Merge pull request #12149 from reyoung/feature/combine_open_files_and_double_buffer

Change and polish readers

paddle/fluid/API.spec

@@ -180,13 +180,13 @@ paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, default
 paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
-paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, None, 1, True))
+paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.shuffle ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.batch ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.double_buffer ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,))
-paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
 paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.Preprocessor.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -171,7 +171,12 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
 
   for (size_t i = 0; i < fetch_tensors.size(); ++i) {
     auto &var_name = fetch_tensors[i];
-    auto &vars = fetched_vars.at(var_name);
+    auto fetched_var_it = fetched_vars.find(var_name);
+    PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
+                   "Cannot find fetched variable.(Perhaps the main_program "
+                   "is not set to ParallelExecutor)");
+
+    auto &vars = fetched_var_it->second;
 
     temp_nodes->emplace_back(new ir::Node("fetch", ir::Node::Type::kOperation));
     auto *op = new FetchOpHandle(temp_nodes->back().get(), fetch_data, i,

paddle/fluid/framework/lod_tensor.cc

@@ -312,19 +312,22 @@ void WriteToRecordIO(recordio::Writer *writer,
   writer->Write(buffer.str());
 }
 
-std::vector<LoDTensor> ReadFromRecordIO(
-    recordio::Scanner *scanner, const platform::DeviceContext &dev_ctx) {
-  std::vector<LoDTensor> result;
-  if (scanner->HasNext()) {
+bool ReadFromRecordIO(recordio::Scanner *scanner,
+                      const platform::DeviceContext &dev_ctx,
+                      std::vector<LoDTensor> *result_ptr) {
+  if (!scanner->HasNext()) {
+    return false;
+  }
   std::istringstream sin(scanner->Next());
   uint32_t sz;
   sin.read(reinterpret_cast<char *>(&sz), sizeof(uint32_t));
+  auto &result = *result_ptr;
   result.resize(sz);
   for (uint32_t i = 0; i < sz; ++i) {
     DeserializeFromStream(sin, &result[i], dev_ctx);
   }
-  }
-  return result;
+
+  return true;
 }
 
 std::vector<LoDTensor> LoDTensor::SplitLoDTensor(

paddle/fluid/framework/lod_tensor.h

@@ -223,8 +223,9 @@ extern void WriteToRecordIO(recordio::Writer* writer,
                             const std::vector<LoDTensor>& tensor,
                             const platform::DeviceContext& dev_ctx);
 
-extern std::vector<LoDTensor> ReadFromRecordIO(
-    recordio::Scanner* scanner, const platform::DeviceContext& dev_ctx);
+extern bool ReadFromRecordIO(recordio::Scanner* scanner,
+                             const platform::DeviceContext& dev_ctx,
+                             std::vector<LoDTensor>* result_ptr);
 
 /*
  * Convert between length-based LoD and offset-based LoD.

paddle/fluid/framework/lod_tensor_test.cc

@@ -301,11 +301,12 @@ static void TestRecordIO() {
   {
     std::unique_ptr<std::istream> stream_ptr(stream);
     recordio::Scanner scanner(std::move(stream_ptr));
-    auto tensors = ReadFromRecordIO(&scanner, ctx);
+    std::vector<framework::LoDTensor> tensors;
+    ASSERT_TRUE(ReadFromRecordIO(&scanner, ctx, &tensors));
     ASSERT_EQ(tensors.size(), static_cast<size_t>(2));
     assert_tensor_ok(tensors[0]);
     assert_tensor_ok(tensors[1]);
-    tensors = ReadFromRecordIO(&scanner, ctx);
+    ASSERT_TRUE(ReadFromRecordIO(&scanner, ctx, &tensors));
     ASSERT_EQ(tensors.size(), static_cast<size_t>(2));
     assert_tensor_ok(tensors[0]);
     assert_tensor_ok(tensors[1]);

paddle/fluid/framework/reader.cc

@@ -67,7 +67,8 @@ void ReaderBase::Start() {
   }
 }
 
-ReaderBase::~ReaderBase() { Shutdown(); }
+ReaderBase::~ReaderBase() {}
 
+DecoratedReader::~DecoratedReader() { reader_->Shutdown(); }
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/reader.h

@@ -25,8 +25,6 @@
 namespace paddle {
 namespace framework {
 
-enum ReaderStatus { kRunning, kStopped };
-
 class ReaderBase {
  public:
   virtual void ReadNext(std::vector<LoDTensor>* out);
@@ -48,6 +46,8 @@ class ReaderBase {
 
   virtual void StartImpl() {}
 
+  enum ReaderStatus { kRunning, kStopped };
+
   ReaderStatus status_{kRunning};
 
   mutable std::mutex mu_;
@@ -74,6 +74,8 @@ class DecoratedReader : public ReaderBase,
     reader_->InsertDecoratedReader(shared_from_this());
   }
 
+  ~DecoratedReader();
+
  protected:
   void ShutdownImpl() override { reader_->Shutdown(); }
 

paddle/fluid/operators/reader/CMakeLists.txt

@@ -15,12 +15,13 @@ function(reader_library TARGET_NAME)
         PARENT_SCOPE)
 endfunction()
 
-reader_library(open_files_op SRCS open_files_op.cc)
+cc_library(buffered_reader SRCS buffered_reader.cc DEPS reader simple_threadpool)
+reader_library(open_files_op SRCS open_files_op.cc DEPS buffered_reader)
 reader_library(create_random_data_generator_op SRCS create_random_data_generator_op.cc)
 reader_library(create_shuffle_reader_op SRCS create_shuffle_reader_op.cc)
 reader_library(create_batch_reader_op SRCS create_batch_reader_op.cc)
 reader_library(create_recordio_file_reader_op SRCS create_recordio_file_reader_op.cc)
-reader_library(create_double_buffer_reader_op SRCS create_double_buffer_reader_op.cc)
+reader_library(create_double_buffer_reader_op SRCS create_double_buffer_reader_op.cc DEPS buffered_reader)
 reader_library(create_multi_pass_reader_op SRCS create_multi_pass_reader_op.cc)
 reader_library(create_custom_reader_op SRCS create_custom_reader_op.cc)
 reader_library(create_py_reader_op SRCS create_py_reader_op.cc)

