Merge branch 'develop' into add-async-listen-and-serv-op

Dockerfile

@@ -49,7 +49,11 @@ ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
 RUN curl -s -q https://glide.sh/get | sh
 
 # Install TensorRT
-# The unnecessary files has been removed to make the library small. It only contains include and lib now.
+# following TensorRT.tar.gz is not the default official one, we do two miny changes:
+# 1. Remove the unnecessary files to make the library small. TensorRT.tar.gz only contains include and lib now,
+#    and its size is only one-third of the official one.
+# 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle.
+#    See https://github.com/PaddlePaddle/Paddle/issues/10129 for details.
 RUN wget -qO- http://paddlepaddledeps.bj.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
     tar -xz -C /usr/local && \
     cp -rf /usr/local/TensorRT/include /usr && \

cmake/tensorrt.cmake

@@ -30,4 +30,6 @@ if(TENSORRT_FOUND)
 
     message(STATUS "Current TensorRT header is ${TENSORRT_INCLUDE_DIR}/NvInfer.h. "
         "Current TensorRT version is v${TENSORRT_MAJOR_VERSION}. ")
+    include_directories(${TENSORRT_INCLUDE_DIR})
+    list(APPEND EXTERNAL_LIBS ${TENSORRT_LIBRARY})
 endif()

doc/fluid/api/data.rst

+==================================
+Data Reader Interface and DataSets
+==================================
+
+..  toctree::
+    :maxdepth: 1
+
+    data/data_reader.rst
+    data/image.rst
+    data/dataset.rst

doc/fluid/api/data/data_reader.rst

+=====================
+Data Reader Interface
+=====================
+
+
+DataTypes
+=========
+
+..  autofunction:: paddle.v2.data_type.dense_array
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.integer_value
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.integer_value_sequence
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.integer_value_sub_sequence
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_binary_vector
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_binary_vector_sequence
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_binary_vector_sub_sequence
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_float_vector
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_float_vector_sequence
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_float_vector_sub_sequence
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_non_value_slot
+    :noindex:
+
+..  autofunction:: paddle.v2.data_type.sparse_value_slot
+    :noindex:
+
+..  autoclass:: paddle.v2.data_type.InputType
+    :members:
+    :noindex:
+
+DataFeeder
+==========
+
+..  automodule:: paddle.v2.data_feeder
+    :members:
+    :noindex:
+
+Reader
+======
+
+..  automodule:: paddle.v2.reader
+    :members:
+    :noindex:
+
+..  automodule:: paddle.v2.reader.creator
+    :members:
+    :noindex:
+
+minibatch
+=========
+
+..  automodule:: paddle.v2.minibatch
+    :members:
+    :noindex:

doc/fluid/api/data/dataset.rst

+Dataset
+=======
+
+..  automodule:: paddle.dataset
+    :members:
+    :noindex:
+
+mnist
++++++
+
+..  automodule:: paddle.dataset.mnist
+    :members:
+    :noindex:
+
+cifar
++++++
+
+..  automodule:: paddle.dataset.cifar
+    :members:
+    :noindex:
+
+conll05
++++++++
+
+..  automodule:: paddle.dataset.conll05
+    :members: get_dict,get_embedding,test
+    :noindex:
+
+imdb
+++++
+
+..  automodule:: paddle.dataset.imdb
+    :members:
+    :noindex:
+
+imikolov
+++++++++
+
+..  automodule:: paddle.dataset.imikolov
+    :members:
+    :noindex:
+
+movielens
++++++++++
+
+..  automodule:: paddle.dataset.movielens
+    :members:
+    :noindex:
+
+..  autoclass:: paddle.dataset.movielens.MovieInfo
+    :noindex:
+
+..  autoclass:: paddle.dataset.movielens.UserInfo
+    :noindex:
+
+sentiment
++++++++++
+
+..  automodule:: paddle.dataset.sentiment
+    :members:
+    :noindex:
+
+uci_housing
++++++++++++
+
+..  automodule:: paddle.dataset.uci_housing
+    :members:
+    :noindex:
+
+wmt14
++++++
+
+..  automodule:: paddle.dataset.wmt14
+    :members:
+    :noindex:
+
+wmt16
++++++
+
+..  automodule:: paddle.dataset.wmt16
+    :members:
+    :noindex:

