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

README.md

- 
+
 <p align="center">
 <img align="center" src="doc/imgs/logo.png", width=1600>
 <p>

cmake/cudnn.cmake

@@ -60,9 +60,8 @@ else()
     set(CUDNN_FOUND OFF)
 endif()
 
-if(CUDNN_FOUND)
-    file(READ ${CUDNN_INCLUDE_DIR}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
-
+macro(find_cudnn_version cudnn_header_file) 
+    file(READ ${cudnn_header_file} CUDNN_VERSION_FILE_CONTENTS)
     get_filename_component(CUDNN_LIB_PATH ${CUDNN_LIBRARY} DIRECTORY)
 
     string(REGEX MATCH "define CUDNN_VERSION +([0-9]+)"
@@ -93,10 +92,15 @@ if(CUDNN_FOUND)
             math(EXPR CUDNN_VERSION
                 "${CUDNN_MAJOR_VERSION} * 1000 +
                  ${CUDNN_MINOR_VERSION} * 100 + ${CUDNN_PATCHLEVEL_VERSION}")
+            message(STATUS "Current cuDNN header is ${cudnn_header_file} "
+              "Current cuDNN version is v${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION}. ")
         endif()
-
-        message(STATUS "Current cuDNN header is ${CUDNN_INCLUDE_DIR}/cudnn.h. "
-            "Current cuDNN version is v${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION}. ")
-
     endif()
+endmacro()
+
+if(CUDNN_FOUND)
+  find_cudnn_version(${CUDNN_INCLUDE_DIR}/cudnn.h) 
+  if (NOT CUDNN_MAJOR_VERSION) 
+    find_cudnn_version(${CUDNN_INCLUDE_DIR}/cudnn_version.h) 
+  endif()
 endif()

paddle/fluid/framework/distributed_strategy.proto

@@ -43,6 +43,12 @@ message GradientMergeConfig {
   optional bool avg = 2 [ default = true ];
 }
 
+message DGCConfig {
+  optional int32 rampup_begin_step = 1 [ default = 0 ];
+  optional int32 rampup_step = 2 [ default = 1 ];
+  repeated float sparsity = 3;
+}
+
 message LarsConfig {
   optional float lars_coeff = 1 [ default = 0.001 ];
   optional float lars_weight_decay = 2 [ default = 0.0005 ];
@@ -114,6 +120,7 @@ message DistributedStrategy {
   optional AMPConfig amp_configs = 102;
   optional LocalSGDConfig localsgd_configs = 103;
   optional GradientMergeConfig gradient_merge_configs = 104;
+  optional DGCConfig dgc_configs = 105;
   optional PipelineConfig pipeline_configs = 106;
   optional AsyncConfig a_sync_configs = 107;
   optional LarsConfig lars_configs = 108;

paddle/fluid/imperative/partial_grad_engine.cc

@@ -885,12 +885,14 @@ void PartialGradTask::RunEachOp(OpBase *op) {
   if (create_graph_) {
     auto double_grad_node = CreateGradOpNode(op->InnerOp(), tmp_ins, tmp_outs,
                                              op->Attrs(), op->place());
-    if (double_grad_node) {
-      VLOG(10) << "Create " << double_grad_node->size()
-               << " double grad op(s) for " << op->Type()
-               << ", pending ops: " << GradPendingOpTypes(*double_grad_node);
-      double_grad_nodes_.emplace_back(std::move(double_grad_node));
-    }
+    PADDLE_ENFORCE_NOT_NULL(
+        double_grad_node,
+        platform::errors::NotFound("The Op %s doesn't have any grad op.",
+                                   op->Type()));
+    VLOG(10) << "Create " << double_grad_node->size()
+             << " double grad op(s) for " << op->Type()
+             << ", pending ops: " << GradPendingOpTypes(*double_grad_node);
+    double_grad_nodes_.emplace_back(std::move(double_grad_node));
   }
 
   VLOG(10) << "There are " << grads_to_accumulate_.size() << " to sum gradient";

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

 /* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
 http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -83,23 +80,10 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
     nvinfer1::ILayer* layer = nullptr;
 
     if (engine_->with_dynamic_shape()) {
-      auto use_fp16 = engine_->WithFp16();
       plugin::DynamicPluginTensorRT* plugin = nullptr;
-      if (use_fp16) {
-#ifdef SUPPORTS_CUDA_FP16
-        plugin = new plugin::EmbEltwiseLayernormPluginDynamic<half>(
-            input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
-            eps);
-#else
-        plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
-            input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
-            eps);
-#endif
-      } else {
-        plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
-            input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
-            eps);
-      }
+      plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
+          input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
+          eps);
       layer = engine_->AddPluginV2(input_ids.data(), input_num, plugin);
     } else {
       PADDLE_THROW(platform::errors::Fatal(

paddle/fluid/inference/tensorrt/engine.h

@@ -200,9 +200,23 @@ class TensorRTEngine {
   void Deserialize(const std::string& engine_serialized_data) {
     freshDeviceId();
     infer_ptr<nvinfer1::IRuntime> runtime(createInferRuntime(&logger_));
-    infer_engine_.reset(runtime->deserializeCudaEngine(
-        engine_serialized_data.c_str(), engine_serialized_data.size(),
-        &inference::Singleton<plugin::PluginFactoryTensorRT>::Global()));
+    if (with_dynamic_shape_) {
+#if IS_TRT_VERSION_GE(6000)
+      infer_engine_.reset(runtime->deserializeCudaEngine(
+          engine_serialized_data.c_str(), engine_serialized_data.size(),
+          nullptr));
+#else
+
+      PADDLE_THROW(platform::errors::PreconditionNotMet(
+          "To enable dynamic shape support, the TensorRT version should be "
+          "greater than 6.0.0"));
+
+#endif
+    } else {
+      infer_engine_.reset(runtime->deserializeCudaEngine(
+          engine_serialized_data.c_str(), engine_serialized_data.size(),
+          &inference::Singleton<plugin::PluginFactoryTensorRT>::Global()));
+    }
     PADDLE_ENFORCE(infer_engine_ != nullptr,
                    "build cuda engine failed when deserialize engine info.!");
   }

paddle/fluid/inference/tensorrt/helper.h

@@ -56,6 +56,9 @@ static nvinfer1::IRuntime* createInferRuntime(nvinfer1::ILogger* logger) {
   return static_cast<nvinfer1::IRuntime*>(
       dy::createInferRuntime_INTERNAL(logger, NV_TENSORRT_VERSION));
 }
+static nvinfer1::IPluginRegistry* getPluginRegistry() {
+  return static_cast<nvinfer1::IPluginRegistry*>(dy::getPluginRegistry());
+}
 
 // A logger for create TensorRT infer builder.
 class NaiveLogger : public nvinfer1::ILogger {

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

@@ -33,53 +33,29 @@ namespace plugin {
 
 template <typename T>
 int EmbEltwiseLayernormPluginDynamic<T>::initialize() {
-  int nb_emb = embs_.size();
-  std::vector<void *> ptr_vector(nb_emb);
-  std::vector<std::vector<half>> emb_fp16(nb_emb);
-
-  if (sizeof(T) == sizeof(float)) {
-    // FP32
-    for (int i = 0; i < nb_emb; ++i) {
-      ptr_vector[i] = embs_[i];
-    }
-  } else {
-    // FP16
-    for (int i = 0; i < nb_emb; ++i) {
-      auto emb_size = emb_sizes_[i];
-      auto &tmp = emb_fp16[i];
-      tmp.resize(emb_size);
-
-      for (int j = 0; j < emb_size; ++j) {
-        tmp[j] = static_cast<half>(embs_[i][j]);
-      }
-      ptr_vector[i] = tmp.data();
-    }
-  }
   embs_gpu_.resize(embs_.size());
   for (int i = 0; i < embs_.size(); i++) {
-    cudaMalloc(&embs_gpu_[i], sizeof(T) * emb_sizes_[i]);
-    cudaMemcpy(embs_gpu_[i], ptr_vector[i], emb_sizes_[i] * sizeof(T),
-               cudaMemcpyHostToDevice);
+    if (embs_[i]) {
+      cudaMalloc(&embs_gpu_[i], sizeof(float) * emb_sizes_[i]);
+      cudaMemcpy(embs_gpu_[i], embs_[i], emb_sizes_[i] * sizeof(float),
+                 cudaMemcpyHostToDevice);
+    }
   }
 
-  cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
-  cudaMemcpy(bias_gpu_, bias_, bias_size_ * sizeof(float),
-             cudaMemcpyHostToDevice);
-  cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
-  cudaMemcpy(scale_gpu_, scale_, scale_size_ * sizeof(float),
-             cudaMemcpyHostToDevice);
-
-  return 0;
-}
+  if (bias_) {
+    cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
+    cudaMemcpy(bias_gpu_, bias_, bias_size_ * sizeof(float),
+               cudaMemcpyHostToDevice);
+  }
+  if (scale_) {
+    cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
+    cudaMemcpy(scale_gpu_, scale_, scale_size_ * sizeof(float),
+               cudaMemcpyHostToDevice);
+  }
 
-template <typename T>
-size_t EmbEltwiseLayernormPluginDynamic<T>::getSerializationSize() const {
   return 0;
 }
 
-template <typename T>
-void EmbEltwiseLayernormPluginDynamic<T>::serialize(void *buffer) const {}
-
 template <typename T>
 nvinfer1::DimsExprs EmbEltwiseLayernormPluginDynamic<T>::getOutputDimensions(
     int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,

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

@@ -44,8 +44,42 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
         hidden_size_(hidden_size),
         eps_(eps) {}
 
-  EmbEltwiseLayernormPluginDynamic(void const* serialData,
-                                   size_t serialLength) {}
+  EmbEltwiseLayernormPluginDynamic(void const* serial_data,
+                                   size_t serial_length) {
+    DeserializeValue(&serial_data, &serial_length, &emb_sizes_);
+
+    embs_gpu_.resize(emb_sizes_.size());
+    embs_.resize(emb_sizes_.size());
+    for (size_t i = 0; i < emb_sizes_.size(); i++) {
+      cudaMalloc(&embs_gpu_[i], sizeof(float) * emb_sizes_[i]);
+      cudaMemcpy(embs_gpu_[i], serial_data, emb_sizes_[i] * sizeof(float),
+                 cudaMemcpyHostToDevice);
+      reinterpret_cast<char const*&>(serial_data) +=
+          emb_sizes_[i] * sizeof(float);
+      serial_length -= emb_sizes_[i] * sizeof(float);
+      embs_[i] = nullptr;
+    }
+    DeserializeValue(&serial_data, &serial_length, &bias_size_);
+    DeserializeValue(&serial_data, &serial_length, &scale_size_);
+
+    cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
+    cudaMemcpy(bias_gpu_, serial_data, bias_size_ * sizeof(float),
+               cudaMemcpyHostToDevice);
+    bias_ = nullptr;
+    reinterpret_cast<char const*&>(serial_data) += bias_size_ * sizeof(float);
+    serial_length -= bias_size_ * sizeof(float);
+
+    cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
+    cudaMemcpy(scale_gpu_, serial_data, scale_size_ * sizeof(float),
+               cudaMemcpyHostToDevice);
+    scale_ = nullptr;
+    reinterpret_cast<char const*&>(serial_data) += scale_size_ * sizeof(float);
+    serial_length -= scale_size_ * sizeof(float);
+
+    DeserializeValue(&serial_data, &serial_length, &hidden_size_);
+    DeserializeValue(&serial_data, &serial_length, &eps_);
+  }
+
   nvinfer1::IPluginV2DynamicExt* clone() const override {
     return new EmbEltwiseLayernormPluginDynamic(
         embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_, hidden_size_,
@@ -58,36 +92,66 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
   int getNbOutputs() const override { return 1; }
   int initialize() override;
 
-  size_t getSerializationSize() const override;
-  void serialize(void* buffer) const override;
+  size_t getSerializationSize() const override {
+    int sum_num = 0;
+    sum_num += SerializedSize(emb_sizes_);
+
+    for (size_t i = 0; i < emb_sizes_.size(); i++) {
+      sum_num += emb_sizes_[i] * sizeof(float);
+    }
+
+    sum_num += SerializedSize(bias_size_);
+    sum_num += SerializedSize(scale_size_);
+
+    sum_num += (bias_size_ + scale_size_) * sizeof(float);
+    sum_num += SerializedSize(hidden_size_);
+    sum_num += SerializedSize(eps_);
+    // sum_num += SerializedSize(with_fp16_);
+
+    return sum_num;
+  }
+
+  void serialize(void* buffer) const override {
+    // SerializeValue(&buffer, with_fp16_);
+    SerializeValue(&buffer, emb_sizes_);
+    for (size_t i = 0; i < emb_sizes_.size(); i++) {
+      SerializeCudaPointer(&buffer, embs_gpu_[i], emb_sizes_[i]);
+    }
+    SerializeValue(&buffer, bias_size_);
+    SerializeValue(&buffer, scale_size_);
+    SerializeCudaPointer(&buffer, bias_gpu_, bias_size_);
+    SerializeCudaPointer(&buffer, scale_gpu_, scale_size_);
+    SerializeValue(&buffer, hidden_size_);
+    SerializeValue(&buffer, eps_);
+  }
 
   nvinfer1::DimsExprs getOutputDimensions(
       int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
       nvinfer1::IExprBuilder& expr_builder) override;
 
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc* inOut,
-                                 int nbInputs, int nbOutputs) override;
+                                 const nvinfer1::PluginTensorDesc* in_out,
+                                 int nb_inputs, int nb_outputs) override;
 
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
-                       int nbInputs,
+                       int nb_inputs,
                        const nvinfer1::DynamicPluginTensorDesc* out,
-                       int nbOutputs) override {}
+                       int nb_outputs) override {}
 
   size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
-                          int nbInputs,
+                          int nb_inputs,
                           const nvinfer1::PluginTensorDesc* outputs,
-                          int nbOutputs) const override {
+                          int nb_outputs) const override {
     return 0;
   }
 
-  int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
-              const nvinfer1::PluginTensorDesc* outputDesc,
+  int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
+              const nvinfer1::PluginTensorDesc* output_desc,
               const void* const* inputs, void* const* outputs, void* workspace,
               cudaStream_t stream) override;
   nvinfer1::DataType getOutputDataType(int index,
-                                       const nvinfer1::DataType* inputTypes,
-                                       int nbInputs) const override;
+                                       const nvinfer1::DataType* input_types,
+                                       int nb_inputs) const override;
 
   void destroy() override { delete this; }
 
@@ -99,7 +163,7 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
   // data on devices
   float* bias_gpu_;
   float* scale_gpu_;
-  std::vector<T*> embs_gpu_;
+  std::vector<float*> embs_gpu_;
 
   std::vector<int> emb_sizes_;
   int bias_size_;
@@ -107,6 +171,49 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
   int hidden_size_;
   float eps_;
 };
+
+class EmbEltwiseLayernormPluginV2Creator : public nvinfer1::IPluginCreator {
+ public:
+  EmbEltwiseLayernormPluginV2Creator() {}
+  const char* getPluginName() const override {
+    return "fused_embedding_eltwise_layernorm_plugin";
+  }
+
+  const char* getPluginVersion() const override { return "1"; }
+
+  const nvinfer1::PluginFieldCollection* getFieldNames() override {
+    return &field_collection_;
+  }
+
+  nvinfer1::IPluginV2* createPlugin(
+      const char* name, const nvinfer1::PluginFieldCollection* fc) override {
+    return nullptr;
+  }
+
+  nvinfer1::IPluginV2* deserializePlugin(const char* name,
+                                         const void* serial_data,
+                                         size_t serial_length) override {
+    return new EmbEltwiseLayernormPluginDynamic<float>(serial_data,
+                                                       serial_length);
+  }
+
+  void setPluginNamespace(const char* lib_namespace) override {
+    plugin_namespace_ = lib_namespace;
+  }
+
+  const char* getPluginNamespace() const override {
+    return plugin_namespace_.c_str();
+  }
+
+ private:
+  std::string plugin_namespace_;
+  std::string plugin_name_;
+  nvinfer1::PluginFieldCollection field_collection_;
+  std::vector<nvinfer1::PluginField> plugin_attributes_;
+};
+
+REGISTER_TRT_PLUGIN_V2(EmbEltwiseLayernormPluginV2Creator);
+
 #endif
 }  // namespace plugin
 }  // namespace tensorrt

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

@@ -132,9 +132,6 @@ int GeluPlugin::enqueue(int batch_size, const void* const* inputs,
 
 // Dynamic Plugin below.
 #if IS_TRT_VERSION_GE(6000)
-size_t GeluPluginDynamic::getSerializationSize() const { return 0; }
-
-void GeluPluginDynamic::serialize(void* buffer) const {}
 
 nvinfer1::DimsExprs GeluPluginDynamic::getOutputDimensions(
     int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,

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

@@ -30,8 +30,8 @@ class GeluPlugin : public PluginTensorRT {
 
   // It was used for tensorrt deserialization.
   // It should not be called by users.
-  GeluPlugin(void const* serialData, size_t serialLength) {
-    deserializeBase(serialData, serialLength);
+  GeluPlugin(void const* serial_data, size_t serial_length) {
+    deserializeBase(serial_data, serial_length);
   }
 
   ~GeluPlugin() {}
@@ -43,8 +43,8 @@ class GeluPlugin : public PluginTensorRT {
   bool supportsFormat(nvinfer1::DataType type,
                       nvinfer1::PluginFormat format) const override;
   nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs,
-                                     int nbInputDims) override;
-  int enqueue(int batchSize, const void* const* inputs, void** outputs,
+                                     int nb_input_dims) override;
+  int enqueue(int batch_size, const void* const* inputs, void** outputs,
               void* workspace, cudaStream_t stream) override;
 
  protected:
@@ -64,7 +64,7 @@ class GeluPlugin : public PluginTensorRT {
 class GeluPluginDynamic : public DynamicPluginTensorRT {
  public:
   GeluPluginDynamic() {}
-  GeluPluginDynamic(void const* serialData, size_t serialLength) {}
+  GeluPluginDynamic(void const* serial_data, size_t serial_length) {}
 
   ~GeluPluginDynamic() {}
   nvinfer1::IPluginV2DynamicExt* clone() const override {
@@ -75,39 +75,79 @@ class GeluPluginDynamic : public DynamicPluginTensorRT {
   int getNbOutputs() const override { return 1; }
   int initialize() override { return 0; }
 
-  size_t getSerializationSize() const override;
-  void serialize(void* buffer) const override;
+  size_t getSerializationSize() const override { return 0; }
+  void serialize(void* buffer) const override {}
 
   nvinfer1::DimsExprs getOutputDimensions(
-      int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs,
-      nvinfer1::IExprBuilder& exprBuilder) override;
+      int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
+      nvinfer1::IExprBuilder& expr_builder) override;
 
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc* inOut,
-                                 int nbInputs, int nbOutputs) override;
+                                 const nvinfer1::PluginTensorDesc* in_out,
+                                 int nb_inputs, int nb_outputs) override;
 
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
-                       int nbInputs,
+                       int nb_inputs,
                        const nvinfer1::DynamicPluginTensorDesc* out,
-                       int nbOutputs) override {}
+                       int nb_outputs) override {}
 
   size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
-                          int nbInputs,
+                          int nb_inputs,
                           const nvinfer1::PluginTensorDesc* outputs,
-                          int nbOutputs) const override {
+                          int nb_outputs) const override {
     return 0;
   }
 
-  int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
-              const nvinfer1::PluginTensorDesc* outputDesc,
+  int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
+              const nvinfer1::PluginTensorDesc* output_desc,
               const void* const* inputs, void* const* outputs, void* workspace,
               cudaStream_t stream) override;
   nvinfer1::DataType getOutputDataType(int index,
-                                       const nvinfer1::DataType* inputTypes,
-                                       int nbInputs) const override;
+                                       const nvinfer1::DataType* input_types,
+                                       int nb_inputs) const override;
 
   void destroy() override { delete this; }
 };
+
+class GeluPluginV2Creator : public nvinfer1::IPluginCreator {
+ public:
+  GeluPluginV2Creator() {}
+  const char* getPluginName() const override { return "gelu_plugin"; }
+
+  const char* getPluginVersion() const override { return "1"; }
+
+  const nvinfer1::PluginFieldCollection* getFieldNames() override {
+    return &field_collection_;
+  }
+
+  nvinfer1::IPluginV2* createPlugin(
+      const char* name, const nvinfer1::PluginFieldCollection* fc) override {
+    return nullptr;
+  }
+
+  nvinfer1::IPluginV2* deserializePlugin(const char* name,
+                                         const void* serial_data,
+                                         size_t serial_length) override {
+    auto plugin = new GeluPluginDynamic(serial_data, serial_length);
+    return plugin;
+  }
+
+  void setPluginNamespace(const char* lib_namespace) override {
+    plugin_namespace_ = lib_namespace;
+  }
+
+  const char* getPluginNamespace() const override {
+    return plugin_namespace_.c_str();
+  }
+
+ private:
+  std::string plugin_namespace_;
+  std::string plugin_name_;
+  nvinfer1::PluginFieldCollection field_collection_{0, nullptr};
+  std::vector<nvinfer1::PluginField> plugin_attributes_;
+};
+
+REGISTER_TRT_PLUGIN_V2(GeluPluginV2Creator);
 #endif
 
