Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_dist_resnet_ut_in_py36

test=develop

Dockerfile

@@ -22,6 +22,27 @@ ENV HOME /root
 # Add bash enhancements
 COPY ./paddle/scripts/docker/root/ /root/
 
+# Prepare packages for Python
+RUN apt-get update && \
+    apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
+    libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
+    xz-utils tk-dev libffi-dev liblzma-dev
+
+# Install Python3.6
+RUN mkdir -p /root/python_build/ && wget -q https://www.sqlite.org/2018/sqlite-autoconf-3250300.tar.gz && \
+    tar -zxf sqlite-autoconf-3250300.tar.gz && cd sqlite-autoconf-3250300 && \
+    ./configure -prefix=/usr/local && make -j8 && make install && cd ../ && rm sqlite-autoconf-3250300.tar.gz && \
+    wget -q https://www.python.org/ftp/python/3.6.0/Python-3.6.0.tgz && \
+    tar -xzf Python-3.6.0.tgz && cd Python-3.6.0 && \
+    CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
+    make -j8 > /dev/null && make altinstall > /dev/null
+
+# Install Python3.7
+RUN wget -q https://www.python.org/ftp/python/3.7.0/Python-3.7.0.tgz && \
+    tar -xzf Python-3.7.0.tgz && cd Python-3.7.0 && \
+    CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
+    make -j8 > /dev/null && make altinstall > /dev/null
+
 RUN apt-get update && \
     apt-get install -y --allow-downgrades patchelf \
     python3 python3-dev python3-pip \
@@ -74,6 +95,12 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 RUN pip3 install -U wheel && \
     pip3 install -U docopt PyYAML sphinx==1.5.6 && \
     pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
+    pip3.6 install -U wheel && \
+    pip3.6 install -U docopt PyYAML sphinx==1.5.6 && \
+    pip3.6 install sphinx-rtd-theme==0.1.9 recommonmark && \
+    pip3.7 install -U wheel && \
+    pip3.7 install -U docopt PyYAML sphinx==1.5.6 && \
+    pip3.7 install sphinx-rtd-theme==0.1.9 recommonmark && \
     easy_install -U pip && \
     pip install -U pip setuptools wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
@@ -82,22 +109,34 @@ RUN pip3 install -U wheel && \
 RUN pip3 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
     pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
     pip3 install opencv-python && \
+    pip3.6 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
+    pip3.6 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip3.6 install opencv-python && \
+    pip3.7 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
+    pip3.7 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip3.7 install opencv-python && \
     pip install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
     pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
     pip install opencv-python
 
 #For docstring checker
 RUN pip3 install pylint pytest astroid isort
+RUN pip3.6 install pylint pytest astroid isort
+RUN pip3.7 install pylint pytest astroid isort
 RUN pip install pylint pytest astroid isort LinkChecker
 
 COPY ./python/requirements.txt /root/
 RUN pip3 install -r /root/requirements.txt
+RUN pip3.6 install -r /root/requirements.txt
+RUN pip3.7 install -r /root/requirements.txt
 RUN pip install -r /root/requirements.txt
 
 # To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
 # the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
 RUN apt-get install -y libssl-dev libffi-dev
 RUN pip3 install certifi urllib3[secure]
+RUN pip3.6 install certifi urllib3[secure]
+RUN pip3.7 install certifi urllib3[secure]
 RUN pip install certifi urllib3[secure]
 
 

paddle/fluid/framework/CMakeLists.txt

@@ -116,8 +116,14 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
 cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
 
+if (NOT WIN32)
+cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto)
 cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
-    shape_inference data_transform lod_tensor profiler)
+    shape_inference data_transform lod_tensor profiler transfer_scope_cache)
+else()
+cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
+    shape_inference data_transform lod_tensor)
+endif(NOT WIN32)
 
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
 

paddle/fluid/framework/executor.cc

@@ -20,6 +20,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/ngraph_operator.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/reader.h"
+#include "paddle/fluid/framework/transfer_scope_cache.h"
 #include "paddle/fluid/operators/detail/macros.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -391,8 +392,8 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
 
   int64_t max_memory_size = GetEagerDeletionThreshold();
   std::unique_ptr<GarbageCollector<Tensor>> gc;
-  // WhileOp would set keep_kids to false
-  // WhileGradOp would need the scopes created in WhileOp
+  // WhileOp would set keep_kids to true,
+  // because WhileGradOp needs the scopes created in WhileOp.
   // Perhaps, we should not perform eager deletion in WhileOp
   // The scopes and variables created by WhileOp would be deleted
   // in WhileGradOp.

paddle/fluid/framework/naive_executor.cc

@@ -83,6 +83,7 @@ void NaiveExecutor::Run() {
   for (auto &op : ops_) {
     VLOG(3) << std::this_thread::get_id() << " run " << op->Type()
             << " on scope " << scope_;
+    op->SetIsCalledByExecutor(false);
     op->Run(*scope_, place_);
   }
 }

paddle/fluid/framework/operator.cc

@@ -22,6 +22,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/shape_inference.h"
+#include "paddle/fluid/framework/transfer_scope_cache.h"
 #include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -33,11 +34,6 @@ DEFINE_bool(check_nan_inf, false,
 namespace paddle {
 namespace framework {
 
-// Combine two hash values to a single hash.
-inline size_t CombineHash(size_t seed, size_t a) {
-  return (seed ^ a) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
-}
-
 std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority = {
     std::make_tuple(platform::CUDAPlace(0), LibraryType::kCUDNN),
     std::make_tuple(platform::CUDAPlace(0), LibraryType::kPlain),
@@ -797,17 +793,6 @@ void OperatorWithKernel::TransferInplaceVarsBack(
 Scope* OperatorWithKernel::TryTransferData(
     const Scope& scope, const OpKernelType& expected_kernel_key,
     std::vector<std::string>* transfered_inplace_vars) const {
-// In the inference scenerio, the scopes will be reused across the batches, so
-// the `new_scope` here will result in GPU memroy explosion over the running of
-// operators.
-// We use a thread_local cache to fix that issue, the key in the cache is the
-// combination of the `scope` argument, from_kernel_type, target_kernel_type.
-// Have a discussion with @Superjomn or the inference developers if some changes
-// on this logic for this macro might not tested on the other scenerios.
-#ifdef PADDLE_ON_INFERENCE
-  thread_local std::unordered_map<size_t, Scope*> infer_transfer_scope_cache;
-#endif
-
   Scope* new_scope = nullptr;
   for (auto& var_name_item : Inputs()) {
     for (auto& var_name : var_name_item.second) {
@@ -838,23 +823,23 @@ Scope* OperatorWithKernel::TryTransferData(
       VLOG(30) << "Transform Variable " << var_name << " from "
                << kernel_type_for_var << " to " << expected_kernel_key;
 