doc/fluid/api/data/image.rst

+Image Interface
+===============
+
+..  automodule:: paddle.v2.image
+    :members:

doc/fluid/api/index_en.rst

@@ -16,3 +16,4 @@ Fluid
     profiler.rst
     regularizer.rst
     io.rst
+    data.rst

doc/fluid/design/dynamic_rnn/rnn_design_en.md

+# Varient Length supported RNN Design
+For the learning of variable length sequences, the existing mainstream frameworks such as tensorflow, pytorch, caffe2, mxnet and so on all use padding.
+
+Different-length sequences in a mini-batch will be padded with zeros and transformed to same length.
+
+The existing RNN implementations of the PaddlePaddle is `RecurrentLayerGroup`, 
+which supports the variable length sequences without padding. 
+This doc will design fluid's RNN based on this idea.
+
+## Multi-layer sequence data format `LODTensor`
+At present, Paddle stores data in one mini-batch in one-dimensional array.
+
+`Argument.sequenceStartPositions` is used to store information for each sentence.
+
+In Paddle, `Argument.subSequenceStartPositions` is used to store 2 levels of sequence information, while higher dimensional sequences can not be supported.
+
+In order to support the storage of `N-level` sequences, we define sequence information as the following data structure.
+
+
+```c++
+std::shared_ptr<std::vector<std::vector<int>>> lod_start_pos_;
+```
+
+Or more clearly defined here
+
+```c++
+typedef std::vector<int> level_t;
+std::vector<level_t> lod_start_pos;
+```
+Each `level_t` here stores a level of offset information consistent with paddle's current practice.
+
+In order to transmit sequence information more transparently, we have introduced a new tensor called `LODTensor`[1].
+Its tensor-related interfaces all inherit directly from `Tensor`, but it also adds serial-related interfaces.
+Thus, when working with a `LODTensor`, ordinary `Op` is used directly as `Tensor`.
+The `Op` of the operation sequence will additionally operate the relevant interface of the `LODTensor` variable-length sequence operation.
+
+The definition of `LODTensor` is as follows:
+
+
+```c++
+class LODTensor : public Tensor {
+public:
+  size_t Levels() const { return seq_start_positions_.size(); }
+  size_t Elements(int level = 0) const {
+    return seq_start_positions_[level].size();
+  }
+  // slice of level[elem_begin: elem_end]
+  // NOTE low performance in slice seq_start_positions_.
+  // TODO should call Tensor's Slice.
+  LODTensor LODSlice(int level, int elem_begin, int elem_end) const;
+
+  // slice with tensor's data shared with this.
+  LODTensor LODSliceShared(int level, int elem_begin, int elem_end) const;
+
+  // copy other's lod_start_pos_, to share LOD info.
+  // NOTE the LOD info sould not be changed.
+  void ShareConstLODFrom(const LODTensor &other) {
+    lod_start_pos_ = other.lod_start_pos_;
+  }
+  // copy other's lod_start_pos_'s content, free to mutate.
+  void ShareMutableLODFrom(const LODTensor &other) {
+    lod_start_pos_ = std::make_shared <
+                     std::vector<std::vector<int>>(other.lod_start_pos_.begin(),
+                                                   other.lod_start_pos_.end());
+  }
+
+private:
+  std::shared_ptr<std::vector<std::vector<int>>> lod_start_pos_;
+};
+```
+Among them, `lod_start_pos_` uses `shared_ptr` to reduce the cost of storage and replication.
+`LODTensor` can be thought as an extension of `Tensor`, which is almost completely compatible with the original `Tensor`.
+
+## How to support the framework