 }  // namespace plugin

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

@@ -152,10 +152,6 @@ inline void TransposeQKV(const int batch, const int seq_len,
 
 int QkvToContextPluginDynamic::initialize() { return 0; }
 
-size_t QkvToContextPluginDynamic::getSerializationSize() const { return 0; }
-
-void QkvToContextPluginDynamic::serialize(void *buffer) const {}
-
 nvinfer1::DimsExprs QkvToContextPluginDynamic::getOutputDimensions(
     int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
     nvinfer1::IExprBuilder &expr_builder) {

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

+// Copyright (c) 2020 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.
+
 // Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -37,7 +51,13 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
         scale_(scale),
         ban_fp16_(ban_fp16) {}
 
-  QkvToContextPluginDynamic(void const* serialData, size_t serialLength) {}
+  QkvToContextPluginDynamic(void const* serial_data, size_t serial_length) {
+    DeserializeValue(&serial_data, &serial_length, &hidden_);
+    DeserializeValue(&serial_data, &serial_length, &head_number_);
+    DeserializeValue(&serial_data, &serial_length, &head_size_);
+    DeserializeValue(&serial_data, &serial_length, &scale_);
+    DeserializeValue(&serial_data, &serial_length, &ban_fp16_);
+  }
   nvinfer1::IPluginV2DynamicExt* clone() const override {
     return new QkvToContextPluginDynamic(hidden_, head_number_, head_size_,
                                          scale_, ban_fp16_);
@@ -47,26 +67,36 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
   int getNbOutputs() const override { return 1; }
   int initialize() override;
 
-  size_t getSerializationSize() const override;
-  void serialize(void* buffer) const override;
+  size_t getSerializationSize() const override {
+    return SerializedSize(hidden_) + SerializedSize(head_number_) +
+           SerializedSize(head_size_) + SerializedSize(scale_) +
+           SerializedSize(ban_fp16_);
+  }
+  void serialize(void* buffer) const override {
+    SerializeValue(&buffer, hidden_);
+    SerializeValue(&buffer, head_number_);
+    SerializeValue(&buffer, head_size_);
+    SerializeValue(&buffer, scale_);
+    SerializeValue(&buffer, ban_fp16_);
+  }
 
   nvinfer1::DimsExprs getOutputDimensions(
       int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
       nvinfer1::IExprBuilder& expr_builder) override;
 
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc* inOut,
-                                 int nbInputs, int nbOutputs) override;
+                                 const nvinfer1::PluginTensorDesc* in_out,
+                                 int nb_inputs, int nb_outputs) override;
 
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
-                       int nbInputs,
+                       int nb_inputs,
                        const nvinfer1::DynamicPluginTensorDesc* out,
-                       int nbOutputs) override {}
+                       int nb_outputs) override {}
 
   size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
-                          int nbInputs,
+                          int nb_inputs,
                           const nvinfer1::PluginTensorDesc* outputs,
-                          int nbOutputs) const override {
+                          int nb_outputs) const override {
     return 0;
   }
 
@@ -75,8 +105,8 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
               const void* const* inputs, void* const* outputs, void* workspace,
               cudaStream_t stream) override;
   nvinfer1::DataType getOutputDataType(int index,
-                                       const nvinfer1::DataType* inputTypes,
-                                       int nbInputs) const override;
+                                       const nvinfer1::DataType* input_types,
+                                       int nb_inputs) const override;
 
   void destroy() override { delete this; }
 
@@ -87,6 +117,45 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
   float scale_;
   bool ban_fp16_;
 };
+
+class QkvToContextPluginV2Creator : public nvinfer1::IPluginCreator {
+ public:
+  QkvToContextPluginV2Creator() {}
+  const char* getPluginName() const override { return "qkv_to_context_plugin"; }
+
+  const char* getPluginVersion() const override { return "1"; }
+
+  const nvinfer1::PluginFieldCollection* getFieldNames() override {
+    return &field_collection_;
+  }
+
+  nvinfer1::IPluginV2* createPlugin(
+      const char* name, const nvinfer1::PluginFieldCollection* fc) override {
+    return nullptr;
+  }
+
+  nvinfer1::IPluginV2* deserializePlugin(const char* name,
+                                         const void* serial_data,
+                                         size_t serial_length) override {
+    auto plugin = new QkvToContextPluginDynamic(serial_data, serial_length);
+    return plugin;
+  }
+
+  void setPluginNamespace(const char* lib_namespace) override {
+    plugin_namespace_ = lib_namespace;
+  }
+
+  const char* getPluginNamespace() const override {
+    return plugin_namespace_.c_str();
+  }
+
+ private:
+  std::string plugin_namespace_;
+  std::string plugin_name_;
+  nvinfer1::PluginFieldCollection field_collection_;
+  std::vector<nvinfer1::PluginField> plugin_attributes_;
+};
+REGISTER_TRT_PLUGIN_V2(QkvToContextPluginV2Creator);
 #endif
 
 }  // namespace plugin

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

@@ -32,18 +32,14 @@ namespace plugin {
 
 int SkipLayerNormPluginDynamic::initialize() {
   cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
-  cudaMemcpy(bias_gpu_, bias_, bias_size_ * sizeof(float),
+  cudaMemcpy(bias_gpu_, bias_.data(), bias_size_ * sizeof(float),
              cudaMemcpyHostToDevice);
   cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
-  cudaMemcpy(scale_gpu_, scale_, scale_size_ * sizeof(float),
+  cudaMemcpy(scale_gpu_, scale_.data(), scale_size_ * sizeof(float),
              cudaMemcpyHostToDevice);
   return 0;
 }
 
-size_t SkipLayerNormPluginDynamic::getSerializationSize() const { return 0; }
-
-void SkipLayerNormPluginDynamic::serialize(void *buffer) const {}
-
 nvinfer1::DimsExprs SkipLayerNormPluginDynamic::getOutputDimensions(
     int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
     nvinfer1::IExprBuilder &expr_builder) {

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

@@ -29,61 +29,84 @@ namespace plugin {
 #if IS_TRT_VERSION_GE(6000)
 class SkipLayerNormPluginDynamic : public DynamicPluginTensorRT {
  public:
-  explicit SkipLayerNormPluginDynamic(float* bias, float* scale, int bias_size,
-                                      int scale_size, const float eps,
-                                      bool ban_fp16)
-      : bias_(bias),
-        scale_(scale),
-        bias_size_(bias_size),
+  explicit SkipLayerNormPluginDynamic(const float* bias, const float* scale,
+                                      int bias_size, int scale_size,
+                                      const float eps, bool ban_fp16)
+      : bias_size_(bias_size),
         scale_size_(scale_size),
         eps_(eps),
-        ban_fp16_(ban_fp16) {}
-  SkipLayerNormPluginDynamic(void const* serialData, size_t serialLength) {}
+        ban_fp16_(ban_fp16) {
+    bias_.resize(bias_size);
+    scale_.resize(scale_size);
+    std::copy(bias, bias + bias_size, bias_.data());
+    std::copy(scale, scale + scale_size, scale_.data());
+  }
+  SkipLayerNormPluginDynamic(void const* serial_data, size_t serial_length) {
+    DeserializeValue(&serial_data, &serial_length, &bias_);
+    DeserializeValue(&serial_data, &serial_length, &scale_);
+    DeserializeValue(&serial_data, &serial_length, &bias_size_);
+    DeserializeValue(&serial_data, &serial_length, &scale_size_);
+    DeserializeValue(&serial_data, &serial_length, &eps_);
+    DeserializeValue(&serial_data, &serial_length, &ban_fp16_);
+  }
+
   nvinfer1::IPluginV2DynamicExt* clone() const override {
-    return new SkipLayerNormPluginDynamic(bias_, scale_, bias_size_,
-                                          scale_size_, eps_, ban_fp16_);
+    return new SkipLayerNormPluginDynamic(
+        bias_.data(), scale_.data(), bias_size_, scale_size_, eps_, ban_fp16_);
   }
 
   const char* getPluginType() const override { return "skip_layernorm_plugin"; }
   int getNbOutputs() const override { return 1; }
   int initialize() override;
 
-  size_t getSerializationSize() const override;
-  void serialize(void* buffer) const override;
+  size_t getSerializationSize() const override {
+    size_t ser_size = SerializedSize(bias_) + SerializedSize(scale_) +
+                      SerializedSize(bias_size_) + SerializedSize(scale_size_) +
+                      SerializedSize(eps_) + SerializedSize(eps_);
+    return ser_size;
+  }
+  void serialize(void* buffer) const override {
+    SerializeValue(&buffer, bias_);
+    SerializeValue(&buffer, scale_);
+    SerializeValue(&buffer, bias_size_);
+    SerializeValue(&buffer, scale_size_);
+    SerializeValue(&buffer, eps_);
+    SerializeValue(&buffer, ban_fp16_);
+  }
 
   nvinfer1::DimsExprs getOutputDimensions(
       int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
       nvinfer1::IExprBuilder& expr_builder) override;
 
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc* inOut,
-                                 int nbInputs, int nbOutputs) override;
+                                 const nvinfer1::PluginTensorDesc* in_out,
+                                 int nb_inputs, int nb_outputs) override;
 
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
-                       int nbInputs,
+                       int nb_inputs,
                        const nvinfer1::DynamicPluginTensorDesc* out,
-                       int nbOutputs) override {}
+                       int nb_outputs) override {}
 
   size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
-                          int nbInputs,
+                          int nb_inputs,
                           const nvinfer1::PluginTensorDesc* outputs,
-                          int nbOutputs) const override {
+                          int nb_outputs) const override {
     return 0;
   }
 
-  int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
-              const nvinfer1::PluginTensorDesc* outputDesc,
+  int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
+              const nvinfer1::PluginTensorDesc* output_desc,
               const void* const* inputs, void* const* outputs, void* workspace,
               cudaStream_t stream) override;
   nvinfer1::DataType getOutputDataType(int index,
-                                       const nvinfer1::DataType* inputTypes,
-                                       int nbInputs) const override;
+                                       const nvinfer1::DataType* input_types,
+                                       int nb_inputs) const override;
 
   void destroy() override { delete this; }
 
  private:
-  float* bias_;
-  float* scale_;
+  std::vector<float> bias_;
+  std::vector<float> scale_;
 
   float* bias_gpu_;
   float* scale_gpu_;
@@ -94,6 +117,45 @@ class SkipLayerNormPluginDynamic : public DynamicPluginTensorRT {
   float eps_;
   bool ban_fp16_;
 };
+
+class SkipLayerNormPluginV2Creator : public nvinfer1::IPluginCreator {
+ public:
+  SkipLayerNormPluginV2Creator() {}
+  const char* getPluginName() const override { return "skip_layernorm_plugin"; }
+
+  const char* getPluginVersion() const override { return "1"; }
+
+  const nvinfer1::PluginFieldCollection* getFieldNames() override {
+    return &field_collection_;
+  }
+
+  nvinfer1::IPluginV2* createPlugin(
+      const char* name, const nvinfer1::PluginFieldCollection* fc) override {
+    return nullptr;
+  }
+
+  nvinfer1::IPluginV2* deserializePlugin(const char* name,
+                                         const void* serial_data,
+                                         size_t serial_length) override {
+    auto plugin = new SkipLayerNormPluginDynamic(serial_data, serial_length);
+    return plugin;
+  }
+
+  void setPluginNamespace(const char* lib_namespace) override {
+    plugin_namespace_ = lib_namespace;
+  }
+
+  const char* getPluginNamespace() const override {
+    return plugin_namespace_.c_str();
+  }
+
+ private:
+  std::string plugin_namespace_;
+  std::string plugin_name_;
+  nvinfer1::PluginFieldCollection field_collection_;
+  std::vector<nvinfer1::PluginField> plugin_attributes_;
+};
+REGISTER_TRT_PLUGIN_V2(SkipLayerNormPluginV2Creator);
 #endif
 
 }  // namespace plugin

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

@@ -175,11 +175,24 @@ class DynamicPluginTensorRT : public nvinfer1::IPluginV2DynamicExt {
   void serializeBase(void*& buffer) const;  // NOLINT
 
  private:
-  std::string name_space_{"paddle_trt"};
-  std::string plugin_base_{"plugin_dynamic"};
+  std::string name_space_;
+  std::string plugin_base_;
 };
 #endif
 
+template <typename T>
+class TrtPluginRegistrarV2 {
+ public:
+  TrtPluginRegistrarV2() { getPluginRegistry()->registerCreator(creator, ""); }
+
+ private:
+  T creator;
+};
+
+#define REGISTER_TRT_PLUGIN_V2(name)                                     \
+  static paddle::inference::tensorrt::plugin::TrtPluginRegistrarV2<name> \
+      plugin_registrar_##name {}
+
 }  // namespace plugin
 }  // namespace tensorrt
 }  // namespace inference

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

@@ -128,6 +128,12 @@ inline void DeserializeValue(void const** buffer, size_t* buffer_size,
   return details::Serializer<T>::Deserialize(buffer, buffer_size, value);
 }
 
+template <typename T>
+inline void SerializeCudaPointer(void** buffer, T* value, int size) {
+  cudaMemcpy((*buffer), value, size * sizeof(T), cudaMemcpyDeviceToHost);
+  reinterpret_cast<char*&>(*buffer) += size * sizeof(T);
+}
+
 }  // namespace plugin
 }  // namespace tensorrt
 }  // namespace inference

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

@@ -433,6 +433,25 @@ if(WITH_GPU AND TENSORRT_FOUND)
     inference_analysis_test(test_trt_dynamic_shape_ernie SRCS trt_dynamic_shape_ernie_test.cc
             EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} 
             ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4)
+
+    set(TEST_TRT_ERNIE_UNSER_MODEL "${TRT_MODEL_INSTALL_DIR}/ernie_test/ernie_model_4_unserialized/")
+    if (NOT EXISTS ${TEST_TRT_ERNIE_UNSER_MODEL})
+        inference_download_and_uncompress(${TEST_TRT_ERNIE_MODEL} ${INFERENCE_URL}/tensorrt_test "ernie_model_4_unserialized.tgz")
+    endif()
+
+    inference_analysis_test(test_trt_dynamic_shape_ernie_serialize SRCS trt_dynamic_shape_ernie_deserialize_test.cc
+            EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} 
+            ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4_unserialized)
+
+    set(TEST_TRT_ERNIE_SER_MODEL "${TRT_MODEL_INSTALL_DIR}/ernie_test/ernie_model_4_serialized/")
+    if (NOT EXISTS ${TEST_TRT_ERNIE_SER_MODEL})
+        inference_download_and_uncompress(${TEST_TRT_ERNIE_MODEL} ${INFERENCE_URL}/tensorrt_test "ernie_model_4_serialized.tgz")
+    endif()
+
+    inference_analysis_test(test_trt_dynamic_shape_ernie_deserialize SRCS trt_dynamic_shape_ernie_deserialize_test.cc
+            EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} 
+            ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4_serialized)
+
 endif()
 
 set(LITE_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/lite")

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

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+
+#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
+
+namespace paddle {
+namespace inference {
+
+void run(const AnalysisConfig& config, std::vector<float>* out_data) {
+  auto predictor = CreatePaddlePredictor(config);
+  auto input_names = predictor->GetInputNames();
+
+  int run_batch = 1;
+  const int run_seq_len = 128;
+
+  std::vector<int64_t> tmp_input;
+  std::vector<float> tmp_four_input;
+  tmp_input.reserve(run_batch * run_seq_len);
+  tmp_four_input.reserve(run_batch * run_seq_len);
+
+  int64_t i0[run_seq_len] = {
+      1,    3558, 4,   75,  491, 89, 340, 313, 93,   4,   255,   10, 75,    321,
+      4095, 1902, 4,   134, 49,  75, 311, 14,  44,   178, 543,   15, 12043, 2,
+      75,   201,  340, 9,   14,  44, 486, 218, 1140, 279, 12043, 2};
+  int64_t i1[run_seq_len] = {
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  int64_t i2[run_seq_len] = {0,  1,  2,  3,  4,  5,  6,  7,  8,  9,
+                             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+                             30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
+  float i3[run_seq_len] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+                           1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+                           1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+                           1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
+
+  // first input
+  auto input_t = predictor->GetInputTensor(input_names[0]);
+  input_t->Reshape({run_batch, run_seq_len, 1});
+  input_t->copy_from_cpu(i0);
+
+  // second input
+  auto input_t2 = predictor->GetInputTensor(input_names[1]);
+  input_t2->Reshape({run_batch, run_seq_len, 1});
+  input_t2->copy_from_cpu(i1);
+
+  // third input.
+  auto input_t3 = predictor->GetInputTensor(input_names[2]);
+  input_t3->Reshape({run_batch, run_seq_len, 1});
+  input_t3->copy_from_cpu(i2);
+
+  auto input_t4 = predictor->GetInputTensor(input_names[3]);
+  input_t4->Reshape({run_batch, run_seq_len, 1});
+  input_t4->copy_from_cpu(i3);
+
+  ASSERT_TRUE(predictor->ZeroCopyRun());
+
+  auto output_names = predictor->GetOutputNames();
+  auto output_t = predictor->GetOutputTensor(output_names[0]);
+  std::vector<int> output_shape = output_t->shape();
+  int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
+                                std::multiplies<int>());
+  out_data->resize(out_num);
+  output_t->copy_to_cpu(out_data->data());
+}
+
+void trt_ernie(bool with_fp16, std::vector<float> result) {
+  AnalysisConfig config;
+  std::string model_dir = FLAGS_infer_model;
+  SetConfig(&config, model_dir, true /* use_gpu */);
+
+  config.SwitchUseFeedFetchOps(false);
+
+  int head_number = 12;
+  int batch = 1;
+  int min_seq_len = 1;
+  int max_seq_len = 128;
+  int opt_seq_len = 128;
+
+  std::vector<int> min_shape = {batch, min_seq_len, 1};
+  std::vector<int> max_shape = {batch, max_seq_len, 1};
+  std::vector<int> opt_shape = {batch, opt_seq_len, 1};
+  // Set the input's min, max, opt shape
+  std::map<std::string, std::vector<int>> min_input_shape = {
+      {"read_file_0.tmp_0", min_shape},
+      {"read_file_0.tmp_1", min_shape},
+      {"read_file_0.tmp_2", min_shape},
+      {"stack_0.tmp_0", {batch, head_number, min_seq_len, min_seq_len}}};
+  std::map<std::string, std::vector<int>> max_input_shape = {
+      {"read_file_0.tmp_0", max_shape},
+      {"read_file_0.tmp_1", max_shape},
+      {"read_file_0.tmp_2", max_shape},
+      {"stack_0.tmp_0", {batch, head_number, max_seq_len, max_seq_len}}};
+  std::map<std::string, std::vector<int>> opt_input_shape = {
+      {"read_file_0.tmp_0", opt_shape},
+      {"read_file_0.tmp_1", opt_shape},
+      {"read_file_0.tmp_2", opt_shape},
+      {"stack_0.tmp_0", {batch, head_number, opt_seq_len, opt_seq_len}}};
+
+  auto precision = AnalysisConfig::Precision::kFloat32;
+  if (with_fp16) {
+    precision = AnalysisConfig::Precision::kHalf;
+  }
+  config.EnableTensorRtEngine(1 << 30, 1, 5, precision, true, false);
+  config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
+                                opt_input_shape);
+  std::vector<float> out_data;
+  run(config, &out_data);
+  for (size_t i = 0; i < out_data.size(); i++) {
+    EXPECT_NEAR(result[i], out_data[i], 1e-6);
+  }
+}
+
+TEST(AnalysisPredictor, no_fp16) {
+  std::vector<float> result = {0.597841, 0.219972, 0.182187};
+  trt_ernie(false, result);
+}
+
+TEST(AnalysisPredictor, fp16) {
+#ifdef SUPPORTS_CUDA_FP16
+  std::vector<float> result = {0.598336, 0.219558, 0.182106};
+  trt_ernie(true, result);
+#endif
+}
+
+}  // namespace inference
+}  // namespace paddle