paddle/fluid/operators/reader/buffered_reader.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/operators/reader/buffered_reader.h"
+#include <vector>
+
+namespace paddle {
+namespace operators {
+namespace reader {
+BufferedReader::~BufferedReader() { reader_->Shutdown(); }
+BufferedReader::BufferedReader(
+    const std::shared_ptr<framework::ReaderBase> &reader,
+    const platform::Place &place, size_t buffer_size)
+    : framework::DecoratedReader(reader),
+      thread_pool_(1),
+      place_(place),
+      buffer_size_(buffer_size) {
+  cpu_buffer_.resize(buffer_size);
+  gpu_buffer_.resize(buffer_size);
+  ReadTillBufferFullAsync();
+}
+void BufferedReader::ReadTillBufferFullAsync() {
+  PADDLE_ENFORCE_EQ(position_.size(), 0U);
+  for (size_t i = 0; i < buffer_size_; ++i) {
+    ReadAsync(i);
+  }
+}
+void BufferedReader::ReadAsync(size_t i) {
+  position_.emplace(thread_pool_.enqueue([this, i]() -> size_t {
+    TensorVec &cpu = cpu_buffer_[i];
+    reader_->ReadNext(&cpu);
+
+    if (cpu.empty()) {
+      return -1UL;
+    }
+
+    if (platform::is_gpu_place(place_)) {
+      TensorVec &gpu = gpu_buffer_[i];
+      gpu.resize(cpu.size());
+      for (size_t i = 0; i < cpu.size(); ++i) {
+        framework::TensorCopySync(cpu[i], place_, &gpu[i]);
+        gpu[i].set_lod(cpu[i].lod());
+      }
+    }
+    return i;
+  }));
+}
+void BufferedReader::ShutdownImpl() {
+  reader_->Shutdown();
+  while (!position_.empty()) {
+    position_.pop();
+  }
+  prev_pos_ = -1UL;
+}
+void BufferedReader::StartImpl() {
+  reader_->Start();
+  ReadTillBufferFullAsync();
+}
+void BufferedReader::ReadNextImpl(std::vector<framework::LoDTensor> *out) {
+  if (position_.empty()) {
+    out->clear();
+    return;
+  }
+  size_t i = position_.front().get();
+  position_.pop();
+
+  if (i == -1UL) {
+    ReadNextImpl(out);
+    return;
+  }
+
+  *out = platform::is_gpu_place(place_) ? gpu_buffer_[i] : cpu_buffer_[i];
+
+  // Do not push current position into ReadAsync. Push the previous position
+  // Since all computation in fluid are async, change the data of
+  // current position may cause data error.
+  if (prev_pos_ != -1Ul) {
+    ReadAsync(prev_pos_);
+  }
+  prev_pos_ = i;
+}
+
+}  // namespace reader
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reader/buffered_reader.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 <list>
+#include <queue>
+#include <vector>
+#include "ThreadPool.h"
+#include "paddle/fluid/framework/reader.h"
+
+namespace paddle {
+namespace operators {
+namespace reader {
+
+class BufferedReader : public framework::DecoratedReader {
+  using TensorVec = std::vector<framework::LoDTensor>;
+  using VecFuture = std::future<TensorVec>;
+
+ public:
+  BufferedReader(const std::shared_ptr<framework::ReaderBase>& reader,
+                 const platform::Place& place, size_t buffer_size);
+
+  ~BufferedReader() override;
+
+ private:
+  void ReadTillBufferFullAsync();
+
+  void ReadAsync(size_t i);
+
+ protected:
+  void ShutdownImpl() override;
+  void StartImpl() override;
+  void ReadNextImpl(std::vector<framework::LoDTensor>* out) override;
+
+ private:
+  ThreadPool thread_pool_;
+  platform::Place place_;
+  const size_t buffer_size_;
+
+  std::queue<std::future<size_t>> position_;
+
+  // The buffer for reading data.
+  // NOTE: the simplest way to implement buffered reader is do not use any
+  // buffer, just read async and create futures as buffer size. However, to
+  // malloc tensors every time is extremely slow. Here we store all data in
+  // buffers and prevent alloc every time.
+  std::vector<TensorVec> cpu_buffer_;
+  std::vector<TensorVec> gpu_buffer_;
+  size_t prev_pos_{-1UL};
+};
+
+}  // namespace reader
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reader/create_double_buffer_reader_op.cc

@@ -12,83 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <thread>  // NOLINT
-
-#include "paddle/fluid/operators/reader/blocking_queue.h"
+#include "paddle/fluid/operators/reader/buffered_reader.h"
 #include "paddle/fluid/operators/reader/reader_op_registry.h"
 
 namespace paddle {
 namespace operators {
 namespace reader {
-
-// 'Double buffer' means we shall maintain two batches of input data at the same
-// time. So the kCacheSize shoul be at least 2.
-static constexpr size_t kCacheSize = 3;
-// There will be two bacthes out of the channel during training:
-// 1. the one waiting to be sent to the channel
-// 2. the one just be received from the channel, which is also being used by
-// subsequent operators.
-// So the channel size should be kChacheSize - 2
-static constexpr size_t kChannelSize = 1;  // kCacheSize - 2
-
-class DoubleBufferReader : public framework::DecoratedReader {
- public:
-  explicit DoubleBufferReader(
-      const std::shared_ptr<ReaderBase>& reader,
-      platform::Place target_place = platform::CPUPlace())
-      : DecoratedReader(reader), place_(target_place) {
-    cpu_tensor_cache_.resize(kCacheSize);
-    gpu_tensor_cache_.resize(kCacheSize);
-#ifdef PADDLE_WITH_CUDA
-    if (platform::is_gpu_place(place_)) {
-      for (size_t i = 0; i < kCacheSize; ++i) {
-        ctxs_.emplace_back(new platform::CUDADeviceContext(
-            boost::get<platform::CUDAPlace>(place_)));
-      }
-    }
-#endif
-    StartPrefetcher();
-  }
-
-  void ReadNextImpl(std::vector<framework::LoDTensor>* out) override;
-
-  ~DoubleBufferReader() { EndPrefetcher(); }
-
- private:
-  void ShutdownImpl() override {
-    EndPrefetcher();
-    reader_->Shutdown();
-  }
-
-  void StartImpl() override {
-    reader_->Start();
-    StartPrefetcher();
-  }
-
-  void StartPrefetcher() {
-    channel_ = new reader::BlockingQueue<size_t>(kChannelSize);
-    prefetcher_ = std::thread([this] { PrefetchThreadFunc(); });
-  }
-
-  void EndPrefetcher() {
-    channel_->Close();
-    if (prefetcher_.joinable()) {
-      prefetcher_.join();
-    }
-    delete channel_;
-    channel_ = nullptr;
-  }
-
-  void PrefetchThreadFunc();
-
-  std::thread prefetcher_;
-  reader::BlockingQueue<size_t>* channel_;
-  platform::Place place_;
-  std::vector<std::vector<framework::LoDTensor>> cpu_tensor_cache_;
-  std::vector<std::vector<framework::LoDTensor>> gpu_tensor_cache_;
-  std::vector<std::unique_ptr<platform::DeviceContext>> ctxs_;
-};
-
 class CreateDoubleBufferReaderOp : public framework::OperatorBase {
  public:
   using framework::OperatorBase::OperatorBase;
@@ -118,8 +47,8 @@ class CreateDoubleBufferReaderOp : public framework::OperatorBase {
       place = platform::CUDAPlace(static_cast<int>(num));
     }
 
