dout_data, item_size * sizeof(T));
+          dx_data[0] = cvm_data[0];
+          dx_data[1] = cvm_data[1];
+          dx_data += item_size;
+          dout_data += item_size;
+        } else {
+          std::memcpy(dx_data + offset, dout_data, item_size * sizeof(T));
+          dx_data[0] = cvm_data[0];
+          dx_data[1] = cvm_data[1];
+          dx_data += item_size + offset;
+          dout_data += item_size;
+        }
+      }
+      cvm_data += offset;
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/fake_quantize_op.cu

@@ -235,11 +235,13 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
 
     int g_find_max;
     memory::Copy(platform::CPUPlace(), &g_find_max, gpu_place, find_max,
-                 sizeof(int), 0);
+                 sizeof(int), ctx.stream());
+    ctx.Wait();
     if (g_find_max) {
       int len;
       memory::Copy(platform::CPUPlace(), &len, gpu_place, out_size_data,
-                   sizeof(int), 0);
+                   sizeof(int), ctx.stream());
+      ctx.Wait();
       FindAbsMaxFunctor<platform::CUDADeviceContext, T>()(ctx, scale_arr, len,
                                                           out_scale_data);
     }
@@ -258,25 +260,26 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
     const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
 
     T accum;
-    memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
-                 sizeof(T), 0);
     T state;
-    memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
-                 sizeof(T), 0);
     T scale;
+    memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
+                 sizeof(T), ctx.stream());
+    memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
+                 sizeof(T), ctx.stream());
     memory::Copy(platform::CPUPlace(), &scale, gpu_place, cur_scale, sizeof(T),
-                 0);
-
+                 ctx.stream());
+    ctx.Wait();
     state = rate * state + 1;
     accum = rate * accum + scale;
     scale = accum / state;
 
     memory::Copy(gpu_place, out_accum->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &accum, sizeof(T), 0);
+                 platform::CPUPlace(), &accum, sizeof(T), ctx.stream());
     memory::Copy(gpu_place, out_state->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &state, sizeof(T), 0);
+                 platform::CPUPlace(), &state, sizeof(T), ctx.stream());
     memory::Copy(gpu_place, out_scale->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &scale, sizeof(T), 0);
+                 platform::CPUPlace(), &scale, sizeof(T), ctx.stream());
+    ctx.Wait();
   }
 };
 

paddle/fluid/operators/math/selected_rows_functor.cc

@@ -296,6 +296,7 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
     auto input_height = has_value_input->height();
     framework::SelectedRows& out = *output;
     std::set<int64_t> merged_row_set;
+    size_t row_num = 0;
     for (auto* input : inputs) {
       if (input->rows().size() == 0) {
         continue;
@@ -305,42 +306,71 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
                         "dimension except for the first one");
       PADDLE_ENFORCE_EQ(input_height, input->height(),
                         "all input should have same height");
+      row_num += input->rows().size();
       merged_row_set.insert(input->rows().begin(), input->rows().end());
     }
-    std::vector<int64_t> merge_rows(merged_row_set.begin(),
-                                    merged_row_set.end());
-    if (sorted_result) {
-      std::sort(merge_rows.begin(), merge_rows.end());
-    }
-    std::unordered_map<int64_t, size_t> rows_to_id;
-    for (size_t i = 0; i < merge_rows.size(); ++i) {
-      rows_to_id[merge_rows[i]] = i;
-    }
-    out.set_rows(merge_rows);
+
     out.set_height(input_height);
     out.mutable_value()->mutable_data<T>(
         framework::make_ddim(
-            {static_cast<int64_t>(merge_rows.size()), input_width}),
+            {static_cast<int64_t>(merged_row_set.size()), input_width}),
         context.GetPlace());
+    auto* out_data = out.mutable_value()->data<T>();
 
-    math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
-    constant_functor(context, out.mutable_value(), 0.0);
+    if (merged_row_set.size() == row_num && !sorted_result) {
+      // no duplicated ids, just concat the result together
+      std::vector<int64_t> merge_rows;
+      merge_rows.reserve(row_num);
+      // concat rows
+      for (auto* in : inputs) {
+        merge_rows.insert(merge_rows.end(), in->rows().begin(),
+                          in->rows().end());
+      }
+      out.set_rows(merge_rows);
+      auto in_place = inputs[0]->place();
+      auto out_place = out.place();
+      int64_t copied_numel = 0;
+      for (auto* in : inputs) {
+        auto* in_data = in->value().data<T>();
+        auto in_numel = in->value().numel();
+        memory::Copy(boost::get<platform::CPUPlace>(out_place),
+                     out_data + copied_numel,
+                     boost::get<platform::CPUPlace>(in_place), in_data,
+                     in_numel * sizeof(T));
+        copied_numel += in_numel;
+      }
+    } else {
+      std::vector<int64_t> merge_rows(merged_row_set.begin(),
+                                      merged_row_set.end());
 