-#ifdef PADDLE_ON_INFERENCE
-      size_t infer_cache_key =
-          CombineHash(OpKernelType::Hash()(kernel_type_for_var),
-                      OpKernelType::Hash()(expected_kernel_key));
-      infer_cache_key =
-          CombineHash(infer_cache_key, std::hash<const Scope*>()(&scope));
-
-      auto it = infer_transfer_scope_cache.find(infer_cache_key);
-      if (it != infer_transfer_scope_cache.end()) {
-        new_scope = infer_transfer_scope_cache[infer_cache_key];
-      } else {
-        new_scope = &scope.NewScope();
-        infer_transfer_scope_cache[infer_cache_key] = new_scope;
-      }
-#endif
-
-      if (new_scope == nullptr) {
+      // In the inference scenerio, the scopes will be reused across the
+      // batches, so the `new_scope` here will result in GPU memroy explosion
+      // over the  running of operators.
+      // We use a thread_local cache to fix that issue, the key in the cache is
+      // the combination of the `scope` argument, from_kernel_type,
+      // target_kernel_type.
+      // Have a discussion with @Superjomn or the inference developers if some
+      // changes on this logic for this macro might not tested on the other
+      // scenerios.
+      // If this op is not called by an Executor or ParallelExecutor, it should
+      // called by a NaiveExecutor, the NaiveExecutor will cache the scopes and
+      // variables, that behavior a lot different.
+      if (!run_by_executor_) {
+        new_scope = TryCreateTransferScope(kernel_type_for_var,
+                                           expected_kernel_key, &scope);
+      }
+      if (!new_scope) {
         new_scope = &scope.NewScope();
       }
 

paddle/fluid/framework/operator.h

@@ -127,6 +127,8 @@ class OperatorBase {
   //! Get all outputs variable names
   virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
 
+  void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
+
  protected:
   std::string type_;
   // NOTE: in case of OpGrad, inputs_ contains:
@@ -139,6 +141,8 @@ class OperatorBase {
   // IG (Inputs Gradients)
   VariableNameMap outputs_;
   AttributeMap attrs_;
+  // Whether this operator executes in an Executor.
+  bool run_by_executor_{true};
 
  private:
   void GenerateTemporaryNames();

paddle/fluid/framework/transfer_scope_cache.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/transfer_scope_cache.h"
+
+namespace paddle {
+namespace framework {
+
+std::unordered_map<size_t, Scope*>& global_transfer_data_cache() {
+  thread_local auto* x = new std::unordered_map<size_t, Scope*>;
+  return *x;
+}
+
+std::unordered_set<Scope*>& global_transfer_scope_cache() {
+  thread_local auto* x = new std::unordered_set<Scope*>;
+  return *x;
+}
+
+Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
+                              const Scope* scope) {
+  Scope* new_scope{nullptr};
+  size_t infer_cache_key =
+      CombineHash(OpKernelType::Hash()(type0), OpKernelType::Hash()(type1));
+  infer_cache_key =
+      CombineHash(infer_cache_key, std::hash<const Scope*>()(scope));
+
+  auto it = global_transfer_data_cache().find(infer_cache_key);
+  if (it != global_transfer_data_cache().end()) {
+    new_scope = global_transfer_data_cache()[infer_cache_key];
+  } else {
+    new_scope = &scope->NewScope();
+    global_transfer_data_cache()[infer_cache_key] = new_scope;
+  }
+  global_transfer_scope_cache().insert(new_scope);
+  return new_scope;
+}
+
+void RemoveKidsFromTransferScopeCache(Scope* scope) {
+  auto it = global_transfer_scope_cache().find(scope);
+  if (it != global_transfer_scope_cache().end()) {
+    global_transfer_scope_cache().erase(it);
+  }
+  for (auto* s : scope->kids()) {
+    auto it = global_transfer_scope_cache().find(s);
+    if (it != global_transfer_scope_cache().end()) {
+      global_transfer_scope_cache().erase(it);
+    }
+  }
+
+  // remove global transfer data cache
+  auto& cache = global_transfer_data_cache();
+  for (auto it = cache.begin(); it != cache.end();) {
+    if (it->second == scope)
+      it = cache.erase(it);
+    else
+      it++;
+  }
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/transfer_scope_cache.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <thread>  // NOLINT
+#include <unordered_map>
+#include <unordered_set>
+#include "paddle/fluid/framework/op_kernel_type.h"
+#include "paddle/fluid/framework/scope.h"
+
+namespace paddle {
+namespace framework {
+
+std::unordered_map<size_t, Scope*>& global_transfer_data_cache();
+
+std::unordered_set<Scope*>& global_transfer_scope_cache();
+
+// Combine two hash values to a single hash.
+static size_t CombineHash(size_t seed, size_t a) {
+  return (seed ^ a) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+}
+
+Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
+                              const Scope* scope);
+
+void RemoveKidsFromTransferScopeCache(Scope* scope);
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/inference/CMakeLists.txt

@@ -4,6 +4,7 @@ endif()
 # analysis and tensorrt must be added before creating static library,
 # otherwise, there would be undefined reference to them in static library.
 add_subdirectory(analysis)
+add_subdirectory(utils)
 if (TENSORRT_FOUND)
   add_subdirectory(tensorrt)
 endif()

paddle/fluid/inference/api/CMakeLists.txt

@@ -30,7 +30,9 @@ cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc)
 cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager)
 cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS scope lod_tensor enforce)
 cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc)
-cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder DEPS zero_copy_tensor)
+cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS
+           lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config
+           analysis_config paddle_pass_builder zero_copy_tensor reset_tensor_array)
 
