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

Dockerfile

@@ -24,7 +24,7 @@ COPY ./paddle/scripts/docker/root/ /root/
 
 RUN apt-get update && \
     apt-get install -y --allow-downgrades \
-    git python-pip python-dev openssh-server bison \
+    git python-pip python-dev python-opencv openssh-server bison \
     libnccl2=2.1.2-1+cuda8.0 libnccl-dev=2.1.2-1+cuda8.0 \
     wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
     curl sed grep graphviz libjpeg-dev zlib1g-dev  \
@@ -76,8 +76,7 @@ RUN easy_install -U pip && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark
 
 RUN pip install pre-commit 'ipython==5.3.0' && \
-    pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
-    pip install opencv-python
+    pip install 'ipykernel==4.6.0' 'jupyter==1.0.0'
 
 #For docstring checker
 RUN pip install pylint pytest astroid isort

benchmark/fluid/Dockerfile

 FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
-RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop
+RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop python-opencv
 RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/libnccl.so
 RUN pip install -U pip
-RUN pip install -U kubernetes opencv-python paddlepaddle
+RUN pip install -U kubernetes paddlepaddle
 
 # IMPORTANT:
 # Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.

benchmark/fluid/fluid_benchmark.py

@@ -69,6 +69,11 @@ def parse_args():
         type=int,
         default=1,
         help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
+    parser.add_argument(
+        '--cpus',
+        type=int,
+        default=1,
+        help='If cpus > 1, will use ParallelDo to run, else use Executor.')
     parser.add_argument(
         '--data_set',
         type=str,
@@ -85,8 +90,8 @@ def parse_args():
         help='If set, use nvprof for CUDA.')
     parser.add_argument(
         '--no_test',
-        action='store_false',
-        help='If set, test the testset during training.')
+        action='store_true',
+        help='If set, do not test the testset during training.')
     parser.add_argument(
         '--memory_optimize',
         action='store_true',
@@ -229,9 +234,9 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
             print("Pass: %d, Iter: %d, Loss: %f\n" %
                   (pass_id, iters, np.mean(train_losses)))
         print_train_time(start_time, time.time(), num_samples)
-        print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses)))
+        print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses))),
         # evaluation
-        if not args.no_test and batch_acc != None:
+        if not args.no_test and batch_acc:
             pass_test_acc = test(exe, infer_prog, test_reader, feeder,
                                  batch_acc)
             print(", Test Accuracy: %f" % pass_test_acc)
@@ -310,7 +315,7 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
                 print("Pass %d, batch %d, loss %s" %
                       (pass_id, batch_id, np.array(loss)))
         print_train_time(start_time, time.time(), num_samples)
-        if not args.no_test and batch_acc != None:
+        if not args.no_test and batch_acc:
             test_acc = test(startup_exe, infer_prog, test_reader, feeder,
                             batch_acc)
             print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))

benchmark/fluid/models/mnist.py

@@ -69,15 +69,30 @@ def get_model(args):
     images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
+    if args.device == 'CPU' and args.cpus > 1:
+        places = fluid.layers.get_places(args.cpus)
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            predict = cnn_model(pd.read_input(images))
+            label = pd.read_input(label)
+            cost = fluid.layers.cross_entropy(input=predict, label=label)
+            avg_cost = fluid.layers.mean(x=cost)
+            batch_acc = fluid.layers.accuracy(input=predict, label=label)
+
+            pd.write_output(avg_cost)
+            pd.write_output(batch_acc)
+
+        avg_cost, batch_acc = pd()
+        avg_cost = fluid.layers.mean(avg_cost)
+        batch_acc = fluid.layers.mean(batch_acc)
+    else:
         # Train program
         predict = cnn_model(images)
         cost = fluid.layers.cross_entropy(input=predict, label=label)
         avg_cost = fluid.layers.mean(x=cost)
 
         # Evaluator
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-    batch_acc = fluid.layers.accuracy(
-        input=predict, label=label, total=batch_size_tensor)
+        batch_acc = fluid.layers.accuracy(input=predict, label=label)
 
     # inference program
     inference_program = fluid.default_main_program().clone()

benchmark/fluid/models/resnet.py

@@ -132,18 +132,33 @@ def get_model(args):
 
     input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-    predict = model(input, class_dim)
+
+    if args.device == 'CPU' and args.cpus > 1:
+        places = fluid.layers.get_places(args.cpus)
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            predict = model(pd.read_input(input), class_dim)
+            label = pd.read_input(label)
             cost = fluid.layers.cross_entropy(input=predict, label=label)
             avg_cost = fluid.layers.mean(x=cost)
+            batch_acc = fluid.layers.accuracy(input=predict, label=label)
 
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-    batch_acc = fluid.layers.accuracy(
-        input=predict, label=label, total=batch_size_tensor)
+            pd.write_output(avg_cost)
+            pd.write_output(batch_acc)
+
+        avg_cost, batch_acc = pd()
+        avg_cost = fluid.layers.mean(avg_cost)
+        batch_acc = fluid.layers.mean(batch_acc)
+    else:
+        predict = model(input, class_dim)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+        batch_acc = fluid.layers.accuracy(input=predict, label=label)
 
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):
         inference_program = fluid.io.get_inference_program(
-            target_vars=[batch_acc, batch_size_tensor])
+            target_vars=[batch_acc])
 
     optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
 

benchmark/fluid/models/stacked_dynamic_lstm.py

@@ -101,9 +101,8 @@ def get_model(args):
     loss = fluid.layers.mean(x=loss)
 
     # add acc
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
     batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
-                shape=[1], dtype='int64'), total=batch_size_tensor)
+                shape=[1], dtype='int64'))
 
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):

cmake/external/grpc.cmake

@@ -45,6 +45,7 @@ ExternalProject_Add(
     #    checkout and clean other dirs under third_party
     # 4. remove .git, and package the directory.
     URL "http://paddlepaddledeps.bj.bcebos.com/grpc-v1.8.x.tar.gz"
+    URL_MD5  "c9c58ee7d0e8929a63155af6a2ecdbd0"
     PREFIX          ${GRPC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CONFIGURE_COMMAND ""

doc/fluid/howto/optimization/host_memory_profiling_cn.md

+## 堆内存分析和优化
+
+计算机程序都可能有内存泄漏的风险。**内存泄漏**一般是由于程序在堆(heap)上分配了内存而没有释放，随着程序的运行占用的内存越来越大，一方面会影响程序的稳定性，可能让运行速度越来越慢，或者造成oom，甚至会影响运行程序的机器的稳定性，造成宕机。
+
+
+目前有很多内存泄漏分析工具，比较经典的有[valgrind](http://valgrind.org/docs/manual/quick-start.html#quick-start.intro), [gperftools](https://gperftools.github.io/gperftools/)。
+
+因为Fluid是用Python驱动C++ core来运行，valgrind直接分析非常困难，需要自己编译debug版本的、带valgrind支持的专用Python版本，而且输出的信息中大部分是Python自己的符号和调用信息，分析起来很困难，另外使用valgrind会让程序运行速度变得非常慢，所以不建议使用。
+
+本教程主要介绍[gperftools](https://gperftools.github.io/gperftools/)的使用。
+
+gperftool主要支持以下四个功能：
+
+- thread-caching malloc
+- heap-checking using tcmalloc
+- heap-profiling using tcmalloc
+- CPU profiler
+
+Paddle也提供了基于gperftool的[CPU性能分析教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/cpu_profiling_cn.md)。
+
+对于堆内存的分析，主要用到thread-caching malloc和heap-profiling using tcmalloc。
+
+## 使用流程
+#### 环境
+本教程基于paddle提供的Docker开发环境paddlepaddle/paddle:latest-dev，基于Ubuntu 16.04.4 LTS环境。
+
+#### 使用流程
+
+- 安装google-perftools
+
+```
+apt-get install libunwind-dev 
+apt-get install google-perftools
+```
+
+- 安装pprof
+
+```
+go get -u github.com/google/pprof
+```
+
+- 设置运行环境
+
+```
+export PPROF_PATH=/root/gopath/bin/pprof
+export PPROF_BINARY_PATH=/root/gopath/bin/pprof
+export LD_PRELOAD=/usr/lib/libtcmalloc.so.4
+```
+
+- 使用heap profile来运行python程序。本质上是周期性的对堆的分配情况做一次快照。
+
+```
+# HEAPPROFILE 设置生成的堆分析文件的目录和文件前缀
+# HEAP_PROFILE_ALLOCATION_INTERVAL 设置每分配多少存储dump一次dump，默认1GB
+env HEAPPROFILE="./perf_log/test.log" HEAP_PROFILE_ALLOCATION_INTERVAL=209715200 python trainer.py
+```
+
+随着程序的运行，会在perf_log这个文件夹下生成很多文件，如下：
+
+```
+-rw-r--r-- 1 root root 1.0M Jun  1 15:00 test.log.0001.heap
+-rw-r--r-- 1 root root 1.0M Jun  1 15:00 test.log.0002.heap
+-rw-r--r-- 1 root root 1.0M Jun  1 15:00 test.log.0003.heap
+-rw-r--r-- 1 root root 1.0M Jun  1 15:00 test.log.0004.heap
+-rw-r--r-- 1 root root 1.0M Jun  1 15:00 test.log.0005.heap
+-rw-r--r-- 1 root root 1.0M Jun  1 15:00 test.log.0006.heap
+```
+
+- 使用pprof对heap文件进行分析。分析有两种模式：
+	- 完整模式。会对当前heap做一个分析，显示目前分配内存一些调用路径。
+
+	```
+	pprof --pdf python test.log.0012.heap
+	```
+	上述命令会生成一个profile00x.pdf的文件，可以直接打开，例如：[memory_cpu_allocator](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_cpu_allocator.pdf)。从下图可以看出，在CPU版本fluid的运行过程中，分配存储最多的模块式CPUAllocator. 而别的模块相对而言分配内存较少，所以被忽略了，这对于分配内存泄漏是很不方便的，因为泄漏是一个缓慢的过程，在这种图中是无法看到的。
+	
+	![result](https://user-images.githubusercontent.com/3048612/40964027-a54033e4-68dc-11e8-836a-144910c4bb8c.png)
+	
+	- Diff模式。可以对两个时刻的heap做diff，把一些内存分配没有发生变化的模块去掉，而把增量部分显示出来。
+	```
+	pprof --pdf --base test.log.0010.heap python test.log.1045.heap
+	```
+	生成的结果为：[`memory_leak_protobuf`](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_leak_protobuf.pdf)
+	
+	从图中可以看出：ProgramDesc这个结构，在两个版本之间增长了200MB+，所以这里有很大的内存泄漏的可能性，最终结果也确实证明是这里造成了泄漏。
+	
+	![result](https://user-images.githubusercontent.com/3048612/40964057-b434d5e4-68dc-11e8-894b-8ab62bcf26c2.png)
+	![result](https://user-images.githubusercontent.com/3048612/40964063-b7dbee44-68dc-11e8-9719-da279f86477f.png)
+	