-    auto* out_data = out.mutable_value()->data<T>();
+      if (sorted_result) {
+        std::sort(merge_rows.begin(), merge_rows.end());
+      }
 
-    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
-    for (auto* input : inputs) {
-      if (input->rows().size() == 0) {
-        continue;
+      out.set_rows(merge_rows);
+
+      math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
+      constant_functor(context, out.mutable_value(), 0.0);
+
+      std::unordered_map<int64_t, size_t> rows_to_id;
+      for (size_t i = 0; i < merge_rows.size(); ++i) {
+        rows_to_id[merge_rows[i]] = i;
       }
-      auto* input_data = input->value().data<T>();
-      auto& input_rows = input->rows();
-
-      for (size_t i = 0; i < input_rows.size(); i++) {
-        size_t out_i = rows_to_id[input_rows[i]];
-        elementwise_add_to<platform::CPUDeviceContext, T>(
-            context, &blas, static_cast<size_t>(input_width),
-            &input_data[i * input_width], &out_data[out_i * input_width]);
+
+      auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
+      for (auto* input : inputs) {
+        if (input->rows().size() == 0) {
+          continue;
+        }
+        auto* input_data = input->value().data<T>();
+        auto& input_rows = input->rows();
+
+        for (size_t i = 0; i < input_rows.size(); i++) {
+          size_t out_i = rows_to_id[input_rows[i]];
+          elementwise_add_to<platform::CPUDeviceContext, T>(
+              context, &blas, static_cast<size_t>(input_width),
+              &input_data[i * input_width], &out_data[out_i * input_width]);
+        }
       }
     }
   }

paddle/fluid/operators/math/selected_rows_functor_test.cc

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
+
+#include <memory>
 #include <vector>
 #include "gtest/gtest.h"
+
 #include "paddle/fluid/operators/math/math_function.h"
 
 TEST(selected_rows_functor, cpu_add) {
@@ -360,6 +363,69 @@ TEST(selected_rows_functor, cpu_merge_add_multi) {
   }
 }
 
+TEST(selected_rows_functor, cpu_merge_add_multi_noduplicated) {
+  paddle::platform::CPUPlace cpu_place;
+  paddle::platform::CPUDeviceContext ctx(cpu_place);
+  paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext,
+                                       float>
+      set_const;
+
+  int64_t height = 10;
+  int64_t row_numel = 8;
+
+  std::vector<int64_t> rows1{1, 3, 5, 7, 9};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows1{
+      new paddle::framework::SelectedRows(rows1, height)};
+  auto* in1_value = selected_rows1->mutable_value();
+  in1_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows1.size()), row_numel}),
+      cpu_place);
+  set_const(ctx, in1_value, 1.0);
+
+  std::vector<int64_t> rows2{0, 2, 4, 6, 8};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
+      new paddle::framework::SelectedRows(rows2, height)};
+  auto* in2_value = selected_rows2->mutable_value();
+  in2_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows2.size()), row_numel}),
+      cpu_place);
+  set_const(ctx, in2_value, 2.0);
+
+  std::unique_ptr<paddle::framework::SelectedRows> output{
+      new paddle::framework::SelectedRows()};
+  output->set_height(height);
+  paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
+                                             float>
+      merge_add_functor;
+
+  std::vector<const paddle::framework::SelectedRows*> inputs;
+  inputs.push_back(selected_rows1.get());
+  inputs.push_back(selected_rows2.get());
+  merge_add_functor(ctx, inputs, output.get());
+
+  EXPECT_EQ(output->height(), height);
+  EXPECT_EQ(output->value().dims(),
+            paddle::framework::make_ddim({10, row_numel}));
+
+  std::vector<int64_t> ret_rows{1, 3, 5, 7, 9, 0, 2, 4, 6, 8};
+  EXPECT_EQ(output->rows(), ret_rows);
+
+  auto* out_data = output->value().data<float>();
+  for (size_t i = 0; i < ret_rows.size(); ++i) {
+    float data_value = 0;
+    if (i < 5) {
+      data_value = 1.0;
+    } else {
+      data_value = 2.0;
+    }
+    for (size_t j = 0; j < static_cast<size_t>(row_numel); ++j) {
+      EXPECT_EQ(out_data[i * row_numel + j], data_value);
+    }
+  }
+}
+
 TEST(selected_rows_functor, cpu_sum_to) {
   paddle::platform::CPUPlace cpu_place;
   paddle::platform::CPUDeviceContext ctx(cpu_place);