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

@@ -120,7 +120,7 @@ void trt_ernie(bool with_fp16, std::vector<float> result) {
   if (with_fp16) {
     precision = AnalysisConfig::Precision::kHalf;
   }
-  config.EnableTensorRtEngine(1 << 30, 1, 5, precision, false, true);
+  config.EnableTensorRtEngine(1 << 30, 1, 5, precision, false, false);
   config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                 opt_input_shape);
   std::vector<float> out_data;

paddle/fluid/operators/clip_op.h

@@ -25,17 +25,23 @@ namespace operators {
 using framework::Tensor;
 using platform::Transform;
 
+#ifdef __NVCC__
+template <typename T, typename UnaryOperation>
+__global__ void ClipCudaKernel(const T* input, T* out, int num,
+                               UnaryOperation op) {
+  int idx = threadIdx.x + blockDim.x * blockIdx.x;
+  if (idx < num) {
+    out[idx] = op(input[idx]);
+  }
+}
+#endif
+
 template <typename T>
 class ClipFunctor {
  public:
   explicit ClipFunctor(const T min, const T max) : min_(min), max_(max) {}
   HOSTDEVICE T operator()(const T& x) const {
-    if (x < min_)
-      return min_;
-    else if (x > max_)
-      return max_;
-    else
-      return x;
+    return x < min_ ? min_ : x > max_ ? max_ : x;
   }
 
  private:
@@ -97,9 +103,20 @@ class ClipKernel : public framework::OpKernel<T> {
       T* out_data = out->mutable_data<T>(context.GetPlace());
       const T* x_data = x->data<T>();
       int64_t numel = x->numel();
-      Transform<DeviceContext> trans;
-      trans(context.template device_context<DeviceContext>(), x_data,
-            x_data + numel, out_data, ClipFunctor<T>(min, max));
+      if (platform::is_gpu_place(context.GetPlace())) {
+#ifdef __NVCC__
+        int threads = 256;
+        int blocks = (numel + threads - 1) / threads;
+        ClipCudaKernel<T, ClipFunctor<T>><<<
+            blocks, threads, 0,
+            context.template device_context<platform::CUDADeviceContext>()
+                .stream()>>>(x_data, out_data, numel, ClipFunctor<T>(min, max));
+#endif
+      } else {
+        Transform<DeviceContext> trans;
+        trans(context.template device_context<DeviceContext>(), x_data,
+              x_data + numel, out_data, ClipFunctor<T>(min, max));
+      }
     } else if (x_var->IsType<framework::SelectedRows>()) {
       auto* x = context.Input<framework::SelectedRows>("X");
       auto* out = context.Output<framework::SelectedRows>("Out");

paddle/fluid/operators/controlflow/compare_op.cc

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/controlflow/compare_op.h"
+#include <algorithm>
 #include <string>
+#include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
 
 namespace paddle {
 namespace operators {
@@ -85,14 +88,22 @@ class CompareOp : public framework::OperatorWithKernel {
     auto dim_x = context->GetInputDim("X");
     auto dim_y = context->GetInputDim("Y");
 
-    PADDLE_ENFORCE_GE(dim_x.size(), dim_y.size(),
-                      platform::errors::InvalidArgument(
-                          "The size of dim_y should not be greater than "
-                          "dim_x's, but received dim_y: %d > dim_x: %d.\n",
-                          dim_y.size(), dim_x.size()));
-
-    context->SetOutputDim("Out", context->GetInputDim("X"));
-    context->ShareLoD("X", "Out");
+    if (context->GetInputDim("X") == context->GetInputDim("Y")) {
+      context->ShareDim("X", /*->*/ "Out");
+      context->ShareLoD("X", /*->*/ "Out");
+    } else {
+      int max_dim = std::max(dim_x.size(), dim_y.size());
+      int axis = std::abs(dim_x.size() - dim_y.size());
+      std::vector<int> x_dims_array(max_dim);
+      std::vector<int> y_dims_array(max_dim);
+      std::vector<int> out_dims_array(max_dim);
+      GetBroadcastDimsArrays(dim_x, dim_y, x_dims_array.data(),
+                             y_dims_array.data(), out_dims_array.data(),
+                             max_dim, axis);
+      context->SetOutputDim("Out", framework::make_ddim(out_dims_array));
+      // to do
+      context->ShareLoD("X", /*->*/ "Out");
+    }
   }
 
   framework::OpKernelType GetExpectedKernelType(

paddle/fluid/operators/conv_cudnn_helper.h

@@ -162,19 +162,7 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
       workspace_size = GetWorkspaceSize(args, algo);
 
       if (workspace_size > workspace_size_limit) {
-        has_got_workspace_size = false;
-        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
-                   "the workspace size request("
-                << workspace_size << ") exceeds the limit("
-                << workspace_size_limit << ")";
-      }
-      if (!has_got_workspace_size) {
-        PADDLE_ENFORCE_CUDA_SUCCESS(
-            platform::dynload::cudnnGetConvolutionForwardAlgorithm(
-                args.handle, args.idesc.desc(), args.wdesc.desc(),
-                args.cdesc.desc(), args.odesc.desc(),
-                CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-                workspace_size_limit, &algo));
+        workspace_size_limit = workspace_size;
       }
 #else
       PADDLE_ENFORCE_CUDA_SUCCESS(
@@ -303,19 +291,8 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
 #endif
       workspace_size = GetWorkspaceSize(args, algo);
       if (workspace_size > workspace_size_limit) {
+        workspace_size_limit = workspace_size;
         has_got_workspace_size = false;
-        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
-                   "the workspace size request("
-                << workspace_size << ") exceeds the limit("
-                << workspace_size_limit << ")";
-      }
-      if (!has_got_workspace_size) {
-        PADDLE_ENFORCE_CUDA_SUCCESS(
-            platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
-                args.handle, args.wdesc.desc(), args.odesc.desc(),
-                args.cdesc.desc(), args.idesc.desc(),
-                CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
-                workspace_size_limit, &algo));
       }
 #else
       PADDLE_ENFORCE_CUDA_SUCCESS(
@@ -432,19 +409,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
       algo = (perf_results.get())[best_algo_idx].algo;
       workspace_size = GetWorkspaceSize(args, algo);
       if (workspace_size > workspace_size_limit) {
-        has_got_workspace_size = false;
-        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
-                   "the workspace size request("
-                << workspace_size << ") exceeds the limit("
-                << workspace_size_limit << ")";
-      }
-      if (!has_got_workspace_size) {
-        PADDLE_ENFORCE_CUDA_SUCCESS(
-            platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
-                args.handle, args.idesc.desc(), args.odesc.desc(),
-                args.cdesc.desc(), args.wdesc.desc(),
-                CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
-                workspace_size_limit, &algo));
+        workspace_size = workspace_size_limit;
       }
 #else
       PADDLE_ENFORCE_CUDA_SUCCESS(

paddle/fluid/operators/elementwise/elementwise_op_function.h

@@ -197,6 +197,40 @@ void CommonForwardBroadcastCPU(const framework::Tensor *x,
 }
 
 #ifdef __NVCC__
+template <typename Functor, typename T, typename OutType>
+__global__ void ElementwiseKernel(const T *x, const T *y, OutType *out, int pre,
+                                  int n, int post, int total, Functor func) {
+  int tid = threadIdx.x + blockDim.x * blockIdx.x;
+  int idx = tid / post % n;
+  if (tid < total) {
+    out[tid] = func(x[tid], y[idx]);
+  }
+}
+
+template <typename Functor, typename T, typename OutType>
+void ComputeElementwiseCUDA(const framework::Tensor *x,
+                            const framework::Tensor *y, framework::Tensor *z,
+                            int pre, int n, int post,
+                            const platform::CUDADeviceContext &ctx,
+                            Functor func, const bool is_xsize_larger = true) {
+  const T *x_data = x->data<T>();
+  const T *y_data = y->data<T>();
+  OutType *out_data = z->mutable_data<OutType>(ctx.GetPlace());
+
+  int numel = pre * n * post;
+  int threads = 256;
+  int blocks = (numel + threads - 1) / threads;
+  if (is_xsize_larger) {
+    ElementwiseKernel<Functor, T,
+                      OutType><<<blocks, threads, 0, ctx.stream()>>>(
+        x_data, y_data, out_data, pre, n, post, numel, func);
+  } else {
+    ElementwiseKernel<Functor, T,
+                      OutType><<<blocks, threads, 0, ctx.stream()>>>(
+        y_data, x_data, out_data, pre, n, post, numel, func);
+  }
+}
+
 template <typename Functor, typename T, typename OutType = T>
 __global__ void CommonForwardBroadcastCUDAKernel(
     const int *x_strides_array, const int *y_strides_array,
@@ -1908,6 +1942,16 @@ void ElementwiseComputeEx(const framework::ExecutionContext &ctx,
         ctx, x, y, z, x_dims, y_dims, func, axis, is_xsize_larger);
     return;
   }
+
+  if (platform::is_gpu_place(ctx.GetPlace())) {
+#ifdef __NVCC__
+    ComputeElementwiseCUDA<Functor, T, OutType>(
+        x, y, z, pre, n, post,
+        ctx.template device_context<platform::CUDADeviceContext>(), func,
+        is_xsize_larger);
+#endif
+    return;
+  }
   if (post == 1) {
     functor.RunRowWise(n, pre);
     return;

paddle/fluid/operators/fused/conv_fusion_op.cu

@@ -204,11 +204,17 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
     auto x_dims = framework::vectorize(transformed_input.dims());
     auto f_dims = framework::vectorize(filter->dims());
     if (!exhaustive_search) {
+      int perf_count;
+      int best_algo_idx = 0;
+      size_t tmp_size = 0;
+      std::unique_ptr<cudnnConvolutionFwdAlgoPerf_t[]> perf_results(
+          new cudnnConvolutionFwdAlgoPerf_t[kNUM_CUDNN_FWD_ALGS]);
       PADDLE_ENFORCE_CUDA_SUCCESS(
-          platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+          platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
               handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-              cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-              workspace_size_limit, &algo));
+              cudnn_output_desc, kNUM_CUDNN_FWD_ALGS, &perf_count,
+              perf_results.get()));
+      algo = (perf_results.get())[best_algo_idx].algo;
       VLOG(3) << "cuDNN forward algo " << algo;
     } else {
       std::function<cudnnConvolutionFwdAlgo_t()> search_func =

paddle/fluid/operators/fused/fusion_conv_inception_op.cu

@@ -179,16 +179,23 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
           out_desc[i], cudnn_dtype, 4, out_dims[i].data(),
           out_strides[i].data()));
+
+      int perf_count;
+      int best_algo_idx = 0;
+      size_t tmp_size = 0;
+      std::unique_ptr<cudnnConvolutionFwdAlgoPerf_t[]> perf_results(
+          new cudnnConvolutionFwdAlgoPerf_t[kNUM_CUDNN_FWD_ALGS]);
       PADDLE_ENFORCE_CUDA_SUCCESS(
-          platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+          platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
               handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
-              CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-              workspace_size_limit, &algo[i]));
-      size_t tmp_size = 0;
+              kNUM_CUDNN_FWD_ALGS, &perf_count, perf_results.get()));
+      algo[i] = (perf_results.get())[best_algo_idx].algo;
+
       PADDLE_ENFORCE_CUDA_SUCCESS(
           platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
               handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
               algo[i], &tmp_size));
+
       workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
     }
     cudnnActivationDescriptor_t cudnn_act_desc =

paddle/fluid/operators/math/bert_encoder_functor.cu

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <cuda_runtime.h>
+#include <algorithm>
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/operators/math/bert_encoder_functor.h"

paddle/fluid/operators/p_norm_op.cu

@@ -99,39 +99,25 @@ __global__ void PnormGradient(const T* x, const T* x_norm, const T* y_grad,
                               const float porder, const int pre,
                               const int axis_n, const int post, const T eps,
                               T* x_grad) {
-  typedef cub::BlockReduce<T, BlockDim> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage_sum;
   // dx = (x/pnorm_broadcast).pow(p-1) * norm_dy.broadcast * sign(x)
   int num = pre * post;
   auto porder_grad = static_cast<T>(porder - 1.0f);
   for (int i = blockIdx.x; i < num; i += gridDim.x) {
-    T sum = 0.0;
-    __shared__ T row_sum;
-    __shared__ T row_sqrt_norm;
-    __shared__ T row_norm;
+    __shared__ T pnorm_i;
+    __shared__ T yout_i;
 
     auto base = (i / post) * post * axis_n + (i % post);
 
-    for (int j = threadIdx.x; j < axis_n; j += blockDim.x) {
-      int index = base + j * post;
-      sum += x[index] * y_grad[index];
-    }
-    T reduce_result = BlockReduce(temp_storage_sum).Sum(sum);
-
     if (threadIdx.x == 0) {
-      row_sum = reduce_result;
-      row_sqrt_norm = x_norm[i];
-      row_norm = row_sqrt_norm * row_sqrt_norm;
+      pnorm_i = x_norm[i];
+      yout_i = y_grad[i];
     }
-    __syncthreads();
 
-    const T pnorm_i = x_norm[i];
-    const T yout_i = y_grad[i];
+    __syncthreads();
 
     for (int j = threadIdx.x; j < axis_n; j += blockDim.x) {
       int index = base + j * post;
       const T x_ij = inline_abs(x[index]);
-      const T dy_ij = y_grad[index];
       x_grad[index] = inline_pow(x_ij, porder_grad) /
                       (inline_pow(pnorm_i, porder_grad) + eps) * yout_i *
                       inline_sign(x[index]);

paddle/fluid/platform/dynload/cudnn.h

@@ -54,7 +54,6 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
   __macro(cudnnSetTensorNdDescriptor);                    \
   __macro(cudnnGetTensorNdDescriptor);                    \
   __macro(cudnnGetConvolutionNdForwardOutputDim);         \
-  __macro(cudnnGetConvolutionForwardAlgorithm);           \
   __macro(cudnnCreateTensorDescriptor);                   \
   __macro(cudnnDestroyTensorDescriptor);                  \
   __macro(cudnnCreateFilterDescriptor);                   \
@@ -102,7 +101,6 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
   __macro(cudnnDropoutGetStatesSize);                     \
   __macro(cudnnSetDropoutDescriptor);                     \
   __macro(cudnnCreateRNNDescriptor);                      \
-  __macro(cudnnSetRNNDescriptor);                         \
   __macro(cudnnGetRNNParamsSize);                         \
   __macro(cudnnGetRNNWorkspaceSize);                      \
   __macro(cudnnGetRNNTrainingReserveSize);                \
@@ -126,12 +124,19 @@ CUDNN_DNN_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 #if CUDNN_VERSION >= 3000
 #define CUDNN_DNN_ROUTINE_EACH_AFTER_R3(__macro)           \
   __macro(cudnnGetConvolutionBackwardFilterWorkspaceSize); \
-  __macro(cudnnGetConvolutionBackwardDataAlgorithm);       \
-  __macro(cudnnGetConvolutionBackwardFilterAlgorithm);     \
   __macro(cudnnGetConvolutionBackwardDataWorkspaceSize);
 CUDNN_DNN_ROUTINE_EACH_AFTER_R3(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 #endif
 
+// APIs available after R3:
+#if CUDNN_VERSION >= 3000 && CUDNN_VERSION < 8000
+#define CUDNN_DNN_ROUTINE_EACH_AFTER_R3_LESS_R8(__macro) \
+  __macro(cudnnGetConvolutionBackwardFilterAlgorithm);   \
+  __macro(cudnnGetConvolutionForwardAlgorithm);          \
+  __macro(cudnnSetRNNDescriptor);
+CUDNN_DNN_ROUTINE_EACH_AFTER_R3_LESS_R8(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
+#endif
+
 // APIs available after R4:
 #if CUDNN_VERSION >= 4007
 #define CUDNN_DNN_ROUTINE_EACH_AFTER_R4(__macro)    \
@@ -183,6 +188,12 @@ CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
   __macro(cudnnGetBatchNormalizationTrainingExReserveSpaceSize);
 CUDNN_DNN_ROUTINE_EACH_AFTER_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 #endif
+
+#if CUDNN_VERSION >= 8000
+#define CUDNN_DNN_ROUTINE_EACH_R8(__macro) __macro(cudnnSetRNNDescriptor_v8);
+CUDNN_DNN_ROUTINE_EACH_R8(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
+#endif
+
 }  // namespace dynload
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/dynload/tensorrt.cc

@@ -13,18 +13,62 @@
    limitations under the License. */
 
 #include "paddle/fluid/platform/dynload/tensorrt.h"
+#include <string>
 
 namespace paddle {
 namespace platform {
 namespace dynload {
 
 std::once_flag tensorrt_dso_flag;
-void *tensorrt_dso_handle;
+void* tensorrt_dso_handle;
 
 #define DEFINE_WRAP(__name) DynLoad__##__name __name
 
 TENSORRT_RAND_ROUTINE_EACH(DEFINE_WRAP);
 
+void* GetTensorRtHandle() {
+#if defined(__APPLE__) || defined(__OSX__)
+  std::string dso_name = "libnvinfer.dylib";
+#elif defined(_WIN32)
+  std::string dso_name = "nvinfer.dll";
+#else
+  std::string dso_name = "libnvinfer.so";
+#endif
+
+#if !defined(_WIN32)
+  int dynload_flags = RTLD_LAZY | RTLD_LOCAL;
+#else
+  int dynload_flags = 0;
+#endif  // !_WIN32
+
+  void* dso_handle = dlopen(dso_name.c_str(), dynload_flags);
+
+  if (nullptr == dso_handle) {
+    auto error_msg =
+        "TensorRT dynamic library (%s) that Paddle depends on is not "
+        "configured correctly. (error code is %s)\n"
+        "  Suggestions:\n"
+        "  1. Check if TensorRT "
+        "is installed correctly and its version is matched with paddlepaddle "
+        "you installed.\n"
+        "  2. Configure TensorRT dynamic library environment variables as "
+        "follows:\n"
+        "  - Linux: set LD_LIBRARY_PATH by `export LD_LIBRARY_PATH=...`\n"
+        "  - Windows: set PATH by `set PATH=XXX;%PATH%`\n"
+        "  - Mac: set  DYLD_LIBRARY_PATH by `export DYLD_LIBRARY_PATH=...` "
+        "[Note: After Mac OS 10.11, using the DYLD_LIBRARY_PATH is "
+        "impossible unless System Integrity Protection (SIP) is disabled.]";
+#if !defined(_WIN32)
+    auto errorno = dlerror();
+#else
+    auto errorno = GetLastError();
+#endif  // !_WIN32
+    std::cerr << string::Sprintf(error_msg, dso_name, errorno);
+  }
+
+  return dso_handle;
+}
+
 }  // namespace dynload
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/dynload/tensorrt.h

@@ -27,6 +27,8 @@ namespace paddle {
 namespace platform {
 namespace dynload {
 
+void* GetTensorRtHandle();
+
 extern std::once_flag tensorrt_dso_flag;
 extern void* tensorrt_dso_handle;
 
@@ -36,8 +38,7 @@ extern void* tensorrt_dso_handle;
     auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {          \
       using tensorrt_func = decltype(&::__name);                              \
       std::call_once(tensorrt_dso_flag, []() {                                \
-        tensorrt_dso_handle =                                                 \
-            paddle::platform::dynload::GetTensorRtDsoHandle();                \
+        tensorrt_dso_handle = paddle::platform::dynload::GetTensorRtHandle(); \
         PADDLE_ENFORCE_NOT_NULL(tensorrt_dso_handle,                          \
                                 platform::errors::Unavailable(                \
                                     "Load tensorrt %s failed", #__name));     \
@@ -53,7 +54,8 @@ extern void* tensorrt_dso_handle;
 
 #define TENSORRT_RAND_ROUTINE_EACH(__macro) \
   __macro(createInferBuilder_INTERNAL);     \
-  __macro(createInferRuntime_INTERNAL);
+  __macro(createInferRuntime_INTERNAL);     \
+  __macro(getPluginRegistry);
 
