Merge branch 'develop' into refine_relu_test

doc/v2/design/mkl/mkldnn.md

@@ -5,7 +5,7 @@
 充分展现英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。
 
 <div align="center">
-<img src="image/overview.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/overview.png"><br/>
 Figure 1. PaddlePaddle on IA
 </div>
 
@@ -42,16 +42,43 @@ Figure 1. PaddlePaddle on IA
 
 MKL，MKLML以及MKL-DNN三者关系如下表：
 
-| Name        |  Open Source     | License     | Descriptions  |
-| :---------- | :--------------- | :---------- | :------------ |
-|   MKL       |     No           | Proprietary | Accelerate math processing routines |
-|   MKLML     |     No           | Proprietary | Small package of MKL, especially for Machine Learning |
-|   MKL-DNN   |     Yes          | Apache 2.0  | Accelerate primitives processing routines especially for Deep Neural Networks  |
+<table>
+<thead>
+<tr>
+<th>Name</th>
+<th>Open Source</th>
+<th>License</th>
+<th>Descriptions</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>MKL</td>
+<td>No</td>
+<td>Proprietary</td>
+<td>Accelerate math processing routines</td>
+</tr>
+<tr>
+<td>MKLML</td>
+<td>No</td>
+<td>Proprietary</td>
+<td>Small package of MKL, especially for Machine Learning</td>
+</tr>
+
+<tr>
+<td>MKL-DNN</td>
+<td>Yes</td>
+<td>Apache 2.0</td>
+<td>Accelerate primitives processing routines especially for Deep Neural Networks</td>
+</tr>
+
+</tbody>
+</table>
 
 MKLML可以与MKL-DNN共同使用，以此达到最好的性能。
 
 <div align="center">
-<img src="image/engine.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/engine.png"><br/>
 Figure 2. PaddlePaddle with MKL Engines
 </div>
 
@@ -103,7 +130,7 @@ MKL-DNN的库目前只有动态库`libmkldnn.so`。
 所以我们定义了一个`MKLDNNMatrix`用于管理MKL-DNN数据的不同格式以及相互之间的转换。
 
 <div align="center">
-<img src="image/matrix.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/matrix.png"><br/>
 Figure 3. MKLDNNMatrix
 </div>
 
@@ -113,7 +140,7 @@ Figure 3. MKLDNNMatrix
 子类只需要使用定义好的接口，实现具体的函数功能即可。
 
 <div align="center">
-<img src="image/layers.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/layers.png"><br/>
 Figure 4. MKLDNNLayer
 </div>
 
@@ -150,7 +177,7 @@ Figure 4. MKLDNNLayer
 所以整体上，在实现每个子类的时候就不需要关心分支的事情了。
 
 <div align="center">
-<img src="image/gradients.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/gradients.png"><br/>
 Figure 5. Merge Gradients
 </div>
 

doc/v2/images/glossary_rnn.dot

+digraph G{
+	subgraph cluster_timestep0 {
+		label="recurrent timestep i-1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc0_0 [label="fc 0"]
+		fc0_1 [label="fc 1"]
+		fc0_2 [label="fc 2"]
+
+		fc0_0 -> fc0_1
+		fc0_1 -> fc0_2
+	}
+
+	subgraph cluster_timestep1 {
+		label="recurrent timestep i"
+		node [style=filled];
+		fc1_0 [label="fc 0"]
+		fc1_1 [label="fc 1"]
+		fc1_2 [label="fc 2"]
+		color=blue
+
+		fc1_0 -> fc1_1
+		fc1_1 -> fc1_2
+	}
+
+	subgraph cluster_timestep2 {
+		label="recurrent timestep i+1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc2_0 [label="fc 0"]
+		fc2_1 [label="fc 1"]
+		fc2_2 [label="fc 2"]
+
+		fc2_0 -> fc2_1
+		fc2_1 -> fc2_2
+	}
+	
+	
+	fc0_1 -> fc1_1 [style="dotted" constraint=false]
+	fc1_1 -> fc2_1 [style="dotted" constraint=false]
+
+}
\ No newline at end of file