paddle/contrib/inference/demo/simple_on_word2vec.cc

@@ -65,7 +65,10 @@ void Main(bool use_gpu) {
 }
 
 TEST(demo, word2vec_cpu) { Main(false /*use_gpu*/); }
+
+#ifdef PADDLE_WITH_CUDA
 TEST(demo, word2vec_gpu) { Main(true /*use_gpu*/); }
+#endif
 
 }  // namespace demo
 }  // namespace paddle

paddle/contrib/inference/paddle_inference_api.h

@@ -63,6 +63,7 @@ class PaddlePredictor {
   struct Config;
   PaddlePredictor() = default;
   PaddlePredictor(const PaddlePredictor&) = delete;
+  PaddlePredictor& operator=(const PaddlePredictor&) = delete;
 
   // Predict an record.
   // The caller should be responsible for allocating and releasing the memory of
@@ -76,7 +77,7 @@ class PaddlePredictor {
   virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
 
   // Destroy the Predictor.
-  virtual ~PaddlePredictor() {}
+  virtual ~PaddlePredictor() = default;
 
   // The common configs for all the predictors.
   struct Config {

paddle/contrib/inference/paddle_inference_api_impl.cc

@@ -54,7 +54,8 @@ std::string num2str(T a) {
 }
 }  // namespace
 
-bool NativePaddlePredictor::Init() {
+bool NativePaddlePredictor::Init(
+    std::shared_ptr<framework::Scope> parent_scope) {
   VLOG(3) << "Predictor::init()";
 
   if (config_.use_gpu) {
@@ -62,9 +63,15 @@ bool NativePaddlePredictor::Init() {
   } else {
     place_ = paddle::platform::CPUPlace();
   }
+  if (parent_scope) {
+    scope_ = parent_scope;
+    sub_scope_ = &(parent_scope->NewScope());
+  } else {
     paddle::framework::InitDevices(false);
-  executor_.reset(new paddle::framework::Executor(place_));
     scope_.reset(new paddle::framework::Scope());
+  }
+
+  executor_.reset(new paddle::framework::Executor(place_));
 
   // Initialize the inference program
   if (!config_.model_dir.empty()) {
@@ -83,13 +90,8 @@ bool NativePaddlePredictor::Init() {
     return false;
   }
   ctx_ = executor_->Prepare(*inference_program_, 0);
-
-  // Create temporary variables first, so that the first batch do not need to
-  // create variables in the runtime. This is the logics of the old inference
-  // API.
-  // TODO(Superjomn) this should be modified when `Clone` is valid for
-  // multi-thread application.
-  executor_->CreateVariables(*inference_program_, scope_.get(), 0);
+  executor_->CreateVariables(
+      *inference_program_, sub_scope_ ? sub_scope_ : scope_.get(), 0);
 
   // Get the feed_target_names and fetch_target_names
   feed_target_names_ = inference_program_->GetFeedTargetNames();
@@ -97,6 +99,13 @@ bool NativePaddlePredictor::Init() {
   return true;
 }
 
+NativePaddlePredictor::~NativePaddlePredictor() {
+  if (sub_scope_) {
+    PADDLE_ENFORCE_NOT_NULL(scope_, "Should have parent scope!");
+    scope_->DeleteScope(sub_scope_);
+  }
+};
+
 bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
                                 std::vector<PaddleTensor> *output_data) {
   VLOG(3) << "Predictor::predict";
@@ -121,8 +130,9 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
   }
   // Run the inference program
   // if share variables, we need not create variables
-  executor_->RunPreparedContext(ctx_.get(),
-                                scope_.get(),
+  executor_->RunPreparedContext(
+      ctx_.get(),
+      sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
       &feed_targets,
       &fetch_targets,
       false /* don't create variable eatch time */);
@@ -138,7 +148,7 @@ std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
   VLOG(3) << "Predictor::clone";
   std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
 
-  if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init()) {
+  if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(scope_)) {
     LOG(ERROR) << "fail to call Init";
     return nullptr;
   }
@@ -266,7 +276,7 @@ CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
   }
 
   std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
-  if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init()) {
+  if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;
   }
   return std::move(predictor);

paddle/contrib/inference/paddle_inference_api_impl.h

@@ -34,14 +34,15 @@ class NativePaddlePredictor : public PaddlePredictor {
   explicit NativePaddlePredictor(const NativeConfig &config)
       : config_(config) {}
 
-  bool Init();
+  // will only create sub scope if have global scope
+  bool Init(std::shared_ptr<framework::Scope> parent_scope);
 
   bool Run(const std::vector<PaddleTensor> &inputs,
            std::vector<PaddleTensor> *output_data) override;
 
   std::unique_ptr<PaddlePredictor> Clone() override;
 
-  ~NativePaddlePredictor() override{};
+  ~NativePaddlePredictor() override;
 
  private:
   bool SetFeed(const std::vector<PaddleTensor> &input_datas,
@@ -52,11 +53,13 @@ class NativePaddlePredictor : public PaddlePredictor {
   NativeConfig config_;
   platform::Place place_;
   std::unique_ptr<framework::Executor> executor_;
-  std::unique_ptr<framework::Scope> scope_;
+  std::shared_ptr<framework::Scope> scope_;
   std::unique_ptr<framework::ExecutorPrepareContext> ctx_;
   std::unique_ptr<framework::ProgramDesc> inference_program_;
   std::vector<std::string> feed_target_names_;
   std::vector<std::string> fetch_target_names_;
+  // Do not use unique_ptr, use parent scope to delete
+  framework::Scope *sub_scope_{nullptr};
 };
 
 }  // namespace paddle

paddle/fluid/framework/CMakeLists.txt

@@ -87,7 +87,7 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
 framework_proto glog lod_rank_table feed_fetch_method)
 
 
-cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor)
+cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

paddle/fluid/framework/details/CMakeLists.txt

@@ -36,5 +36,6 @@ cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_ha
         device_context broadcast_op_handle)
 cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
         device_context gather_op_handle)
+cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor)
 #cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
 #        device_context reduce_op_handle )

paddle/fluid/framework/details/execution_strategy.h

@@ -22,6 +22,7 @@ struct ExecutionStrategy {
   size_t num_threads_{0};
   bool use_event_{true};
   bool allow_op_delay_{false};
+  size_t num_iteration_per_drop_scope_{100};
 };
 