-    out->Reset(framework::MakeDecoratedReader<DoubleBufferReader>(
-        underlying_reader, place));
+    out->Reset(framework::MakeDecoratedReader<BufferedReader>(underlying_reader,
+                                                              place, 2));
   }
 };
 
@@ -146,51 +75,6 @@ class CreateDoubleBufferReaderOpMaker : public DecoratedReaderMakerBase {
   }
 };
 
-void DoubleBufferReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
-  size_t cached_tensor_id;
-  if (channel_->Receive(&cached_tensor_id)) {
-    if (platform::is_gpu_place(place_)) {
-      *out = gpu_tensor_cache_[cached_tensor_id];
-    } else {
-      // CPU place
-      *out = cpu_tensor_cache_[cached_tensor_id];
-    }
-  } else {
-    out->clear();
-  }
-}
-
-void DoubleBufferReader::PrefetchThreadFunc() {
-  VLOG(5) << "A new prefetch thread starts.";
-  size_t cached_tensor_id = 0;
-  while (true) {
-    auto& cpu_batch = cpu_tensor_cache_[cached_tensor_id];
-    reader_->ReadNext(&cpu_batch);
-    if (cpu_batch.empty()) {
-      // The underlying reader have no next data.
-      break;
-    }
-    if (platform::is_gpu_place(place_)) {
-      auto& gpu_batch = gpu_tensor_cache_[cached_tensor_id];
-      gpu_batch.resize(cpu_batch.size());
-      for (size_t i = 0; i < cpu_batch.size(); ++i) {
-        // TODO(fengjiayi): Use asynchronous TensorCopy instead
-        framework::TensorCopySync(cpu_batch[i], place_, &gpu_batch[i]);
-        gpu_batch[i].set_lod(cpu_batch[i].lod());
-      }
-    }
-    if (!channel_->Send(cached_tensor_id)) {
-      VLOG(5) << "WARNING: The double buffer channel has been closed. The "
-                 "prefetch thread will terminate.";
-      break;
-    }
-    ++cached_tensor_id;
-    cached_tensor_id %= kCacheSize;
-  }
-  channel_->Close();
-  VLOG(5) << "Prefetch thread terminates.";
-}
-
 }  // namespace reader
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/reader/create_py_reader_op.cc

@@ -33,6 +33,8 @@ class PyReader : public framework::FileReader {
     if (!success) out->clear();
   }
 
+  ~PyReader() { queue_->Close(); }
+
   void Shutdown() override { queue_->Close(); }
 
   void Start() override { queue_->ReOpen(); }

paddle/fluid/operators/reader/create_recordio_file_reader_op.cc

@@ -33,11 +33,14 @@ class RecordIOFileReader : public framework::FileReader {
 
  protected:
   void ReadNextImpl(std::vector<framework::LoDTensor>* out) override {
+    std::unique_ptr<std::lock_guard<std::mutex>> guard;
     if (ThreadSafe) {
-      std::lock_guard<std::mutex> guard(*mutex_);
-      *out = framework::ReadFromRecordIO(&scanner_, dev_ctx_);
-    } else {
-      *out = framework::ReadFromRecordIO(&scanner_, dev_ctx_);
+      guard.reset(new std::lock_guard<std::mutex>(*mutex_));
+    }
+
+    bool ok = framework::ReadFromRecordIO(&scanner_, dev_ctx_, out);
+    if (!ok) {
+      out->clear();
     }
   }
 

paddle/fluid/operators/reader/create_shuffle_reader_op.cc

@@ -48,9 +48,9 @@ class ShuffleReader : public framework::DecoratedReader {
 
  private:
   void ShutdownImpl() override {
+    reader_->Shutdown();
     buffer_.clear();
     iteration_pos_ = 0;
-    reader_->Shutdown();
   }
 
   void StartImpl() override {

paddle/fluid/operators/reader/open_files_op.cc

@@ -12,150 +12,200 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <cmath>
+#include <stdexcept>
 #include <thread>  // NOLINT
-
+#include "ThreadPool.h"
+#include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/operators/reader/blocking_queue.h"
+#include "paddle/fluid/operators/reader/buffered_reader.h"
 #include "paddle/fluid/operators/reader/reader_op_registry.h"
 
 namespace paddle {
 namespace operators {
 namespace reader {
 
-class MultiFileReader : public framework::ReaderBase {
+class IReaderContainer {
+ public:
+  virtual ~IReaderContainer() {}
+  virtual void AppendReader(
+      std::unique_ptr<framework::ReaderBase>&& readers) = 0;
+  virtual void Stop() = 0;
+  virtual void Start() = 0;
+  virtual void ReadNext(std::vector<framework::LoDTensor>* out) = 0;
+};
+
+class OrderedReaderContainer : public IReaderContainer {
  public:
-  MultiFileReader(const std::vector<std::string>& file_names, size_t thread_num,
-                  size_t buffer_size)
-      : buffer_size_(buffer_size) {
-    readers_.reserve(file_names.size());
-    for (const std::string& f_name : file_names) {
-      readers_.emplace_back(CreateReaderByFileName(f_name));
+  void AppendReader(std::unique_ptr<framework::ReaderBase>&& reader) override {
+    pending_.emplace(std::move(reader));
+  }
+
+  void Stop() override {
+    while (!pending_.empty()) {
+      MoveFrontPendingToDone();
     }
-    prefetchers_.resize(thread_num);
-    StartNewScheduler();
   }
 