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc

paddle/fluid/inference/api/analysis_predictor.cc

@@ -31,6 +31,7 @@
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 #endif
 #include "paddle/fluid/inference/utils/singleton.h"
+#include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/cpu_helper.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -174,7 +175,6 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
   inference::Timer timer;
   timer.tic();
   // set feed variable
-  std::vector<framework::LoDTensor> feeds;
   framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
   if (!SetFeed(inputs, scope)) {
     LOG(ERROR) << "fail to set feed";
@@ -215,17 +215,29 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
     framework::DDim ddim = framework::make_ddim(inputs[i].shape);
     void *input_ptr;
     if (inputs[i].dtype == PaddleDType::INT64) {
-      input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
+      input_ptr = input.mutable_data<int64_t>(ddim, place_);
     } else if (inputs[i].dtype == PaddleDType::FLOAT32) {
-      input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
+      input_ptr = input.mutable_data<float>(ddim, place_);
     } else {
       LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
       return false;
     }
 
+    if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
                   inputs[i].data.length());
+    } else {
+#ifdef PADDLE_WITH_CUDA
+      auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
+      memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
+                   platform::CPUPlace(), inputs[i].data.data(),
+                   inputs[i].data.length(),
+                   0);  // stream 0 for sync copy
+#else
+      PADDLE_THROW("Not compile with CUDA, should not reach here.");
+#endif
+    }
     // TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
     framework::LoD lod;
     for (auto &level : inputs[i].lod) {

paddle/fluid/inference/api/api_impl.cc

@@ -24,6 +24,7 @@ limitations under the License. */
 #include "paddle/fluid/inference/api/api_impl.h"
 #include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/helper.h"
+#include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/cpu_helper.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -138,7 +139,6 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
   Timer timer;
   timer.tic();
   // set feed variable
-  std::vector<framework::LoDTensor> feeds;
   framework::Scope *scope = sub_scope_ != nullptr ? sub_scope_ : scope_.get();
   if (!SetFeed(inputs, scope)) {
     LOG(ERROR) << "fail to set feed";
@@ -194,17 +194,30 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
     framework::DDim ddim = framework::make_ddim(inputs[i].shape);
     void *input_ptr;
     if (inputs[i].dtype == PaddleDType::INT64) {
-      input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
+      input_ptr = input.mutable_data<int64_t>(ddim, place_);
     } else if (inputs[i].dtype == PaddleDType::FLOAT32) {
-      input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
+      input_ptr = input.mutable_data<float>(ddim, place_);
     } else {
       LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
       return false;
     }
 
+    if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
                   inputs[i].data.length());
+    } else {
+#ifdef PADDLE_WITH_CUDA
+      auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
+      memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
+                   platform::CPUPlace(), inputs[i].data.data(),
+                   inputs[i].data.length(),
+                   0);  // stream 0 for sync copy
+#else
+      PADDLE_THROW("Not compile with CUDA, should not reach here.");
+#endif
+    }
+
     // TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
     framework::LoD lod;
     for (auto &level : inputs[i].lod) {

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

@@ -46,8 +46,6 @@ if(WITH_GPU)
     endif()
   endif(NOT WIN32)
 endif()
-
-include_directories("D:/Paddle/")
 include_directories("${PADDLE_LIB}")
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")

paddle/fluid/inference/utils/CMakeLists.txt

+cc_library(benchmark SRCS benchmark.cc DEPS enforce)
+cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)

paddle/fluid/inference/utils/benchmark.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/inference/utils/benchmark.h"
+#include <sstream>
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+
+std::string Benchmark::SerializeToString() const {
+  std::stringstream ss;
+  ss << "-----------------------------------------------------\n";
+  ss << "name\t";
+  ss << "batch_size\t";
+  ss << "num_threads\t";
+  ss << "latency\t";
+  ss << "qps";
+  ss << '\n';
+
+  ss << name_ << "\t";
+  ss << batch_size_ << "\t";
+  ss << num_threads_ << "\t";
+  ss << latency_ << "\t";
+  ss << 1000 / latency_;
+  ss << '\n';
+  return ss.str();
+}
+void Benchmark::PersistToFile(const std::string &path) const {
+  std::ofstream file(path, std::ios::app);
+  PADDLE_ENFORCE(file.is_open(), "Can not open %s to add benchmark", path);
+  file << SerializeToString();
+  file.flush();
+  file.close();
+}
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/utils/benchmark.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <fstream>
+#include <iostream>
+
+namespace paddle {
+namespace inference {
+
+/*
+ * Helper class to calculate the performance.
+ */
+struct Benchmark {
+  int batch_size() const { return batch_size_; }
+  void SetBatchSize(int x) { batch_size_ = x; }
+
+  int num_threads() const { return num_threads_; }
+  void SetNumThreads(int x) { num_threads_ = x; }
+
+  bool use_gpu() const { return use_gpu_; }
+  void SetUseGpu() { use_gpu_ = true; }
+
+  int latency() const { return latency_; }
+  void SetLatency(int x) { latency_ = x; }
+
+  const std::string& name() const { return name_; }
+  void SetName(const std::string& name) { name_ = name; }
+
+  std::string SerializeToString() const;
+  void PersistToFile(const std::string& path) const;
+
+ private:
+  bool use_gpu_{false};
+  int batch_size_{0};
+  int latency_;
+  int num_threads_{1};
+  std::string name_;
+};
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/utils/benchmark_tester.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/inference/utils/benchmark.h"
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+
+using namespace paddle::inference;
+TEST(Benchmark, basic) {
+  Benchmark benchmark;
+  benchmark.SetName("key0");
+  benchmark.SetBatchSize(10);
+  benchmark.SetUseGpu();
+  benchmark.SetLatency(220);
+  LOG(INFO) << "benchmark:\n" << benchmark.SerializeToString();
+}
+
+TEST(Benchmark, PersistToFile) {
+  Benchmark benchmark;
+  benchmark.SetName("key0");
+  benchmark.SetBatchSize(10);
+  benchmark.SetUseGpu();
+  benchmark.SetLatency(220);
+
+  benchmark.PersistToFile("1.log");
+  benchmark.PersistToFile("1.log");
+  benchmark.PersistToFile("1.log");
+}
\ No newline at end of file

paddle/fluid/memory/allocation/retry_allocator_test.cc

@@ -41,7 +41,7 @@ TEST(RetryAllocator, RetryAllocator) {
 
   size_t thread_num = 32;
   size_t sleep_time = 40;
-  size_t extra_time = 2;
+  size_t extra_time = 10;
 