 TENSORRT_RAND_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP)
 

paddle/scripts/paddle_build.sh

@@ -958,6 +958,7 @@ function parallel_test() {
     ut_total_startTime_s=`date +%s`
     mkdir -p ${PADDLE_ROOT}/build
     cd ${PADDLE_ROOT}/build
+    pip install ${PADDLE_ROOT}/build/python/dist/*whl
     if [ "$WITH_GPU" == "ON" ];then
         parallel_test_base_gpu
     else

python/paddle/fleet/base/distributed_strategy.py

@@ -604,6 +604,15 @@ class DistributedStrategy(object):
         else:
             print("WARNING: lars should have value of bool type")
 
+    @property
+    def lars_configs(self):
+        return get_msg_dict(self.strategy.lars_configs)
+
+    @lars_configs.setter
+    def lars_configs(self, configs):
+        check_configs_key(self.strategy.lars_configs, configs, "lars_configs")
+        assign_configs_value(self.strategy.lars_configs, configs)
+
     @property
     def lamb(self):
         return self.strategy.lamb

python/paddle/fleet/base/fleet_base.py

@@ -279,8 +279,11 @@ class Fleet(object):
             # for more examples, please reference https://github.com/PaddlePaddle/Fleet
 
         """
+        context = {}
         # cache original feed forward program
         self.origin_main_program = loss.block.program
+        context["origin_main_program"] = self.origin_main_program
+        context["loss"] = loss
         if startup_program == None:
             self.origin_startup_program = \
                 paddle.default_startup_program().clone(for_test=False)
@@ -288,6 +291,8 @@ class Fleet(object):
         else:
             self.origin_startup_program = \
                 startup_program.clone(for_test=False)
+        context["origin_startup_program"] = startup_program
+        context["role_maker"] = self._role_maker
 
         # compile time
         distributed_optimizer_list = \
@@ -317,6 +322,9 @@ class Fleet(object):
 
         valid_strategy = self.strategy_compiler._get_valid_strategy(
             self.user_defined_strategy, can_not_apply_optimizer_list)
+
+        context["valid_strategy"] = valid_strategy
+
         self.valid_strategy = valid_strategy
 
         optimize_ops = []
@@ -334,6 +342,8 @@ class Fleet(object):
                 parameter_list=parameter_list,
                 no_grad_set=no_grad_set)
 
+        context["program_optimize_ops"] = optimize_ops
+        context["program_params_grads"] = params_grads
         if graph_optimizer:
             optimize_ops, params_grads = graph_optimizer.minimize(
                 loss,
@@ -344,12 +354,13 @@ class Fleet(object):
             # if a graph optimizer takes effect, mostly
             # optimizers_ops and params_grads are None
             # i.e. users can not modify current computation graph anymore
+            context["graph_optimize_ops"] = optimize_ops
+            context["graph_optimize_grads"] = params_grads
+
         if self._runtime_handle is None:
-            self._runtime_handle = RuntimeFactory()._create_runtime(
-                valid_strategy, self._role_maker, optimize_ops, params_grads)
+            self._runtime_handle = RuntimeFactory()._create_runtime(context)
 
         if self._util is None:
-            self._util = UtilFactory()._create_util(
-                valid_strategy, self._role_maker, optimize_ops, params_grads)
+            self._util = UtilFactory()._create_util(context)
 
         return optimize_ops, params_grads

python/paddle/fleet/base/meta_optimizer_factory.py

@@ -19,6 +19,7 @@ from ..meta_optimizers import GraphExecutionOptimizer
 from ..meta_optimizers import PipelineOptimizer
 from ..meta_optimizers import LocalSGDOptimizer
 from ..meta_optimizers import LarsOptimizer
+from ..meta_optimizers import DGCOptimizer
 
 __all__ = ["MetaOptimizerFactory"]
 
@@ -30,6 +31,7 @@ meta_optimizer_names = [
     "PipelineOptimizer",
     "LocalSGDOptimizer",
     "LarsOptimizer",
+    "DGCOptimizer",
 ]
 
 

python/paddle/fleet/base/runtime_factory.py

@@ -18,10 +18,8 @@ class RuntimeFactory(object):
     def __init__(self):
         pass
 
-    def _create_runtime(self, final_dist_strategy, role_maker, opt_ops,
-                        params_grads):
-        if role_maker._is_collective:
+    def _create_runtime(self, context):
+        if context["role_maker"]._is_collective:
             collective_runtime = CollectiveRuntime()
-            collective_runtime._set_basic_info(final_dist_strategy, role_maker,
-                                               opt_ops, params_grads)
+            collective_runtime._set_basic_info(context)
             return collective_runtime

python/paddle/fleet/base/util_factory.py

@@ -20,11 +20,10 @@ __all__ = ['UtilBase']
 
 
 class UtilFactory(object):
-    def _create_util(self, dist_strategy, role_maker, optimize_ops,
-                     params_grads):
+    def _create_util(self, context):
         util = UtilBase()
-        util._set_strategy(dist_strategy)
-        util._set_role_maker(role_maker)
+        util._set_strategy(context["valid_strategy"])
+        util._set_role_maker(context["role_maker"])
         return util
 
 

python/paddle/fleet/cloud_utils.py

+# Copyright (c) 2020 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 os
+import paddle
+from paddle.fleet.launch_utils import get_cluster, logger
+
+
+def get_cloud_cluster(args_node_ips, selected_gpus, args_port=6170):
+    """
+    args_node_ips, args_node_ip:string
+    """
+    #you can automatically get ip info while using paddlecloud multi nodes mode.
+    node_ips = os.getenv("PADDLE_TRAINERS")
+    assert node_ips is not None, "PADDLE_TRAINERS should not be None"
+
+    node_ip = os.getenv("POD_IP")
+    assert node_ip is not None, "POD_IP should not be None"
+
+    node_rank = os.getenv("PADDLE_TRAINER_ID")
+    assert node_rank is not None, "PADDLE_TRAINER_ID should not be None"
+
+    node_ips = node_ips.split(",")
+    num_nodes = len(node_ips)
+    node_rank = int(node_rank)
+
+    if args_node_ips != "127.0.0.1" and args_node_ips != ",".join(node_ips):
+        logger.warning(
+            "Please NOTE: When using paddlecloud, cluster_node_ips is \
+automatically got from PADDLE_TRAINERS(multi nodes) or POD_IP(single node).\
+Your input cluster_node_ips: {} doesn't equals to IPs: {} from \
+paddlecloud environment.".format(args_node_ips, node_ips))
+
+    started_port = args_port
+    print("num_nodes:", num_nodes)
+    if num_nodes > 1:
+        try:
+            paddle_port = int(os.getenv("PADDLE_PORT", ""))
+            paddle_port_num = int(os.getenv("TRAINER_PORTS_NUM", ""))
+
+            if paddle_port_num >= len(
+                    selected_gpus) and paddle_port != args_port:
+                logger.warning("Use Cloud specified port:{}.".format(
+                    paddle_port))
+                started_port = paddle_port
+
+        except Exception as e:
+            print(e)
+            pass
+
+    if started_port is None:
+        started_port = 6170
+
+    logger.debug("parsed from args:node_ips:{} \
+        node_ip:{} node_rank:{} started_port:{}"
+                 .format(node_ips, node_ip, node_rank, started_port))
+
+    ports = [x for x in range(started_port, started_port + len(selected_gpus))]
+    cluster, pod = get_cluster(node_ips, node_ip, ports, selected_gpus)
+    return cluster, cluster.pods[node_rank]
+
+
+def use_paddlecloud():
+    node_ips = os.getenv("PADDLE_TRAINERS")
+    node_ip = os.getenv("POD_IP")
+    node_rank = os.getenv("PADDLE_TRAINER_ID")
+    if node_ips is None or node_ip is None or node_rank is None:
+        return False
+    else:
+        return True
+
+
+def get_trainers_num():
+    return int(os.getenv("PADDLE_TRAINERS_NUM", "1"))

python/paddle/fleet/launch.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.
+"""
+paddle.distributed.launch is a module that spawns multiple distributed 
+process on each training node for gpu training and cpu training.
+Usage:
+    In both of single node training or multiple node training, this module 
+launch a process on each of the given gpu card or cpu machine.
+    GPU training:
+    1. for single node training with all visible gpu cards:
+       fleetrun your_training_py (arg1 arg2 and all others)
+    2. for single node training with [0,4) cards
+       fleetrun --gpus="0,1,2,3" your_training_py (arg1 arg2 and all others)
+    3. for multiple node training such as two node:192.168.0.16, 192.168.0.17
+        on 192.168.0.16:
+            fleetrun --ips="192.168.0.16,192.168.0.17" --node_ip=192.168.0.16 \
+                your_training_py (arg1 arg2 and all others)
+        on 192.168.0.17:
+            fleetrun --ips="192.168.0.16,192.168.0.17" \
+                --node_ip=192.168.0.17 \
+                your_training_py (arg1 arg2 and all others)
+    CPU training:
+    1. for single node training with multi servers and workers:
+        fleetrun --server_num=1 --worker_num=4 your_training_py (arg1 arg2 and all others)
+    2. for multiple node training such as two node:192.168.0.16, 192.168.0.17 \
+        with 2 servers and  4 workers.
+        on 192.168.0.16:
+            fleetrun --servers="192.168.0.16:6170,192.168.0.17:6171" \
+                --workers="192.168.0.16:6172,192.168.0.17:6173,192.168.0.16:6174,192.168.0.17:6175" \
+                your_training_py (arg1 arg2 and all others)
+        on 192.168.0.17:
+            fleetrun --servers="192.168.0.16:6170,192.168.0.17:6171" \
+                --workers="192.168.0.16:6172,192.168.0.17:6173,192.168.0.16:6174,192.168.0.17:6175" \
+                your_training_py (arg1 arg2 and all others)
+"""
+
+from __future__ import print_function
+import sys
+from sys import version
+import subprocess
+import os
+import time
+import six
+import copy
+from argparse import ArgumentParser, REMAINDER
+import paddle
+import paddle.fluid as fluid
+
+from paddle.fleet.launch_utils import *
+import paddle.fleet.cloud_utils as cloud_utils
+
+
+def _print_arguments(args):
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
+
+
+def _parse_args():
+    """
+    Helper function parsing the command line options
+    @retval ArgumentParser
+    """
+    parser = ArgumentParser(
+        description='''start paddle training using multi-process mode.
+see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
+''')
+
+    #Optional arguments for the launch helper
+    parser.add_argument(
+        "--ips",
+        type=str,
+        default="127.0.0.1",
+        help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..")
+    parser.add_argument(
+        "--gpus",
+        type=str,
+        default=None,
+        help="It's for gpu training and the training process will run on the gpus,"
+        "each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training."
+    )
+
+    parser.add_argument(
+        "--servers", type=str, default="", help="User defined servers ip:port")
+    parser.add_argument(
+        "--workers", type=str, default="", help="User defined workers ip:port")
+    parser.add_argument(
+        "--worker_num", type=int, default=2, help="number of workers")
+
+    parser.add_argument(
+        "--server_num", type=int, default=2, help="number of servers")
+
+    parser.add_argument(
+        "--log_dir",
+        type=str,
+        help="The path for each process's log.If it's not set, the log will printed to default pipe."
+    )
+    #positional
+    parser.add_argument(
+        "training_script",
+        type=str,
+        help="The full path to the single GPU training "
+        "program/script to be launched in parallel, "
+        "followed by all the arguments for the "
+        "training script")
+
+    #rest from the training program
+    parser.add_argument('training_script_args', nargs=REMAINDER)
+    return parser.parse_args()
+
+
+def get_cluster_from_args(args, gpus):
+    node_ips = [x.strip() for x in args.ips.split(',')]
+    if len(node_ips) == 1:
+        node_ip = node_ips[0]
+    else:
+        _, node_ip = get_host_name_ip()
+
+    # node_ip = args.node_ip
+    assert node_ip in node_ips, "Can't find your local ip {%s} in node_ips:{%s}" \
+                % (node_ip, node_ips)
+    node_rank = node_ips.index(node_ip)
+
+    logger.debug("parsed from args:node_ips:{} node_ip:{} node_rank:{}".format(
+        node_ips, node_ip, node_rank))
+
+    free_ports = None
+    if not cloud_utils.use_paddlecloud() and len(
+            node_ips) <= 1 and os.environ.get('FLAGS_START_PORT') is None:
+        free_ports = find_free_ports(len(gpus))
+        if free_ports is not None:
+            free_ports = list(free_ports)
+    else:
+        start_port = 6070
+        if os.environ.get('FLAGS_START_PORT') is not None:
+            start_port = os.environ.get('FLAGS_START_PORT')
+
+        free_ports = [x for x in range(start_port, start_port + len(gpus))]
+
+    return get_cluster(node_ips, node_ip, free_ports, gpus)
+
+
+def get_gpus(gpus):
+    if gpus is None:
+        gpus_num = fluid.core.get_cuda_device_count()
+        gpus = [str(x) for x in range(0, gpus_num)]
+    else:
+        cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
+        if cuda_visible_devices is None or cuda_visible_devices == "":
+            gpus = [x.strip() for x in gpus.split(',')]
+        else:
+            # change gpus into relative values
+            # e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.gpus=4,5,6,7;
+            # therefore gpus=0,1,2,3
+            cuda_visible_devices_list = cuda_visible_devices.split(',')
+            for x in gpus.split(','):
+                assert x in cuda_visible_devices_list, "Can't find "\
+                "your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\
+                % (x, cuda_visible_devices)
+            gpus = [
+                cuda_visible_devices_list.index(x.strip())
+                for x in gpus.split(',')
+            ]
+
+    return gpus
+
+
+def launch_collective(args):
+    # parse arguments, used for cloud-single-machine and local
+    gpus = get_gpus(args.gpus)
+    trainers_num = cloud_utils.get_trainers_num()
+    logger.debug("parsed from args trainerss_num:{} gpus:{}".format(
+        trainers_num, gpus))
+
+    cluster = None
+    pod = None
+
+    if cloud_utils.use_paddlecloud() and trainers_num != 1:
+        cluster, pod = cloud_utils.get_cloud_cluster(args.ips, gpus)
+        logger.info("get cluster from cloud:{}".format(cluster))
+    else:
+        # trainers_num = 1 or not use paddlecloud ips="a,b"
+        cluster, pod = get_cluster_from_args(args, gpus)
+        logger.info("get cluster from args:{}".format(cluster))
+
+    procs = start_local_trainers(
+        cluster,
+        pod,
+        training_script=args.training_script,
+        training_script_args=args.training_script_args,
+        log_dir=args.log_dir)
+
+    while True:
+        alive = watch_local_trainers(procs, cluster.trainers_nranks())
+
+        if not alive:
+            logger.info("Local procs complete, POD info:{}".format(pod))
+            break
+
+        time.sleep(3)
+
+
+def launch_ps(args):
+    worker_num = args.worker_num
+    server_num = args.server_num
+    start_port = 6170
+    if os.environ.get('FLAGS_START_PORT') is not None:
+        start_port = os.environ.get('FLAGS_START_PORT')
+    default_env = os.environ.copy()
+    current_env = copy.copy(default_env)
+    current_env.pop("http_proxy", None)
+    current_env.pop("https_proxy", None)
+    procs = []
+    cmds = []
+    log_fns = []
+    ports = range(start_port, start_port + server_num, 1)
+    default_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports])
+    user_endpoints = ""
+    if args.servers == "":
+        user_endpoints = default_endpoints
+    else:
+        user_endpoints = args.servers
+    user_endpoints_ips = [x.split(":")[0] for x in user_endpoints.split(",")]
+    user_endpoints_port = [x.split(":")[1] for x in user_endpoints.split(",")]
+    for i in range(server_num):
+        current_env.update({
+            "PADDLE_PSERVERS_IP_PORT_LIST": user_endpoints,
+            "PADDLE_PORT": user_endpoints_port[i],
+            "TRAINING_ROLE": "PSERVER",
+            "PADDLE_TRAINERS_NUM": str(worker_num),
+            "POD_IP": user_endpoints_ips[i]
+        })
+
+        cmd = [sys.executable, "-u", args.training_script
+               ] + args.training_script_args
+        cmds.append(cmd)
+        if args.log_dir is not None:
+            os.system("mkdir -p {}".format(args.log_dir))
+            fn = open("%s/serverlog.%d" % (args.log_dir, i), "w")
+            log_fns.append(fn)
+            proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
+        else:
+            proc = subprocess.Popen(cmd, env=current_env)
+        procs.append(proc)
+
+    for i in range(worker_num):
+        current_env.update({
+            "PADDLE_PSERVERS_IP_PORT_LIST": user_endpoints,
+            "PADDLE_TRAINERS_NUM": str(worker_num),
+            "TRAINING_ROLE": "TRAINER",
+            "PADDLE_TRAINER_ID": str(i)
+        })
+        cmd = [sys.executable, "-u", args.training_script
+               ] + args.training_script_args
+        cmds.append(cmd)
+        if args.log_dir is not None:
+            os.system("mkdir -p {}".format(args.log_dir))
+            fn = open("%s/workerlog.%d" % (args.log_dir, i), "w")
+            log_fns.append(fn)
+            proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
+        else:
+            proc = subprocess.Popen(cmd, env=current_env)
+        procs.append(proc)
+
+    # only wait worker to finish here
+    for i, proc in enumerate(procs):
+        if i < server_num:
+            continue
+        procs[i].wait()
+        if len(log_fns) > 0:
+            log_fns[i].close()
+
+    print("all workers exit, going to finish parameter server", file=sys.stderr)
+    for i in range(server_num):
+        if len(log_fns) > 0:
+            log_fns[i].close()
+        procs[i].terminate()
+    print("all parameter server are killed", file=sys.stderr)
+
+
+def launch():
+    args = _parse_args()
+    logger = get_logger()
+    _print_arguments(args)
+    ps_args = ['--worker_num', '--server_num', '--servers', '--workers']
+    collective_args = ['--ips', '--gpus']
+    has_ps_args = [
+        ps_arg for ps_arg in ps_args if ps_arg in " ".join(sys.argv[1:-1])
+    ]
+    has_collective_args = [
+        co_arg for co_arg in collective_args
+        if co_arg in " ".join(sys.argv[1:-1])
+    ]
+    if len(has_ps_args) > 0 or fluid.core.get_cuda_device_count() == 0:
+        logger.info("Run cpu parameter-sever mode.")
+        launch_ps(args)
+    elif len(has_collective_args) > 0:
+        logger.info("Run gpu collective mode.")
+        launch_collective(args)
+    else:
+        logger.warning(
+            "Not found distinct args. Default use gpu collective mode")
+        launch_collective(args)
+
+
+if __name__ == "__main__":
+    launch()