doc/v2/images/glossary_rnn_with_memory.dot

+digraph G{
+	subgraph cluster_timestep0 {
+		label="recurrent timestep i-1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc0_0 [label="fc 0"]
+		fc0_1 [label="fc 1"]
+		fc0_2 [label="fc 2"]
+		m0 [label="memory"]
+		fc0_0 -> fc0_1
+		fc0_1 -> fc0_2
+		fc0_1 -> m0
+		m0 -> fc0_1
+	}
+
+	subgraph cluster_timestep1 {
+		label="recurrent timestep i"
+		node [style=filled];
+		fc1_0 [label="fc 0"]
+		fc1_1 [label="fc 1"]
+		fc1_2 [label="fc 2"]
+		m1 [label="memory"]
+		color=blue
+		fc1_0 -> fc1_1
+		fc1_1 -> fc1_2
+		fc1_1 -> m1
+		m1 -> fc1_1
+	}
+
+	subgraph cluster_timestep2 {
+		label="recurrent timestep i+1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc2_0 [label="fc 0"]
+		fc2_1 [label="fc 1"]
+		fc2_2 [label="fc 2"]
+		m2 [label="memory"]
+		fc2_0 -> fc2_1
+		fc2_1 -> fc2_2
+		fc2_1 -> m2
+		m2 -> fc2_1
+	}
+	
+	
+	m0 -> m1 [style="dotted" constraint=false]
+	m1 -> m2 [style="dotted" constraint=false]
+
+}
\ No newline at end of file

doc/v2/images/simple_full_hierarchical_recurrent.dot

+digraph G {
+  rankdir=LR;
+
+  subgraph cluster_t0 {
+    a [label="4"]
+    b [label="5"]
+    c [label="2"]
+  }
+  
+  subgraph cluster_t1 {
+    d [label="0"]
+    e [label="9"]
+  }
+
+  subgraph cluster_t2 {
+    f [label="8"]
+    g [label="1"]
+    h [label="4"]
+  }
+
+  a -> b;
+  b -> c;
+  c -> d [constraint=false];
+
+  d -> e;
+  e -> f [constraint=false];
+  
+  f -> g;
+  g -> h;
+}
\ No newline at end of file

doc/v2/images/simple_full_recurrent.dot

+digraph G {
+  rankdir=LR;
+  a [label="4"]
+  b [label="5"]
+  c [label="2"]
+  d [label="0"]
+  e [label="9"]
+  f [label="8"]
+  g [label="1"]
+  h [label="4"]
+
+  a -> b;
+  b -> c;
+  c -> d;
+  d -> e;
+  e -> f;
+  f -> g;
+  g -> h;
+}
\ No newline at end of file

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -49,8 +49,10 @@ void FetchOpHandle::RunImpl() {
       platform::DeviceContextPool::Instance().Get(platform::CPUPlace());
   for (auto *input : inputs_) {
     auto *var = static_cast<VarHandle *>(input);
+    if (var->generated_op_) {
       var->generated_op_->Wait(cpu_ctx);
     }
+  }
   tensors_.resize(inputs_.size());
   auto *var_handle = static_cast<VarHandle *>(inputs_[0]);
   auto &var_name = var_handle->name_;

paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc

@@ -36,8 +36,10 @@ void NCCLAllReduceOpHandle::RunImpl() {
     // Wait input done
     for (auto *in : inputs_) {
       auto &p = static_cast<VarHandle *>(in)->place_;
+      if (in->generated_op_) {
         in->generated_op_->Wait(dev_ctxes_[p]);
       }
+    }
 
     auto &var_name = static_cast<VarHandle *>(this->inputs_[0])->name_;
     int dtype = -1;

paddle/fluid/framework/details/send_op_handle.cc

@@ -32,8 +32,10 @@ void SendOpHandle::RunImpl() {
     if (in->DebugString() == "dummy") {  // HACK
       continue;
     }
+    if (in->generated_op_) {
       in->generated_op_->Wait(dev_ctxes_[p]);
     }
+  }
   auto &tmp_scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
   // FIXME(wuyi): can not use RunAndRecordEvent here, for it will cause dead
   // lock.