   // Reserve to perform more tests in the future
   std::vector<std::shared_ptr<Allocator>> allocators;

paddle/fluid/operators/distributed/grpc_client.cc

@@ -22,6 +22,8 @@ limitations under the License. */
 #include "paddle/fluid/operators/distributed/request_handler.h"
 #include "paddle/fluid/platform/profiler.h"
 
+DECLARE_bool(rpc_disable_reuse_port);
+
 namespace paddle {
 namespace operators {
 namespace distributed {
@@ -383,6 +385,9 @@ std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
   // Channel configurations:
   grpc::ChannelArguments args;
   args.SetInt(GRPC_ARG_MAX_RECONNECT_BACKOFF_MS, 2000);
+  if (FLAGS_rpc_disable_reuse_port) {
+    args.SetInt(GRPC_ARG_ALLOW_REUSEPORT, 0);
+  }
   args.SetCompressionAlgorithm(GRPC_COMPRESS_NONE);
   args.SetMaxSendMessageSize(std::numeric_limits<int>::max());
   args.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());

paddle/fluid/operators/distributed/grpc_server.cc

@@ -20,6 +20,8 @@ limitations under the License. */
 
 using ::grpc::ServerAsyncResponseWriter;
 
+DECLARE_bool(rpc_disable_reuse_port);
+
 namespace paddle {
 namespace operators {
 namespace distributed {
@@ -252,6 +254,20 @@ void AsyncGRPCServer::WaitServerReady() {
   VLOG(40) << "AsyncGRPCServer WaitSeverReady";
 }
 
+// Define an option subclass in order to disable SO_REUSEPORT for the
+// server socket.
+// Come from:
+// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
+class NoReusePortOption : public ::grpc::ServerBuilderOption {
+ public:
+  void UpdateArguments(::grpc::ChannelArguments* args) override {
+    args->SetInt(GRPC_ARG_ALLOW_REUSEPORT, 0);
+  }
+
+  void UpdatePlugins(std::vector<std::unique_ptr<::grpc::ServerBuilderPlugin>>*
+                         plugins) override {}
+};
+
 void AsyncGRPCServer::StartServer() {
   ::grpc::ServerBuilder builder;
   builder.AddListeningPort(bind_address_, ::grpc::InsecureServerCredentials(),
@@ -259,6 +275,10 @@ void AsyncGRPCServer::StartServer() {
 
   builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
   builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
+  if (FLAGS_rpc_disable_reuse_port) {
+    builder.SetOption(
+        std::unique_ptr<::grpc::ServerBuilderOption>(new NoReusePortOption));
+  }
   builder.RegisterService(&service_);
 
   for (auto t : rpc_call_map_) {

paddle/fluid/operators/distributed/sendrecvop_utils.cc

@@ -22,6 +22,8 @@ limitations under the License. */
 #include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
 #include "paddle/fluid/operators/distributed/variable_response.h"
 
+DEFINE_bool(rpc_disable_reuse_port, false, "Disable SO_REUSEPORT or not.");
+
 namespace paddle {
 namespace operators {
 namespace distributed {

paddle/fluid/operators/lookup_sparse_table_op.cc

@@ -67,6 +67,7 @@ class LookupSparseTableOp : public framework::OperatorBase {
                       framework::proto::VarType::FP32,
                       "The sparse table only support FP32");
     w_t->Get(ids_t, out_t, true, is_test);
+    out_t->set_lod(ids_t.lod());
   }
 };
 

paddle/fluid/operators/sum_op.h

@@ -127,6 +127,9 @@ class SumKernel : public framework::OpKernel<T> {
         math::scatter::MergeAdd<DeviceContext, T> merge_add;
         merge_add(context.template device_context<DeviceContext>(), inputs,
                   out);
+
+        out->SyncIndex();
+
       } else {
         // no data, just set a empty out tensor.
         out->mutable_value()->mutable_data<T>(framework::make_ddim({0}),

paddle/fluid/operators/tensor_array_to_tensor_op.cc

@@ -106,9 +106,9 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
     out_inx_dim[0] = inx.size();
     out_inx.Resize(out_inx_dim);
 
+    auto &local_scope = scope.NewScope();
     std::string var_name = "out_index";
-    framework::Variable *tmp_index_var =
-        const_cast<framework::Scope &>(scope).Var(var_name);
+    framework::Variable *tmp_index_var = local_scope.Var(var_name);
     auto &tmp_index_tensor =
         *(tmp_index_var->GetMutable<paddle::framework::LoDTensor>());
     tmp_index_tensor.Resize(out_inx_dim);
@@ -128,12 +128,12 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
     out_dims[axis] = out_dim_sum;
     out.Resize(out_dims);
 
-    LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
-    // Invoke Reshape Op
+    LodTensorArray2LodTensorVector(local_scope, base_name, Input("X"), &names);
+    // Invoke concat Op
     auto concat_op = framework::OpRegistry::CreateOp(
         "concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
 
-    concat_op->Run(scope, place);
+    concat_op->Run(local_scope, place);
   }
 };
 

paddle/fluid/platform/dynload/cublas.cc

@@ -32,6 +32,9 @@ CUBLAS_BLAS_ROUTINE_EACH_R2(DEFINE_WRAP);
 CUBLAS_BLAS_ROUTINE_EACH_R3(DEFINE_WRAP);
 #endif
 
+#ifdef CUBLAS_BLAS_ROUTINE_EACH_R4
+CUBLAS_BLAS_ROUTINE_EACH_R4(DEFINE_WRAP);
+#endif
 }  // namespace dynload
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/dynload/cublas.h