+### Replace `Tensor` with `LoDTensor`
+To implement the passing of `LODTensor`, most `Tensor` in the framework need to be replaced with `LODTensor`.
+Simple implementation, directly **replace all previous `Tensor` with `LODTensor`** , where you can directly modify the `Tensor` interface created in `pybind.cc`.
+
+In addition, the user may need to perceive the existence of a sequence (such as the sequence of the visualization needs to parse the output sequence in the model), so some of the serial operation APIs also need to be exposed to the python layer.
+
+### Transmit `lod_start_pos` along with the Op call chain
+`lod_start_pos` is passed along with the Op call chain
+The framework needs to support the following features to implement the transmit of `lod_start_pos`:
+
+1. Implement the transfer as `shared_ptr`
+    - Do not modify the contents of `lod_start_pos` as a consumer
+    - Modify producer of `lod_start_pos` as producer
+    - Conventions consumer only needs to copy `shared_ptr` passed over
+    - producer needs to create its own independent memory to store its own independent modifications and expose `shared_ptr` to subsequent consumer
+    - Since the transfer process is implemented by copying `shared_ptr`, the framework only needs to pass `lod_start_pos` once.
+
+2. Op is transparent enough not to sense `lod_start_pos`
+3. Producer Op that needs to modify `lod_start_pos` can update its `lod_start_pos` data when `Run`
+
+## sorted by length
+After sorting by length, the batch size from the forward time step will naturally decrement, and you can directly plug it into Net to do the batch calculation.
+
+For example, the original input:
+
+```
+origin:
+xxxx
+xx
+xxx
+
+-> sorted:
+xxxx
+xxx
+xx
+```
+
+After `SegmentInputs`, there will be 4 time steps, the input of each time step is as follows (vertical arrangement)
+
+```
+0    1    2    3
+x    x    x    x
+x    x    x
+x    x
+```
+
+In order to track the changes before and after sorting, use here
+
+```c++
+struct SortedSeqItem {
+   void *start{nullptr};
+   void *end{nullptr};
+};
+
+std::vector<SortedSeqItem> sorted_seqs;
+```
+To track the position of the sequence after sorting, and add a new interface
+
+```c++
+std::vector<SortedSeqItem> SortBySeqLen(const LODTensor& tensor);
+```
+Due to the sequence of input sequences, the following existing interfaces need to be modified:
+
+- InitMemories, memory needs to be rearranged according to `sorted_seqs`
+- SetmentInputs
+- ConcatOutputs
+
+In addition, because `sorted_seqs` needs to be multiplexed with `RecurrentGradientOp`, it will become a new output of `RecurrentOp`.
+It is passed in as an input to `RecurrentGradientOp`.
+
+## InitMemories
+Due to the sequence change, the order of the elements on the `boot_memories` batch also needs to be rearranged accordingly.
+
+## SegmentInputs
+
+`SegmentInputs` relies on the information of `sorted_seqs` to cut the original sequence from the horizontal to the input of each step in the sorted sequence order.
+
+the transition is as follows:
+```
+origin:
+xxxx
+xx
+xxx
+
+   |
+   |
+  \ /
+   !
+0    1    2    3
+x    x    x    x
+x    x    x
+x    x
+```
+## ConcatOutputs
+`ConcatOutputs` needs
+
+- Restore the output of each time step back to the original input sequence order (to prevent the order of Infer phase from being upset)
+- Concat each sequence as a regular mini-batch representation
+
+## references
+1. [Level of details](https://en.wikipedia.org/wiki/Level_of_detail)