paddle/fluid/inference/CMakeLists.txt

@@ -21,6 +21,8 @@ endif()
 
 if(WITH_TESTING)
   add_subdirectory(tests/book)
+  # analysis test depends the models that generate by python/paddle/fluid/tests/book
+  add_subdirectory(analysis)
 endif()
 
 if (TENSORRT_FOUND)

paddle/fluid/inference/analysis/CMakeLists.txt

+cc_library(dot SRCS dot.cc)

paddle/fluid/inference/analysis/dot.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/dot.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+size_t Dot::counter = 0;
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/dot.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 implements some helper classes and methods for DOT programming
+ * support. It will give a visualization of the graph and that helps to debug
+ * the logics of each Pass.
+ */
+#pragma once
+
+#include <glog/logging.h>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * A Dot template that helps to build a DOT graph definition.
+ */
+class Dot {
+ public:
+  static size_t counter;
+
+  struct Attr {
+    std::string key;
+    std::string value;
+
+    Attr(const std::string& key, const std::string& value)
+        : key(key), value(value) {}
+
+    std::string repr() const {
+      std::stringstream ss;
+      ss << key << "=" << '"' << value << '"';
+      return ss.str();
+    }
+  };
+
+  struct Node {
+    std::string name;
+    std::vector<Attr> attrs;
+
+    Node(const std::string& name, const std::vector<Attr>& attrs)
+        : name(name),
+          attrs(attrs),
+          id_("node_" + std::to_string(Dot::counter++)) {}
+
+    std::string id() const { return id_; }
+
+    std::string repr() const {
+      std::stringstream ss;
+      CHECK(!name.empty());
+      ss << id_;
+      for (size_t i = 0; i < attrs.size(); i++) {
+        if (i == 0) {
+          ss << "[label=" << '"' << name << '"' << " ";
+        }
+        ss << attrs[i].repr();
+        ss << ((i < attrs.size() - 1) ? " " : "]");
+      }
+      return ss.str();
+    }
+
+   private:
+    std::string id_;
+  };
+
+  struct Edge {
+    std::string source;
+    std::string target;
+    std::vector<Attr> attrs;
+
+    Edge(const std::string& source, const std::string& target,
+         const std::vector<Attr>& attrs)
+        : source(source), target(target), attrs(attrs) {}
+
+    std::string repr() const {
+      std::stringstream ss;
+      CHECK(!source.empty());
+      CHECK(!target.empty());
+      ss << source << "->" << target;
+      for (size_t i = 0; i < attrs.size(); i++) {
+        if (i == 0) {
+          ss << "[";
+        }
+        ss << attrs[i].repr();
+        ss << ((i < attrs.size() - 1) ? " " : "]");
+      }
+      return ss.str();
+    }
+  };
+
+  Dot() = default;
+
+  explicit Dot(const std::vector<Attr>& attrs) : attrs_(attrs) {}
+
+  void AddNode(const std::string& name, const std::vector<Attr>& attrs) {
+    CHECK(!nodes_.count(name)) << "duplicate Node '" << name << "'";
+    nodes_.emplace(name, Node{name, attrs});
+  }
+
+  void AddEdge(const std::string& source, const std::string& target,
+               const std::vector<Attr>& attrs) {
+    CHECK(!source.empty());
+    CHECK(!target.empty());
+    auto sid = nodes_.at(source).id();
+    auto tid = nodes_.at(target).id();
+    edges_.emplace_back(sid, tid, attrs);
+  }
+
+  // Compile to DOT language codes.
+  std::string Build() const {
+    std::stringstream ss;
+    const std::string indent = "   ";
+    ss << "digraph G {" << '\n';
+
+    // Add graph attrs
+    for (const auto& attr : attrs_) {
+      ss << indent << attr.repr() << '\n';
+    }
+    // add nodes
+    for (auto& item : nodes_) {
+      ss << indent << item.second.repr() << '\n';
+    }
+    // add edges
+    for (auto& edge : edges_) {
+      ss << indent << edge.repr() << '\n';
+    }
+    ss << "} // end G";
+    return ss.str();
+  }
+
+ private:
+  std::unordered_map<std::string, Node> nodes_;
+  std::vector<Edge> edges_;
+  std::vector<Attr> attrs_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/engine.h