 }  //  namespace details

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.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/details/scope_buffered_ssa_graph_executor.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/executor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor(
+    ExecutionStrategy strategy, std::vector<Scope *> local_scopes,
+    std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
+    std::unique_ptr<SSAGraphExecutor> &&underlying_executor)
+    : strategy_(std::move(strategy)),
+      underlying_executor_(std::move(underlying_executor)),
+      local_scopes_(std::move(local_scopes)),
+      var_infos_(std::move(var_infos)),
+      places_(std::move(places)) {}
+
+FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  if (drop_scope_counter_ == 0) {
+    // Create local scopes.
+    for (auto it = local_scopes_.rbegin(); it != local_scopes_.rend(); ++it) {
+      auto &scope = *it;
+      Scope &local_scope = scope->NewScope();
+      *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
+          &local_scope;
+
+      for (auto &info : var_infos_) {
+        if (scope->FindVar(info.name_) != nullptr) {
+          continue;
+        }
+
+        if (info.persistable_) {  // Persistable
+          InitializeVariable(scope->Var(info.name_), info.type_);
+        } else {
+          InitializeVariable(local_scope.Var(info.name_), info.type_);
+        }
+      }
+    }
+  }
+
+  auto fetch_data = underlying_executor_->Run(fetch_tensors);
+  drop_scope_counter_ += 1;
+  if (!fetch_tensors.empty() ||
+      drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
+    drop_scope_counter_ = 0;
+    // Wait All computational streams
+    for (auto p : places_) {
+      platform::DeviceContextPool::Instance().Get(p)->Wait();
+    }
+    for (auto &scope : local_scopes_) {
+      auto &local_scope =
+          *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
+      scope->DeleteScope(local_scope);
+    }
+  }
+  return fetch_data;
+}
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.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 <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/execution_strategy.h"
+#include "paddle/fluid/framework/details/ssa_graph_executor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/platform/place.h"
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct VariableInfo {
+  std::string name_;
+  proto::VarType::Type type_;
+  bool persistable_;
+};
+
+class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  ScopeBufferedSSAGraphExecutor(
+      ExecutionStrategy strategy, std::vector<Scope*> local_scopes,
+      std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
+      std::unique_ptr<SSAGraphExecutor>&& underlying_executor);
+  FeedFetchList Run(const std::vector<std::string>& fetch_tensors) override;
+
+ private:
+  size_t drop_scope_counter_{0};
+
+  ExecutionStrategy strategy_;
+  std::unique_ptr<SSAGraphExecutor> underlying_executor_;
+  std::vector<Scope*> local_scopes_;
+  std::vector<VariableInfo> var_infos_;
+  std::vector<platform::Place> places_;
+};
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/ssa_graph_executor.cc

@@ -17,10 +17,6 @@
 namespace paddle {
 namespace framework {
 namespace details {
-
-SSAGraphExecutor::SSAGraphExecutor(std::unique_ptr<SSAGraph> &&graph)
-    : graph_(std::move(graph)) {}
-
 SSAGraphExecutor::~SSAGraphExecutor() {}
 
 }  // namespace details

paddle/fluid/framework/details/ssa_graph_executor.h

@@ -28,15 +28,11 @@ class SSAGraphExecutor {
   DISABLE_COPY_AND_ASSIGN(SSAGraphExecutor);
 
  public:
-  // Steal graph inside
-  explicit SSAGraphExecutor(std::unique_ptr<SSAGraph> &&graph);
+  SSAGraphExecutor() {}
 
   virtual ~SSAGraphExecutor();
 
   virtual FeedFetchList Run(const std::vector<std::string> &fetch_tensors) = 0;
-
- protected:
-  std::unique_ptr<SSAGraph> graph_;
 };
 }  // namespace details
 }  // namespace framework

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -21,7 +21,7 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
     const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
     const std::vector<platform::Place> &places,
     std::unique_ptr<SSAGraph> &&graph)
-    : SSAGraphExecutor(std::move(graph)),
+    : graph_(std::move(graph)),
       pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
                                        : nullptr),
       local_scopes_(local_scopes),
@@ -189,7 +189,9 @@ void ThreadedSSAGraphExecutor::RunOp(
     BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
   auto op_run = [ready_var_q, op, this] {
     try {
+      if (VLOG_IS_ON(10)) {
         VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
+      }
       op->Run(strategy_.use_event_);
       VLOG(10) << op << " " << op->Name() << " Done ";
       running_ops_--;

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -51,6 +51,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
              details::OpHandleBase *op);
 
  private:
+  std::unique_ptr<SSAGraph> graph_;
   std::unique_ptr<::ThreadPool> pool_;
   std::vector<Scope *> local_scopes_;
   std::vector<platform::Place> places_;

paddle/fluid/framework/parallel_executor.cc

@@ -23,6 +23,7 @@ limitations under the License. */
 #endif
 
 #include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
+#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -42,8 +43,6 @@ class ParallelExecutorPrivate {
 #ifdef PADDLE_WITH_CUDA
   std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
 #endif
-
-  std::vector<std::tuple<std::string, proto::VarType::Type, bool>> var_types_;
   bool own_local_scope;
 };
 
@@ -92,9 +91,18 @@ ParallelExecutor::ParallelExecutor(
       local_scopes.empty()) {  // Is CUDA
     BCastParamsToGPUs(bcast_vars);
   }
-// Startup Program has been run. All local scopes has correct parameters.
+  // Startup Program has been run. All local scopes has correct parameters.
 
-// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
+  // Step 2. Create vars in each scope;
+  std::vector<details::VariableInfo> var_infos;
+  for (auto *var : main_program.Block(0).AllVars()) {
+    var_infos.emplace_back();
+    var_infos.back().name_ = var->Name();
+    var_infos.back().type_ = var->GetType();
+    var_infos.back().persistable_ = var->Persistable();
+  }
+
+// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
 // ncclOp
 #ifdef PADDLE_WITH_CUDA
   details::MultiDevSSAGraphBuilder builder(
@@ -105,16 +113,15 @@ ParallelExecutor::ParallelExecutor(
                                            params, member_->local_scopes_,
                                            build_strategy);
 #endif
+
   auto graph = builder.Build(main_program);
 
   member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
       exec_strategy, member_->local_scopes_, places, std::move(graph)));
 
-  // Step 3. Create vars in each scope;
-  for (auto *var : main_program.Block(0).AllVars()) {
-    member_->var_types_.emplace_back(var->Name(), var->GetType(),
-                                     var->Persistable());
-  }
+  member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
+      exec_strategy, member_->local_scopes_, std::move(var_infos),
+      member_->places_, std::move(member_->executor_)));
 }
 
 void ParallelExecutor::BCastParamsToGPUs(
@@ -169,42 +176,9 @@ void ParallelExecutor::BCastParamsToGPUs(
 void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
                            const std::string &fetched_var_name) {
   platform::RecordBlock b(0);
-  // Create local scopes.
-  for (auto it = member_->local_scopes_.rbegin();
-       it != member_->local_scopes_.rend(); ++it) {
-    auto &scope = *it;
-    Scope &local_scope = scope->NewScope();
-    *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
-        &local_scope;
-
-    for (auto &name_type_pair : member_->var_types_) {
-      if (scope->FindVar(std::get<0>(name_type_pair)) != nullptr) {
-        continue;
-      }
-
-      if (std::get<2>(name_type_pair)) {  // Persistable
-        InitializeVariable(scope->Var(std::get<0>(name_type_pair)),
-                           std::get<1>(name_type_pair));
-      } else {
-        InitializeVariable(local_scope.Var(std::get<0>(name_type_pair)),
-                           std::get<1>(name_type_pair));
-      }
-    }
-  }
-
   auto fetch_data = member_->executor_->Run(fetch_tensors);
   *member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
       fetch_data;
-
-  // Wait All computational streams
-  for (auto p : member_->places_) {
-    platform::DeviceContextPool::Instance().Get(p)->Wait();
-  }
-  for (auto &scope : member_->local_scopes_) {
-    auto &local_scope =
-        *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
-    scope->DeleteScope(local_scope);
-  }
 }
 
 void ParallelExecutor::FeedTensorsIntoLocalScopes(

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -15,3 +15,9 @@ cc_test(test_subgraph_splitter
         DEPS analysis paddle_fluid tensor
         ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
 set_tests_properties(test_subgraph_splitter PROPERTIES DEPENDS test_word2vec)
+
+cc_test(test_dfg_graphviz_draw_pass
+        SRCS dfg_graphviz_draw_pass_tester.cc
+        DEPS analysis
+        ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
+set_tests_properties(test_dfg_graphviz_draw_pass PROPERTIES DEPENDS test_word2vec)