-  void ReadNextImpl(std::vector<framework::LoDTensor>* out) override;
+  void Start() override { std::swap(done_, pending_); }
 
-  ~MultiFileReader() { EndScheduler(); }
+  void ReadNext(std::vector<framework::LoDTensor>* out) override {
+    if (!pending_.empty()) {
+      pending_.front()->ReadNext(out);
+      if (out->empty()) {
+        MoveFrontPendingToDone();
+        ReadNext(out);
+      }
+    } else {
+      out->clear();
+    }
+  }
 
  private:
-  void ShutdownImpl() override { EndScheduler(); }
-
-  void StartImpl() override { StartNewScheduler(); }
-
-  void StartNewScheduler();
-  void EndScheduler();
-  void ScheduleThreadFunc();
-  void PrefetchThreadFunc(size_t reader_idx, size_t thread_idx);
-
-  std::vector<std::unique_ptr<framework::ReaderBase>> readers_;
-  std::thread scheduler_;
-  std::vector<std::thread> prefetchers_;
-  size_t buffer_size_;
-  reader::BlockingQueue<size_t>* waiting_reader_idx_;
-  reader::BlockingQueue<size_t>* available_thread_idx_;
-  reader::BlockingQueue<std::vector<framework::LoDTensor>>* buffer_;
+  void MoveFrontPendingToDone() {
+    pending_.front()->Shutdown();
+    pending_.front()->Start();
+    done_.emplace(move(pending_.front()));
+    pending_.pop();
+  }
+
+  std::queue<std::unique_ptr<framework::ReaderBase>> pending_;
+  std::queue<std::unique_ptr<framework::ReaderBase>> done_;
 };
 
-void MultiFileReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
-  if (!buffer_->Receive(out)) {
-    out->clear();
+class PreemptiveReaderContainer : public IReaderContainer {
+  using ReaderList = std::list<std::unique_ptr<framework::ReaderBase>>;
+
+  struct FutureItem {
+    std::vector<framework::LoDTensor> data_;
+    ReaderList::iterator reader_it_;
+    std::exception_ptr exception_;
+  };
+
+  using FutureList = std::list<std::future<FutureItem>>;
+
+ public:
+  explicit PreemptiveReaderContainer(size_t thread_num) : pool_(thread_num) {}
+
+  void Stop() override {
+    if (!pending_.empty()) {
+      for (auto& reader : pending_) {
+        reader->Shutdown();
       }
-}
-
-void MultiFileReader::StartNewScheduler() {
-  size_t thread_num = prefetchers_.size();
-  waiting_reader_idx_ = new reader::BlockingQueue<size_t>(readers_.size());
-  available_thread_idx_ = new reader::BlockingQueue<size_t>(thread_num);
-  buffer_ = new reader::BlockingQueue<std::vector<framework::LoDTensor>>(
-      buffer_size_);
-
-  for (size_t i = 0; i < readers_.size(); ++i) {
-    waiting_reader_idx_->Send(i);
-  }
-  waiting_reader_idx_->Close();
-  for (size_t i = 0; i < thread_num; ++i) {
-    available_thread_idx_->Send(i);
-  }
-
-  scheduler_ = std::thread([this] { ScheduleThreadFunc(); });
-}
-
-void MultiFileReader::EndScheduler() {
-  available_thread_idx_->Close();
-  buffer_->Close();
-  waiting_reader_idx_->Close();
-  if (scheduler_.joinable()) {
-    scheduler_.join();
-  }
-  delete buffer_;
-  delete available_thread_idx_;
-  delete waiting_reader_idx_;
-}
-
-void MultiFileReader::ScheduleThreadFunc() {
-  VLOG(5) << "MultiFileReader schedule thread starts.";
-  size_t completed_thread_num = 0;
-  size_t thread_idx;
-  while (available_thread_idx_->Receive(&thread_idx)) {
-    std::thread& prefetcher = prefetchers_[thread_idx];
-    if (prefetcher.joinable()) {
-      prefetcher.join();
-    }
-    size_t reader_idx;
-    if (waiting_reader_idx_->Receive(&reader_idx)) {
-      // Still have files to read. Start a new prefetch thread.
-      prefetcher = std::thread([this, reader_idx, thread_idx] {
-        PrefetchThreadFunc(reader_idx, thread_idx);
-      });
-    } else {
-      // No more file to read.
-      ++completed_thread_num;
-      if (completed_thread_num == prefetchers_.size()) {
-        buffer_->Close();
-        break;
+      for (auto& fu : futures_) {
+        fu.wait();
+      }
+      futures_.clear();
+      for (auto& reader : pending_) {
+        reader->Start();
+        done_.emplace_back(std::move(reader));
       }
+      pending_.clear();
+      bool timeout;
+      complete_queue_.PopAll(1000, &timeout);
+      PADDLE_ENFORCE(!timeout);
     }
   }
-  // If users invoke Shutdown() when scheduler is running, it will close the
-  // 'avaiable_thread_idx_' and prefecther threads have no way to tell scheduler
-  // to release their resource. So a check is needed before scheduler ends.
-  for (auto& p : prefetchers_) {
-    if (p.joinable()) {
-      p.join();
+
+  void Start() override {
+    for (auto& reader : done_) {
+      AppendReader(std::move(reader));
     }
+    done_.clear();
   }
-  VLOG(5) << "MultiFileReader schedule thread terminates.";
-}
 