doc/fluid/design/onnx/onnx_convertor.md

+# Background
+
+[ONNX (Open Neural Network Exchange)](https://github.com/onnx/onnx) bridges different deep learning frameworks by providing an open source graph format for models. The models trained in other frameworks can be converted into the ONNX format to execute inference by utilizing the built-in operators in ONNX - this is called a **frontend**. With the inverse conversion (called a **backend**), different frameworks can share any models supported by ONNX in principle. Now most mainstream frameworks have joined the ONNX community, e.g. Caffe2, PyTorch, and MXNet etc. And there is a momentum driving more and more vendors to begin supporting ONNX or even choose ONNX as the only machine learning runtime in their devices.
+
+Therefore, it is necessary to enable the conversion between PaddlePaddle and ONNX. This design doc is aimed at implementing a convertor, mainly for converting between **Fluid** models and ONNX (it is very likely that we may support older v2 models in the future). A complete convertor should be bidirectional - with a frontend AND a backend, but considering the importance, the we will start with the frontend i.e. Fluid models to ONNX models.
+
+
+# How it works
+
+ONNX has a [working list of operators](https://github.com/onnx/onnx/blob/master/docs/Operators.md) which is versioned.
+
+When prioritizing implementation of a frontend over a backend, choice of coverage of Fluid -> ONNX operators comes down to choices of models to be supported (see section `Supported models`). Eventually, this will allow us to reach a really-wide coverage of all operators.
+
+Here are a few major considerations when it comes to converting models:
+
+- **Op-level conversion**: How to map the inputs, attributes, and outputs of each Paddle operator to those of the ONNX operator. In several cases, these require transformations. For each direction (frontend vs. backend), a different conversion mapping is needed.
+- **Parameters (weights) initialization**: Setting initial parameters on different nodes.
+- **Tensor data type mapping** (Note: Some ONNX data types are not supported in Fluid)
+- **Network representation adaption**: Fluid `ProgramDesc` include nested blocks. Since ONNX is free of nesting, the `ProgramDesc` ops need to be traversed to only include ops from the global scope in the root block. The variables used as inputs and outputs should also be in this scope.
+- **Model validation**: There are two kinds of validations that are necessary:
+   1. We need to ensure that the inference outputs of the ops in run inside a model are the same as those when running the ONNX converted ops through an alternative ONNX backend.
+   2. Checking to see if the generated nodes on the graph are validated by the internal ONNX checkers.
+- **Versioning**: ONNX versions its op listing over versions. In fact, it has versioning on 3 different levels: ops, graphs, and ONNX models. This requires that we are conscious about versioning the convertor and updating tests and op convertor logic for each release. It also implies that we release pre-trained ONNX models upon each version release.
+
+One thing that makes this conversion more feasible in Fluid's case is the use of a static IR - the `ProgramDesc` - as opposed to a dynamic graph, as created in the cases of frameworks like PyTorch.
+
+
+# Project structure
+
+<p align="center">
+<img src="./images/project_structure.png"/>
+</p>
+
+The project contains four important parts:
+
+* **fluid**: The directory that contains wrappers for fluid related APIs. Fluid has provided some low-level APIs to parse or generate the inference model. However, directly using these low-level APIs makes the code tediously long. This module wraps low-level APIs to provide simplified interfaces.
+
+* **onnx**: This is a Python package provided by ONNX containing helpers for creating nodes, graphs, and eventually binary protobuf models with initializer parameters.
+
+* **onnx_fluid**: Contains two-way mapping (Fluid -> ONNX ops and ONNX -> Fluid ops). Called from `convert.py`, the program uses this mapping along with modifier functions to construct ONNX nodes with the help of ONNX's `make_node` helper. It also contains mapping between datatypes and tensor deprecation / amplification logic.
+
+* **convert.py**: The interface exposed to users. This will traverse the global program blocks/variables and construct the write-able model.