python/paddle/fleet/launch_utils.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 functools
+import logging
+import socket
+import time
+import os
+import signal
+import copy
+import sys
+import subprocess
+from contextlib import closing
+import socket
+
+logger = logging.getLogger("root")
+logger.propagate = False
+
+
+class Cluster(object):
+    def __init__(self, hdfs):
+        self.job_server = None
+        self.pods = []
+        self.hdfs = None
+        self.job_stage_flag = None
+
+    def __str__(self):
+        return "job_server:{} pods:{} job_stage_flag:{} hdfs:{}".format(
+            self.job_server, [str(pod) for pod in self.pods],
+            self.job_stage_flag, self.hdfs)
+
+    def __eq__(self, cluster):
+        if len(self.pods) != len(cluster.pods):
+            return False
+
+        for a, b in zip(self.pods, cluster.pods):
+            if a != b:
+                return False
+
+        if self.job_stage_flag != cluster.job_stage_flag:
+            return False
+
+        return True
+
+    def __ne__(self, cluster):
+        return not self.__eq__(cluster)
+
+    def update_pods(cluster):
+        self.pods = copy.copy(cluster.pods)
+
+    def trainers_nranks(self):
+        return len(self.trainers_endpoints())
+
+    def pods_nranks(self):
+        return len(self.pods)
+
+    def trainers_endpoints(self):
+        r = []
+        for pod in self.pods:
+            for t in pod.trainers:
+                r.append(t.endpoint)
+        return r
+
+    def pods_endpoints(self):
+        r = []
+        for pod in self.pods:
+            ep = "{}:{}".format(pod.addr, pod.port)
+            assert pod.port != None and pod.addr != None, "{} not a valid endpoint".format(
+                ep)
+            r.append(ep)
+
+        return r
+
+    def get_pod_by_id(self, pod_id):
+        for pod in self.pods:
+            if str(pod_id) == str(pod.id):
+                return pod
+
+        return None
+
+
+class JobServer(object):
+    def __init__(self):
+        self.endpoint = None
+
+    def __str__(self):
+        return "{}".format(self.endpoint)
+
+    def __eq__(self, j):
+        return self.endpint == j.endpoint
+
+    def __ne__(self, j):
+        return not self == j
+
+
+class Trainer(object):
+    def __init__(self):
+        self.gpus = []
+        self.endpoint = None
+        self.rank = None
+
+    def __str__(self):
+        return "gpu:{} endpoint:{} rank:{}".format(self.gpus, self.endpoint,
+                                                   self.rank)
+
+    def __eq__(self, t):
+        if len(self.gpus) != len(t.gpus):
+            return False
+
+        if self.endpoint != t.endpoint or \
+                self.rank != t.rank:
+            return False
+
+        for a, b in zip(self.gpus, t.gpus):
+            if a != b:
+                return False
+
+        return True
+
+    def __ne__(self, t):
+        return not self == t
+
+    def rank(self):
+        return self.rank
+
+
+class Pod(object):
+    def __init__(self):
+        self.rank = None
+        self.id = None
+        self.addr = None
+        self.port = None
+        self.trainers = []
+        self.gpus = []
+
+    def __str__(self):
+        return "rank:{} id:{} addr:{} port:{} visible_gpu:{} trainers:{}".format(
+            self.rank, self.id, self.addr, self.port, self.gpus,
+            [str(t) for t in self.trainers])
+
+    def __eq__(self, pod):
+        if self.rank != pod.rank or \
+                self.id != pod.id or \
+                self.addr != pod.addr or \
+                self.port != pod.port:
+            logger.debug("pod {} != pod".format(self, pod))
+            return False
+
+        if len(self.trainers) != len(pod.trainers):
+            logger.debug("trainers {} != {}".format(self.trainers,
+                                                    pod.trainers))
+            return False
+
+        for i in range(len(self.trainers)):
+            if self.trainers[i] != pod.trainers[i]:
+                logger.debug("trainer {} != {}".format(self.trainers[i],
+                                                       pod.trainers[i]))
+                return False
+
+        return True
+
+    def __ne__(self, pod):
+        return not self == pod
+
+    def parse_response(self, res_pods):
+        pass
+
+    def rank(self):
+        return self.rank
+
+    def get_visible_gpus(self):
+        r = ""
+        for g in self.gpus:
+            r += "{},".format(g)
+
+        assert r != "", "this pod {} can't see any gpus".format(self)
+
+        r = r[:-1]
+        return r
+
+
+def get_logger(log_level=20, name="root"):
+    logger = logging.getLogger(name)
+    logger.setLevel(log_level)
+
+    log_handler = logging.StreamHandler()
+    log_format = logging.Formatter(
+        '%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
+    log_handler.setFormatter(log_format)
+    logger.addHandler(log_handler)
+
+    return logger
+
+
+def get_cluster(node_ips, node_ip, paddle_ports, selected_gpus):
+    assert type(paddle_ports) is list, "paddle_ports must be list"
+    cluster = Cluster(hdfs=None)
+    trainer_rank = 0
+    for node_rank, ip in enumerate(node_ips):
+        pod = Pod()
+        pod.rank = node_rank
+        pod.addr = ip
+        for i in range(len(selected_gpus)):
+            trainer = Trainer()
+            trainer.gpus.append(selected_gpus[i])
+            trainer.endpoint = "%s:%d" % (ip, paddle_ports[i])
+            trainer.rank = trainer_rank
+            trainer_rank += 1
+
+            pod.trainers.append(trainer)
+        cluster.pods.append(pod)
+
+    pod_rank = node_ips.index(node_ip)
+    return cluster, cluster.pods[pod_rank]
+
+
+def terminate_local_procs(procs):
+    for p in procs:
+        if p.proc.poll() is None:
+            p.proc.terminate()
+            p.log_fn.close()
+            logger.debug("terminate process id:{}".format(p.proc.pid))
+
+    #wait all process terminiated
+    time.sleep(3)
+    for step in range(0, 50):
+        alive = False
+        for p in procs:
+            if p.proc.poll() is None:  # not termniate
+                os.kill(p.proc.pid, signal.SIGKILL)
+                alive = True
+
+        if not alive:
+            logger.info("terminate all the procs")
+            return
+
+        time.sleep(3)
+
+    logger.fatal("can't kill all process and exit")
+    exit(1)
+
+
+def get_host_name_ip():
+    try:
+        host_name = socket.gethostname()
+        host_ip = socket.gethostbyname(host_name)
+        return host_name, host_ip
+    except:
+        return None
+
+
+def add_arguments(argname, type, default, help, argparser, **kwargs):
+    """Add argparse's argument.
+    Usage:
+    .. code-block:: python
+        parser = argparse.ArgumentParser()
+        add_argument("name", str, "Jonh", "User name.", parser)
+        args = parser.parse_args()
+    """
+    type = distutils.util.strtobool if type == bool else type
+    argparser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)
+
+
+def find_free_ports(num):
+    def __free_port():
+        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
+            s.bind(('', 0))
+            return s.getsockname()[1]
+
+    port_set = set()
+    step = 0
+    while True:
+        port = __free_port()
+        if port not in port_set:
+            port_set.add(port)
+
+        if len(port_set) >= num:
+            return port_set
+
+        step += 1
+        if step > 100:
+            print(
+                "can't find avilable port and use the specified static port now!"
+            )
+            return None
+
+    return None
+
+
+class TrainerProc(object):
+    def __init__(self):
+        self.proc = None
+        self.log_fn = None
+        self.log_offset = None
+        self.rank = None
+        self.local_rank = None
+        self.cmd = None
+
+
+def start_local_trainers(cluster,
+                         pod,
+                         training_script,
+                         training_script_args,
+                         log_dir=None):
+    current_env = copy.copy(os.environ.copy())
+    #paddle broadcast ncclUniqueId use socket, and
+    #proxy maybe make trainers unreachable, so delete them.
+    #if we set them to "", grpc will log error message "bad uri"
+    #so just delete them.
+    current_env.pop("http_proxy", None)
+    current_env.pop("https_proxy", None)
+
+    procs = []
+    for idx, t in enumerate(pod.trainers):
+        proc_env = {
+            "FLAGS_selected_gpus": "%s" % ",".join([str(g) for g in t.gpus]),
+            "PADDLE_TRAINER_ID": "%d" % t.rank,
+            "PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint,
+            "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(),
+            "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints())
+        }
+
+        current_env.update(proc_env)
+
+        logger.debug("trainer proc env:{}".format(current_env))
+
+        cmd = [sys.executable, "-u", training_script] + training_script_args
+
+        logger.info("start trainer proc:{} env:{}".format(cmd, proc_env))
+
+        fn = None
+        if log_dir is not None:
+            os.system("mkdir -p {}".format(log_dir))
+            fn = open("%s/workerlog.%d" % (log_dir, idx), "a")
+            proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
+        else:
+            proc = subprocess.Popen(cmd, env=current_env)
+
+        tp = TrainerProc()
+        tp.proc = proc
+        tp.rank = t.rank
+        tp.local_rank = idx
+        tp.log_fn = fn
+        tp.log_offset = fn.tell() if fn else None
+        tp.cmd = cmd
+
+        procs.append(tp)
+
+    return procs
+
+
+def pull_worker_log(tp):
+    if tp.log_fn:
+        with open(tp.log_fn.name, 'r') as fin:
+            fin.seek(tp.log_offset, 0)
+            for line in fin:
+                try:
+                    sys.stdout.write(line)
+                except UnicodeEncodeError:
+                    sys.stdout.write(
+                        'UnicodeEncodeError occurs at this line. '
+                        'Please refer to the original log file "%s"\n' %
+                        tp.log_fn.name)
+            tp.log_offset = fin.tell()
+
+
+def watch_local_trainers(procs, nranks):
+    try:
+        error = False
+        error_rank = []
+        # wait all process finish or one error
+        alive = False
+        for p in procs:
+            if p.log_fn and p.local_rank == 0:
+                pull_worker_log(p)
+
+            ret = p.proc.poll()
+            if ret is None:
+                alive = True
+            elif ret != 0:
+                error = True
+                error_rank.append(p.rank)
+
+        if error:
+            terminate_local_procs(procs)
+            exit(1)
+
+    except KeyboardInterrupt:
+        logger.warning("KeyboardInterrupt, exit")
+        terminate_local_procs(procs)
+        raise
+    except SystemExit:
+        logger.error(
+            "ABORT!!! Out of all {} trainers, the trainer process with rank={} was aborted. Please check its log.".
+            format(nranks, error_rank))
+        terminate_local_procs(procs)
+        raise
+    except:
+        logger.error(
+            "ABORT!!! Out of all {} trainers, the trainer process with rank={} was aborted. Please check its log.".
+            format(nranks, error_rank))
+        terminate_local_procs(procs)
+        raise
+
+    return alive

python/paddle/fleet/meta_optimizers/__init__.py

@@ -18,6 +18,7 @@ from .graph_execution_optimizer import GraphExecutionOptimizer
 from .pipeline_optimizer import PipelineOptimizer
 from .localsgd_optimizer import LocalSGDOptimizer
 from .lars_optimizer import LarsOptimizer
+from .dgc_optimizer import DGCOptimizer
 
 __all__ = [
     'AMPOptimizer',
@@ -26,4 +27,5 @@ __all__ = [
     'PipelineOptimizer',
     'LocalSGDOptimizer',
     'LarsOptimizer',
+    'DGCOptimizer',
 ]

python/paddle/fleet/meta_optimizers/dgc_optimizer.py

+#   Copyright (c) 2020 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
+
+from paddle.fluid.optimizer import Momentum, DGCMomentumOptimizer
+from .meta_optimizer_base import MetaOptimizerBase
+import logging
+
+__all__ = ["DGCOptimizer"]
+
+
+class DGCOptimizer(MetaOptimizerBase):
+    def __init__(self, optimizer):
+        super(DGCOptimizer, self).__init__(optimizer)
+        self.inner_opt = optimizer
+        self.dgc_opt = None
+        # we do not allow meta optimizer to be inner optimizer currently
+        self.meta_optimizers_white_list = []
+
+    def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
+                        user_defined_strategy):
+        super(DGCOptimizer, self)._set_basic_info(
+            loss, role_maker, user_defined_optimizer, user_defined_strategy)
+
+        opt = self.inner_opt
+        if not isinstance(opt, Momentum):
+            return
+
+        configs = self.user_defined_strategy.dgc_configs
+        if len(configs['sparsity']) == 0:
+            # default is [0.999]
+            configs['sparsity'] = [0.999]
+
+        self.dgc_opt = DGCMomentumOptimizer(
+            learning_rate=opt._learning_rate,
+            momentum=opt._momentum,
+            rampup_begin_step=configs['rampup_begin_step'],
+            rampup_step=configs['rampup_step'],
+            sparsity=configs['sparsity'],
+            parameter_list=opt._parameter_list,
+            use_nesterov=opt._use_nesterov,
+            num_trainers=self.role_maker.worker_num(),
+            regularization=opt.regularization,
+            grad_clip=opt._grad_clip,
+            name=opt._name)
+
+    def _can_apply(self):
+        if self.user_defined_strategy.dgc:
+            if not isinstance(self.inner_opt, Momentum):
+                logging.warn("dgc only works on Momentum optimizer")
+                return False
+            if self.role_maker.worker_num() <= 1:
+                logging.warn("dgc only works on multi cards")
+                return False
+
+            return True
+
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.dgc = False
+        dist_strategy.dgc_configs = {
+            'rampup_begin_step': 0,
+            'rampup_step': 1,
+            'sparsity': [0.999]
+        }
+
+    def backward(self,
+                 loss,
+                 startup_program=None,
+                 parameter_list=None,
+                 no_grad_set=None,
+                 callbacks=None):
+        return self.dgc_opt.backward(loss, startup_program, parameter_list,
+                                     no_grad_set, callbacks)
+
+    def minimize_impl(self,
+                      loss,
+                      startup_program=None,
+                      parameter_list=None,
+                      no_grad_set=None):
+        optimize_ops, params_grads = \
+            self.dgc_opt.minimize(loss, startup_program,
+                                      parameter_list, no_grad_set)
+        return optimize_ops, params_grads

python/paddle/fleet/meta_optimizers/meta_optimizer_base.py

@@ -40,7 +40,8 @@ class MetaOptimizerBase(object):
             return True
 
     def _disable_strategy(self, dist_strategy):
-        raise NotImplementedError("you should implement disable strategy")
+        raise NotImplementedError("you should implement disable strategy in {}".
+                                  format(type(self).__name__))
 
     def minimize_impl(self,
                       loss,

python/paddle/fleet/runtime/runtime_base.py

@@ -19,11 +19,8 @@ class RuntimeBase(object):
     def __init__(self):
         pass
 
-    def _set_basic_info(self, loss, role_maker, optimizer, strategy):
-        self.loss = loss
-        self.role_maker = role_maker
-        self.optimizer = optimizer
-        self.strategy = strategy
+    def _set_basic_info(self, context):
+        self.context = context
 
     def _run_worker(self):
         pass

python/paddle/fluid/annotations.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 functools
-import sys
-
-__all__ = ['deprecated']
-
-
-def deprecated(since, instead, extra_message=""):
-    def decorator(func):
-        err_msg = "API {0} is deprecated since {1}. Please use {2} instead.".format(
-            func.__name__, since, instead)
-        if len(extra_message) != 0:
-            err_msg += "\n"
-            err_msg += extra_message
-
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            print(err_msg, file=sys.stderr)
-            return func(*args, **kwargs)
-
-        wrapper.__doc__ += "\n    "
-        wrapper.__doc__ += err_msg
-        return wrapper
-
-    return decorator

python/paddle/fluid/dygraph/dygraph_to_static/program_translator.py

@@ -36,7 +36,6 @@ from paddle.fluid.wrapped_decorator import signature_safe_contextmanager
 from paddle.fluid.dygraph.base import param_guard
 from paddle.fluid.data_feeder import check_type
 from paddle.fluid.dygraph.dygraph_to_static.partial_program import partial_program_from
-from paddle.fluid.annotations import deprecated
 
 __all__ = ['ProgramTranslator', 'convert_to_static']
 

python/paddle/fluid/dygraph/layers.py

@@ -129,6 +129,45 @@ class Layer(core.Layer):
         for layer in self.sublayers():
             layer.eval()
 
+    def apply(self, fn):
+        """
+        Applies ``fn`` recursively to every sublayer (as returned by ``.sublayers()``)
+        as well as self. Typical use includes initializing the parameters of a model.
+
+        Parameters:
+            fn (function): a function to be applied to each sublayer
+
+        Returns:
+            Layer: self
+
+        Example::
+            .. code-block:: python
+
+              import paddle
+              import paddle.nn as nn
+              
+              paddle.enable_imperative()
+              
+              net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
+
+              def init_weights(layer):
+                  if type(layer) == nn.Linear:
+                      print('before init weight:', layer.weight.numpy())
+                      new_weight = paddle.fill_constant(layer.weight.shape, layer.weight.dtype, value=0.9)
+                      layer.weight.set_value(new_weight)
+                      print('after init weight:', layer.weight.numpy())
+
+              net.apply(init_weights)
+
+              print(net.state_dict())
+        """
+        for layer in self.sublayers():
+            layer.apply(fn)
+
+        fn(self)
+
+        return self
+
     def full_name(self):
         """Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
 

python/paddle/fluid/layers/device.py

@@ -20,12 +20,12 @@ from __future__ import print_function
 from .layer_function_generator import autodoc
 from ..framework import unique_name
 from ..layer_helper import LayerHelper
-from ..annotations import deprecated
+from paddle.utils import deprecated
 
 __all__ = []
 
 
-@deprecated(since='0.15.0', instead="ParallelExecutor")
+@deprecated(since='0.15.0', update_to="paddle.fluid.ParallelExecutor")
 @autodoc()
 def get_places(device_count=None, device_type=None):
     helper = LayerHelper('get_places', **locals())

python/paddle/fluid/layers/nn.py

@@ -37,6 +37,7 @@ from functools import reduce
 from .. import core
 from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
 import paddle
+from paddle.utils import deprecated
 
 __all__ = [
     'fc',
@@ -11614,6 +11615,7 @@ Examples:
     return _elementwise_op(LayerHelper('elementwise_sub', **locals()))
 
 
+@deprecated(since="2.0.0", update_to="paddle.multiply")
 def elementwise_mul(x, y, axis=-1, act=None, name=None):
     """
     :alias_main: paddle.elementwise_mul

python/paddle/fluid/optimizer.py

@@ -47,9 +47,8 @@ __all__ = [
     'AdamOptimizer', 'AdamaxOptimizer', 'DpsgdOptimizer',
     'DecayedAdagradOptimizer', 'RMSPropOptimizer', 'FtrlOptimizer', 'Adadelta',
     'AdadeltaOptimizer', 'ModelAverage', 'LarsMomentum',
-    'LarsMomentumOptimizer', 'DGCMomentumOptimizer', 'LambOptimizer',
-    'ExponentialMovingAverage', 'PipelineOptimizer', 'LookaheadOptimizer',
-    'RecomputeOptimizer'
+    'LarsMomentumOptimizer', 'LambOptimizer', 'ExponentialMovingAverage',
+    'PipelineOptimizer', 'LookaheadOptimizer', 'RecomputeOptimizer'
 ]
 
 

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

@@ -27,6 +27,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_communicator_async)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_geo)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_half_async)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_sync)
+list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_api_input)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_checkpoint)
 list(APPEND MIXED_DIST_TEST_OPS test_collective_optimizer)
@@ -38,6 +39,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_fleet_amp_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_gradient_merge_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_localsgd_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_lars_meta_optimizer)
+list(APPEND MIXED_DIST_TEST_OPS test_fleet_dgc_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_private_function)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_graph_executor)
 foreach(TEST_OP ${MIXED_DIST_TEST_OPS})
@@ -387,6 +389,7 @@ if(WITH_DISTRIBUTE)
         py_test_modules(test_dgc_op MODULES test_dgc_op)
         py_test_modules(test_dgc_momentum_op MODULES test_dgc_momentum_op)
         py_test_modules(test_dgc_optimizer MODULES test_dgc_optimizer)
+        py_test_modules(test_fleet_dgc_meta_optimizer MODULES test_fleet_dgc_meta_optimizer)
     else()
         # if not with dgc, must close all dgc tests
         list(REMOVE_ITEM DIST_TEST_OPS "test_dist_mnist_dgc_nccl")
@@ -399,6 +402,7 @@ if(WITH_DISTRIBUTE)
             py_test_modules(test_fleet_checkpoint MODULES test_fleet_checkpoint)
         endif()
         bash_test_modules(test_launch_ps MODULES test_launch_ps.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
+        bash_test_modules(test_fleet_launch MODULES test_fleet_launch.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
 
         set(dist_ut_port 20001)
         foreach(TEST_OP ${DIST_TEST_OPS})

python/paddle/fluid/tests/unittests/dygraph_to_static/test_bert.py

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
+from paddle.fluid.dygraph.io import VARIABLE_FILENAME
 
 from bert_dygraph_model import PretrainModelLayer
 from bert_utils import get_bert_config, get_feed_data_reader
@@ -28,9 +29,11 @@ place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace(
 SEED = 2020
 STEP_NUM = 10
 PRINT_STEP = 2
+MODEL_SAVE_PATH = "./bert.inference.model"
+DY_STATE_DICT_SAVE_PATH = "./bert.dygraph"
 