-void MultiFileReader::PrefetchThreadFunc(size_t reader_idx, size_t thread_idx) {
-  VLOG(5) << "The prefetch thread of file idx '" << reader_idx << "' starts.";
-  std::unique_ptr<framework::ReaderBase>& reader = readers_[reader_idx];
-  while (true) {
-    std::vector<framework::LoDTensor> ins;
-    reader->ReadNext(&ins);
-    if (ins.empty()) {
-      reader->Shutdown();
-      reader->Start();
-      break;
+  void ReadNext(std::vector<framework::LoDTensor>* out) override {
+    if (!pending_.empty()) {
+      auto future_it = complete_queue_.Pop();
+      FutureItem item = future_it->get();
+      if (item.exception_) {
+        for (auto it = futures_.begin(); it != futures_.end(); ++it) {
+          if (it != future_it) {
+            it->wait();  // Wait all other threads complete.
+          }
+        }
+        std::rethrow_exception(item.exception_);
+
+      } else if (item.data_.empty()) {  // reader done.
+        done_.emplace_back(std::move(*item.reader_it_));
+        pending_.erase(item.reader_it_);
+        futures_.erase(future_it);
+        ReadNext(out);
+      } else {
+        *out = item.data_;
+        // continue read async
+        ReadAsync(item.reader_it_, &future_it);
       }
+    } else {
+      out->clear();
+    }
+  }
+
+ private:
+  void AppendReader(std::unique_ptr<framework::ReaderBase>&& reader) override {
+    pending_.emplace_back(std::move(reader));
+    auto reader_it = pending_.end();
+    --reader_it;
+
+    futures_.emplace_back();
+    auto future_it = futures_.end();
+    --future_it;
+
+    ReadAsync(reader_it, &future_it);
+  }
+
+  void ReadAsync(const ReaderList::iterator& reader_it,
+                 FutureList::iterator* future_it_ptr) {
+    auto& future_it = *future_it_ptr;
+    *future_it = pool_.enqueue([reader_it, future_it, this] {
       try {
-      buffer_->Send(std::move(ins));
-    } catch (paddle::platform::EnforceNotMet e) {
-      VLOG(5) << "WARNING: The buffer channel has been closed. The prefetch "
-                 "thread of file idx '"
-              << reader_idx << "' will terminate.";
-      break;
+        FutureItem item;
+        item.reader_it_ = reader_it;
+        (*reader_it)->ReadNext(&item.data_);
+        if (item.data_.empty()) {
+          (*reader_it)->Shutdown();
+          (*reader_it)->Start();
         }
+        complete_queue_.Push(future_it);
+        return item;
+      } catch (...) {
+        FutureItem item;
+        item.exception_ = std::current_exception();
+        complete_queue_.Push(future_it);
+        return item;
+      }
+    });
   }
 
-  if (!available_thread_idx_->Send(thread_idx)) {
-    VLOG(5) << "WARNING: The available_thread_idx_ channel has been closed. "
-               "Fail to send thread_idx.";
+  FutureList futures_;
+  ThreadPool pool_;
+  framework::BlockingQueue<FutureList::iterator> complete_queue_;
+  std::list<std::unique_ptr<framework::ReaderBase>> pending_;
+  std::list<std::unique_ptr<framework::ReaderBase>> done_;
+};
+
+class MultiFileReader : public framework::ReaderBase {
+ public:
+  MultiFileReader(const std::vector<std::string>& file_names,
+                  std::unique_ptr<IReaderContainer>&& container)
+      : container_(std::move(container)) {
+    for (auto& fn : file_names) {
+      container_->AppendReader(CreateReaderByFileName(fn));
     }
-  VLOG(5) << "The prefetch thread of file idx '" << reader_idx
-          << "' terminates.";
-}
+  }
+
+  ~MultiFileReader() { container_->Stop(); }
+
+ protected:
+  void ReadNextImpl(std::vector<framework::LoDTensor>* out) override {
+    container_->ReadNext(out);
+  }
+  void ShutdownImpl() override { container_->Stop(); }
+  void StartImpl() override { container_->Start(); }
+
+ private:
+  std::unique_ptr<IReaderContainer> container_;
+};
 
 class OpenFilesOp : public framework::OperatorBase {
  public:
@@ -173,13 +223,27 @@ class OpenFilesOp : public framework::OperatorBase {
                       "shape concat's length.");
     const auto& file_names = Attr<std::vector<std::string>>("file_names");
     PADDLE_ENFORCE(!file_names.empty(), "No file to be read!");
-    const size_t thread_num = Attr<int>("thread_num");
-    const size_t buffer_size = Attr<int>("buffer_size");
+    bool is_test = Attr<bool>("is_test");
 
     auto* out = scope.FindVar(Output("Out"))
                     ->template GetMutable<framework::ReaderHolder>();
-    out->Reset(
-        std::make_shared<MultiFileReader>(file_names, thread_num, buffer_size));
+    std::unique_ptr<IReaderContainer> container;
+
+    if (is_test) {
+      container.reset(new OrderedReaderContainer());
+    } else {
+      container.reset(new PreemptiveReaderContainer(
+          static_cast<size_t>(Attr<int>("thread_num"))));
+    }
+
+    std::shared_ptr<framework::ReaderBase> reader(
+        new MultiFileReader(file_names, std::move(container)));
+    auto buffer_size = Attr<int>("buffer_size");
+    if (buffer_size > 1) {
+      reader = framework::MakeDecoratedReader<BufferedReader>(
+          reader, platform::CPUPlace(), buffer_size);
+    }
+    out->Reset(reader);
   }
 };
 
@@ -187,9 +251,7 @@ class OpenFilesOpMaker : public FileReaderMakerBase {
  protected:
   void Apply() override {
     AddAttr<std::vector<std::string>>("file_names", "Files to be read.");
-    AddAttr<int>("thread_num", "The maximal concurrent prefetch thread number.")
-        .GreaterThan(0);
-    AddAttr<int>("buffer_size", "The size of prefetch buffer.").GreaterThan(0);
+    AddAttr<bool>("is_test", "Used for testing data.").SetDefault(false);
 