@@ -90,23 +90,33 @@ CUBLAS_BLAS_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
 
 // APIs available after CUDA 8.0
 #if CUDA_VERSION >= 8000
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmEx);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasSgemmStridedBatched);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasDgemmStridedBatched);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasCgemmStridedBatched);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasZgemmStridedBatched);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasHgemmStridedBatched);
+#define CUBLAS_BLAS_ROUTINE_EACH_R2(__macro) \
+  __macro(cublasGemmEx);                     \
+  __macro(cublasSgemmStridedBatched);        \
+  __macro(cublasDgemmStridedBatched);        \
+  __macro(cublasCgemmStridedBatched);        \
+  __macro(cublasZgemmStridedBatched);        \
+  __macro(cublasHgemmStridedBatched);
+
+CUBLAS_BLAS_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
 #endif
 
 // APIs available after CUDA 9.0
 #if CUDA_VERSION >= 9000
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasSetMathMode);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGetMathMode);
+#define CUBLAS_BLAS_ROUTINE_EACH_R3(__macro) \
+  __macro(cublasSetMathMode);                \
+  __macro(cublasGetMathMode);
+
+CUBLAS_BLAS_ROUTINE_EACH_R3(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
 #endif
 
+// APIs available after CUDA 9.1
 #if CUDA_VERSION >= 9010
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmBatchedEx);
-DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmStridedBatchedEx);
+#define CUBLAS_BLAS_ROUTINE_EACH_R4(__macro) \
+  __macro(cublasGemmBatchedEx);              \
+  __macro(cublasGemmStridedBatchedEx);
+
+CUBLAS_BLAS_ROUTINE_EACH_R4(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
 #endif
 
 #undef DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP

paddle/testing/paddle_gtest_main.cc

@@ -31,6 +31,11 @@ int main(int argc, char** argv) {
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   new_argv.push_back(
       strdup("--tryfromenv=fraction_of_gpu_memory_to_use,allocator_strategy"));
+#elif __clang__
+  new_argv.push_back(
+      strdup("--tryfromenv=use_mkldnn,initial_cpu_memory_in_"
+             "mb,allocator_strategy"));
+  new_argv.push_back(strdup("--undefok=use_mkldnn,initial_cpu_memory_in_mb"));
 #else
   new_argv.push_back(
       strdup("--tryfromenv=use_pinned_memory,use_mkldnn,initial_cpu_memory_in_"

python/paddle/fluid/__init__.py

@@ -91,6 +91,7 @@ def __bootstrap__():
     """
     import sys
     import os
+    import platform
     from . import core
 
     in_test = 'unittest' in sys.modules
@@ -110,14 +111,17 @@ def __bootstrap__():
         print('PLEASE USE OMP_NUM_THREADS WISELY.', file=sys.stderr)
 
     os.environ['OMP_NUM_THREADS'] = str(num_threads)
-
+    sysstr = platform.system()
     read_env_flags = [
-        'use_pinned_memory', 'check_nan_inf', 'benchmark', 'eager_delete_scope',
-        'use_mkldnn', 'use_ngraph', 'initial_cpu_memory_in_mb',
-        'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
-        "dist_threadpool_size", 'eager_delete_tensor_gb', 'allocator_strategy',
+        'check_nan_inf', 'benchmark', 'eager_delete_scope', 'use_mkldnn',
+        'use_ngraph', 'initial_cpu_memory_in_mb', 'init_allocated_mem',
+        'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size",
+        'eager_delete_tensor_gb', 'allocator_strategy',
         'reader_queue_speed_test_mode', 'print_sub_graph_dir'
     ]
+    if 'Darwin' not in sysstr:
+        read_env_flags.append('use_pinned_memory')
+
     if os.name != 'nt':
         read_env_flags.append('warpctc_dir')
         read_env_flags.append('cpu_deterministic')
@@ -129,6 +133,7 @@ def __bootstrap__():
         read_env_flags.append('rpc_send_thread_num')
         read_env_flags.append('rpc_get_thread_num')
         read_env_flags.append('rpc_prefetch_thread_num')
+        read_env_flags.append('rpc_disable_reuse_port')
 
     if core.is_compiled_with_cuda():
         read_env_flags += [

python/paddle/fluid/contrib/utils/__init__.py

@@ -13,8 +13,10 @@
 # limitations under the License.
 
 from __future__ import print_function
-
+from . import lookup_table_utils
+from .lookup_table_utils import *
 from . import hdfs_utils
 from .hdfs_utils import *
 