@@ -14,11 +14,15 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include "paddle/fluid/framework/framework.pb.h"
 
 namespace paddle {
 namespace inference {
 
+struct Buffer;
+enum class DeviceType { UNK = -1, CPU, GPU };
+
 /*
  * EngineBase is the base class of all inference engines. An inference engine
  * takes a paddle program as input, and outputs the result in fluid Tensor
@@ -45,8 +49,20 @@ class EngineBase {
   // Execute the engine, that will run the inference network.
   virtual void Execute(int batch_size) = 0;
 
+  // Return the IO buffer that allocated in engine. One can read/write directly
+  // on the buffer. If the buffer's buffer is nullptr, one can also allocate
+  // memory and maintain it outside the engine.
+  virtual Buffer& buffer(const std::string& name) = 0;
+
   virtual ~EngineBase() {}
 };  // class EngineBase
 
+struct Buffer {
+  void* buffer{nullptr};               // buffer should be allocated only once.
+  int max_size;                        // buffer allocated space.
+  int size;                            // data size.
+  DeviceType device{DeviceType::UNK};  // tells which device this buffer is on.
+};
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/CMakeLists.txt

+nv_library(tensorrt_engine SRCS engine.cc)
 nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
-nv_test(test_tensorrt_engine SRCS test_engine.cc engine.cc DEPS dynload_cuda)
-set(ENGINE_FILE ${CMAKE_CURRENT_SOURCE_DIR}/engine.cc)
+nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
+
 add_subdirectory(convert)

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

 nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS ${FLUID_CORE_MODULES})
-nv_test(test_trt_activation_op SRCS test_activation_op.cc io_converter.cc ${ENGINE_FILE} activation_op.cc 
-  DEPS ${FLUID_CORE_MODULES} activation_op)
+nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
+  DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine)
 nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)

paddle/fluid/inference/tensorrt/engine.cc

@@ -30,16 +30,24 @@ void TensorRTEngine::Build(const DescType& paddle_model) {
 }
 
 void TensorRTEngine::Execute(int batch_size) {
-  infer_context_->enqueue(batch_size, buffers_.data(), *stream_, nullptr);
+  std::vector<void*> buffers;
+  for (auto& buf : buffers_) {
+    PADDLE_ENFORCE_NOT_NULL(buf.buffer, "buffer should be allocated");
+    PADDLE_ENFORCE_GT(buf.max_size, 0);
+    PADDLE_ENFORCE(buf.device == DeviceType::GPU);
+    buffers.push_back(buf.buffer);
+  }
+  infer_context_->enqueue(batch_size, buffers.data(), *stream_, nullptr);
   cudaStreamSynchronize(*stream_);
 }
 
 TensorRTEngine::~TensorRTEngine() {
   // clean buffer
-  for (auto& buffer : buffers_) {
-    if (buffer != nullptr) {
-      PADDLE_ENFORCE_EQ(0, cudaFree(buffer));
-      buffer = nullptr;
+  for (auto& buf : buffers_) {
+    if (buf.buffer != nullptr) {
+      PADDLE_ENFORCE_EQ(0, cudaFree(buf.buffer));
+      buf.buffer = nullptr;
+      buf.max_size = 0;
     }
   }
 }
@@ -59,7 +67,7 @@ void TensorRTEngine::FreezeNetwork() {
   infer_context_.reset(infer_engine_->createExecutionContext());
 
   // allocate GPU buffers.
-  buffers_.resize(buffer_sizes_.size(), nullptr);
+  buffers_.resize(buffer_sizes_.size());
   for (auto& item : buffer_sizes_) {
     if (item.second == 0) {
       auto slot_offset = infer_engine_->getBindingIndex(item.first.c_str());
@@ -67,7 +75,11 @@ void TensorRTEngine::FreezeNetwork() {
                         infer_engine_->getBindingDataType(slot_offset))] *
                     AccumDims(infer_engine_->getBindingDimensions(slot_offset));
     }
-    PADDLE_ENFORCE_EQ(0, cudaMalloc(&buffer(item.first), item.second));
+    auto& buf = buffer(item.first);
+    CHECK(buf.buffer == nullptr);  // buffer should be allocated only once.
+    PADDLE_ENFORCE_EQ(0, cudaMalloc(&buf.buffer, item.second));
+    buf.size = buf.max_size = item.second;
+    buf.device = DeviceType::GPU;
   }
 }
 