 
-def train(bert_config, data_reader):
+def train(bert_config, data_reader, to_static):
     with fluid.dygraph.guard(place):
         fluid.default_main_program().random_seed = SEED
         fluid.default_startup_program().random_seed = SEED
@@ -79,18 +82,74 @@ def train(bert_config, data_reader):
 
             step_idx += 1
             if step_idx == STEP_NUM:
+                if to_static:
+                    fluid.dygraph.jit.save(bert, MODEL_SAVE_PATH)
+                else:
+                    fluid.dygraph.save_dygraph(bert.state_dict(),
+                                               DY_STATE_DICT_SAVE_PATH)
                 break
         return loss, ppl
 
 
 def train_dygraph(bert_config, data_reader):
     program_translator.enable(False)
-    return train(bert_config, data_reader)
+    return train(bert_config, data_reader, False)
 
 
 def train_static(bert_config, data_reader):
     program_translator.enable(True)
-    return train(bert_config, data_reader)
+    return train(bert_config, data_reader, True)
+
+
+def predict_static(data):
+    exe = fluid.Executor(place)
+    # load inference model
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(
+         MODEL_SAVE_PATH, executor=exe, params_filename=VARIABLE_FILENAME)
+    pred_res = exe.run(inference_program,
+                       feed=dict(zip(feed_target_names, data)),
+                       fetch_list=fetch_targets)
+
+    return pred_res
+
+
+def predict_dygraph(bert_config, data):
+    program_translator.enable(False)
+    with fluid.dygraph.guard(place):
+        bert = PretrainModelLayer(
+            config=bert_config, weight_sharing=False, use_fp16=False)
+        model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
+
+        bert.set_dict(model_dict)
+        bert.eval()
+
+        input_vars = [fluid.dygraph.to_variable(x) for x in data]
+        src_ids, pos_ids, sent_ids, input_mask, mask_label, mask_pos, labels = input_vars
+        pred_res = bert(
+            src_ids=src_ids,
+            position_ids=pos_ids,
+            sentence_ids=sent_ids,
+            input_mask=input_mask,
+            mask_label=mask_label,
+            mask_pos=mask_pos,
+            labels=labels)
+        pred_res = [var.numpy() for var in pred_res]
+
+        return pred_res
+
+
+def predict_dygraph_jit(data):
+    with fluid.dygraph.guard(place):
+        bert = fluid.dygraph.jit.load(MODEL_SAVE_PATH)
+        bert.eval()
+
+        src_ids, pos_ids, sent_ids, input_mask, mask_label, mask_pos, labels = data
+        pred_res = bert(src_ids, pos_ids, sent_ids, input_mask, mask_label,
+                        mask_pos, labels)
+        pred_res = [var.numpy() for var in pred_res]
+
+        return pred_res
 
 
 class TestBert(unittest.TestCase):
@@ -104,14 +163,36 @@ class TestBert(unittest.TestCase):
         dygraph_loss, dygraph_ppl = train_dygraph(self.bert_config,
                                                   self.data_reader)
         self.assertTrue(
-            np.allclose(static_loss, static_loss),
-            msg="static_loss: {} \n static_loss: {}".format(static_loss,
-                                                            dygraph_loss))
+            np.allclose(static_loss, dygraph_loss),
+            msg="static_loss: {} \n dygraph_loss: {}".format(static_loss,
+                                                             dygraph_loss))
         self.assertTrue(
             np.allclose(static_ppl, dygraph_ppl),
             msg="static_ppl: {} \n dygraph_ppl: {}".format(static_ppl,
                                                            dygraph_ppl))
 
+        self.verify_predict()
+
+    def verify_predict(self):
+        for data in self.data_reader.data_generator()():
+            dygraph_pred_res = predict_dygraph(self.bert_config, data)
+            static_pred_res = predict_static(data)
+            dygraph_jit_pred_res = predict_dygraph_jit(data)
+
+            for dy_res, st_res, dy_jit_res in zip(
+                    dygraph_pred_res, static_pred_res, dygraph_jit_pred_res):
+                self.assertTrue(
+                    np.allclose(st_res, dy_res),
+                    "dygraph_res: {},\n static_res: {}".format(
+                        dy_res[~np.isclose(st_res, dy_res)],
+                        st_res[~np.isclose(st_res, dy_res)]))
+                self.assertTrue(
+                    np.allclose(st_res, dy_jit_res),
+                    "dygraph_jit_res: {},\n static_res: {}".format(
+                        dy_jit_res[~np.isclose(st_res, dy_jit_res)],
+                        st_res[~np.isclose(st_res, dy_jit_res)]))
+            break
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py

@@ -692,13 +692,20 @@ class TestTrain(unittest.TestCase):
             video_data = np.array([item[0] for item in data]).astype(DATATYPE)
             static_pred_res = self.predict_static(video_data)
             dygraph_pred_res = self.predict_dygraph(video_data)
+            dygraph_jit_pred_res = self.predict_dygraph_jit(video_data)
 
-            for dy_res, st_res in zip(dygraph_pred_res, static_pred_res):
+            for dy_res, st_res, dy_jit_res in zip(
+                    dygraph_pred_res, static_pred_res, dygraph_jit_pred_res):
                 self.assertTrue(
                     np.allclose(st_res, dy_res),
                     "dygraph_res: {},\n static_res: {}".format(
                         dy_res[~np.isclose(st_res, dy_res)],
                         st_res[~np.isclose(st_res, dy_res)]))
+                self.assertTrue(
+                    np.allclose(st_res, dy_jit_res),
+                    "dygraph_jit_res: {},\n static_res: {}".format(
+                        dy_jit_res[~np.isclose(st_res, dy_jit_res)],
+                        st_res[~np.isclose(st_res, dy_jit_res)]))
             break
 
     def predict_dygraph(self, data):
@@ -731,6 +738,17 @@ class TestTrain(unittest.TestCase):
 
         return pred_res
 
+    def predict_dygraph_jit(self, data):
+        with fluid.dygraph.guard(self.place):
+            bmn = fluid.dygraph.jit.load(self.args.infer_dir)
+            bmn.eval()
+
+            x = to_variable(data)
+            pred_res = bmn(x)
+            pred_res = [var.numpy() for var in pred_res]
+
+            return pred_res
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py

@@ -535,9 +535,14 @@ class TestLACModel(unittest.TestCase):
             batch = [np.vstack(var) for var in zip(*batch)]
             dy_pre = self.predict_dygraph(batch)
             st_pre = self.predict_static(batch)
+            dy_jit_pre = self.predict_dygraph_jit(batch)
             self.assertTrue(
                 np.allclose(dy_pre, st_pre),
                 msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
+            self.assertTrue(
+                np.allclose(dy_jit_pre, st_pre),
+                msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre,
+                                                               st_pre))
 
     def predict_dygraph(self, batch):
         words, targets, length = batch
@@ -576,6 +581,16 @@ class TestLACModel(unittest.TestCase):
             fetch_list=fetch_targets)
         return pred_res[0]
 
+    def predict_dygraph_jit(self, batch):
+        words, targets, length = batch
+        with fluid.dygraph.guard(self.place):
+            model = fluid.dygraph.jit.load(self.args.model_save_dir)
+            model.eval()
+
+            pred_res = model(to_variable(words), to_variable(length))
+
+            return pred_res.numpy()
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py

@@ -19,6 +19,7 @@ from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
 from paddle.fluid.dygraph import declarative, ProgramTranslator
+from paddle.fluid.dygraph.io import VARIABLE_FILENAME
 
 import unittest
 
@@ -433,14 +434,15 @@ class Args(object):
     class_dim = 50
     print_step = 1
     train_step = 10
+    place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+    ) else fluid.CPUPlace()
+    model_save_path = model + ".inference.model"
+    dy_state_dict_save_path = model + ".dygraph"
 
 
 def train_mobilenet(args, to_static):
     program_translator.enable(to_static)
-
-    place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
-    ) else fluid.CPUPlace()
-    with fluid.dygraph.guard(place):
+    with fluid.dygraph.guard(args.place):
 
         np.random.seed(SEED)
         fluid.default_startup_program().random_seed = SEED
@@ -461,7 +463,7 @@ def train_mobilenet(args, to_static):
         # 3. reader
         train_reader = fake_data_reader(args.batch_size, args.class_dim)
         train_data_loader = fluid.io.DataLoader.from_generator(capacity=16)
-        train_data_loader.set_sample_list_generator(train_reader, place)
+        train_data_loader.set_sample_list_generator(train_reader)
 
         # 4. train loop
         loss_data = []
@@ -498,17 +500,64 @@ def train_mobilenet(args, to_static):
                 batch_id += 1
                 t_last = time.time()
                 if batch_id > args.train_step:
+                    if to_static:
+                        fluid.dygraph.jit.save(net, args.model_save_path)
+                    else:
+                        fluid.dygraph.save_dygraph(net.state_dict(),
+                                                   args.dy_state_dict_save_path)
                     break
 
     return np.array(loss_data)
 
 
+def predict_static(args, data):
+    exe = fluid.Executor(args.place)
+    # load inference model
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(
+         args.model_save_path, executor=exe, params_filename=VARIABLE_FILENAME)
+
+    pred_res = exe.run(inference_program,
+                       feed={feed_target_names[0]: data},
+                       fetch_list=fetch_targets)
+    return pred_res[0]
+
+
+def predict_dygraph(args, data):
+    program_translator.enable(False)
+    with fluid.dygraph.guard(args.place):
+        if args.model == "MobileNetV1":
+            model = MobileNetV1(class_dim=args.class_dim, scale=1.0)
+        elif args.model == "MobileNetV2":
+            model = MobileNetV2(class_dim=args.class_dim, scale=1.0)
+        # load dygraph trained parameters
+        model_dict, _ = fluid.load_dygraph(args.dy_state_dict_save_path)
+        model.set_dict(model_dict)
+        model.eval()
+
+        pred_res = model(fluid.dygraph.to_variable(data))
+
+        return pred_res.numpy()
+
+
+def predict_dygraph_jit(args, data):
+    with fluid.dygraph.guard(args.place):
+        model = fluid.dygraph.jit.load(args.model_save_path)
+        model.eval()
+
+        pred_res = model(data)
+
+        return pred_res.numpy()
+
+
 class TestMobileNet(unittest.TestCase):
     def setUp(self):
         self.args = Args()
 
     def train(self, model_name, to_static):
         self.args.model = model_name
+        self.args.model_save_path = model_name + ".inference.model"
+        self.args.dy_state_dict_save_path = model_name + ".dygraph"
         out = train_mobilenet(self.args, to_static)
         return out
 
@@ -519,12 +568,36 @@ class TestMobileNet(unittest.TestCase):
             np.allclose(dy_out, st_out),
             msg="dy_out: {}, st_out: {}".format(dy_out, st_out))
 
-    def test_mobileNet(self):
+    def assert_same_predict(self, model_name):
+        self.args.model = model_name
+        self.args.model_save_path = model_name + ".inference.model"
+        self.args.dy_state_dict_save_path = model_name + ".dygraph"
+        local_random = np.random.RandomState(SEED)
+        image = local_random.random_sample([1, 3, 224, 224]).astype('float32')
+        dy_pre = predict_dygraph(self.args, image)
+        st_pre = predict_static(self.args, image)
+        dy_jit_pre = predict_dygraph_jit(self.args, image)
+        self.assertTrue(
+            np.allclose(dy_pre, st_pre),
+            msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
+        self.assertTrue(
+            np.allclose(dy_jit_pre, st_pre),
+            msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
+
+    def test_mobile_net(self):
         # MobileNet-V1
         self.assert_same_loss("MobileNetV1")
         # MobileNet-V2
         self.assert_same_loss("MobileNetV2")
 
+        self.verify_predict()
+
+    def verify_predict(self):
+        # MobileNet-V1
+        self.assert_same_predict("MobileNetV1")
+        # MobileNet-V2
+        self.assert_same_predict("MobileNetV2")
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py

@@ -22,39 +22,33 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.dygraph.jit import dygraph_to_static_func
+from paddle.fluid.dygraph import declarative, ProgramTranslator
 from paddle.fluid.dygraph.nn import BatchNorm, Conv2D, Linear, Pool2D
+from paddle.fluid.dygraph.io import VARIABLE_FILENAME
 
+SEED = 2020
 IMAGENET1000 = 1281167
-base_lr = 0.1
+base_lr = 0.001
 momentum_rate = 0.9
 l2_decay = 1e-4
 batch_size = 8
 epoch_num = 1
 place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
     else fluid.CPUPlace()
+MODEL_SAVE_PATH = "./resnet.inference.model"
+DY_STATE_DICT_SAVE_PATH = "./resnet.dygraph"
+program_translator = ProgramTranslator()
+
+if fluid.is_compiled_with_cuda():
+    fluid.set_flags({'FLAGS_cudnn_deterministic': True})
 
 
 def optimizer_setting(parameter_list=None):
-    total_images = IMAGENET1000
-    step = int(math.ceil(float(total_images) / batch_size))
-    epochs = [30, 60, 90]
-    bd = [step * e for e in epochs]
-
-    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
-    if fluid.in_dygraph_mode():
-        optimizer = fluid.optimizer.Momentum(
-            learning_rate=fluid.layers.piecewise_decay(
-                boundaries=bd, values=lr),
-            momentum=momentum_rate,
-            regularization=fluid.regularizer.L2Decay(l2_decay),
-            parameter_list=parameter_list)
-    else:
-        optimizer = fluid.optimizer.Momentum(
-            learning_rate=fluid.layers.piecewise_decay(
-                boundaries=bd, values=lr),
-            momentum=momentum_rate,
-            regularization=fluid.regularizer.L2Decay(l2_decay))
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=base_lr,
+        momentum=momentum_rate,
+        regularization=fluid.regularizer.L2Decay(l2_decay),
+        parameter_list=parameter_list)
 
     return optimizer
 
@@ -189,8 +183,8 @@ class ResNet(fluid.dygraph.Layer):
             param_attr=fluid.param_attr.ParamAttr(
                 initializer=fluid.initializer.Uniform(-stdv, stdv)))
 
-    @dygraph_to_static_func
-    def forward(self, inputs, label):
+    @declarative
+    def forward(self, inputs):
         y = self.conv(inputs)
         y = self.pool2d_max(y)
         for bottleneck_block in self.bottleneck_block_list:
@@ -199,77 +193,144 @@ class ResNet(fluid.dygraph.Layer):
         y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
         pred = self.out(y)
 
-        loss = fluid.layers.cross_entropy(input=pred, label=label)
-        avg_loss_ = fluid.layers.mean(x=loss)
-        acc_top1_ = fluid.layers.accuracy(input=pred, label=label, k=1)
-        acc_top5_ = fluid.layers.accuracy(input=pred, label=label, k=5)
+        return pred
+
 
-        return pred, avg_loss_, acc_top1_, acc_top5_
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
 
+    return __reader__
 
-def train_resnet_in_static_mode():
+
+def train(to_static):
     """
     Tests model decorated by `dygraph_to_static_output` in static mode. For users, the model is defined in dygraph mode and trained in static mode.
     """
+    with fluid.dygraph.guard(place):
+        np.random.seed(SEED)
+        fluid.default_startup_program().random_seed = SEED
+        fluid.default_main_program().random_seed = SEED
+
+        train_reader = paddle.batch(
+            reader_decorator(paddle.dataset.flowers.train(use_xmap=False)),
+            batch_size=batch_size,
+            drop_last=True)
+        data_loader = fluid.io.DataLoader.from_generator(
+            capacity=5, iterable=True)
+        data_loader.set_sample_list_generator(train_reader)
+
+        resnet = ResNet()
+        optimizer = optimizer_setting(parameter_list=resnet.parameters())
+
+        for epoch in range(epoch_num):
+            total_loss = 0.0
+            total_acc1 = 0.0
+            total_acc5 = 0.0
+            total_sample = 0
+
+            for batch_id, data in enumerate(data_loader()):
+                start_time = time.time()
+                img, label = data
+
+                pred = resnet(img)
+                loss = fluid.layers.cross_entropy(input=pred, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+                acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
+                acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
+
+                avg_loss.backward()
+                optimizer.minimize(avg_loss)
+                resnet.clear_gradients()
+
+                total_loss += avg_loss
+                total_acc1 += acc_top1
+                total_acc5 += acc_top5
+                total_sample += 1
+
+                end_time = time.time()
+                if batch_id % 2 == 0:
+                    print( "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" % \
+                        ( epoch, batch_id, total_loss.numpy() / total_sample, \
+                            total_acc1.numpy() / total_sample, total_acc5.numpy() / total_sample, end_time-start_time))
+                if batch_id == 10:
+                    if to_static:
+                        fluid.dygraph.jit.save(resnet, MODEL_SAVE_PATH)
+                    else:
+                        fluid.dygraph.save_dygraph(resnet.state_dict(),
+                                                   DY_STATE_DICT_SAVE_PATH)
+                    # avoid dataloader throw abort signaal
+                    data_loader._reset()
+                    break
+
+    return total_loss.numpy()
+
+
+def predict_dygraph(data):
+    program_translator.enable(False)
+    with fluid.dygraph.guard(place):
+        resnet = ResNet()
+
+        model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
+        resnet.set_dict(model_dict)
+        resnet.eval()
 
+        pred_res = resnet(fluid.dygraph.to_variable(data))
+
+        return pred_res.numpy()
+
+
+def predict_static(data):
     exe = fluid.Executor(place)
-    startup_prog = fluid.Program()
-    main_prog = fluid.Program()
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(
+         MODEL_SAVE_PATH, executor=exe, params_filename=VARIABLE_FILENAME)
 
-    with fluid.program_guard(main_prog, startup_prog):
+    pred_res = exe.run(inference_program,
+                       feed={feed_target_names[0]: data},
+                       fetch_list=fetch_targets)
 
-        img = fluid.data(name="img", shape=[None, 3, 224, 224], dtype="float32")
-        label = fluid.data(name="label", shape=[None, 1], dtype="int64")
-        label.stop_gradient = True
-        resnet = ResNet()
-        pred, avg_loss_, acc_top1_, acc_top5_ = resnet(img, label)
-        optimizer = optimizer_setting(parameter_list=resnet.parameters())
-        optimizer.minimize(avg_loss_)
-
-    exe.run(startup_prog)
-
-    train_reader = paddle.batch(
-        paddle.dataset.flowers.train(use_xmap=False), batch_size=batch_size)
-
-    for epoch in range(epoch_num):
-        total_loss = 0.0
-        total_acc1 = 0.0
-        total_acc5 = 0.0
-        total_sample = 0
-
-        for batch_id, data in enumerate(train_reader()):
-            start_time = time.time()
-            dy_x_data = np.array(
-                [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
-            if len(np.array([x[1]
-                             for x in data]).astype('int64')) != batch_size:
-                continue
-            y_data = np.array([x[1] for x in data]).astype('int64').reshape(-1,
-                                                                            1)
-
-            avg_loss, acc_top1, acc_top5 = exe.run(
-                main_prog,
-                feed={"img": dy_x_data,
-                      "label": y_data},
-                fetch_list=[avg_loss_, acc_top1_, acc_top5_])
-
-            total_loss += avg_loss
-            total_acc1 += acc_top1
-            total_acc5 += acc_top5
-            total_sample += 1
-
-            end_time = time.time()
-            if batch_id % 2 == 0:
-                print( "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" % \
-                       ( epoch, batch_id, total_loss / total_sample, \
-                         total_acc1 / total_sample, total_acc5 / total_sample, end_time-start_time))
-            if batch_id == 10:
-                break
+    return pred_res[0]
+
+
+def predict_dygraph_jit(data):
+    with fluid.dygraph.guard(place):
+        resnet = fluid.dygraph.jit.load(MODEL_SAVE_PATH)
+        resnet.eval()
+
+        pred_res = resnet(data)
+
+        return pred_res.numpy()
 
 
 class TestResnet(unittest.TestCase):
-    def test_in_static_mode(self):
-        train_resnet_in_static_mode()
+    def train(self, to_static):
+        program_translator.enable(to_static)
+        return train(to_static)
+
+    def verify_predict(self):
+        image = np.random.random([1, 3, 224, 224]).astype('float32')
+        dy_pre = predict_dygraph(image)
+        st_pre = predict_static(image)
+        dy_jit_pre = predict_dygraph_jit(image)
+        self.assertTrue(
+            np.allclose(dy_pre, st_pre),
+            msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
+        self.assertTrue(
+            np.allclose(dy_jit_pre, st_pre),
+            msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
+
+    def test_resnet(self):
+        static_loss = self.train(to_static=True)
+        dygraph_loss = self.train(to_static=False)
+        self.assertTrue(
+            np.allclose(static_loss, dygraph_loss),
+            msg="static_loss: {} \n dygraph_loss: {}".format(static_loss,
+                                                             dygraph_loss))
+        self.verify_predict()
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py

@@ -24,6 +24,7 @@ from paddle.fluid.dygraph.base import to_variable
 from paddle.fluid.dygraph.nn import BatchNorm, Conv2D, Linear, Pool2D
 from paddle.fluid.dygraph import declarative
 from paddle.fluid.dygraph import ProgramTranslator
+from paddle.fluid.dygraph.io import VARIABLE_FILENAME
 