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.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. */
+
+/*
+ * This file create an DFG_GraphvizDrawPass which helps to draw a data flow
+ * graph's structure using graphviz.
+ */
+
+#pragma once
+
+#include <fstream>
+#include <string>
+#include "paddle/fluid/inference/analysis/pass.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * Output a dot file and write to some place.
+ */
+class DFG_GraphvizDrawPass : public DataFlowGraphPass {
+ public:
+  DFG_GraphvizDrawPass(const std::string& dir, const std::string& id)
+      : dir_(dir), id_(id) {}
+
+  bool Initialize() override { return Pass::Initialize(); }
+  void Run(DataFlowGraph* graph) override {
+    auto content = Draw(graph);
+    std::ofstream file(GenDotPath());
+    file.write(content.c_str(), content.size());
+    file.close();
+    LOG(INFO) << "draw dot to " << GenDotPath();
+  }
+
+  bool Finalize() override { return Pass::Finalize(); }
+
+  Pass* CreatePrinterPass(std::ostream& os,
+                          const std::string& banner) const override {
+    return nullptr;
+  }
+
+ private:
+  // Path of the dot file to output.
+  std::string GenDotPath() const {
+    return dir_ + "/" + "graph_" + id_ + ".dot";
+  }
+
+  std::string Draw(DataFlowGraph* graph) { return graph->DotString(); }
+
+  std::string dir_;
+  std::string id_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_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/analysis/dfg_graphviz_draw_pass.h"
+
+#include <gtest/gtest.h>
+#include <fstream>
+#include <string>
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) {
+  auto dfg = ProgramDescToDFG(desc);
+  DFG_GraphvizDrawPass pass("./", "test");
+  pass.Initialize();
+  pass.Run(&dfg);
+
+  // test content
+  std::ifstream file("./graph_test.dot");
+  ASSERT_TRUE(file.is_open());
+
+  std::string line;
+  int no{0};
+  while (std::getline(file, line)) {
+    no++;
+  }
+  ASSERT_EQ(no, 82);
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -20,7 +20,7 @@ limitations under the License. */
 #include "paddle/fluid/platform/cudnn_helper.h"
 #include "paddle/fluid/platform/float16.h"
 
-DEFINE_bool(cudnn_algo_use_autotune, true,
+DEFINE_bool(cudnn_deterministic, true,
             "Whether allow using an autotuning algorithm for convolution "
             "operator. The autotuning algorithm may be non-deterministic. If "
             "false, the algorithm is deterministic.");
@@ -272,7 +272,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     auto handle = dev_ctx.cudnn_handle();
     if (input_grad) {
-      if (FLAGS_cudnn_algo_use_autotune) {
+      if (FLAGS_cudnn_deterministic) {
         PADDLE_ENFORCE(
             platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
                 handle, cudnn_filter_desc,
@@ -297,7 +297,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     }
 
     if (filter_grad) {
-      if (FLAGS_cudnn_algo_use_autotune) {
+      if (FLAGS_cudnn_deterministic) {
         PADDLE_ENFORCE(
             platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
                 handle, cudnn_input_desc, cudnn_output_grad_desc,

paddle/fluid/operators/detail/grpc_client.cc

@@ -38,6 +38,25 @@ void RPCClient::Init() {
   if (rpc_client_.get() == nullptr) {
     rpc_client_.reset(new RPCClient());
   }
+  rpc_client_->InitEventLoop();
+}
+
+void RPCClient::InitEventLoop() {
+  // start the client process thread
+  // TODO(wuyi): can make this in a threadpool
+  client_thread_.reset(new std::thread(std::bind(&RPCClient::Proceed, this)));
+}
+
+RPCClient::~RPCClient() {
+  Wait();
+  cq_.Shutdown();
+  {
+    std::lock_guard<std::mutex> guard(chan_mutex_);
+    for (auto& it : channels_) {
+      it.second.reset();
+    }
+  }
+  client_thread_->join();
 }
 
 bool RPCClient::AsyncSendVariable(const std::string& ep,
@@ -204,70 +223,37 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) {
   req_count_++;
 }
 
-bool RPCClient::Wait() {
-  VLOG(3) << "RPCClient begin Wait()"
-          << " req_count_:" << req_count_;
-  if (req_count_ <= 0) {
-    return true;
-  }
-  const size_t kReqCnt = req_count_;
-  bool a[kReqCnt];
-  std::vector<std::future<void>> waits(req_count_);
-  std::mutex mu;
-
-  for (int i = 0; i < req_count_; i++) {
-    waits[i] = framework::AsyncIO([i, &a, &mu, this] {
-      bool ret = Proceed();
-      std::lock_guard<std::mutex> l(mu);
-      a[i] = ret;
-    });
-  }
-
-  for (int i = 0; i < req_count_; i++) {
-    waits[i].wait();
-  }
-
-  int last_req_count = req_count_;
-  req_count_ = 0;
-
-  for (int i = 0; i < last_req_count; i++) {
-    if (!a[i]) {
-      return false;
-    }
-  }
-
-  return true;
+void RPCClient::Wait() {
+  std::unique_lock<std::mutex> lk(sync_mutex_);
+  sync_cond_.wait(lk, [this] { return req_count_ == 0; });
 }
 
-bool RPCClient::Proceed() {
-  void* tag = NULL;
+void RPCClient::Proceed() {
+  void* tag = nullptr;
   bool ok = false;
 
-  // request counts.
-  if (!cq_.Next(&tag, &ok)) {
-    LOG(ERROR) << "Get meets CompletionQueue error";
-    return false;
-  }
-
-  GPR_ASSERT(ok);
-  PADDLE_ENFORCE(tag);
-
-  // TODO(gongwb): add more retries.
+  while (cq_.Next(&tag, &ok)) {
     BaseProcessor* c = static_cast<BaseProcessor*>(tag);
-  if (!c->status_.ok()) {
-    LOG(ERROR) << "proc param error:" << c->var_h_.String()
+    GPR_ASSERT(ok);
+    PADDLE_ENFORCE(c);
+    if (c->status_.ok()) {
+      c->Process();
+    } else {
+      LOG(ERROR) << "var: " << c->var_h_.String()
                  << " grpc error:" << c->status_.error_message();
-    delete c;
-    return false;
     }
-
-  c->Process();
     delete c;
-  return true;
+    {
+      std::lock_guard<std::mutex> lk(sync_mutex_);
+      req_count_--;
+    }
+    sync_cond_.notify_all();
+  }
 }
+
 std::shared_ptr<grpc::Channel> RPCClient::GetChannel(const std::string& ep) {
   // TODO(Yancey1989): make grpc client completely thread-safe
-  std::unique_lock<std::mutex> lock(mutex_);
+  std::lock_guard<std::mutex> guard(chan_mutex_);
   auto it = channels_.find(ep);
   if (it != channels_.end()) {
     return it->second;

paddle/fluid/operators/detail/grpc_client.h

@@ -17,14 +17,17 @@ limitations under the License. */
 #include <time.h>
 
 #include <chrono>              // NOLINT
+#include <condition_variable>  // NOLINT
 #include <ctime>
 #include <functional>
 #include <iostream>
 #include <map>
 #include <mutex>  // NOLINT
 #include <string>
+#include <thread>  // NOLINT
 #include <vector>
 
+#include "grpc++/channel.h"
 #include "grpc++/generic/generic_stub.h"
 #include "grpc++/grpc++.h"
 #include "grpc++/support/byte_buffer.h"
@@ -164,6 +167,7 @@ class FetchBarrierProcessor : public BaseProcessor {
 class RPCClient {
  public:
   RPCClient() {}
+  ~RPCClient();
 
   static RPCClient* GetInstance();
 
@@ -192,19 +196,28 @@ class RPCClient {
   void AsyncSendFetchBarrier(const std::string& ep,
                              int64_t time_out = 600 * 1000);
 