     AddComment(R"DOC(
       OpenFiles Operator
@@ -197,6 +259,11 @@ class OpenFilesOpMaker : public FileReaderMakerBase {
       An OpenFilesOp creates a MultiFileReader, which is able to
       read data multi-threaded from multiple files.
     )DOC");
+    AddAttr<int>("thread_num",
+                 "The maximal concurrent prefetch thread number. Used only "
+                 "when is_test = False");
+    AddAttr<int>("buffer_size", "The reading buffer of these files.")
+        .GreaterThan(0);
   }
 };
 

paddle/fluid/recordio/scanner.cc

@@ -28,6 +28,7 @@ Scanner::Scanner(std::unique_ptr<std::istream> &&stream)
 
 Scanner::Scanner(const std::string &filename)
     : stream_(new std::ifstream(filename)), parser_(*stream_) {
+  PADDLE_ENFORCE(static_cast<bool>(*stream_), "Cannot open file %s", filename);
   Reset();
 }
 

paddle/scripts/paddle_build.sh

@@ -600,11 +600,11 @@ function main() {
       cicheck)
         cmake_gen ${PYTHON_ABI:-""}
         build
-        assert_api_not_changed
         run_test
         gen_capi_package
         gen_fluid_inference_lib
         test_fluid_inference_lib
+        assert_api_not_changed
         ;;
       *)
         print_usage

python/paddle/fluid/layers/io.py

@@ -12,14 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import contextlib
+import multiprocessing
+import threading
 
-from .. import core
-from ..framework import convert_np_dtype_to_dtype_, default_main_program, default_startup_program, Program
-from ..unique_name import generate as unique_name
+from ..data_feeder import DataFeeder
 from control_flow import BlockGuard
-from ..layer_helper import LayerHelper
+from layer_function_generator import templatedoc
+from .. import core
 from ..executor import global_scope
-from layer_function_generator import generate_layer_fn, templatedoc
+from ..framework import convert_np_dtype_to_dtype_, default_main_program, \
+    default_startup_program, program_guard, Program
+from ..layer_helper import LayerHelper
+from ..unique_name import generate as unique_name
 
 __all__ = [
     'data', 'open_recordio_file', 'open_files', 'read_file', 'shuffle', 'batch',
@@ -445,7 +449,12 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
     return monkey_patch_reader_methods(main_prog_var)
 
 
-def py_reader(capacity, shapes, dtypes, lod_levels=None):
+def py_reader(capacity,
+              shapes,
+              dtypes,
+              lod_levels=None,
+              name=None,
+              use_double_buffer=True):
     """
     Create a reader and blocking queue for data feeding in Python
     
@@ -458,10 +467,13 @@ def py_reader(capacity, shapes, dtypes, lod_levels=None):
     using `close()` method when unused.
 
     Args:
+       use_double_buffer(bool): Whether use double buffer or not.
        capacity(int): The maximum capacity of the BlockingQueue.
-       shapes(list): List of tuples which declaring data shapes.
-       dtypes(list): List of strs which declaring data type. 
-       lod_levels(list): List of ints which declaring data lod_level.
+       shapes(list|tuple): List of tuples which declaring data shapes.
+       dtypes(list|tuple): List of strs which declaring data type.
+       lod_levels(list|tuple): List of ints which declaring data lod_level.
+       name(basestring): The prefix Python queue name and Reader name. None will
+            be generated automatically.
 
     Returns:
        tuple(Variable, BlockingQueue):
@@ -502,15 +514,23 @@ def py_reader(capacity, shapes, dtypes, lod_levels=None):
     if lod_levels is None:
         lod_levels = [0] * len(shapes)
 
+    if name is None:
         queue_name = unique_name('lod_tensor_blocking_queue')
+        reader_name = unique_name('create_py_reader')
+        double_buffer_name = unique_name('double_buffer')
+    else:
+        queue_name = "_".join([name, "queue"])
+        reader_name = "_".join([name, "reader"])
+        double_buffer_name = "_".join([name, "double_buffer"])
+
     var = global_scope().var(queue_name)
     feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
 
     startup_blk = default_startup_program().current_block()
-    startup_var = startup_blk.create_var(name=unique_name('create_py_reader'))
+    startup_var = startup_blk.create_var(name=reader_name)
     startup_blk.append_op(
         type='create_py_reader',
-        inputs={'blocking_queue': queue_name},
+        inputs={'blocking_queue': [queue_name]},
         outputs={'Out': [startup_var]},
         attrs={
             'shape_concat': shape_concat,
@@ -524,17 +544,96 @@ def py_reader(capacity, shapes, dtypes, lod_levels=None):
     main_prog_var = _copy_reader_var_(default_main_program().current_block(),
                                       startup_var)
 
-    return monkey_patch_reader_methods(main_prog_var), feed_queue
+    reader = monkey_patch_reader_methods(main_prog_var)
+    if use_double_buffer:
+        double_buffer_reader = double_buffer(reader, name=double_buffer_name)
+        # we return a double buffer reader. However, the reset method comes from
+        # py_reader.
+        double_buffer_reader.reset = reader.reset
+        reader = double_buffer_reader
+
+    # monkey patch py_reader special methods
+    reader.queue = feed_queue
+    current_reset_method = reader.reset
+    reader.thread = None
+    reader.tensor_provider = None
+    reader.exited = False
+
+    def start_provide_thread(func):
+        def __provider_thread__():
+            for tensors in func():
+                array = core.LoDTensorArray()
+                for item in tensors:
+                    if not isinstance(item, core.LoDTensor):
+                        tmp = core.LoDTensor()
+                        tmp.set(item, core.CPUPlace())
+                        item = tmp
+
+                    array.append(item)
+
+                if reader.exited:
+                    break
+                feed_queue.push(array)
+                if reader.exited:
+                    break
+            feed_queue.close()
+
+        reader.thread = threading.Thread(target=__provider_thread__)
+        reader.thread.start()
+
+    def __set_tensor_provider__(func):
+        reader.tensor_provider = func
+
+    def __set_paddle_reader__(paddle_reader):
+        with program_guard(Program(), Program()):
+            feed_list = []
+            counter = 0
+            for dtype, shape, lod_level in zip(dtypes, shapes, lod_levels):
+                name = str(counter)
+                feed_list.append(
+                    data(
+                        name=name,
+                        dtype=dtype,
+                        shape=shape,
+                        lod_level=lod_level))
+                counter += 1
+
+            feeder = DataFeeder(feed_list=feed_list, place=core.CPUPlace())
+            paddle_reader = feeder.decorate_reader(
+                paddle_reader, multi_devices=False)
+
+        def __tensor_provider__():
+            for slots in paddle_reader():
+                yield [slots[str(idx)] for idx in xrange(counter)]
+
+        __set_tensor_provider__(__tensor_provider__)
+
+    def __reset__():
+        current_reset_method()
+        if reader.thread is not None and reader.tensor_provider is not None:
+            reader.exited = True
+            reader.thread.join()
+            reader.exited = False
+
+    def __start__():
+        start_provide_thread(reader.tensor_provider)
+
+    reader.reset = __reset__
+    reader.decorate_tensor_provider = __set_tensor_provider__
+    reader.decorate_paddle_reader = __set_paddle_reader__
+    reader.start = __start__
+
+    return reader
 