@@ -113,7 +125,7 @@ void TensorRTEngine::DeclareOutput(const std::string& name) {
 }
 
 void* TensorRTEngine::GetOutputInGPU(const std::string& name) {
-  return buffer(name);
+  return buffer(name).buffer;
 }
 
 void TensorRTEngine::GetOutputInCPU(const std::string& name, void* dst,
@@ -123,11 +135,13 @@ void TensorRTEngine::GetOutputInCPU(const std::string& name, void* dst,
   PADDLE_ENFORCE(it != buffer_sizes_.end());
   PADDLE_ENFORCE_GT(it->second, 0);
   PADDLE_ENFORCE_GE(max_size, it->second);
-  PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(dst, buffer(name), it->second,
+  auto& buf = buffer(name);
+  PADDLE_ENFORCE_NOT_NULL(buf.buffer, "buffer should be allocated before");
+  PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(dst, buf.buffer, it->second,
                                        cudaMemcpyDeviceToHost, *stream_));
 }
 
-void*& TensorRTEngine::buffer(const std::string& name) {
+Buffer& TensorRTEngine::buffer(const std::string& name) {
   PADDLE_ENFORCE(infer_engine_ != nullptr, "call FreezeNetwork first.");
   auto it = buffer_sizes_.find(name);
   PADDLE_ENFORCE(it != buffer_sizes_.end());
@@ -137,9 +151,12 @@ void*& TensorRTEngine::buffer(const std::string& name) {
 
 void TensorRTEngine::SetInputFromCPU(const std::string& name, void* data,
                                      size_t size) {
-  void* buf = buffer(name);
-  PADDLE_ENFORCE_EQ(
-      0, cudaMemcpyAsync(buf, data, size, cudaMemcpyHostToDevice, *stream_));
+  auto& buf = buffer(name);
+  PADDLE_ENFORCE_NOT_NULL(buf.buffer);
+  PADDLE_ENFORCE_LE(size, buf.max_size, "buffer is too small");
+  PADDLE_ENFORCE(buf.device == DeviceType::GPU);
+  PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(buf.buffer, data, size,
+                                       cudaMemcpyHostToDevice, *stream_));
 }
 
 void TensorRTEngine::SetITensor(const std::string& name,

paddle/fluid/inference/tensorrt/engine.h

@@ -87,7 +87,9 @@ class TensorRTEngine : public EngineBase {
   // these memory directly for acceleration, for example, output the converted
   // data directly to the buffer to save data copy overhead.
   // NOTE this should be used after calling `FreezeNetwork`.
-  void*& buffer(const std::string& name);
+  Buffer& buffer(const std::string& name) override;
+
+  cudaStream_t* stream() { return stream_; }
 
   // Fill an input from CPU memory with name and size.
   void SetInputFromCPU(const std::string& name, void* data, size_t size);
@@ -116,7 +118,7 @@ class TensorRTEngine : public EngineBase {
   cudaStream_t* stream_;
   nvinfer1::ILogger& logger_;
 
-  std::vector<void*> buffers_;
+  std::vector<Buffer> buffers_;
   // max data size for the buffers.
   std::unordered_map<std::string /*name*/, size_t /*max size*/> buffer_sizes_;
   std::unordered_map<std::string /*name*/, nvinfer1::ITensor* /*ITensor*/>

paddle/fluid/operators/load_op.cc

@@ -46,19 +46,6 @@ class LoadOp : public framework::OperatorBase {
     auto *tensor = out_var->GetMutable<framework::LoDTensor>();
 