-  bool Wait();
+  void Wait();
+  // InitEventLoop should only be called by Init()
+  void InitEventLoop();
 
  private:
-  bool Proceed();
+  void Proceed();
   std::shared_ptr<grpc::Channel> GetChannel(const std::string& ep);
   // Init is called by GetInstance.
   static void Init();
 
  private:
   grpc::CompletionQueue cq_;
-  std::map<std::string, std::shared_ptr<grpc::Channel>> channels_;
+  std::unordered_map<std::string, std::shared_ptr<grpc::Channel>> channels_;
+  std::unique_ptr<std::thread> client_thread_;
+
+  // mutex for Wait client sync
+  std::mutex sync_mutex_;
+  std::condition_variable sync_cond_;
   std::atomic<int64_t> req_count_{0};
-  std::mutex mutex_;
+
+  // mutex for GetChannel thread safety
+  std::mutex chan_mutex_;
   static std::unique_ptr<RPCClient> rpc_client_;
   static std::once_flag init_flag_;
   DISABLE_COPY_AND_ASSIGN(RPCClient);

paddle/fluid/operators/detail/grpc_server.cc

@@ -68,9 +68,7 @@ class RequestSend final : public RequestBase {
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
         reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
   }
-
   virtual ~RequestSend() {}
-
   std::string GetReqName() override { return request_->Varname(); }
 
   void Process() override {
@@ -82,7 +80,6 @@ class RequestSend final : public RequestBase {
     framework::Variable* outvar = nullptr;
 
     request_handler_->Handle(varname, scope, invar, &outvar);
-
     status_ = FINISH;
     responder_.Finish(reply_, ::grpc::Status::OK,
                       reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
@@ -125,7 +122,6 @@ class RequestGet final : public RequestBase {
       SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
                             &reply_);
     }
-
     status_ = FINISH;
     responder_.Finish(reply_, ::grpc::Status::OK,
                       reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
@@ -170,10 +166,9 @@ class RequestPrefetch final : public RequestBase {
 
     SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
                           &reply_);
-
-    status_ = FINISH;
     responder_.Finish(reply_, ::grpc::Status::OK,
                       reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
+    status_ = FINISH;
   }
 
  protected:

paddle/fluid/operators/detail/grpc_server_test.cc

@@ -113,10 +113,6 @@ void StartServer() {
   std::thread server_thread(
       std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
 
-  // FIXME(gongwb): don't use hard time.
-  sleep(10);
-  LOG(INFO) << "got nccl id and stop server...";
-  g_rpc_service->ShutDown();
   server_thread.join();
 }
 
@@ -127,7 +123,7 @@ TEST(PREFETCH, CPU) {
   std::thread server_thread(StartServer);
   g_rpc_service->WaitServerReady();
 
-  detail::RPCClient client;
+  detail::RPCClient* client = detail::RPCClient::GetInstance();
   int port = g_rpc_service->GetSelectedPort();
   std::string ep = paddle::string::Sprintf("127.0.0.1:%d", port);
 
@@ -141,8 +137,8 @@ TEST(PREFETCH, CPU) {
     std::string in_var_name("ids");
     std::string out_var_name("out");
 
-    client.AsyncPrefetchVariable(ep, ctx, scope, in_var_name, out_var_name);
-    client.Wait();
+    client->AsyncPrefetchVariable(ep, ctx, scope, in_var_name, out_var_name);
+    client->Wait();
     auto var = scope.Var(out_var_name);
     auto value = var->GetMutable<framework::SelectedRows>()->value();
     auto ptr = value.mutable_data<float>(place);
@@ -152,6 +148,7 @@ TEST(PREFETCH, CPU) {
     }
   }
 
+  g_rpc_service->ShutDown();
   server_thread.join();
   LOG(INFO) << "begin reset";
   g_rpc_service.reset(nullptr);

paddle/fluid/operators/detection/box_coder_op.cc

@@ -22,21 +22,21 @@ class BoxCoderOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
                    "Input(PriorBox) of BoxCoderOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("PriorBoxVar"),
-                   "Input(PriorBoxVar) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("TargetBox"),
                    "Input(TargetBox) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("OutputBox"),
                    "Output(OutputBox) of BoxCoderOp should not be null.");
 
     auto prior_box_dims = ctx->GetInputDim("PriorBox");
-    auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
     auto target_box_dims = ctx->GetInputDim("TargetBox");
 
     PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
                       "The rank of Input of PriorBoxVar must be 2");
     PADDLE_ENFORCE_EQ(prior_box_dims[1], 4, "The shape of PriorBox is [N, 4]");
+    if (ctx->HasInput("PriorBoxVar")) {
+      auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
       PADDLE_ENFORCE_EQ(prior_box_dims, prior_box_var_dims);
+    }
 
     auto code_type = GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
     if (code_type == BoxCodeType::kEncodeCenterSize) {
@@ -71,9 +71,11 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
         "of the coordinate system. [xmax, ymax] is the right bottom "
         "coordinate of the anchor box.");
     AddInput("PriorBoxVar",
-             "(Tensor, default Tensor<float>) "
+             "(Tensor, default Tensor<float>, optional) "
              "PriorBoxVar is a 2-D Tensor with shape [M, 4] holds M group "
-             "of variance.");
+             "of variance. PriorBoxVar will set all elements to 1 by "
+             "default.")
+        .AsDispensable();
     AddInput(
         "TargetBox",
         "(LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape "
@@ -131,5 +133,6 @@ width and height.
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker,
                   paddle::framework::EmptyGradOpMaker);
-REGISTER_OP_CPU_KERNEL(box_coder, ops::BoxCoderKernel<float>,
-                       ops::BoxCoderKernel<double>);
+REGISTER_OP_CPU_KERNEL(
+    box_coder, ops::BoxCoderKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::BoxCoderKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/detection/box_coder_op.cu

@@ -48,15 +48,18 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
                           target_box_data[row_idx * len + 1] +
                           (normalized == false);
 
-    output[idx * len] = (target_box_center_x - prior_box_center_x) /
-                        prior_box_width / prior_box_var_data[col_idx * len];
-    output[idx * len + 1] = (target_box_center_y - prior_box_center_y) /
-                            prior_box_height /
-                            prior_box_var_data[col_idx * len + 1];
-    output[idx * len + 2] = log(fabs(target_box_width / prior_box_width)) /
-                            prior_box_var_data[col_idx * len + 2];
-    output[idx * len + 3] = log(fabs(target_box_height / prior_box_height)) /
-                            prior_box_var_data[col_idx * len + 3];
+    output[idx * len] =
+        (target_box_center_x - prior_box_center_x) / prior_box_width;
+    output[idx * len + 1] =
+        (target_box_center_y - prior_box_center_y) / prior_box_height;
+    output[idx * len + 2] = log(fabs(target_box_width / prior_box_width));
+    output[idx * len + 3] = log(fabs(target_box_height / prior_box_height));
+    if (prior_box_var_data) {
+      output[idx * len] /= prior_box_var_data[col_idx * len];
+      output[idx * len + 1] /= prior_box_var_data[col_idx * len + 1];
+      output[idx * len + 2] /= prior_box_var_data[col_idx * len + 2];
+      output[idx * len + 3] /= prior_box_var_data[col_idx * len + 3];
+    }
   }
 }
 
@@ -79,20 +82,31 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
     T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
                             prior_box_data[col_idx * len + 1]) /
                            2;
-
-    T target_box_width = exp(prior_box_var_data[col_idx * len + 2] *
+    T target_box_width, target_box_height;
+    T target_box_center_x, target_box_center_y;
+    if (prior_box_var_data) {
+      target_box_width = exp(prior_box_var_data[col_idx * len + 2] *
                              target_box_data[idx * len + 2]) *
                          prior_box_width;
-    T target_box_height = exp(prior_box_var_data[col_idx * len + 3] *
+      target_box_height = exp(prior_box_var_data[col_idx * len + 3] *
                               target_box_data[idx * len + 3]) *
                           prior_box_height;
-    T target_box_center_x = prior_box_var_data[col_idx * len] *
+      target_box_center_x = prior_box_var_data[col_idx * len] *
                                 target_box_data[idx * len] * prior_box_width +
                             prior_box_center_x;
-    T target_box_center_y = prior_box_var_data[col_idx * len + 1] *
+      target_box_center_y = prior_box_var_data[col_idx * len + 1] *
                                 target_box_data[idx * len + 1] *
                                 prior_box_height +
                             prior_box_center_y;
+    } else {
+      target_box_width = exp(target_box_data[idx * len + 2]) * prior_box_width;
+      target_box_height =
+          exp(target_box_data[idx * len + 3]) * prior_box_height;
+      target_box_center_x =
+          target_box_data[idx * len] * prior_box_width + prior_box_center_x;
+      target_box_center_y = target_box_data[idx * len + 1] * prior_box_height +
+                            prior_box_center_y;
+    }
 
     output[idx * len] = target_box_center_x - target_box_width / 2;
     output[idx * len + 1] = target_box_center_y - target_box_height / 2;
@@ -103,7 +117,7 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
   }
 }
 
-template <typename T>
+template <typename DeviceContext, typename T>
 class BoxCoderCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
@@ -114,6 +128,11 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
     auto* output_box = context.Output<framework::Tensor>("OutputBox");
 