 
 def open_files(filenames,
                shapes,
                lod_levels,
                dtypes,
-               thread_num=1,
+               thread_num=None,
                buffer_size=None,
                pass_num=1,
-               for_parallel=True):
+               is_test=None):
     """
     Open files
 
@@ -547,14 +646,14 @@ def open_files(filenames,
        shapes(list): List of tuples which declaring data shapes.
        lod_levels(list): List of ints which declaring data lod_level.
        dtypes(list): List of strs which declaring data type.
-       thread_num(int): The maximal concurrent prefetch thread number.
-       buffer_size(int|None): The size of prefetch buffer. If it is setted None, 
-            buffer size will be thread_num * 3.
-            Default: None
+       thread_num(None): The number of thread to read files.
+            Default: min(len(filenames), cpu_number).
+       buffer_size(None): The buffer size of reader. Default: 3 * thread_num
        pass_num(int): Number of passes to run.
-       for_parallel(Bool): Set it as True if you are going to run 
-            subsequent operators in parallel.
-            Default: True
+       is_test(bool|None): Whether `open_files` used for testing or not. If it
+            is used for testing, the order of data generated is same as the file
+            order. Otherwise, it is not guaranteed the order of data is same
+            between every epoch. [Default: False].
 
     Returns:
        Variable: A Reader Variable via which we can get file data.
@@ -566,15 +665,21 @@ def open_files(filenames,
                                                      './data2.recordio'],
                                              shapes=[(3,224,224), (1)],
                                              lod_levels=[0, 0],
-                                             dtypes=['float32', 'int64'],
-                                             thread_num=2,
-                                             buffer_size=2)
+                                             dtypes=['float32', 'int64'])
 
          # Via the reader, we can use 'read_file' layer to get data:
          image, label = fluid.layers.io.read_file(reader)
     """
+    if thread_num is None:
+        thread_num = min(len(filenames), multiprocessing.cpu_count())
+    else:
+        thread_num = int(thread_num)
+
     if buffer_size is None:
-        buffer_size = thread_num * 3
+        buffer_size = 3 * thread_num
+    else:
+        buffer_size = int(buffer_size)
+
     if isinstance(filenames, basestring):
         filenames = [filenames]
     dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
@@ -588,17 +693,18 @@ def open_files(filenames,
     multi_file_reader_name = unique_name('multi_file_reader')
     startup_blk = default_startup_program().current_block()
     startup_reader = startup_blk.create_var(name=multi_file_reader_name)
-    startup_blk.append_op(
-        type='open_files',
-        outputs={'Out': [startup_reader]},
-        attrs={
+    attrs = {
         'shape_concat': shape_concat,
         'lod_levels': lod_levels,
         'ranks': ranks,
         'file_names': filenames,
         'thread_num': thread_num,
         'buffer_size': buffer_size
-        })
+    }
+    if is_test is not None:
+        attrs['is_test'] = is_test
+    startup_blk.append_op(
+        type='open_files', outputs={'Out': [startup_reader]}, attrs=attrs)
 
     startup_reader.desc.set_dtypes(dtypes)
     startup_reader.persistable = True

python/paddle/fluid/tests/demo/pyreader.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.
+
+import numpy
+
+import paddle
+import paddle.dataset.mnist as mnist
+import paddle.fluid as fluid
+import paddle.v2
+
+
+def network(is_train):
+    reader = fluid.layers.py_reader(
+        capacity=10,
+        shapes=((-1, 784), (-1, 1)),
+        dtypes=('float32', 'int64'),
+        name="train_reader" if is_train else "test_reader")
+    img, label = fluid.layers.read_file(reader)
+
+    hidden = img
+
+    for i in xrange(2):
+        hidden = fluid.layers.fc(input=hidden, size=100, act='tanh')
+        hidden = fluid.layers.dropout(
+            hidden, dropout_prob=0.5, is_test=not is_train)
+
+    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=prediction, label=label)
+    return fluid.layers.mean(loss), reader
+
+
+def main():
+    train_prog = fluid.Program()
+    startup_prog = fluid.Program()
+
+    with fluid.program_guard(train_prog, startup_prog):
+        with fluid.unique_name.guard():
+            loss, train_reader = network(True)
+            adam = fluid.optimizer.Adam(learning_rate=0.01)
+            adam.minimize(loss)
+
+    test_prog = fluid.Program()
+    test_startup = fluid.Program()
+    with fluid.program_guard(test_prog, test_startup):
+        with fluid.unique_name.guard():
+            test_loss, test_reader = network(False)
+
+    fluid.Executor(fluid.CUDAPlace(0)).run(startup_prog)
+    fluid.Executor(fluid.CUDAPlace(0)).run(test_startup)
+
+    trainer = fluid.ParallelExecutor(
+        use_cuda=True, loss_name=loss.name, main_program=train_prog)
+
+    tester = fluid.ParallelExecutor(
+        use_cuda=True, share_vars_from=trainer, main_program=test_prog)
+
+    train_reader.decorate_paddle_reader(
+        paddle.v2.reader.shuffle(
+            paddle.batch(mnist.train(), 512), buf_size=8192))
+
+    test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
+
+    for epoch_id in xrange(10):
+        train_reader.start()
+        try:
+            while True:
+                print 'train_loss', numpy.array(
+                    trainer.run(fetch_list=[loss.name]))
+        except fluid.core.EOFException:
+            print 'End of epoch', epoch_id
+            train_reader.reset()
+
+        test_reader.start()
+        try:
+            while True:
+                print 'test loss', numpy.array(
+                    tester.run(fetch_list=[test_loss.name]))
+        except fluid.core.EOFException:
+            print 'End of testing'
+            test_reader.reset()
+
+
+if __name__ == '__main__':
+    main()

python/paddle/fluid/tests/demo/text_classification/convert_data_to_recordio.py

@@ -31,7 +31,10 @@ def load_vocab(filename):
 