     DeserializeFromStream(fin, tensor, *dev_ctx);
-
-    if (platform::is_gpu_place(place)) {
-      // copy CPU to GPU
-      framework::LoDTensor cpu_tensor;
-      cpu_tensor.ShareDataWith(*tensor);
-      cpu_tensor.set_lod(tensor->lod());
-
-      // reset tensor
-      out_var->Clear();
-      tensor = out_var->GetMutable<framework::LoDTensor>();
-      tensor->set_lod(cpu_tensor.lod());
-      TensorCopy(cpu_tensor, place, *dev_ctx, tensor);
-    }
   }
 };
 

paddle/fluid/platform/profiler.cc

@@ -463,7 +463,7 @@ void SetProfileListener() {
   std::mt19937 rng;
   rng.seed(std::random_device()());
   std::uniform_int_distribution<std::mt19937::result_type> dist6(
-      1, std::numeric_limits<int64_t>::max());
+      1, std::numeric_limits<std::mt19937::result_type>::max());
   profiler_lister_id = dist6(rng);
 }
 int64_t ListenerId() { return profiler_lister_id; }

python/paddle/dataset/wmt16.py

@@ -96,7 +96,7 @@ def __get_dict_size(src_dict_size, trg_dict_size, src_lang):
     src_dict_size = min(src_dict_size, (TOTAL_EN_WORDS if src_lang == "en" else
                                         TOTAL_DE_WORDS))
     trg_dict_size = min(trg_dict_size, (TOTAL_DE_WORDS if src_lang == "en" else
-                                        TOTAL_ENG_WORDS))
+                                        TOTAL_EN_WORDS))
     return src_dict_size, trg_dict_size
 
 

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

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy
+import numpy as np
 import unittest
 
 import paddle.fluid as fluid
@@ -243,7 +243,7 @@ class TestParallelExecutorBase(unittest.TestCase):
             begin = time.time()
             first_loss, = run_executor(
                 exe=exe, feed=feed_dict, fetch_list=[loss.name])
-            first_loss = numpy.array(first_loss)
+            first_loss = np.array(first_loss)
 
             for i in xrange(iter):
                 run_executor(exe=exe, feed=feed_dict, fetch_list=[])
@@ -256,7 +256,7 @@ class TestParallelExecutorBase(unittest.TestCase):
                 print "%.4f Instance per second" % (
                     (batch_size * iter + 2) / (end - begin))
 
-            last_loss = numpy.array(last_loss)
+            last_loss = np.array(last_loss)
 
             print first_loss, last_loss
             # self.assertGreater(first_loss[0], last_loss[0])
@@ -284,8 +284,8 @@ class TestMNIST(TestParallelExecutorBase):
         self.check_network_convergence(simple_fc_net)
         self.check_network_convergence(simple_fc_net, allow_op_delay=True)
 
-        img = numpy.zeros(shape=[32, 784], dtype='float32')
-        label = numpy.ones(shape=[32, 1], dtype='int64')
+        img = np.zeros(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
         self.check_network_convergence(
             simple_fc_net, feed_dict={"image": img,
                                       "label": label})
@@ -294,8 +294,8 @@ class TestMNIST(TestParallelExecutorBase):
         self.check_simple_fc_convergence()
 
     def check_simple_fc_parallel_accuracy(self):
-        img = numpy.zeros(shape=[32, 784], dtype='float32')
-        label = numpy.ones(shape=[32, 1], dtype='int64')
+        img = np.zeros(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
         single_first_loss, single_last_loss = self.check_network_convergence(
             method=simple_fc_net,
             seed=1000,
@@ -319,8 +319,8 @@ class TestMNIST(TestParallelExecutorBase):
 
     def check_batchnorm_fc_convergence(self):
         self.check_network_convergence(fc_with_batchnorm)
-        img = numpy.zeros(shape=[32, 784], dtype='float32')
-        label = numpy.ones(shape=[32, 1], dtype='int64')
+        img = np.zeros(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
         self.check_network_convergence(
             fc_with_batchnorm, feed_dict={"image": img,
                                           "label": label})
@@ -404,9 +404,6 @@ class ModelHyperParams(object):
     dropout = 0.1
 