+    const T* prior_box_data = prior_box->data<T>();
+    const T* target_box_data = target_box->data<T>();
+    const T* prior_box_var_data = nullptr;
+    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
+
     if (target_box->lod().size()) {
       PADDLE_ENFORCE_EQ(target_box->lod().size(), 1,
                         "Only support 1 level of LoD.");
@@ -125,10 +144,6 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     int grid = (row * col + block - 1) / block;
     auto& device_ctx = context.cuda_device_context();
 
-    const T* prior_box_data = prior_box->data<T>();
-    const T* prior_box_var_data = prior_box_var->data<T>();
-    const T* target_box_data = target_box->data<T>();
-
     output_box->mutable_data<T>({row, col, len}, context.GetPlace());
     T* output = output_box->data<T>();
 
@@ -150,5 +165,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(box_coder, ops::BoxCoderCUDAKernel<float>,
-                        ops::BoxCoderCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(
+    box_coder,
+    ops::BoxCoderCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::BoxCoderCUDAKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/detection/box_coder_op.h

@@ -28,19 +28,20 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
   PADDLE_THROW("Not support type %s.", type);
 }
 
-template <typename T>
+template <typename DeviceContext, typename T>
 class BoxCoderKernel : public framework::OpKernel<T> {
  public:
-  void EncodeCenterSize(const framework::Tensor& target_box,
-                        const framework::Tensor& prior_box,
-                        const framework::Tensor& prior_box_var,
+  void EncodeCenterSize(const framework::Tensor* target_box,
+                        const framework::Tensor* prior_box,
+                        const framework::Tensor* prior_box_var,
                         const bool normalized, T* output) const {
-    int64_t row = target_box.dims()[0];
-    int64_t col = prior_box.dims()[0];
-    int64_t len = prior_box.dims()[1];
-    auto* target_box_data = target_box.data<T>();
-    auto* prior_box_data = prior_box.data<T>();
-    auto* prior_box_var_data = prior_box_var.data<T>();
+    int64_t row = target_box->dims()[0];
+    int64_t col = prior_box->dims()[0];
+    int64_t len = prior_box->dims()[1];
+    auto* target_box_data = target_box->data<T>();
+    auto* prior_box_data = prior_box->data<T>();
+    const T* prior_box_var_data = nullptr;
+    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
@@ -65,30 +66,35 @@ class BoxCoderKernel : public framework::OpKernel<T> {
                               (normalized == false);
 
         size_t offset = i * col * len + j * len;
-        output[offset] = (target_box_center_x - prior_box_center_x) /
-                         prior_box_width / prior_box_var_data[j * len];
-        output[offset + 1] = (target_box_center_y - prior_box_center_y) /
-                             prior_box_height / prior_box_var_data[j * len + 1];
+        output[offset] =
+            (target_box_center_x - prior_box_center_x) / prior_box_width;
+        output[offset + 1] =
+            (target_box_center_y - prior_box_center_y) / prior_box_height;
         output[offset + 2] =
-            std::log(std::fabs(target_box_width / prior_box_width)) /
-            prior_box_var_data[j * len + 2];
+            std::log(std::fabs(target_box_width / prior_box_width));
         output[offset + 3] =
-            std::log(std::fabs(target_box_height / prior_box_height)) /
-            prior_box_var_data[j * len + 3];
+            std::log(std::fabs(target_box_height / prior_box_height));
+        if (prior_box_var) {
+          output[offset] /= prior_box_var_data[j * len];
+          output[offset + 1] /= prior_box_var_data[j * len + 1];
+          output[offset + 2] /= prior_box_var_data[j * len + 2];
+          output[offset + 3] /= prior_box_var_data[j * len + 3];
         }
       }
     }
-  void DecodeCenterSize(const framework::Tensor& target_box,
-                        const framework::Tensor& prior_box,
-                        const framework::Tensor& prior_box_var,
+  }
+  void DecodeCenterSize(const framework::Tensor* target_box,
+                        const framework::Tensor* prior_box,
+                        const framework::Tensor* prior_box_var,
                         const bool normalized, T* output) const {
-    int64_t row = target_box.dims()[0];
-    int64_t col = prior_box.dims()[0];
-    int64_t len = prior_box.dims()[1];
+    int64_t row = target_box->dims()[0];
+    int64_t col = prior_box->dims()[0];
+    int64_t len = prior_box->dims()[1];
 
-    auto* target_box_data = target_box.data<T>();
-    auto* prior_box_data = prior_box.data<T>();
-    auto* prior_box_var_data = prior_box_var.data<T>();
+    auto* target_box_data = target_box->data<T>();
+    auto* prior_box_data = prior_box->data<T>();
+    const T* prior_box_var_data = nullptr;
+    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
@@ -103,19 +109,32 @@ class BoxCoderKernel : public framework::OpKernel<T> {
         T prior_box_center_y =
             (prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
 
-        T target_box_center_x = prior_box_var_data[j * len] *
+        T target_box_center_x = 0, target_box_center_y = 0;
+        T target_box_width = 0, target_box_height = 0;
+        if (prior_box_var) {
+          target_box_center_x = prior_box_var_data[j * len] *
                                     target_box_data[offset] * prior_box_width +
                                 prior_box_center_x;
-        T target_box_center_y = prior_box_var_data[j * len + 1] *
+          target_box_center_y = prior_box_var_data[j * len + 1] *
                                     target_box_data[offset + 1] *
                                     prior_box_height +
                                 prior_box_center_y;
-        T target_box_width = std::exp(prior_box_var_data[j * len + 2] *
+          target_box_width = std::exp(prior_box_var_data[j * len + 2] *
                                       target_box_data[offset + 2]) *
                              prior_box_width;
-        T target_box_height = std::exp(prior_box_var_data[j * len + 3] *
+          target_box_height = std::exp(prior_box_var_data[j * len + 3] *
                                        target_box_data[offset + 3]) *
                               prior_box_height;
+        } else {
+          target_box_center_x =
+              target_box_data[offset] * prior_box_width + prior_box_center_x;
+          target_box_center_y = target_box_data[offset + 1] * prior_box_height +
+                                prior_box_center_y;
+          target_box_width =
+              std::exp(target_box_data[offset + 2]) * prior_box_width;
+          target_box_height =
+              std::exp(target_box_data[offset + 3]) * prior_box_height;
+        }
 
         output[offset] = target_box_center_x - target_box_width / 2;
         output[offset + 1] = target_box_center_y - target_box_height / 2;
@@ -147,10 +166,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     bool normalized = context.Attr<bool>("box_normalized");
     T* output = output_box->data<T>();
     if (code_type == BoxCodeType::kEncodeCenterSize) {
-      EncodeCenterSize(*target_box, *prior_box, *prior_box_var, normalized,
+      EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
                        output);
     } else if (code_type == BoxCodeType::kDecodeCenterSize) {
-      DecodeCenterSize(*target_box, *prior_box, *prior_box_var, normalized,
+      DecodeCenterSize(target_box, prior_box, prior_box_var, normalized,
                        output);
     }
   }

paddle/fluid/operators/fetch_barrier_op.cc

@@ -45,13 +45,13 @@ class FetchBarrierOp : public framework::OperatorBase {
 
     auto rpc_client = detail::RPCClient::GetInstance();
 
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
 
     for (auto& ep : eps) {
       VLOG(3) << "fetch barrier, ep: " << ep;
       rpc_client->AsyncSendFetchBarrier(ep);
     }
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
   }
 };
 

paddle/fluid/operators/pool_cudnn_op.cu.cc

@@ -135,7 +135,11 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
 
     PoolingMode pooling_mode;
     if (pooling_type == "max") {
+      if (FLAGS_cudnn_deterministic) {
+        pooling_mode = PoolingMode::kMaximumDeterministic;
+      } else {
         pooling_mode = PoolingMode::kMaximum;
+      }
     } else {
       pooling_mode = PoolingMode::kAverage;
     }

paddle/fluid/operators/prefetch_op.cc

@@ -53,7 +53,7 @@ class PrefetchOp : public framework::OperatorBase {
         VLOG(3) << "don't send no-initialied variable: " << ins[i];
       }
     }
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
   }
 };
 

paddle/fluid/operators/recv_op.cc

@@ -51,7 +51,7 @@ class RecvOp : public framework::OperatorBase {
       rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
     if (sync_mode) {
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
   }
 };

paddle/fluid/operators/reduce_op.h

@@ -135,15 +135,16 @@ class ReduceKernel : public framework::OpKernel<T> {
     } else {
       int ndim = context.Input<Tensor>("X")->dims().size();
       int rdim = context.Attr<std::vector<int>>("dim").size();
-      HANDLE_DIM(6, 5);
-      HANDLE_DIM(6, 4);
-      HANDLE_DIM(6, 3);
-      HANDLE_DIM(6, 2);
-      HANDLE_DIM(6, 1);
-      HANDLE_DIM(5, 4);
-      HANDLE_DIM(5, 3);
-      HANDLE_DIM(5, 2);
-      HANDLE_DIM(5, 1);
+      // comments for accelerating compiling temporarily.
+      //      HANDLE_DIM(6, 5);
+      //      HANDLE_DIM(6, 4);
+      //      HANDLE_DIM(6, 3);
+      //      HANDLE_DIM(6, 2);
+      //      HANDLE_DIM(6, 1);
+      //      HANDLE_DIM(5, 4);
+      //      HANDLE_DIM(5, 3);
+      //      HANDLE_DIM(5, 2);
+      //      HANDLE_DIM(5, 1);
       HANDLE_DIM(4, 3);
       HANDLE_DIM(4, 2);
       HANDLE_DIM(4, 1);

paddle/fluid/operators/send_barrier_op.cc

@@ -49,13 +49,13 @@ class SendBarrierOp : public framework::OperatorBase {
     VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
 