paddle/fluid/operators/reduce_ops/reduce_all_op.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/fluid/operators/reduce_ops/reduce_all_op.h"
 
-REGISTER_REDUCE_OP(reduce_all);
+REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all);
 REGISTER_OP_CPU_KERNEL(reduce_all,
                        ops::ReduceKernel<paddle::platform::CPUDeviceContext,
                                          bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/fluid/operators/reduce_ops/reduce_any_op.h"
 
-REGISTER_REDUCE_OP(reduce_any);
+REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any);
 REGISTER_OP_CPU_KERNEL(reduce_any,
                        ops::ReduceKernel<paddle::platform::CPUDeviceContext,
                                          bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_op.h

@@ -270,3 +270,12 @@ namespace ops = paddle::operators;
   REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__,        \
                     paddle::framework::DefaultGradOpDescMaker<true>);    \
   REGISTER_OPERATOR(op_name##_grad, ops::ReduceGradOp)
+
+#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name)                         \
+  class __##op_name##Maker__ : public ops::ReduceOpMaker {               \
+   protected:                                                            \
+    virtual std::string GetName() const { return #op_name; }             \
+    virtual std::string GetOpType() const { return "Reduce " #op_name; } \
+  };                                                                     \
+  REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__,        \
+                    paddle::framework::EmptyGradOpMaker);

python/paddle/fluid/contrib/tests/test_calibration.py

@@ -147,10 +147,11 @@ class TestCalibrationForResnet50(unittest.TestCase):
                                                    self.data_cache_folder)
         os.system(cmd)
 
-        self.batch_size = 1
-        self.sample_iterations = 50
+        self.batch_size = 1 if os.environ.get('DATASET') == 'full' else 50
+        self.sample_iterations = 50 if os.environ.get(
+            'DATASET') == 'full' else 1
         self.infer_iterations = 50000 if os.environ.get(
-            'DATASET') == 'full' else 50
+            'DATASET') == 'full' else 1
 
     def cache_unzipping(self, target_folder, zip_path):
         if not os.path.exists(target_folder):
@@ -279,15 +280,15 @@ class TestCalibrationForResnet50(unittest.TestCase):
     def test_calibration(self):
         self.download_model()
         print("Start FP32 inference for {0} on {1} images ...").format(
-            self.model, self.infer_iterations)
+            self.model, self.infer_iterations * self.batch_size)
         (fp32_throughput, fp32_latency,
          fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
         print("Start INT8 calibration for {0} on {1} images ...").format(
-            self.model, self.sample_iterations)
+            self.model, self.sample_iterations * self.batch_size)
         self.run_program(
             self.model_cache_folder + "/model", True, algo=self.algo)
         print("Start INT8 inference for {0} on {1} images ...").format(
-            self.model, self.infer_iterations)
+            self.model, self.infer_iterations * self.batch_size)
         (int8_throughput, int8_latency,
          int8_acc1) = self.run_program("calibration_out")
         delta_value = fp32_acc1 - int8_acc1

python/paddle/fluid/layers/nn.py

@@ -196,6 +196,7 @@ __all__ = [
     'npair_loss',
     'pixel_shuffle',
     'fsp_matrix',
+    'continuous_value_model',
 ]
 