+__all__ = lookup_table_utils.__all__
 __all__ = hdfs_utils.__all__

python/paddle/fluid/contrib/utils/lookup_table_utils.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 os
+import time
+import logging
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid import io
+from paddle.fluid import Program
+
+__all__ = [
+    "load_inference_model", "load_persistable_vars",
+    "convert_dist_to_sparse_program"
+]
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+_logger = logging.getLogger("lookup_table_utils")
+_logger.setLevel(logging.INFO)
+
+model_filename = "__model__"
+lookup_table_dir = "__lookup_table__"
+
+
+def __insert_lookup_sparse_table_op(main_program, idx, ids, w, out):
+    main_program.global_block()._insert_op(
+        index=idx,
+        type="lookup_sparse_table",
+        inputs={"Ids": [ids],
+                "W": [w]},
+        outputs={"Out": [out]},
+        attrs={
+            "is_distributed": False,
+            "is_sparse": True,
+            "grad_inplace": False
+        })
+
+
+def __get_prefetch_op_tuples(main_program):
+    # current lookup tables op is split_ids->prefetch->merge_ids
+    prefetch_op_tuples = None
+    op_types = [op.type for op in main_program.global_block().ops]
+
+    for i in range(len(op_types)):
+        if op_types[i] == "prefetch":
+            if op_types[i - 1] == "split_ids" and op_types[i +
+                                                           1] == "merge_ids":
+                split_ids_op_id = i - 1
+                split_ids_inputs = main_program.global_block().ops[i - 1].input(
+                    "Ids")
+                prefetch_op_inputs = main_program.global_block().ops[i].input(
+                    "X")
+                prefetch_op_outputs = main_program.global_block().ops[i].output(
+                    "Out")
+                merge_ids_outputs = main_program.global_block().ops[
+                    i + 1].output("Out")
+
+                need_delete_vars = []
+                need_delete_vars.extend(prefetch_op_inputs)
+                need_delete_vars.extend(prefetch_op_outputs)
+
+                prefetch_op_tuples = (split_ids_op_id, split_ids_inputs,
+                                      merge_ids_outputs, need_delete_vars)
+                break
+    return prefetch_op_tuples
+
+
+def convert_dist_to_sparse_program(main_program):
+    if not main_program._distributed_lookup_table:
+        _logger.warn(
+            "There are no distributed lookup tables need to be converted")
+        return
+
+    # create table param and grad var in pserver program
+    origin_emb_var = "{}.origin".format(main_program._distributed_lookup_table)
+    emb_var = main_program._distributed_lookup_table
+    main_program.global_block()._rename_var(emb_var, origin_emb_var)
+    origin_param_var = main_program.global_block().vars[origin_emb_var]
+
+    param_var = main_program.global_block().create_var(
+        name=emb_var,
+        shape=origin_param_var.shape,
+        dtype=origin_param_var.dtype,
+        type=core.VarDesc.VarType.SELECTED_ROWS,
+        persistable=True)
+    # parameter must be selected rows
+    param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+    main_program._sync_with_cpp()
+
+    prefetch_op_tuples = __get_prefetch_op_tuples(main_program)
+
+    split_ids_id = prefetch_op_tuples[0]
+
+    for idx in range(split_ids_id + 2, split_ids_id - 1, -1):
+        main_program.global_block()._remove_op(idx)
+    main_program.desc.flush()
+
+    in_out_pairs = zip(prefetch_op_tuples[1], prefetch_op_tuples[2])
+
+    for in_out_pair in in_out_pairs:
+        idx = split_ids_id
+        ids = main_program.global_block().vars[in_out_pair[0]]
+        out = main_program.global_block().vars[in_out_pair[1]]
+        __insert_lookup_sparse_table_op(main_program, idx, ids, param_var, out)
+        main_program.desc.flush()
+    return main_program
+
+
+def load_persistable_vars(executor, dirname, program, lookup_table_var):
+    def _is_checkpoint_var(exclude_fluid_vars=None):
+        """
+        the checkpoint will not save or load all the variables.
+        var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
+
+        : param var(Variable)
+        """
+
+        if exclude_fluid_vars is None:
+            exclude_fluid_vars = []
+
+        def is_valid(var):
+            if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
+                    var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
+                    var.desc.type() == core.VarDesc.VarType.RAW:
+                return False
+            # @GRAD are named for gradient variables, checkpoint will not save it.
+            if "@GRAD" in var.name:
+                return False
+            # .trainer_ are named for distribute train variables, checkpoint will not save it.
+            if ".trainer_" in var.name:
+                return False
+
+            # .block is named for distribute train variables, checkpoint will not save it.
+            if ".block" in var.name:
+                return False
+
+            if "tmp_" in var.name:
+                return False
+
+            if var.name in exclude_fluid_vars:
+                return False
+
+            return var.persistable
+
+        return is_valid
+
+    def _load_lookup_table_vars(executor, dirname, main_program,
+                                lookup_table_vars):
+        if not os.path.isdir(dirname):
+            raise ValueError("There is no directory named '%s'", dirname)
+
+        lookup_table_dirname = os.path.join(dirname, lookup_table_dir)
+
+        emb_var_name = lookup_table_vars[0]
+        emb_var = main_program.global_block().var(emb_var_name)
+
+        emb_files = []
+        for emb_name in os.listdir(lookup_table_dirname):
+            if emb_var_name in emb_name:
+                emb_files.append(emb_name)
+
+        convert_program = Program()
+        global_block = convert_program.global_block()
+
+        emb_var = global_block.create_var(
+            name=emb_var.name,
+            shape=emb_var.shape,
+            dtype=emb_var.dtype,
+            type=core.VarDesc.VarType.SELECTED_ROWS,
+            persistable=True)
+        emb_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+
+        sums = []
+
+        for i, emb_file in enumerate(emb_files):
+            var_name = "{}_{}".format(emb_var.name, i)
+            param_var = global_block.create_var(
+                name=var_name,
+                shape=emb_var.shape,
+                dtype=emb_var.dtype,
+                type=core.VarDesc.VarType.SELECTED_ROWS,
+                persistable=True)
+            param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+            global_block.append_op(
+                type='load',
+                inputs={},
+                outputs={'Out': [param_var]},
+                attrs={
+                    'file_path': os.path.join(lookup_table_dirname, var_name)
+                })
+            sums.append(param_var)
+        global_block.append_op(
+            type='sum', inputs={"X": sums}, outputs={'Out': emb_var}, attrs={})
+        global_block.append_op(type='delete_var', inputs={'X': sums})
+        executor.run(convert_program)
+
+    _logger.info("Start Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+    lookup_table_vars = [lookup_table_var]
+
+    io.load_vars(
+        executor,
+        dirname=dirname,
+        main_program=program,
+        predicate=_is_checkpoint_var(lookup_table_vars),
+        filename=None)
+
+    _load_lookup_table_vars(executor, dirname, program, lookup_table_vars)
+
+    _logger.info("Finish Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+
+def load_inference_model(dirname, executor, lookup_table_var_name):
+    if not os.path.isdir(dirname):
+        raise ValueError("There is no directory named '%s'", dirname)
+
+    local_model = os.path.join(dirname, model_filename)
+
+    with open(local_model, "rb") as f:
+        program_desc_str = f.read()
+
+    program = Program.parse_from_string(program_desc_str)
+
+    if not core._is_program_version_supported(program._version()):
+        raise ValueError("Unsupported program version: %d\n" %
+                         program._version())
+
+    # Binary data also need version.
+    load_persistable_vars(executor, dirname, program, lookup_table_var_name)
+
+    feed_target_names = program.desc.get_feed_target_names()
+    fetch_target_names = program.desc.get_fetch_target_names()
+    fetch_targets = [
+        program.global_block().var(name) for name in fetch_target_names
+    ]
+
+    return [program, feed_target_names, fetch_targets]

python/paddle/fluid/framework.py

@@ -1698,6 +1698,7 @@ class Program(object):
 
         p._copy_param_info_from(self)
         p._copy_data_info_from(self)
+        p._copy_dist_param_info_from(self)
         return p
 
     def _prune(self, targets):
@@ -1938,6 +1939,25 @@ class Program(object):
                              "program, with represent the same topology")
         self.global_block()._copy_param_info_from(other.global_block())
 