 
 # load word dict with paddle inner function
-word_dict = load_vocab(sys.argv[1])
+if len(sys.argv) == 1:
+    word_dict = paddle.dataset.imdb.word_dict()
+else:
+    word_dict = load_vocab(sys.argv[1])
 word_dict["<unk>"] = len(word_dict)
 print "Dict dim = ", len(word_dict)
 

python/paddle/fluid/tests/demo/text_classification/train.py

@@ -41,16 +41,14 @@ def network_cfg(is_train, pass_num=100):
             pass_num=pass_num,
             shapes=[[-1, 1], [-1, 1]],
             lod_levels=[1, 0],
-            dtypes=['int64', 'int64'],
-            thread_num=1)
+            dtypes=['int64', 'int64'])
 
         test_file_obj = fluid.layers.open_files(
             filenames=TEST_FILES,
             pass_num=1,
             shapes=[[-1, 1], [-1, 1]],
             lod_levels=[1, 0],
-            dtypes=['int64', 'int64'],
-            thread_num=1)
+            dtypes=['int64', 'int64'])
 
         if is_train:
             file_obj = fluid.layers.shuffle(train_file_obj, buffer_size=1000)

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

@@ -142,8 +142,7 @@ class TestDataBalance(unittest.TestCase):
                 filenames=[self.lod_data_file_name],
                 shapes=[[-1, 3], [-1, 1]],
                 lod_levels=[1, 0],
-                dtypes=['float32', 'int32'],
-                thread_num=1)
+                dtypes=['float32', 'int32'])
             ins, label = fluid.layers.read_file(data_reader)
 
             place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
@@ -156,7 +155,7 @@ class TestDataBalance(unittest.TestCase):
                 main_program=main_prog,
                 build_strategy=build_strategy)
 
-            if (parallel_exe.device_count > self.batch_size):
+            if parallel_exe.device_count > self.batch_size:
                 print("WARNING: Unittest TestDataBalance skipped. \
                     For the result is not correct when device count \
                     is larger than batch size.")
@@ -190,3 +189,7 @@ class TestDataBalance(unittest.TestCase):
     def test_all(self):
         self.main()
         self.main_lod()
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -39,17 +39,17 @@ class TestMultipleReader(unittest.TestCase):
         copyfile('./mnist_0.recordio', './mnist_1.recordio')
         copyfile('./mnist_0.recordio', './mnist_2.recordio')
 
-    def main(self, thread_num):
+    def main(self, is_test=False):
         file_list = [
             './mnist_0.recordio', './mnist_1.recordio', './mnist_2.recordio'
         ]
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             data_files = fluid.layers.open_files(
                 filenames=file_list,
-                thread_num=thread_num,
                 shapes=[(-1, 784), (-1, 1)],
                 lod_levels=[0, 0],
-                dtypes=['float32', 'int64'])
+                dtypes=['float32', 'int64'],
+                is_test=is_test)
             img, label = fluid.layers.read_file(data_files)
 
             if fluid.core.is_compiled_with_cuda():
@@ -71,6 +71,9 @@ class TestMultipleReader(unittest.TestCase):
             self.assertEqual(batch_count, self.num_batch * 3)
 
     def test_main(self):
-        self.main(thread_num=3)  # thread number equals to file number
-        self.main(thread_num=10)  # thread number is larger than file number
-        self.main(thread_num=2)  # thread number is less than file number
+        self.main(is_test=False)
+        self.main(is_test=True)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -33,9 +33,7 @@ def simple_fc_net(use_feed):
             filenames=[MNIST_RECORDIO_FILE],
             shapes=[[-1, 784], [-1, 1]],
             lod_levels=[0, 0],
-            dtypes=['float32', 'int64'],
-            thread_num=1,
-            for_parallel=True)
+            dtypes=['float32', 'int64'])
         reader = fluid.layers.io.double_buffer(reader)
         img, label = fluid.layers.read_file(reader)
     hidden = img
@@ -61,9 +59,7 @@ def fc_with_batchnorm(use_feed):
             filenames=[MNIST_RECORDIO_FILE],
             shapes=[[-1, 784], [-1, 1]],
             lod_levels=[0, 0],
-            dtypes=['float32', 'int64'],
-            thread_num=1,
-            for_parallel=True)
+            dtypes=['float32', 'int64'])
         reader = fluid.layers.io.double_buffer(reader)
         img, label = fluid.layers.read_file(reader)
 

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

@@ -45,12 +45,12 @@ class TestPyReader(unittest.TestCase):
             ) else fluid.CPUPlace()
             executor = fluid.Executor(place)
 
-            data_file, feed_queue = fluid.layers.py_reader(
+            data_file = fluid.layers.py_reader(
                 capacity=self.capacity,
                 dtypes=self.dtypes,
                 lod_levels=self.lod_levels,
                 shapes=self.shapes)
-
+            feed_queue = data_file.queue
             read_out_data = fluid.layers.read_file(data_file)
             self.inputs = []
 

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

@@ -52,11 +52,13 @@ def simple_fc_net(in_size,
                   batch_size,
                   queue_capacity,
                   use_double_buffer=False):
-    reader, feed_queue = fluid.layers.py_reader(
+    reader = fluid.layers.py_reader(
         capacity=queue_capacity,
         shapes=[[-1, in_size], [-1, 1]],
         lod_levels=[0, 0],
-        dtypes=['float32', 'int64'])
+        dtypes=['float32', 'int64'],
+        use_double_buffer=False)
+    feed_queue = reader.queue
     reader = fluid.layers.batch(reader, batch_size=batch_size)
     if use_double_buffer:
         reader = fluid.layers.double_buffer(reader)