+
+
+# Usage
+The converter should be designed to very easy-to-use. Bidirectional conversion between a Fluid inference model and an ONNX binary model will be supported. Model validation will also provided to verify the correctness of converted model.
+
+* Convert Fluid inference model to ONNX binary model
+
+    ```
+    python convert.py --fluid_model <fluid inference model> --onnx_model <ONNX model> validate True
+    ```
+
+* Validate the converted model
+
+    ```
+    python validate.py --fluid_model <fluid inference model> --onnx_model <ONNX model>
+    ```
+
+The conversion and model validation will be completed consecutively, finally output a readable model structure description. And for the converse conversion, users only need to exchange the input and output.
+
+
+# Challenges and mitigation
+
+## Cycles
+
+Cycles are unsupported in ONNX. In Paddle, the `while` op is the most prominent example of a cycle.
+
+*Resolution*: We won't support models with `while`s which can't be substituted until ONNX adds support for such ops.
+
+## Sequences
+
+Sequence processing operators like `sequence_expand`, `sequence_reshape`, `sequence_concat`, and `sequence_pool` are not supported by ONNX as well, because they do not support non-padded datatypes like LoDTensors.
+
+*Resolution*: Since the runtimes using our ONNX exported graphs won't be using LoDTensors in the first place, such sequence operators should be mapped to ONNX ops that will do the necessary transposing ops with the knowledge of the padding and shape of the Tensors.
+
+## Ops that can't easily be mapped
+
+There are ops that just aren't possible to map today:
+
+**Control flow operators**
+
+Paddle supports control flow ops like `If/Else` and `Switch` (if we ignore the CSP operations like `select` for now). ONNX has `If` support in the experimental phase.
+
+*Resolution*: Map Paddle's `If/Else` to ONNX's `If`, but ignore other control flow operators until ONNX brings support for them.
+
+
+**Non-existent in Fluid**
+
+There are several ONNX operators that are not available in Fluid today, e.g. `InstanceNormalization`, `RandomUniform`, `Unsqueeze`, etc.
+
+*Resolution*: For the initial phase, we can choose to not support ops that our models don't care for and are subsequently not available in Fluid. However, for ops that we think might be necessary for Fluid users also, we must implement them on our side and support the ONNX conversion to them. This list is TBD.
+
+
+**Concurrency**
+
+ONNX does not have any considerations for concurrency right now.
+
+*Resolution*: There are two ways to approach this:
+
+a. We choose to not support concurrent models.
+b. We only support `go_op`s (basically threads) shallowly. This could mean that we enqueue `go_op` ops prior to gradient calculations OR even prior to the entire graph, and that's it - since `go_op`s do not have support for backprop anyways. One of the core target use cases of `go_op`: batch reading - can be handled through this approach.
+
+
+**Overloaded in Fluid**
+
+There are ops in ONNX whose job can't be accomplished by a single corresponding Paddle operator (e.g. ), but a collection of operators.
+
+*Resolution*: Chain multiple Paddle operators.
+
+
+## Lack of LoDTensors
+
+As stated above, ONNX only supports simple Tensor values.
+
+*Resolution*: Deprecate to plain old numpy-able tensors.
+
+
+## Reconstruction from deprecated ONNX ops
+
+For higher-level Fluid ops, such as a few offered by the `nn` layer that do not have direct corresponding mappings but can be converted to ONNX by chaining a series of ops without cycles, it would be useful to map them back to the higher-level Fluid ops once converted back from the deprecated ONNX graphs.
+
+*Resolution*: Graphs that have the deprecation from Paddle -> ONNX. When converting back from ONNX, if we encounter the identical graphs by doing a forward search, we can replace the subgraphs with the matching ONNX op.
+
+
+# Supported models
+
+As mentioned above, potential risks may come from the conversion of sequence-related models, including the LodTensor, ```if/else``` and ```while``` operator. So a good choice is to focus on some important feedforward models first, then implement some simple recurrent models.
+
+- Feedforward models: common models selected in PaddleBook, e.g. VGG, ResNet and some other models proposed by application teams.
+- Recurrent models: language model, stacked LSTMs etc.