 kIgnoreIndex = -100
@@ -11202,3 +11203,54 @@ def fsp_matrix(x, y):
         input_param_name='x'))
     helper.append_op(type='fsp', inputs={'X': x, 'Y': y}, outputs={'Out': out})
     return out
+
+
+def continuous_value_model(input, cvm, use_cvm=True):
+    """
+
+    **continuous_value_model layers**
+
+    continuous value model(cvm). Now, it only considers show and click value in CTR project.
+    We assume that input is an embedding vector with cvm_feature, whose shape is [N * D] (D is 2 + embedding dim).
+    If use_cvm is True, it will log(cvm_feature), and output shape is [N * D].
+    If use_cvm is False, it will remove cvm_feature from input, and output shape is [N * (D - 2)].
+    
+    This layer accepts a tensor named input which is ID after embedded(lod level is 1), cvm is a show_click info.
+
+    Args:
+
+        input (Variable): a 2-D LodTensor with shape [N x D], where N is the batch size, D is 2 + the embedding dim. lod level = 1.
+        cvm (Variable):   a 2-D Tensor with shape [N x 2], where N is the batch size, 2 is show and click.
+        use_cvm  (bool):  use cvm or not. if use cvm, the output dim is the same as input
+                          if don't use cvm, the output dim is input dim - 2(remove show and click)
+                          (cvm op is a customized op, which input is a sequence has embedd_with_cvm default, so we need an op named cvm to decided whever use it or not.)
+
+    Returns:
+
+        Variable: A 2-D LodTensor with shape [N x D], if use cvm, D is equal to input dim, if don't use cvm, D is equal to input dim - 2. 
+
+    Examples:
+
+        .. code-block:: python
+
+          input = fluid.layers.data(name="input", shape=[-1, 1], lod_level=1, append_batch_size=False, dtype="int64")#, stop_gradient=False)
+          label = fluid.layers.data(name="label", shape=[-1, 1], append_batch_size=False, dtype="int64")
+          embed = fluid.layers.embedding(
+                            input=input,
+                            size=[100, 11],
+                            dtype='float32')
+          ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1)
+          show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32')
+          show_clk.stop_gradient = True
+          input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True)
+
+    """
+    helper = LayerHelper('cvm', **locals())
+    out = helper.create_variable(dtype=input.dtype)
+    helper.append_op(
+        type='cvm',
+        inputs={'X': [input],
+                'CVM': [cvm]},
+        outputs={'Y': [out]},
+        attrs={"use_cvm": use_cvm})
+    return out

python/paddle/fluid/optimizer.py

@@ -275,15 +275,26 @@ class Optimizer(object):
         self._create_global_learning_rate()
 
         optimize_ops = []
-        for param_and_grad in parameters_and_grads:
-            if param_and_grad[1] is None:
-                continue
-            with param_and_grad[0].block.program._optimized_guard(
-                    param_and_grad), name_scope("optimizer"):
-                if param_and_grad[0].trainable is True:
-                    optimize_op = self._append_optimize_op(global_block,
-                                                           param_and_grad)
-                    optimize_ops.append(optimize_op)
+        if framework.in_dygraph_mode():
+            for param_and_grad in parameters_and_grads:
+                if param_and_grad[1] is None:
+                    continue
+                with param_and_grad[0].block.program._optimized_guard(
+                        param_and_grad):
+                    if param_and_grad[0].trainable is True:
+                        optimize_op = self._append_optimize_op(global_block,
+                                                               param_and_grad)
+                        optimize_ops.append(optimize_op)
+        else:
+            for param_and_grad in parameters_and_grads:
+                if param_and_grad[1] is None:
+                    continue
+                with param_and_grad[0].block.program._optimized_guard(
+                        param_and_grad), name_scope("optimizer"):
+                    if param_and_grad[0].trainable is True:
+                        optimize_op = self._append_optimize_op(global_block,
+                                                               param_and_grad)
+                        optimize_ops.append(optimize_op)
 
         # Get custom finish ops for subclasses
         # FIXME: Need to fix this once we figure out how to handle dependencies

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

+#   Copyright (c) 2019 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.
+
+import numpy as np
+from math import log
+from math import exp
+from op_test import OpTest
+import unittest
+
+
+class TestCVMOp(OpTest):
+    """
+        Test cvm op with discrete one-hot labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cvm"
+        batch_size = 4
+        dims = 11
+        lod = [[1]]
+        self.inputs = {
+            'X': (np.random.uniform(0, 1, [1, dims]).astype("float32"), lod),
+            'CVM': np.array([[0.6, 0.4]]).astype("float32"),
+        }
+        self.attrs = {'use_cvm': False}
+        out = []
+        for index, emb in enumerate(self.inputs["X"][0]):
+            out.append(emb[2:])
+        self.outputs = {'Y': (np.array(out), lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()