+    def _copy_dist_param_info_from(self, other):
+        """
+        Copy the information of distributed information from other program.
+
+        Args:
+            other(Program): Other program
+
+        Returns:
+            None
+        """
+        if not isinstance(other, Program):
+            raise TypeError("_copy_dist_param_info_from should be invoked with "
+                            "Program")
+        self._is_distributed = other._is_distributed
+        self._is_chief = other._is_chief
+        self._slice_vars_and_attrs = other._slice_vars_and_attrs
+        self._endpoints = other._endpoints
+        self._distributed_lookup_table = other._distributed_lookup_table
+
     def _copy_data_info_from(self, other):
         """
         Copy the information of data variables from other program.

python/paddle/fluid/io.py

@@ -165,6 +165,7 @@ def save_vars(executor,
 
         save_vars(
             executor,
+            main_program=main_program,
             dirname=dirname,
             vars=list(filter(predicate, main_program.list_vars())),
             filename=filename)
@@ -172,11 +173,18 @@ def save_vars(executor,
         save_program = Program()
         save_block = save_program.global_block()
 
+        if main_program is None:
+            main_program = default_main_program()
+        if not isinstance(main_program, Program):
+            raise TypeError("program should be as Program type or None")
+
         save_var_map = {}
         for each_var in vars:
             # NOTE: don't save the variable which type is RAW
             if each_var.type == core.VarDesc.VarType.RAW:
                 continue
+            if each_var.name == main_program._distributed_lookup_table:
+                continue
             new_var = _clone_var_in_block_(save_block, each_var)
             if filename is None:
                 save_block.append_op(
@@ -198,6 +206,16 @@ def save_vars(executor,
                 outputs={},
                 attrs={'file_path': os.path.join(dirname, filename)})
 
+        # if there is lookup table, the trainer 0 will notify all pserver to save.
+        if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
+            lookup_table_filename = os.path.join(dirname, "__lookup_table__")
+            attrs = {}
+            attrs['epmap'] = main_program._endpoints
+            attrs['dir'] = lookup_table_filename
+            attrs['lookup_table'] = main_program._distributed_lookup_table
+            save_block.append_op(
+                type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
+
         executor.run(save_program)
 
 
@@ -379,11 +397,22 @@ def load_vars(executor,
         load_prog = Program()
         load_block = load_prog.global_block()
 
+        if main_program is None:
+            main_program = default_main_program()
+        if not isinstance(main_program, Program):
+            raise TypeError("program should be as Program type or None")
+
+        load_slice_vars = []
+        for each_var in main_program._slice_vars_and_attrs:
+            load_slice_vars.append(each_var[2].name)
+
         load_var_map = {}
         for each_var in vars:
             assert isinstance(each_var, Variable)
             if each_var.type == core.VarDesc.VarType.RAW:
                 continue
+            if each_var.name in load_slice_vars:
+                continue
             new_var = _clone_var_in_block_(load_block, each_var)
             if filename is None:
                 load_block.append_op(
@@ -406,9 +435,6 @@ def load_vars(executor,
                 attrs={'file_path': os.path.join(dirname, filename)})
         executor.run(load_prog)
 
-        if main_program is None:
-            main_program = default_main_program()
-
         # load slice vars on pserver, if have it.
         _load_slice_up_vars(executor, dirname,
                             main_program._slice_vars_and_attrs)
@@ -618,13 +644,6 @@ def save_inference_model(dirname,
     if main_program is None:
         main_program = default_main_program()
 
-    # if there is lookup table, the trainer 0 will notify all pserver to save.
-    if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
-        lookup_table_filename = os.path.join(dirname, "__lookup_table__")
-        _save_lookup_tables_by_notify(executor, lookup_table_filename,
-                                      main_program._distributed_lookup_table,
-                                      main_program._endpoints)
-
     # when a pserver and a trainer running on the same machine, mkdir may conflict
     try:
         os.makedirs(dirname)
@@ -642,6 +661,9 @@ def save_inference_model(dirname,
     # it can only be loaded for inference directly. If it's false, the whole
     # original program and related meta are saved so that future usage can be
     # more flexible.
+
+    origin_program = main_program.clone()
+
     if export_for_deployment:
         main_program = main_program.clone()
         global_block = main_program.global_block()
@@ -666,8 +688,11 @@ def save_inference_model(dirname,
         with open(model_basename + ".main_program", "wb") as f:
             f.write(main_program.desc.serialize_to_string())
 
+    main_program._copy_dist_param_info_from(origin_program)
+
     if params_filename is not None:
         params_filename = os.path.basename(params_filename)
+
     save_persistables(executor, dirname, main_program, params_filename)
 
 
@@ -897,6 +922,9 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
         slice_var = var_tuple[2]
         end = start + slice_var.shape[0]
 
+        orig_var_name = orig_var.name
+        orig_var.name = "{}.origin".format(orig_var_name)
+
         clone_orig_var = load_block.create_var(
             name=orig_var.name,
             type=orig_var.type,
@@ -915,7 +943,7 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
             type='load',
             inputs={},
             outputs={'Out': [clone_orig_var]},
-            attrs={'file_path': os.path.join(dirname, clone_orig_var.name)})
+            attrs={'file_path': os.path.join(dirname, orig_var_name)})
         load_block.append_op(
             type="slice",
             inputs={'Input': clone_orig_var},
@@ -924,6 +952,7 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
                    'starts': [start],
                    'ends': [end]})
         need_delete_vars.append(clone_orig_var)
+
     load_block.append_op(
         type='delete_var',
         inputs={'X': need_delete_vars}, )

python/paddle/fluid/layers/control_flow.py

@@ -896,9 +896,10 @@ def array_to_lod_tensor(x, table):
 
 def increment(x, value=1.0, in_place=True):
     """
-    This function performs an operation that increments each value in the
+    This function performs an operation that increments the value in the
     input :math:`x` by an amount: :math:`value` as mentioned in the input
-    parameter. This operation is performed in-place by default.
+    parameter. This operation is performed in-place by default. Notice that
+    the number of elements in :math:`x` must be equal to 1.
 