doc/fluid/getstarted/quickstart_cn.rst

-../../v2/getstarted/quickstart_cn.rst
\ No newline at end of file

doc/fluid/getstarted/quickstart_cn.rst

+快速开始
+========
+
+快速安装
+--------
+
+PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
+执行下面的命令完成快速安装，版本为cpu_avx_openblas：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle
+
+如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+
+更详细的安装和编译方法参考： :ref:`install_steps` 。
+
+快速使用
+--------
+
+创建一个 housing.py 并粘贴此Python代码：
+
+  .. code-block:: python
+
+     import paddle.dataset.uci_housing as uci_housing
+     import paddle.fluid as fluid
+  
+     with fluid.scope_guard(fluid.core.Scope()):
+         # initialize executor with cpu
+         exe = fluid.Executor(place=fluid.CPUPlace())
+         # load inference model 
+         [inference_program, feed_target_names,fetch_targets] =  \
+             fluid.io.load_inference_model(uci_housing.fluid_model(), exe)
+         # run inference
+         result = exe.run(inference_program, 
+                          feed={feed_target_names[0]: uci_housing.predict_reader()}, 
+                          fetch_list=fetch_targets)
+         # print predicted price is $12,273.97 
+         print 'Predicted price: ${:,.2f}'.format(result[0][0][0] * 1000)
+
+执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。

doc/fluid/getstarted/quickstart_en.rst

-../../v2/getstarted/quickstart_en.rst
\ No newline at end of file

doc/fluid/getstarted/quickstart_en.rst

+Quick Start
+============
+
+Quick Install
+-------------
+
+You can use pip to install PaddlePaddle with a single command, supports
+CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
+Simply run the following command to install, the version is cpu_avx_openblas:
+
+  .. code-block:: bash
+
+     pip install paddlepaddle
+
+If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+
+For more details about installation and build: :ref:`install_steps` .
+
+Quick Use
+---------
+
+Create a new file called housing.py, and paste this Python
+code:
+
+
+  .. code-block:: python
+
+     import paddle.dataset.uci_housing as uci_housing
+     import paddle.fluid as fluid
+  
+     with fluid.scope_guard(fluid.core.Scope()):
+         # initialize executor with cpu
+         exe = fluid.Executor(place=fluid.CPUPlace())
+         # load inference model 
+         [inference_program, feed_target_names,fetch_targets] =  \
+             fluid.io.load_inference_model(uci_housing.fluid_model(), exe)
+         # run inference
+         result = exe.run(inference_program, 
+                          feed={feed_target_names[0]: uci_housing.predict_reader()}, 
+                          fetch_list=fetch_targets)
+         # print predicted price is $12,273.97 
+         print 'Predicted price: ${:,.2f}'.format(result[0][0][0] * 1000)
+
+Run :code:`python housing.py` and voila! It should print out a list of predictions
+for the test housing data.

doc/v2/dev/index_en.rst

@@ -6,6 +6,7 @@ PaddlePaddle adheres to the following three sections of code and document specif
 
 
 PaddlePaddle uses git for version control and Docker is used for building and testing environment. The code includes Cuda, C++, Python, Shell and other programming languages，which comply with Google C++ Style, Pep-8, and the code base includes style checking by an automatic inspection tool. Code comments need to follow the Doxygen specification. The code that does not meet the style requirements will fail to compile. We provide the following guidelines for the use of Git, build tests and code development.
+
 ..  toctree::
   :maxdepth: 1
 

doc/v2/faq/local/index_en.rst

 #############################
-Local Training and Prediction
+Parameter Setting
 #############################
 