     // need to wait before sending send_barrier message
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
     if (sync_mode) {
       for (auto& ep : eps) {
         VLOG(3) << "send barrier, ep: " << ep;
         rpc_client->AsyncSendBatchBarrier(ep);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
   }
 };

paddle/fluid/operators/send_op.cc

@@ -59,14 +59,14 @@ class SendOp : public framework::OperatorBase {
         VLOG(3) << "don't send no-initialied variable: " << ins[i];
       }
     }
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
 
     if (sync_mode) {
       for (auto& ep : endpoints) {
         VLOG(3) << "batch barrier, ep: " << ep;
         rpc_client->AsyncSendBatchBarrier(ep);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
 
     if (outs.size() > 0) {
@@ -74,13 +74,13 @@ class SendOp : public framework::OperatorBase {
         VLOG(2) << "getting " << outs[i] << " from " << epmap[i];
         rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
       // tell pservers that current trainer have called fetch
       for (auto& ep : endpoints) {
         VLOG(2) << "send fetch barrier, ep: " << ep;
         rpc_client->AsyncSendFetchBarrier(ep);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
   }
 };

paddle/fluid/operators/test_send_nccl_id.cc

@@ -61,7 +61,6 @@ void StartServer() {
       std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
 
   g_rpc_service->SetCond(detail::kRequestSend);
-  std::cout << "before WaitFanInOfSend" << std::endl;
   g_rpc_service->WaitBarrier(detail::kRequestSend);
 
   LOG(INFO) << "got nccl id and stop server...";
@@ -88,12 +87,12 @@ TEST(SendNcclId, GrpcServer) {
   int port = g_rpc_service->GetSelectedPort();
 
   std::string ep = string::Sprintf("127.0.0.1:%d", port);
-  detail::RPCClient client;
-  LOG(INFO) << "connect to server" << ep;
-  client.AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
-  client.Wait();
-  client.AsyncSendBatchBarrier(ep);
-  client.Wait();
+  detail::RPCClient* client = detail::RPCClient::GetInstance();
+  LOG(INFO) << "connect to server " << ep;
+  client->AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
+  client->Wait();
+  client->AsyncSendBatchBarrier(ep);
+  client->Wait();
 
   server_thread.join();
   g_rpc_service.reset(nullptr);

paddle/fluid/platform/cudnn_helper.h

@@ -22,6 +22,8 @@ limitations under the License. */
 #include "paddle/fluid/platform/float16.h"
 #include "paddle/fluid/platform/macros.h"
 
+DECLARE_bool(cudnn_deterministic);
+
 namespace paddle {
 namespace platform {
 
@@ -76,8 +78,22 @@ enum class DataLayout {  // Not use
 enum class PoolingMode {
   kMaximum,
   kAverage,
+  kMaximumDeterministic,
 };
 
+inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
+  switch (mode) {
+    case PoolingMode::kMaximumDeterministic:
+      return CUDNN_POOLING_MAX_DETERMINISTIC;
+    case PoolingMode::kAverage:
+      return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+    case PoolingMode::kMaximum:
+      return CUDNN_POOLING_MAX;
+    default:
+      PADDLE_THROW("Unexpected pooling mode.");
+  }
+}
+
 template <typename T>
 class CudnnDataType;
 
@@ -293,9 +309,7 @@ class ScopedPoolingDescriptor {
     PADDLE_ENFORCE_EQ(kernel.size(), pads.size());
     PADDLE_ENFORCE_EQ(kernel.size(), strides.size());
     PADDLE_ENFORCE(dynload::cudnnSetPoolingNdDescriptor(
-        desc_, (mode == PoolingMode::kMaximum
-                    ? CUDNN_POOLING_MAX
-                    : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
+        desc_, (GetPoolingMode(mode)),
         CUDNN_PROPAGATE_NAN,  // Always propagate nans.
         kernel.size(), kernel.data(), pads.data(), strides.data()));
     return desc_;

paddle/fluid/platform/device_context.cc

@@ -175,7 +175,6 @@ CUDADeviceContext::~CUDADeviceContext() {
 Place CUDADeviceContext::GetPlace() const { return place_; }
 
 void CUDADeviceContext::Wait() const {
-  std::lock_guard<std::recursive_mutex> guard(mutex_);
   PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
   PADDLE_ENFORCE(cudaGetLastError());
 }

paddle/fluid/platform/device_context.h

@@ -100,7 +100,6 @@ class CUDADeviceContext : public DeviceContext {
 
   template <typename Callback>
   void RecordEvent(cudaEvent_t ev, Callback callback) {
-    std::lock_guard<std::recursive_mutex> guard(mutex_);
     callback();
     PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
   }
@@ -110,8 +109,6 @@ class CUDADeviceContext : public DeviceContext {
 
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
-
-  mutable std::recursive_mutex mutex_;
   cudaStream_t stream_;
   cudnnHandle_t cudnn_handle_;
   cublasHandle_t cublas_handle_;

paddle/fluid/platform/dynload/cublas.h

@@ -45,7 +45,7 @@ extern void *cublas_dso_handle;
       std::call_once(cublas_dso_flag, []() {                                 \
         cublas_dso_handle = paddle::platform::dynload::GetCublasDsoHandle(); \
       });                                                                    \
-      void *p_##__name = dlsym(cublas_dso_handle, #__name);                  \
+      static void *p_##__name = dlsym(cublas_dso_handle, #__name);           \
       return reinterpret_cast<FUNC_TYPE>(p_##__name)(args...);               \
     }                                                                        \
   };                                                                         \

paddle/fluid/platform/dynload/cudnn.h

@@ -39,7 +39,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
         cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \
       });                                                                  \
       EnforceCUDNNLoaded(#__name);                                         \
-      void* p_##__name = dlsym(cudnn_dso_handle, #__name);                 \
+      static void* p_##__name = dlsym(cudnn_dso_handle, #__name);          \
       return reinterpret_cast<cudnn_func>(p_##__name)(args...);            \
     }                                                                      \
   };                                                                       \

paddle/fluid/platform/dynload/cupti.h

@@ -45,7 +45,7 @@ extern void *cupti_dso_handle;
       std::call_once(cupti_dso_flag, []() {                                \
         cupti_dso_handle = paddle::platform::dynload::GetCUPTIDsoHandle(); \
       });                                                                  \
-      void *p_##__name = dlsym(cupti_dso_handle, #__name);                 \
+      static void *p_##__name = dlsym(cupti_dso_handle, #__name);          \
       return reinterpret_cast<cuptiFunc>(p_##__name)(args...);             \
     }                                                                      \
   };                                                                       \

paddle/fluid/platform/dynload/curand.h

@@ -34,7 +34,7 @@ extern void *curand_dso_handle;
       std::call_once(curand_dso_flag, []() {                                 \
         curand_dso_handle = paddle::platform::dynload::GetCurandDsoHandle(); \
       });                                                                    \
-      void *p_##__name = dlsym(curand_dso_handle, #__name);                  \
+      static void *p_##__name = dlsym(curand_dso_handle, #__name);           \
       return reinterpret_cast<curandFunc>(p_##__name)(args...);              \
     }                                                                        \
   };                                                                         \