     Args:
         x (Variable|list): The tensor that has the input values.
@@ -911,7 +912,8 @@ def increment(x, value=1.0, in_place=True):
     Examples:
         .. code-block:: python
 
-          data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
+          data = fluid.layers.data(name='data', shape=[1], dtype='float32',
+                                   append_batch_size=False)
           data = fluid.layers.increment(x=data, value=3.0, in_place=True)
     """
     helper = LayerHelper("increment", **locals())

python/paddle/fluid/layers/nn.py

@@ -6972,14 +6972,14 @@ def prelu(x, mode, param_attr=None, name=None):
     """
     Equation:
 
-        y = \max(0, x) + alpha \min(0, x)
+        y = \max(0, x) + alpha * \min(0, x)
 
     Args:
         x (Variable): The input tensor.
         param_attr(ParamAttr|None): The parameter attribute for the learnable
                        weight (alpha).
-        mode (string): The mode for weight sharing
-		       all: all elements share same weight
+        mode (string): The mode for weight sharing. It supports all, channel
+                       and element. all: all elements share same weight
                        channel:elements in a channel share same weight
                        element:each element has a weight
         name(str|None): A name for this layer(optional). If set None, the layer

python/paddle/fluid/tests/book/test_image_classification.py

@@ -239,7 +239,7 @@ def infer(use_cuda, save_dirname=None):
         assert len(results[0]) == len(transpiler_results[0])
         for i in range(len(results[0])):
             np.testing.assert_almost_equal(
-                results[0][i], transpiler_results[0][i], decimal=5)
+                results[0][i], transpiler_results[0][i], decimal=4)
 
         print("infer results: ", results[0])
 

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

@@ -145,10 +145,15 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
         lod.append(nmsed_num)
         if nmsed_num == 0: continue
 
+        tmp_det_out = []
         for c, indices in nmsed_outs.items():
             for idx in indices:
                 xmin, ymin, xmax, ymax = boxes[n][idx][:]
-                det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
+                tmp_det_out.append(
+                    [c, scores[n][c][idx], xmin, ymin, xmax, ymax])
+        sorted_det_out = sorted(
+            tmp_det_out, key=lambda tup: tup[0], reverse=False)
+        det_outs.extend(sorted_det_out)
 
     return det_outs, lod
 

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -644,6 +644,9 @@ in a single call.")
             else:
                 recv_inputs.append(single_trainer_var)
 
+        self._slice_params_and_optimizes = self._get_slice_vars_and_attrs(
+            endpoint)
+
         # step 3
         # Create a union-find data structure from optimize ops,
         # If two ops are connected, we could add these two ops
@@ -766,7 +769,7 @@ in a single call.")
                                                grad_to_block_id, merged_var,
                                                lr_ops)
 
-# dedup grad to ids list
+        # dedup grad to ids list
         grad_to_block_id = list(set(grad_to_block_id))
         # append global ops
         if global_ops:
@@ -827,8 +830,8 @@ in a single call.")
             attrs=attrs)
 
         # add distributed attrs
-        pserver_program._slice_vars_and_attrs = self._get_slice_vars_and_attrs(
-            endpoint)
+        pserver_program._slice_vars_and_attrs = list(
+            self._slice_params_and_optimizes.values())
 
         pserver_program._sync_with_cpp()
         # save pserver program to generate pserver side startup relatively.
@@ -941,12 +944,12 @@ to transpile() call.")
                     outputs={"Out": startup_tmpvar})
 
         # add slice vars
-        s_prog._slice_vars_and_attrs = self._get_slice_vars_and_attrs(endpoint)
+        s_prog._slice_vars_and_attrs = pserver_program._slice_vars_and_attrs
 
         return s_prog
 
     def _get_slice_vars_and_attrs(self, endpoint):
-        slice_vars_and_attrs = []
+        slice_vars_and_attrs = {}
         block_suffix = "block"
         for param in self.param_grad_ep_mapping[endpoint]["params"]:
             orig_var_name, block_name, _ = self._get_varname_parts(param.name)
@@ -960,8 +963,7 @@ to transpile() call.")
             slice_vars = self.param_var_mapping[orig_var_name]
             for slice_var in slice_vars[:block_idx]:
                 skip_dim0 += slice_var.shape[0]
-            slice_vars_and_attrs.append([orig_var, skip_dim0, param])
-
+            slice_vars_and_attrs[param.name] = [orig_var, skip_dim0, param]
         return slice_vars_and_attrs
 
     # ====================== private transpiler functions =====================
@@ -1662,10 +1664,10 @@ to transpile() call.")
             if key in ["Param", "Grad", "LearningRate"]:
                 continue
             var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
+            param_var = new_inputs["Param"]
             # update accumulator variable shape
-            param_shape = new_inputs["Param"].shape
-            new_shape = self._get_optimizer_input_shape(opt_op.type, key,
-                                                        var.shape, param_shape)
+            new_shape = self._get_optimizer_input_shape(
+                opt_op.type, key, var.shape, param_var.shape)
             tmpvar = pserver_block.create_var(
                 name=var.name,
                 persistable=var.persistable,
@@ -1673,6 +1675,13 @@ to transpile() call.")
                 shape=new_shape)
             new_inputs[key] = tmpvar
 
+            # var shape been changed
+            if new_shape != var.shape:
+                slice_var_args = self._slice_params_and_optimizes[
+                    param_var.name]
+                self._slice_params_and_optimizes[
+                    var.name] = [var, slice_var_args[1], tmpvar]
+
         # change output's ParamOut variable
         outputs = self._get_output_map_from_op(
             self.origin_program.global_block().vars, opt_op)