-TBD
+..  contents::
+
+1. Reduce Memory Consumption
+-------------------
+
+The training procedure of neural networks demands dozens of gigabytes of host memory or serval gigabytes of device memory, which is a rather memory consuming work. The memory consumed by PaddlePaddle framework mainly includes:
+\:
+
+* Cache memory for DataProvider (only on host memory),
+* Memory for neurons' activation information (on both host memory and device memory),
+* Memory for parameters (on both host memory and device memory),
+* Other memory demands.
+
+Other memory demands is mainly used to support the running demand of PaddlePaddle framework itself, such as string allocation，temporary variables, which are not considered currently.
+
+Reduce DataProvider Cache Memory
+++++++++++++++++++++++++++
+
+PyDataProvider works under asynchronous mechanism, it loads together with the data fetch and shuffle procedure in host memory:
+
+..  graphviz::
+
+    digraph {
+        rankdir=LR;
+        Data Files -> Host Memory Pool -> PaddlePaddle Training
+    }
+
+Thus the reduction of the DataProvider cache memory can reduce memory occupancy, meanwhile speed up the data loading procedure before training. However, the size of the memory pool can actually affect the granularity of shuffle，which means a shuffle operation is needed before each data file reading process to ensure the randomness of data when try to reduce the size of the memory pool.
+
+..  literalinclude:: src/reduce_min_pool_size.py
+
+In this way, the memory consumption can be significantly reduced and hence the training procedure can be accelerated. More details are demonstrated in :ref:`api_pydataprovider2`.
+
+The Neurons Activation Memory
+++++++++++++++
+
+Each neuron activation operating in a neural network training process contains certain amount of temporary data such as the activation data (like the output value of a neuron). These data will be used to update parameters in back propagation period. The scale of memory consumed by these data is mainly related with two parameters, which are batch size and the length of each Sequence. Therefore, the neurons activation memory consuming is actually in proportion to the information contains in each mini-batch training.
+
+Two practical ways:
+
+* Reduce batch size. Set a smaller value in network configuration settings(batch_size=1000) can be helpful. But setting batch size to a smaller value may affect the training result due to it is a super parameter of the neural network itself.
+* Shorten the sequence length or cut off those excessively long sequences. For example, if the length of sequences in a dataset are mostly varies between 100 and 200, but there is sequence lengthen out to 10,000, then it’s quite potentially leads to OOM (out of memory), especially in RNN models such as LSTM.
+
+The Parameters Memory
+++++++++
+
+The PaddlePaddle framework supports almost all popular optimizers. Different optimizers have different memory requirement. For example, the :code:`adadelta` consumes approximately 5 times memory
+
+space than the weights parameter’s scale, which means the :code:`adadelta` needs at least :code:`500M` memory if the model file contains all
+
+parameters needs :code:`100M`.
+
+Some optimization algorithms such as :code:`momentum` are worth giving a shot.
+
+2. Tricks To Speed Up Training
+-------------------
+
+The training procedure of PaddlePaddle may be speed up when considering following aspects:\：
+
+* Reduce the time consumption of data loading
+* Speed up training epochs
+* Introduce more computing resources with the utilization of distribute training frameworks
+
+Reduce The Time Consumption of Data Loading
+++++++++++++++++++
+
+
+The \ :code:`pydataprovider`\ holds big potential to speed up the data loading procedure if the cache pool and enable memory cache when use it. The principle of the reduction of :code:`DataProvider` cache pool is basically the same with the method which reduct the memory occupation with the set of a smaller cache pool.
+
+..  literalinclude:: src/reduce_min_pool_size.py
+
+Beside, the interface :code:`@provider` provides a parameter :code:`cache` to control cache. If set it to :code:`CacheType.CACHE_PASS_IN_MEM`, the data after the first :code:`pass` ( a pass means all data have be fed into the network for training) will be cached in memory and no new data will be read from the :code:`python` side in following :code:`pass` , instead from the cached data in memory. This strategy can also drop the time consuming in data loading process.