 SEED = 2020
 np.random.seed(SEED)
@@ -32,6 +33,8 @@ BATCH_SIZE = 8
 EPOCH_NUM = 1
 PRINT_STEP = 2
 STEP_NUM = 10
+MODEL_SAVE_PATH = "./se_resnet.inference.model"
+DY_STATE_DICT_SAVE_PATH = "./se_resnet.dygraph"
 
 place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
     else fluid.CPUPlace()
@@ -377,11 +380,60 @@ def train(train_reader, to_static):
 
                 step_idx += 1
                 if step_idx == STEP_NUM:
+                    if to_static:
+                        configs = fluid.dygraph.jit.SaveLoadConfig()
+                        configs.output_spec = [pred]
+                        fluid.dygraph.jit.save(se_resnext, MODEL_SAVE_PATH,
+                                               [img], configs)
+                    else:
+                        fluid.dygraph.save_dygraph(se_resnext.state_dict(),
+                                                   DY_STATE_DICT_SAVE_PATH)
                     break
         return pred.numpy(), avg_loss.numpy(), acc_top1.numpy(), acc_top5.numpy(
         )
 
 
+def predict_dygraph(data):
+    program_translator = ProgramTranslator()
+    program_translator.enable(False)
+    with fluid.dygraph.guard(place):
+        se_resnext = SeResNeXt()
+
+        model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
+        se_resnext.set_dict(model_dict)
+        se_resnext.eval()
+
+        label = np.random.random([1, 1]).astype("int64")
+        img = fluid.dygraph.to_variable(data)
+        label = fluid.dygraph.to_variable(label)
+        pred_res, _, _, _ = se_resnext(img, label)
+
+        return pred_res.numpy()
+
+
+def predict_static(data):
+    exe = fluid.Executor(place)
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(
+         MODEL_SAVE_PATH, executor=exe, params_filename=VARIABLE_FILENAME)
+
+    pred_res = exe.run(inference_program,
+                       feed={feed_target_names[0]: data},
+                       fetch_list=fetch_targets)
+
+    return pred_res[0]
+
+
+def predict_dygraph_jit(data):
+    with fluid.dygraph.guard(place):
+        se_resnext = fluid.dygraph.jit.load(MODEL_SAVE_PATH)
+        se_resnext.eval()
+
+        pred_res = se_resnext(data)
+
+        return pred_res.numpy()
+
+
 class TestSeResnet(unittest.TestCase):
     def setUp(self):
         self.train_reader = paddle.batch(
@@ -390,6 +442,18 @@ class TestSeResnet(unittest.TestCase):
             batch_size=BATCH_SIZE,
             drop_last=True)
 
+    def verify_predict(self):
+        image = np.random.random([1, 3, 224, 224]).astype('float32')
+        dy_pre = predict_dygraph(image)
+        st_pre = predict_static(image)
+        dy_jit_pre = predict_dygraph_jit(image)
+        self.assertTrue(
+            np.allclose(dy_pre, st_pre),
+            msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
+        self.assertTrue(
+            np.allclose(dy_jit_pre, st_pre),
+            msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
+
     def test_check_result(self):
         pred_1, loss_1, acc1_1, acc5_1 = train(
             self.train_reader, to_static=False)
@@ -409,6 +473,8 @@ class TestSeResnet(unittest.TestCase):
             np.allclose(acc5_1, acc5_2),
             msg="static acc5: {} \ndygraph acc5: {}".format(acc5_1, acc5_2))
 
+        self.verify_predict()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -72,25 +72,40 @@ def create_paddle_case(op_type, callback):
     class PaddleCls(unittest.TestCase):
         def setUp(self):
             self.op_type = op_type
-            self.input_x = np.array([1, 2, 3, 4])
-            self.input_y = np.array([1, 3, 2, 4])
+            self.input_x = np.array([1, 2, 3, 4]).astype(np.int64)
+            self.input_y = np.array([1, 3, 2, 4]).astype(np.int64)
             self.real_result = callback(self.input_x, self.input_y)
+            self.place = fluid.CPUPlace()
+            if core.is_compiled_with_cuda():
+                self.place = paddle.CUDAPlace(0)
 
         def test_api(self):
             with program_guard(Program(), Program()):
-                x = fluid.layers.data(name='x', shape=[4], dtype='int64')
-                y = fluid.layers.data(name='y', shape=[4], dtype='int64')
+                x = fluid.data(name='x', shape=[4], dtype='int64')
+                y = fluid.data(name='y', shape=[4], dtype='int64')
                 op = eval("paddle.%s" % (self.op_type))
                 out = op(x, y)
-                place = fluid.CPUPlace()
-                if core.is_compiled_with_cuda():
-                    place = paddle.CUDAPlace(0)
-                exe = fluid.Executor(place)
+                exe = fluid.Executor(self.place)
                 res, = exe.run(feed={"x": self.input_x,
                                      "y": self.input_y},
                                fetch_list=[out])
             self.assertEqual((res == self.real_result).all(), True)
 
+        def test_broadcast_api_1(self):
+            with program_guard(Program(), Program()):
+                x = paddle.nn.data(name='x', shape=[1, 2, 1, 3], dtype='int32')
+                y = paddle.nn.data(name='y', shape=[1, 2, 3], dtype='int32')
+                op = eval("paddle.%s" % (self.op_type))
+                out = op(x, y)
+                exe = paddle.Executor(self.place)
+                input_x = np.arange(1, 7).reshape((1, 2, 1, 3)).astype(np.int32)
+                input_y = np.arange(0, 6).reshape((1, 2, 3)).astype(np.int32)
+                real_result = callback(input_x, input_y)
+                res, = exe.run(feed={"x": input_x,
+                                     "y": input_y},
+                               fetch_list=[out])
+            self.assertEqual((res == real_result).all(), True)
+
         def test_attr_name(self):
             with program_guard(Program(), Program()):
                 x = fluid.layers.data(name='x', shape=[4], dtype='int32')
@@ -104,6 +119,7 @@ def create_paddle_case(op_type, callback):
     globals()[cls_name] = PaddleCls
 
 
+create_paddle_case('less_than', lambda _a, _b: _a < _b)
 create_paddle_case('less_equal', lambda _a, _b: _a <= _b)
 create_paddle_case('greater_than', lambda _a, _b: _a > _b)
 create_paddle_case('greater_equal', lambda _a, _b: _a >= _b)

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

@@ -39,7 +39,6 @@ class TestDistMnistNCCL2DGC(TestDistBase):
         self._nccl2_mode = True
         self._use_dgc = True
 
-    @unittest.skip(reason="Skip unstable ut")
     def test_dist_train(self):
         import paddle.fluid as fluid
         if fluid.core.is_compiled_with_cuda():
@@ -69,7 +68,6 @@ class TestDistMnistNCCL2DGCMultiCards(TestDistBase):
         self._nccl2_mode = True
         self._use_dgc = True
 
-    @unittest.skip(reason="Skip unstable ut")
     def test_dist_train(self):
         import paddle.fluid as fluid
         if fluid.core.is_compiled_with_cuda():

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

+# Copyright (c) 2020 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 unittest
+import paddle
+import os
+import paddle.fleet as fleet
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+
+class TestFleetDGCOptimizer(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ID"] = "1"
+        os.environ[
+            "PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001,127.0.0.1:36002"
+
+    def net(self):
+        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        fleet.init(role)
+        input_x = paddle.fluid.layers.data(
+            name="x", shape=[32], dtype='float32')
+        input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
+
+        fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
+        fc_2 = paddle.fluid.layers.fc(input=fc_1, size=256, act='tanh')
+        prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
+        cost = paddle.fluid.layers.cross_entropy(
+            input=prediction, label=input_y)
+        avg_cost = paddle.fluid.layers.mean(x=cost)
+
+        strategy = paddle.fleet.DistributedStrategy()
+        strategy.dgc = True
+        strategy.dgc_configs = {
+            "rampup_begin_step": 128,
+            "rampup_step": 100,
+            "sparsity": [0.996, 0.999]
+        }
+        return avg_cost, strategy
+
+    def test_dgc_optimizer(self):
+        avg_cost, strategy = self.net()
+        optimizer = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+
+        ops = [op.type for op in avg_cost.block.ops]
+        self.assertIn('dgc', ops)
+        self.assertIn('dgc_momentum', ops)
+
+    def test_dgc_not_apply_with_adam(self):
+        avg_cost, strategy = self.net()
+        optimizer = paddle.optimizer.Adam(learning_rate=0.01)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+
+        ops = [op.type for op in avg_cost.block.ops]
+        self.assertNotIn('dgc', ops)
+        self.assertNotIn('dgc_momentum', ops)
+
+    def test_dgc_not_apply_with_one_worker(self):
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+
+        avg_cost, strategy = self.net()
+        optimizer = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+
+        ops = [op.type for op in avg_cost.block.ops]
+        self.assertNotIn('dgc', ops)
+        self.assertNotIn('dgc_momentum', ops)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_fleet_launch.sh

+#!/bin/bash
+set -e
+
+
+function test_launch_ps(){
+    fleetrun --server_num=2 --worker_num=2 fleet_ps_training.py 2> ut.elog
+
+    if grep -q "server are killed" ut.elog; then
+        echo "test pserver launch succeed"
+    else
+        echo "test pserver launch failed"
+        exit -1
+    fi
+}
+
+if [[ ${WITH_GPU} == "OFF" ]]; then
+    test_launch_ps
+    exit 0
+fi
+
+test_launch_ps
+# use default values
+fleetrun multi_process.py
+
+# use paddlecloud
+echo "begin test use paddlecloud"
+cluster_node_ips="127.0.0.1,127.0.0.2"
+export PADDLE_TRAINERS_NUM=2
+export POD_IP=127.0.0.1
+export PADDLE_TRAINERS=127.0.0.1,127.0.0.2
+export PADDLE_TRAINER_ID=0
+
+export PADDLE_PORT=35019
+export TRAINER_PORTS_NUM=2
+
+distributed_args="--ips=${cluster_node_ips} --gpus=0,1 --log_dir=testlog"
+CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} multi_process.py
+
+str1="selected_gpus:0 worker_endpoints:127.0.0.1:35019,127.0.0.1:35020,127.0.0.2:35019,127.0.0.2:35020 trainers_num:4 current_endpoint:127.0.0.1:35019 trainer_id:0"
+str2="selected_gpus:1 worker_endpoints:127.0.0.1:35019,127.0.0.1:35020,127.0.0.2:35019,127.0.0.2:35020 trainers_num:4 current_endpoint:127.0.0.1:35020 trainer_id:1"
+file_0="multi_process.check_0.log"
+file_1="multi_process.check_1.log"
+
+echo "paddlecloud params test"
+if grep -q "$str1" "$file_0"; then
+    echo "find trainer 0"
+else
+    echo "not find trainer 0"
+    exit -1
+fi
+
+if grep -q "$str2" "$file_1"; then
+    echo "find trainer 1"
+else
+    echo "not find trainer 1"
+    exit -1
+fi
+
+# test async poll process
+if [ -f $file_0 ]; then
+    rm $file_0
+fi
+if [ -f $file_1 ]; then
+    rm $file_1
+fi
+
+
+unset PADDLE_PORT
+unset TRAINER_PORTS_NUM
+
+echo ""
+echo "paddle.distributed.launch async poll process test"
+if ! CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} multi_process.py abort; then
+    echo "train abort as planned"
+fi
+
+abort_str1="abort>>> selected_gpus:0 worker_endpoints:127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171 trainers_num:4 current_endpoint:127.0.0.1:6170 trainer_id:0"
+
+if grep -q "$abort_str1" "$file_0"; then
+    echo "trainer 0 abort as planned"
+else
+    echo "trainer 0 not abort as planned"
+    exit -1
+fi
+
+if [ ! -f $file_1 ]; then
+    echo "trainer 1 terminate as planned"
+else
+    echo "trainer 1 not terminate as planned"
+    exit -1
+fi
+
+#test for random ports
+file_0_0="test_launch_filelock_0_0.log"
+file_1_0="test_launch_filelock_1_0.log"
+rm -rf $file_0_0 $file_0_1
+
+distributed_args="--gpus=0,1 --log_dir=testlog"
+export PADDLE_LAUNCH_LOG="test_launch_filelock_0"
+CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} find_ports.py
+str_0="worker_endpoints:127.0.0.1:6070,127.0.0.1:6071"

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

@@ -33,8 +33,10 @@ class TestFleetUtil(unittest.TestCase):
         role_maker = None  # should be fleet.PaddleCloudRoleMaker()
         optimize_ops = []
         params_grads = []
-        util = factory._create_util(strategy, role_maker, optimize_ops,
-                                    params_grads)
+        context = {}
+        context["role_maker"] = role_maker
+        context["valid_strategy"] = strategy
+        util = factory._create_util(context)
         self.assertEqual(util.role_maker, None)
 
     def test_get_util(self):

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

@@ -298,16 +298,15 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
 
 class TestDygraphDoubleGradVisitedUniq(TestCase):
     def test_compare(self):
-        value = np.random.uniform(-0.5, 0.5, 100).reshape(10, 2,
-                                                          5).astype("float32")
+        value = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
 
         def model_f(input):
-            linear = fluid.dygraph.Linear(5, 3, bias_attr=False)
+            conv2d = fluid.dygraph.Conv2D(3, 2, 3)
             for i in range(10):
                 if i == 0:
-                    out = linear(input)
+                    out = conv2d(input)
                 else:
-                    out = out + linear(input)
+                    out = out + conv2d(input)
             return out
 
         backward_strategy = fluid.dygraph.BackwardStrategy()
@@ -319,8 +318,14 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
             out = model_f(a)
 
-            dx=fluid.dygraph.grad(outputs=[out],inputs=[a],create_graph=True,retain_graph=True,  \
-                        only_inputs=True,allow_unused=False, backward_strategy=backward_strategy)
+            dx = fluid.dygraph.grad(
+                outputs=[out],
+                inputs=[a],
+                create_graph=True,
+                retain_graph=True,
+                only_inputs=True,
+                allow_unused=False,
+                backward_strategy=backward_strategy)
 
             grad_1 = dx[0].numpy()
 
@@ -334,7 +339,9 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
             grad_2 = a.gradient()
 
-        self.assertTrue(np.array_equal(grad_1, grad_2))
+        self.assertTrue(
+            np.allclose(
+                grad_1, grad_2, rtol=1.e-5, atol=1.e-8, equal_nan=True))
 
 
 if __name__ == '__main__':

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

+#   Copyright (c) 2020 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 paddle
+import paddle.nn as nn
+import paddle.fluid as fluid
+
+import numpy as np
+
+
+class LeNetDygraph(fluid.dygraph.Layer):
+    def __init__(self, num_classes=10, classifier_activation='softmax'):
+        super(LeNetDygraph, self).__init__()
+        self.num_classes = num_classes
+        self.features = nn.Sequential(
+            nn.Conv2D(
+                1, 6, 3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Pool2D(2, 'max', 2),
+            nn.Conv2D(
+                6, 16, 5, stride=1, padding=0),
+            nn.ReLU(),
+            nn.Pool2D(2, 'max', 2))
+
+        if num_classes > 0:
+            self.fc = nn.Sequential(
+                nn.Linear(400, 120),
+                nn.Linear(120, 84),
+                nn.Linear(
+                    84, 10, act=classifier_activation))
+
+    def forward(self, inputs):
+        x = self.features(inputs)
+
+        if self.num_classes > 0:
+            x = fluid.layers.flatten(x, 1)
+            x = self.fc(x)
+        return x
+
+
+def init_weights(layer):
+    if type(layer) == nn.Linear:
+        new_weight = paddle.fill_constant(
+            layer.weight.shape, layer.weight.dtype, value=0.9)
+        layer.weight.set_value(new_weight)
+        new_bias = paddle.fill_constant(
+            layer.bias.shape, layer.bias.dtype, value=-0.1)
+        layer.bias.set_value(new_bias)
+    elif type(layer) == nn.Conv2D:
+        new_weight = paddle.fill_constant(
+            layer.weight.shape, layer.weight.dtype, value=0.7)
+        layer.weight.set_value(new_weight)
+        new_bias = paddle.fill_constant(
+            layer.bias.shape, layer.bias.dtype, value=-0.2)
+        layer.bias.set_value(new_bias)
+
+
+class TestLayerApply(unittest.TestCase):
+    def test_apply_init_weight(self):
+        with fluid.dygraph.guard():
+            net = LeNetDygraph()
+
+            net.apply(init_weights)
+
+            for layer in net.sublayers():
+                if type(layer) == nn.Linear:
+                    np.testing.assert_allclose(layer.weight.numpy(), 0.9)
+                    np.testing.assert_allclose(layer.bias.numpy(), -0.1)
+                elif type(layer) == nn.Conv2D:
+                    np.testing.assert_allclose(layer.weight.numpy(), 0.7)
+                    np.testing.assert_allclose(layer.bias.numpy(), -0.2)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -37,7 +37,7 @@ class TestSortOnCPU(unittest.TestCase):
                 [[[5, 8, 9, 5], [0, 0, 1, 7], [6, 9, 2, 4]],
                  [[5, 2, 4, 2], [4, 7, 7, 9], [1, 7, 0, 6]]],
                 dtype='float32')
-            result, = exe.run(feed={'input': data}, fetch_list=[output[0]])
+            result, = exe.run(feed={'input': data}, fetch_list=[output])
             np_result = np.sort(result)
             self.assertEqual((result == np_result).all(), True)
 
@@ -50,7 +50,7 @@ class TestSortOnCPU(unittest.TestCase):
                 [[[5, 8, 9, 5], [0, 0, 1, 7], [6, 9, 2, 4]],
                  [[5, 2, 4, 2], [4, 7, 7, 9], [1, 7, 0, 6]]],
                 dtype='float32')
-            result, = exe.run(feed={'input': data}, fetch_list=[output[0]])
+            result, = exe.run(feed={'input': data}, fetch_list=[output])
             np_result = np.sort(result, axis=1)
             self.assertEqual((result == np_result).all(), True)
 
@@ -75,7 +75,7 @@ class TestSortDygraph(unittest.TestCase):
         with imperative.guard(self.place):
             var_x = imperative.to_variable(self.input_data)
             out = paddle.sort(var_x)
-            self.assertEqual((np.sort(self.input_data) == out[0].numpy()).all(),
+            self.assertEqual((np.sort(self.input_data) == out.numpy()).all(),
                              True)
 
     def test_api_1(self):
@@ -84,5 +84,4 @@ class TestSortDygraph(unittest.TestCase):
             out = paddle.sort(var_x, axis=-1)
             self.assertEqual(
                 (np.sort(
-                    self.input_data, axis=-1) == out[0].numpy()).all(),
-                True)
+                    self.input_data, axis=-1) == out.numpy()).all(), True)

python/paddle/incubate/hapi/__init__.py

@@ -25,6 +25,7 @@ from . import datasets
 from . import distributed
 from . import vision
 from . import text
+from . import utils
 
 from . import device
 from .device import *
@@ -41,6 +42,7 @@ __all__ = [
     'metrics',
     'vision',
     'text',
+    'utils',
 ] + model.__all__ + device.__all__
 
 monkey_patch_layer()

python/paddle/incubate/hapi/tests/test_uncombined_weight2state_dict.py

+# copyright (c) 2020 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 division
+from __future__ import print_function
+
+import unittest
+
+import numpy as np
+import shutil
+import tempfile
+
+from paddle import fluid
+from paddle.nn import Conv2D, Pool2D, Linear, ReLU, Sequential
+
+from paddle.incubate.hapi.utils import uncombined_weight_to_state_dict
+
+
+class LeNetDygraph(fluid.dygraph.Layer):
+    def __init__(self, num_classes=10, classifier_activation='softmax'):
+        super(LeNetDygraph, self).__init__()
+        self.num_classes = num_classes
+        self.features = Sequential(
+            Conv2D(
+                1, 6, 3, stride=1, padding=1),
+            ReLU(),
+            Pool2D(2, 'max', 2),
+            Conv2D(
+                6, 16, 5, stride=1, padding=0),
+            ReLU(),
+            Pool2D(2, 'max', 2))
+
+        if num_classes > 0:
+            self.fc = Sequential(
+                Linear(400, 120),
+                Linear(120, 84),
+                Linear(
+                    84, 10, act=classifier_activation))
+
+    def forward(self, inputs):
+        x = self.features(inputs)
+
+        if self.num_classes > 0:
+            x = fluid.layers.flatten(x, 1)
+            x = self.fc(x)
+        return x
+
+
+class TestUncombinedWeight2StateDict(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.save_dir = tempfile.mkdtemp()
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.save_dir)
+
+    def test_infer(self):
+        start_prog = fluid.Program()
+        train_prog = fluid.Program()
+
+        x = fluid.data(name='x', shape=[None, 1, 28, 28], dtype='float32')
+
+        with fluid.program_guard(train_prog, start_prog):
+            with fluid.unique_name.guard():
+                x = fluid.data(
+                    name='x', shape=[None, 1, 28, 28], dtype='float32')
+                model = LeNetDygraph()
+                output = model.forward(x)
+
+        excutor = fluid.Executor()
+        excutor.run(start_prog)
+
+        test_prog = train_prog.clone(for_test=True)
+
+        fluid.io.save_params(excutor, self.save_dir, test_prog)
+
+        rand_x = np.random.rand(1, 1, 28, 28).astype('float32')
+        out = excutor.run(program=test_prog,
+                          feed={'x': rand_x},
+                          fetch_list=[output.name],
+                          return_numpy=True)
+
+        state_dict = uncombined_weight_to_state_dict(self.save_dir)
+
+        key2key_dict = {
+            'features.0.weight': 'conv2d_0.w_0',
+            'features.0.bias': 'conv2d_0.b_0',
+            'features.3.weight': 'conv2d_1.w_0',
+            'features.3.bias': 'conv2d_1.b_0',
+            'fc.0.weight': 'linear_0.w_0',
+            'fc.0.bias': 'linear_0.b_0',
+            'fc.1.weight': 'linear_1.w_0',
+            'fc.1.bias': 'linear_1.b_0',
+            'fc.2.weight': 'linear_2.w_0',
+            'fc.2.bias': 'linear_2.b_0'
+        }
+
+        fluid.enable_imperative()
+        dygraph_model = LeNetDygraph()
+
+        converted_state_dict = dygraph_model.state_dict()
+        for k1, k2 in key2key_dict.items():
+            converted_state_dict[k1] = state_dict[k2]
+
+        dygraph_model.set_dict(converted_state_dict)
+
+        dygraph_model.eval()
+        dy_out = dygraph_model(fluid.dygraph.to_variable(rand_x))
+
+        np.testing.assert_allclose(dy_out.numpy(), out[0], atol=1e-5)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/incubate/hapi/utils.py