 
-import numpy as np
-
-
 def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
     """
     Pad the instances to the max sequence length in batch, and generate the
@@ -533,9 +530,8 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
             opt.minimize(loss)
 
             batch_size = 32
-            image = numpy.random.normal(size=(batch_size,
-                                              784)).astype('float32')
-            label = numpy.random.randint(0, 10, (batch_size, 1), dtype="int64")
+            image = np.random.normal(size=(batch_size, 784)).astype('float32')
+            label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
 
             place = fluid.CUDAPlace(0)
             exe = fluid.Executor(place)
@@ -552,12 +548,12 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
 
             for i in xrange(5):
                 test_loss, = test_exe.run([loss.name], feed=feed_dict)
-                test_loss = numpy.array(test_loss)
+                test_loss = np.array(test_loss)
 
                 train_loss, = train_exe.run([loss.name], feed=feed_dict)
-                train_loss = numpy.array(train_loss)
+                train_loss = np.array(train_loss)
                 self.assertTrue(
-                    numpy.allclose(
+                    np.allclose(
                         train_loss, test_loss, atol=1e-8),
                     "Train loss: " + str(train_loss) + "\n Test loss:" +
                     str(test_loss))
@@ -712,7 +708,7 @@ class TestCRFModel(unittest.TestCase):
             data = train_data()
             for i in xrange(10):
                 cur_batch = next(data)
-                print map(numpy.array,
+                print map(np.array,
                           pe.run(feed=feeder.feed(cur_batch),
                                  fetch_list=[avg_cost.name]))[0]
 
@@ -721,3 +717,84 @@ class TestCRFModel(unittest.TestCase):
 
     def test_update_dense_parameter(self):
         self.check_network_convergence(is_sparse=False)
+
+
+# test fetch all the variables of global_block
+
+import paddle.dataset.flowers as flowers
+import math
+
+
+def Lenet(data, class_dim):
+    conv1 = fluid.layers.conv2d(data, 32, 5, 1, act=None)
+    bn1 = fluid.layers.batch_norm(conv1, act='relu')
+    pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
+    conv2 = fluid.layers.conv2d(pool1, 50, 5, 1, act=None)
+    bn2 = fluid.layers.batch_norm(conv2, act='relu')
+    pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
+
+    fc1 = fluid.layers.fc(pool2, size=500, act='relu')
+    fc2 = fluid.layers.fc(fc1, size=class_dim, act='softmax')
+
+    return fc2
+
+
+class TestFetchOp(unittest.TestCase):
+    def parallel_exe(self, train_inputs, seed):
+        main = fluid.Program()
+        startup = fluid.Program()
+        startup.random_seed = seed
+        with fluid.program_guard(main, startup):
+            data = fluid.layers.data(
+                name='image', shape=[3, 224, 224], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            out = Lenet(data, class_dim=102)
+            loss = fluid.layers.cross_entropy(input=out, label=label)
+            loss = fluid.layers.mean(loss)
+
+            opt = fluid.optimizer.Momentum(
+                learning_rate=0.1,
+                momentum=0.9,
+                regularization=fluid.regularizer.L2Decay(1e-4))
+
+            opt.minimize(loss)
+
+            # TODO(zcd): I found that onece the memory optimizer is open,
+            # parallel_exe doesn't fetch some variable, such as conv2d_0.b_0@GRAD,
+            # conv2d_1.b_0@GRAD. Those variables should not be pruned.
+            # fluid.memory_optimize(main)
+
+            place = fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+            exe.run(startup)
+
+            feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
+            pe = fluid.ParallelExecutor(
+                use_cuda=True, loss_name=loss.name, main_program=main)
+
+            fetch_list = []
+            all_vars = main.global_block().vars
+            for k, v in all_vars.iteritems():
+                if 'tmp' not in k and k[0] is not '_' or v.persistable:
+                    fetch_list.append(k)
+
+            for data in train_inputs:
+                ret = pe.run(fetch_list, feed=feeder.feed(data))
+                for i in range(len(fetch_list)):
+                    assert not math.isnan(np.sum(ret[i])) and \
+                           not math.isinf(np.sum(ret[i]))
+
+    def test_update_sparse_parameter(self):
+        tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
+        tst_reader_iter = tst_reader()
+
+        iters = 3
+        train_inputs = []
+        for i in range(iters):
+            train_inputs.append(tst_reader_iter.next())
+
+        self.parallel_exe(train_inputs, seed=1)
+
+
+if __name__ == '__main__':
+    unittest.main()