paddle/fluid/platform/dynload/nccl.h

@@ -37,7 +37,7 @@ extern void* nccl_dso_handle;
       std::call_once(nccl_dso_flag, []() {                               \
         nccl_dso_handle = paddle::platform::dynload::GetNCCLDsoHandle(); \
       });                                                                \
-      void* p_##__name = dlsym(nccl_dso_handle, #__name);                \
+      static void* p_##__name = dlsym(nccl_dso_handle, #__name);         \
       return reinterpret_cast<nccl_func>(p_##__name)(args...);           \
     }                                                                    \
   };                                                                     \

paddle/fluid/platform/dynload/tensorrt.h

@@ -40,7 +40,7 @@ extern void* tensorrt_dso_handle;
             paddle::platform::dynload::GetTensorRtDsoHandle();          \
         PADDLE_ENFORCE(tensorrt_dso_handle, "load tensorrt so failed"); \
       });                                                               \
-      void* p_##__name = dlsym(tensorrt_dso_handle, #__name);           \
+      static void* p_##__name = dlsym(tensorrt_dso_handle, #__name);    \
       PADDLE_ENFORCE(p_##__name, "load %s failed", #__name);            \
       return reinterpret_cast<tensorrt_func>(p_##__name)(args...);      \
     }                                                                   \

paddle/fluid/platform/dynload/warpctc.h

@@ -40,7 +40,7 @@ extern void* warpctc_dso_handle;
       std::call_once(warpctc_dso_flag, []() {                                  \
         warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \
       });                                                                      \
-      void* p_##_name = dlsym(warpctc_dso_handle, #__name);                    \
+      static void* p_##_name = dlsym(warpctc_dso_handle, #__name);             \
       return reinterpret_cast<warpctcFunc>(p_##_name)(args...);                \
     }                                                                          \
   };                                                                           \

paddle/fluid/pybind/pybind.cc

@@ -519,6 +519,14 @@ All parameter, weight, gradient are variables in Paddle.
           [](const ExecutionStrategy &self) { return self.allow_op_delay_; },
           [](ExecutionStrategy &self, bool allow_op_delay) {
             self.allow_op_delay_ = allow_op_delay;
+          })
+      .def_property(
+          "num_iteration_per_drop_scope",
+          [](const ExecutionStrategy &self) {
+            return self.num_iteration_per_drop_scope_;
+          },
+          [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
+            self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
           });
   py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
 

python/paddle/fluid/__init__.py

@@ -120,7 +120,7 @@ def __bootstrap__():
     ]
     if core.is_compiled_with_cuda():
         read_env_flags += [
-            'fraction_of_gpu_memory_to_use', 'cudnn_algo_use_autotune'
+            'fraction_of_gpu_memory_to_use', 'cudnn_deterministic'
         ]
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])

python/paddle/fluid/layers/nn.py

@@ -81,6 +81,8 @@ __all__ = [
     'label_smooth',
     'roi_pool',
     'dice_loss',
+    'image_resize',
+    'image_resize_short',
     'resize_bilinear',
     'gather',
     'random_crop',
@@ -3929,22 +3931,25 @@ def dice_loss(input, label, epsilon=0.00001):
     return reduce_mean(dice_score)
 
 
-def resize_bilinear(input, out_shape=None, scale=None, name=None):
+def image_resize(input,
+                 out_shape=None,
+                 scale=None,
+                 name=None,
+                 resample='BILINEAR'):
     """
-    The mathematical meaning of resize bilinear layer is
-    Bilinear interpolation.
-    Bilinear interpolation is an extension of linear interpolation for
-    interpolating functions of two variables (e.g. H-direction and
-    W-direction in this layer) on a rectilinear 2D grid.
+    Resize a batch of images.
 
-    For details, please refer to Wikipedia:
-    https://en.wikipedia.org/wiki/Bilinear_interpolation
+    The input must be a tensor of the shape (num_batches, channels, in_h, in_w), 
+    and the resizing only applies on the last two dimensions(hight and width).
+
+    Supporting resample methods:
+        'BILINEAR' : Bilinear interpolation
 
     Args:
-        input (Variable): The input tensor of resize bilinear layer,
+        input (Variable): The input tensor of image resize layer,
                           This is a 4-D tensor of the shape
                           (num_batches, channels, in_h, in_w).
-        out_shape(list|tuple|Variable|None): Output shape of resize bilinear
+        out_shape(list|tuple|Variable|None): Output shape of image resize
                                     layer, the shape is (out_h, out_w).
                                     Default: None
         scale(float|None): The multiplier for the input height or width.
@@ -3953,6 +3958,8 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
                          Default: None
         name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
+        resample(str): The resample method. It can only be 'BILINEAR' currently.
+                       Default: 'BILINEAR'
 
     Returns:
         out (Variable): The output is a 4-D tensor of the shape
@@ -3961,8 +3968,12 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
     Examples:
         .. code-block:: python
 
-            out = fluid.layers.resize_bilinear(input, out_shape=[12, 12])
+            out = fluid.layers.image_resize(input, out_shape=[12, 12])
     """
+    resample_methods = {'BILINEAR': 'bilinear_interp'}
+    if resample not in resample_methods:
+        raise ValueError(
+            "The 'resample' of image_resize can only be 'BILINEAR' currently.")
     if out_shape is None and scale is None:
         raise ValueError("One of out_shape and scale must not be None")
     helper = LayerHelper('bilinear_interp', **locals())
@@ -3990,7 +4001,7 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
 
     out = helper.create_tmp_variable(dtype)
     helper.append_op(
-        type="bilinear_interp",
+        type=resample_methods[resample],
         inputs=inputs,
         outputs={"Out": out},
         attrs={"out_h": out_h,
@@ -3998,6 +4009,55 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
     return out
 
 
+def resize_bilinear(input, out_shape=None, scale=None, name=None):
+    """
+    This is an alias of layer 'image_resize' with bilinear interpolation.
+
+    The mathematical meaning of resize bilinear layer is
+    Bilinear interpolation.
+    Bilinear interpolation is an extension of linear interpolation for
+    interpolating functions of two variables (e.g. H-direction and
+    W-direction in this layer) on a rectilinear 2D grid.
+
+    For details, please refer to Wikipedia:
+    https://en.wikipedia.org/wiki/Bilinear_interpolation
+    """
+
+    return image_resize(input, out_shape, scale, name, 'BILINEAR')
+
+
+def image_resize_short(input, out_short_len, resample='BILINEAR'):
+    """
+    Resize a batch of images. The short edge of input images will be 
+    resized to the given 'out_short_len'. The long edge of input images 
+    will be resized proportionately to make images' length-width ratio 
+    constant.
+
+    Args:
+        input (Variable): The input tensor of image resize layer,
+                          This is a 4-D tensor of the shape
+                          (num_batches, channels, in_h, in_w).
+        out_short_len(int): The length of output images' short edge.
+
+    Returns:
+        out (Variable): The output is a 4-D tensor of the shape
+                        (num_batches, channls, out_h, out_w).
+    """
+    in_shape = input.shape
+    if len(in_shape) != 4:
+        raise ValueError(
+            "The rank of input must be 4 (num_batches, channels, in_h, in_w).")
+    hw = in_shape[2:4]
+    short_idx = hw.index(min(hw))
+    long_idx = 1 - short_idx
+    out_shape = list(hw)
+    out_shape[short_idx] = out_short_len
+    out_shape[long_idx] = int(
+        float(out_shape[long_idx]) * (float(out_short_len) / float(hw[
+            short_idx])) + 0.5)
+    return image_resize(input=input, out_shape=out_shape, resample=resample)
+
+
 def gather(input, index):
     """
     Output is obtained by gathering entries of the outer-most dimension 

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

@@ -43,12 +43,10 @@ list(REMOVE_ITEM TEST_OPS test_warpctc_op)
 list(REMOVE_ITEM TEST_OPS test_dist_train)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
+# TODO(wuyi): this test hungs on CI, will add it back later
+list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
 py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
 py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
-# FIXME(Yancey1989): this test would cost much more time on CUDAPlace
-# since load cudnn libraries, so we use a longer timeout to make this
-# unit test stability.
-set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 30)

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

@@ -120,6 +120,32 @@ class TestBoxCoderOp(OpTest):
         self.outputs = {'OutputBox': output_box}
 
 
+class TestBoxCoderOpWithoutBoxVar(OpTest):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        lod = [[0, 1, 2, 3, 4, 5]]
+        prior_box = np.random.random((10, 4)).astype('float32')
+        prior_box_var = np.ones((10, 4)).astype('float32')
+        target_box = np.random.random((5, 10, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        output_box = batch_box_coder(prior_box, prior_box_var, target_box,
+                                     lod[0], code_type, box_normalized)
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
 class TestBoxCoderOpWithLoD(OpTest):
     def test_check_output(self):
         self.check_output()