@@ -12,13 +12,171 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import inspect
 import numpy as np
 
+from collections import OrderedDict
 from paddle import fluid
 from paddle.fluid.framework import Variable
 from paddle.fluid.executor import global_scope
 
+__all__ = ['uncombined_weight_to_state_dict']
+
+
+def uncombined_weight_to_state_dict(weight_dir):
+    """
+    Convert uncombined weight which getted by using `fluid.io.save_params` or `fluid.io.save_persistables` to state_dict
+
+    Args:
+        weight_dir (str): weight direcotory path.
+
+    Returns:
+        OrderDict: weight dict.
+
+    Examples:
+        .. code-block:: python
+
+            import os
+
+            from paddle import fluid
+            from paddle.nn import Conv2D, Pool2D, Linear, ReLU, Sequential
+            from paddle.incubate.hapi.utils import uncombined_weight_to_state_dict
+
+
+            class LeNetDygraph(fluid.dygraph.Layer):
+                def __init__(self, num_classes=10, classifier_activation='softmax'):
+                    super(LeNetDygraph, self).__init__()
+                    self.num_classes = num_classes
+                    self.features = Sequential(
+                        Conv2D(
+                            1, 6, 3, stride=1, padding=1),
+                        ReLU(),
+                        Pool2D(2, 'max', 2),
+                        Conv2D(
+                            6, 16, 5, stride=1, padding=0),
+                        ReLU(),
+                        Pool2D(2, 'max', 2))
+
+                    if num_classes > 0:
+                        self.fc = Sequential(
+                            Linear(400, 120),
+                            Linear(120, 84),
+                            Linear(
+                                84, 10, act=classifier_activation))
+
+                def forward(self, inputs):
+                    x = self.features(inputs)
+
+                    if self.num_classes > 0:
+                        x = fluid.layers.flatten(x, 1)
+                        x = self.fc(x)
+                    return x
+
+            # save weight use fluid.io.save_params
+            save_dir = 'temp'
+            if not os.path.exists(save_dir):
+                os.makedirs(save_dir)
+
+            start_prog = fluid.Program()
+            train_prog = fluid.Program()
+
+            x = fluid.data(name='x', shape=[None, 1, 28, 28], dtype='float32')
+
+            with fluid.program_guard(train_prog, start_prog):
+                with fluid.unique_name.guard():
+                    x = fluid.data(
+                        name='x', shape=[None, 1, 28, 28], dtype='float32')
+                    model = LeNetDygraph()
+                    output = model.forward(x)
+
+            excutor = fluid.Executor()
+            excutor.run(start_prog)
+
+            test_prog = train_prog.clone(for_test=True)
+
+            fluid.io.save_params(excutor, save_dir, test_prog)
+
+            # convert uncombined weight to state dict
+            state_dict = uncombined_weight_to_state_dict(save_dir)
+
+            key2key_dict = {
+                'features.0.weight': 'conv2d_0.w_0',
+                'features.0.bias': 'conv2d_0.b_0',
+                'features.3.weight': 'conv2d_1.w_0',
+                'features.3.bias': 'conv2d_1.b_0',
+                'fc.0.weight': 'linear_0.w_0',
+                'fc.0.bias': 'linear_0.b_0',
+                'fc.1.weight': 'linear_1.w_0',
+                'fc.1.bias': 'linear_1.b_0',
+                'fc.2.weight': 'linear_2.w_0',
+                'fc.2.bias': 'linear_2.b_0'
+            }
+
+            fluid.enable_imperative()
+            dygraph_model = LeNetDygraph()
+
+            converted_state_dict = dygraph_model.state_dict()
+            for k1, k2 in key2key_dict.items():
+                converted_state_dict[k1] = state_dict[k2]
+
+            # dygraph model load state dict which converted from uncombined weight
+            dygraph_model.set_dict(converted_state_dict)
+    """
+
+    def _get_all_params_name(dir):
+        params_name = []
+        dir = os.path.expanduser(dir)
+
+        dir_len = len(dir)
+        for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
+            for fname in sorted(fnames):
+                path = os.path.join(root[dir_len:], fname)
+                params_name.append(path)
+
+        return params_name
+
+    class Load(fluid.dygraph.Layer):
+        def __init__(self):
+            super(Load, self).__init__()
+
+        def forward(self, filename):
+            weight = self.create_parameter(
+                shape=[1],
+                dtype='float32',
+                default_initializer=fluid.initializer.ConstantInitializer(0.0))
+            self._helper.append_op(
+                type='load',
+                inputs={},
+                outputs={'Out': [weight]},
+                attrs={'file_path': filename})
+            return weight
+
+    params_name_list = _get_all_params_name(weight_dir)
+    if not fluid.in_dygraph_mode():
+        dygraph_enabled = False
+        fluid.enable_imperative()
+    else:
+        dygraph_enabled = True
+
+    load = Load()
+    state_dict = OrderedDict()
+
+    for param_name in params_name_list:
+        param_path = os.path.join(weight_dir, param_name)
+        weight = load(param_path)
+        try:
+            weight = weight.numpy()
+        except Exception as e:
+            print(e)
+
+        state_dict[param_name] = weight
+
+    if not dygraph_enabled:
+        fluid.disable_imperative()
+
+    return state_dict
+
 
 def to_list(value):
     if value is None:

python/paddle/tensor/search.py

@@ -46,8 +46,7 @@ def argsort(x, axis=-1, descending=False, name=None):
 	:alias_main: paddle.argsort
 	:alias: paddle.argsort,paddle.tensor.argsort,paddle.tensor.search.argsort
 
-    This OP sorts the input along the given axis, and returns sorted output
-    data Varibale and its corresponding index Variable with the same shape as ``x``.
+    This OP sorts the input along the given axis, and returns the corresponding index tensor for the sorted output values. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True.
 
     Args:
         x(Tensor): An input N-D Tensor with type float32, float64, int16,
@@ -84,26 +83,26 @@ def argsort(x, axis=-1, descending=False, name=None):
             out2 = paddle.argsort(x=x, axis=0)
             out3 = paddle.argsort(x=x, axis=1)
             print(out1.numpy())
-	    #[[[0 3 1 2]
-	    #  [0 1 2 3]
-	    #  [2 3 0 1]]
+            #[[[0 3 1 2]
+            #  [0 1 2 3]
+            #  [2 3 0 1]]
             # [[1 3 2 0]
-	    #  [0 1 2 3]
-	    #  [2 0 3 1]]]
+            #  [0 1 2 3]
+            #  [2 0 3 1]]]
             print(out2.numpy())
-	    #[[[0 1 1 1]
-	    #  [0 0 0 0]
-	    #  [1 1 1 0]]
-	    # [[1 0 0 0]
-	    #  [1 1 1 1]
-	    #  [0 0 0 1]]]
+            #[[[0 1 1 1]
+            #  [0 0 0 0]
+            #  [1 1 1 0]]
+            # [[1 0 0 0]
+            #  [1 1 1 1]
+            #  [0 0 0 1]]]
             print(out3.numpy())
-	    #[[[1 1 1 2]
-	    #  [0 0 2 0]
-	    #  [2 2 0 1]]
-	    # [[2 0 2 0]
-	    #  [1 1 0 2]
-	    #  [0 2 1 1]]]
+            #[[[1 1 1 2]
+            #  [0 0 2 0]
+            #  [2 2 0 1]]
+            # [[2 0 2 0]
+            #  [1 1 0 2]
+            #  [0 2 1 1]]]
     """
     if in_dygraph_mode():
         _, ids = core.ops.argsort(x, 'axis', axis, 'descending', descending)
@@ -381,8 +380,7 @@ def sort(x, axis=-1, descending=False, name=None):
 	:alias_main: paddle.sort
 	:alias: paddle.sort,paddle.tensor.sort,paddle.tensor.search.sort
 
-    This OP sorts the input along the given axis, and returns sorted output
-    data Tensor and its corresponding index Tensor with the same shape as ``x``.
+    This OP sorts the input along the given axis, and returns the sorted output tensor. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True.
 
     Args:
         x(Tensor): An input N-D Tensor with type float32, float64, int16,
@@ -397,9 +395,7 @@ def sort(x, axis=-1, descending=False, name=None):
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.
     Returns:
-        tuple: A tuple of sorted data tensor(with the same shape and data
-        type as ``x``) and the sorted indices(with the same shape as ``x``
-        and with data type int64).
+        Tensor: sorted tensor(with the same shape and data type as ``x``).
     Examples:
         .. code-block:: python
             import paddle
@@ -417,38 +413,31 @@ def sort(x, axis=-1, descending=False, name=None):
             out1 = paddle.sort(x=x, axis=-1)
             out2 = paddle.sort(x=x, axis=0)
             out3 = paddle.sort(x=x, axis=1)
-            print(out1[0].numpy())
-	    #[[[5. 5. 8. 9.]
-	    #  [0. 0. 1. 7.]
-	    #  [2. 4. 6. 9.]]
-	    # [[2. 2. 4. 5.]
-	    #  [4. 7. 7. 9.]
-	    #  [0. 1. 6. 7.]]]
-            print(out1[1].numpy())
-	    #[[[0 3 1 2]
-	    #  [0 1 2 3]
-	    #  [2 3 0 1]]
-            # [[1 3 2 0]
-	    #  [0 1 2 3]
-	    #  [2 0 3 1]]]
-            print(out2[0].numpy())
+            print(out1.numpy())
+            #[[[5. 5. 8. 9.]
+            #  [0. 0. 1. 7.]
+            #  [2. 4. 6. 9.]]
+            # [[2. 2. 4. 5.]
+            #  [4. 7. 7. 9.]
+            #  [0. 1. 6. 7.]]]
+            print(out2.numpy())
             #[[[5. 2. 4. 2.]
-	    #  [0. 0. 1. 7.]
-	    #  [1. 7. 0. 4.]]
-	    # [[5. 8. 9. 5.]
-	    #  [4. 7. 7. 9.]
-	    #  [6. 9. 2. 6.]]]
-            print(out3[0].numpy())
+            #  [0. 0. 1. 7.]
+            #  [1. 7. 0. 4.]]
+            # [[5. 8. 9. 5.]
+            #  [4. 7. 7. 9.]
+            #  [6. 9. 2. 6.]]]
+            print(out3.numpy())
             #[[[0. 0. 1. 4.]
-	    #  [5. 8. 2. 5.]
-	    #  [6. 9. 9. 7.]]
-	    # [[1. 2. 0. 2.]
-	    #  [4. 7. 4. 6.]
-	    #  [5. 7. 7. 9.]]]
+            #  [5. 8. 2. 5.]
+            #  [6. 9. 9. 7.]]
+            # [[1. 2. 0. 2.]
+            #  [4. 7. 4. 6.]
+            #  [5. 7. 7. 9.]]]
     """
     if in_dygraph_mode():
-        out, ids = core.ops.argsort(x, 'axis', axis, 'descending', descending)
-        return out, ids
+        out, _ = core.ops.argsort(x, 'axis', axis, 'descending', descending)
+        return out
     helper = LayerHelper("sort", **locals())
     out = helper.create_variable_for_type_inference(
         dtype=x.dtype, stop_gradient=False)
@@ -461,7 +450,7 @@ def sort(x, axis=-1, descending=False, name=None):
                  'Indices': ids},
         attrs={'axis': axis,
                'descending': descending})
-    return out, ids
+    return out
 
 
 def where(condition, x, y, name=None):

python/paddle/utils/__init__.py

@@ -16,8 +16,9 @@ from .plot import Ploter
 from .profiler import ProfilerOptions
 from .profiler import Profiler
 from .profiler import get_profiler
+from .deprecated import deprecated
 
-__all__ = ['dump_config', 'Ploter']
+__all__ = ['dump_config', 'Ploter', 'deprecated']
 
 #TODO: define new api under this directory
 # __all__ = ['unique_name',

python/paddle/utils/deprecated.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.
+"""
+decorator to deprecate a function or class
+"""
+
+import warnings
+import functools
+import paddle
+
+
+def deprecated(update_to="", since="", reason=""):
+    """Decorate a function to signify its deprecation.
+
+       This function wraps a method that will soon be removed and does two things:
+           - The docstring of the API will be modified to include a notice
+             about deprecation."
+           - Raises a :class:`~exceptions.DeprecatedWarning` when old API is called.
+       Args:
+           since(str): The version at which the decorated method is considered deprecated.
+           update_to(str): The new API users should use.
+           reason(str): The reason why the API is deprecated.
+       Returns:
+           decorator: decorated function or class.
+    """
+
+    def decorator(func):
+        """construct warning message, and return a decorated function or class."""
+        assert isinstance(update_to, str), 'type of "update_to" must be str.'
+        assert isinstance(since, str), 'type of "since" must be str.'
+        assert isinstance(reason, str), 'type of "reason" must be str.'
+
+        _since = since.strip()
+        _update_to = update_to.strip()
+        _reason = reason.strip()
+
+        msg = 'API "{}.{}" is deprecated'.format(func.__module__, func.__name__)
+        if len(_since) > 0:
+            msg += " since {}".format(_since)
+        msg += ", and may be removed in future versions."
+        if len(_update_to) > 0:
+            assert _update_to.startswith(
+                "paddle."
+            ), 'Argument update_to must start with "paddle.", your value is "{}"'.format(
+                update_to)
+            msg += ' Use "{}" instead.'.format(_update_to)
+        if len(_reason) > 0:
+            msg += "\n reason: {}".format(_reason)
+
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            """deprecated warning should be fired in 3 circumstances:
+               1. current version is develop version, i.e. "0.0.0", because we assume develop version is always the latest version.
+               2. since version is empty, in this case, API is deprecated in all versions.
+               3. current version is newer than since version.
+            """
+            v_current = [int(i) for i in paddle.__version__.split(".")]
+            v_current += [0] * (4 - len(v_current))
+            v_since = [int(i) for i in _since.split(".")]
+            v_since += [0] * (4 - len(v_since))
+            if paddle.__version__ == "0.0.0" or _since == "" or v_current >= v_since:
+                warnings.simplefilter('always',
+                                      DeprecationWarning)  # turn off filter
+                warnings.warn(msg, category=DeprecationWarning, stacklevel=2)
+                warnings.simplefilter('default',
+                                      DeprecationWarning)  # reset filter
+            return func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator

python/setup.py.in

@@ -475,6 +475,11 @@ with redirect_stdout():
         cmdclass={
             'install_headers': InstallHeaders,
             'install': InstallCommand,
+        },
+        entry_points={
+            'console_scripts': [
+                'fleetrun = paddle.fleet.launch:launch'
+            ]
         }
     )
 

tools/dockerfile/Dockerfile.centos

@@ -13,7 +13,7 @@ ENV PATH /opt/rh/devtoolset-2/root/usr/bin:$PATH
 ENV LD_LIBRARY_PATH /opt/rh/devtoolset-2/root/usr/lib64:/opt/rh/devtoolset-2/root/usr/lib:/usr/local/lib64:/usr/local/lib:${LD_LIBRARY_PATH}
 ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
 
-RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz graphviz wget curl-devel
+RUN yum install -y gettext-devel sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz graphviz wget curl-devel
 COPY build_scripts /build_scripts
 RUN bash build_scripts/build.sh
 RUN bash build_scripts/install_nccl2.sh && \
@@ -22,6 +22,13 @@ RUN rm -rf build_scripts
 
 ENV SSL_CERT_FILE=/opt/_internal/certs.pem
 
+# git 2.17.1
+RUN wget -q https://paddle-ci.gz.bcebos.com/git-2.17.1.tar.gz && \
+  tar -xvf git-2.17.1.tar.gz && \
+  cd git-2.17.1 && \
+  ./configure --prefix=/usr/local && \
+  make -j8 && make install 
+
 # for paddle
 RUN wget --no-check-certificate -qO- https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
     tar -xz -C /usr/local && \

tools/dockerfile/ci_dockerfile.sh

@@ -3,9 +3,9 @@ function make_ubuntu_dockerfile(){
   dockerfile_name="Dockerfile.cuda10_cudnn7_gcc82_ubuntu16"
   sed 's/<baseimg>/10.1-cudnn7-devel-ubuntu16.04/g' ./Dockerfile.ubuntu >${dockerfile_name}
   sed -i 's#liblzma-dev#liblzma-dev openmpi-bin openmpi-doc libopenmpi-dev#g' ${dockerfile_name} 
+  dockerfile_line=`wc -l ${dockerfile_name}|awk '{print $1}'`
   sed -i "${dockerfile_line}i RUN wget --no-check-certificate  -q https://paddle-edl.bj.bcebos.com/hadoop-2.7.7.tar.gz && \
      tar -xzf  hadoop-2.7.7.tar.gz && mv hadoop-2.7.7 /usr/local/" ${dockerfile_name} 
-  dockerfile_line=`wc -l ${dockerfile_name}|awk '{print $1}'`
   sed -i 's#<install_gcc>#WORKDIR /usr/bin \
       COPY tools/dockerfile/build_scripts /build_scripts \
       RUN bash /build_scripts/install_gcc.sh gcc82 \&\& rm -rf /build_scripts \
@@ -24,6 +24,9 @@ function make_centos_dockerfile(){
   sed 's/<baseimg>/9.0-cudnn7-devel-centos6/g' Dockerfile.centos >${dockerfile_name}
   sed -i 's#COPY build_scripts /build_scripts#COPY tools/dockerfile/build_scripts ./build_scripts#g' ${dockerfile_name} 
   dockerfile_line=`wc -l ${dockerfile_name}|awk '{print $1}'`
+  sed -i "${dockerfile_line}i RUN ln -s /usr/lib64/libz.so /usr/local/lib/libz.so && \
+     ln -s /usr/local/lib/libnccl.so /usr/local/cuda/lib64/ && \
+     rm -rf /usr/include/NvInfer*" ${dockerfile_name}
   sed -i "${dockerfile_line}i RUN wget --no-check-certificate  -q https://paddle-edl.bj.bcebos.com/hadoop-2.7.7.tar.gz && \
      tar -xzf  hadoop-2.7.7.tar.gz && mv hadoop-2.7.7 /usr/local/" ${dockerfile_name}
 }