+
+
+Accelerating Training Epochs
+++++++++++++
+
+Sparse training is supported in PaddlePaddle. The features needs to be trained is any of :code:`sparse_binary_vector`, :code:`sparse_vector` and :code:`integer_value` . Meanwhile, the Layer interacts with the training data need to turn the Parameter to sparse updating mode by setting :code:`sparse_update=True`.
+Take :code:`word2vec` as an example, to train a language distance, one needs to predict the middle word with two words prior to it and next to it. The DataProvider of this task is:
+
+..  literalinclude:: src/word2vec_dataprovider.py
+
+The configuration of this task is:
+
+..  literalinclude:: src/word2vec_config.py
+
+Introduce More Computing Resources
+++++++++++++++++++
+
+More computing resources can be introduced with following manners:
+* Single CPU platform training
+
+  * Use multi-threading by set :code:`trainer_count`。
+
+* Single GPU platform training
+
+  * Set :code:`use_gpu` to train on single GPU.
+  * Set :code:`use_gpu` and :code:`trainer_count` to enable multiple GPU training support.
+
+* Cluster Training
+
+  * Refer to :ref:`cluster_train` 。
+
+3. Assign GPU Devices
+------------------
+
+Assume a computing platform consists of 4 GPUs which serial number from 0 to 3:
+
+* Method1: specify a GPU as computing device by set:
+ `CUDA_VISIBLE_DEVICES <http://www.acceleware.com/blog/cudavisibledevices-masking-gpus>`_
+
+..      code-block:: bash
+
+        env CUDA_VISIBLE_DEVICES=2,3 paddle train --use_gpu=true --trainer_count=2
+
+* Method2: Assign by —gpu_id:
+
+..      code-block:: bash
+
+        paddle train --use_gpu=true --trainer_count=2 --gpu_id=2
+
+
+4. How to Fix Training Termination Caused By :code:`Floating point exception` During Training.
+------------------------------------------------------------------------
+
+Paddle binary catches floating exceptions during runtime, it will be terminated when NaN or Inf occurs. Floating exceptions are mostly caused by float overflow, divide by zero. There are three main reasons may raise such exception:
+
+* Parameters or gradients during training are oversize, which leads to float overflow during calculation.
+* The model failed to converge and diverges to a big value.
+* Parameters may converge to a singular value due to bad training data. If the scale of input data is too big and contains millions of parameter values, float overflow error may arise when operating matrix multiplication.
+
+Two ways to solve this problem:
+
+1. Set :code:`gradient_clipping_threshold` as:
+
+..  code-block:: python
+
+    optimizer = paddle.optimizer.RMSProp(
+        learning_rate=1e-3,
+        gradient_clipping_threshold=10.0,
+        regularization=paddle.optimizer.L2Regularization(rate=8e-4))
+
+Details can refer to example `nmt_without_attention  <https://github.com/PaddlePaddle/models/blob/develop/nmt_without_attention/train.py#L35>`_ 示例。
+
+2. Set :code:`error_clipping_threshold` as:
+
+..  code-block:: python
+
+    decoder_inputs = paddle.layer.fc(
+        act=paddle.activation.Linear(),
+        size=decoder_size * 3,
+        bias_attr=False,
+        input=[context, current_word],
+        layer_attr=paddle.attr.ExtraLayerAttribute(
+            error_clipping_threshold=100.0))
+
+Details can refer to example `machine translation <https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/train.py#L66>`_ 。
+
+The main difference between these two methods are:
+
+1. They both block the gradient, but happen in different occasions，the former one happens when then :code:`optimzier` updates the network parameters while the latter happens when the back propagation computing of activation functions.
+2. The block target are different, the former blocks the trainable parameters’ gradient while the later blocks the gradient to be propagated to prior layers.
+
+Moreover, Such problems may be fixed with smaller learning rates or data normalization.
+
+5.  Fetch Multi Layers’ Prediction Result With Infer Interface
+-----------------------------------------------
+
+* Join the layer to be used as :code:`output_layer` layer to the input parameters of  :code:`paddle.inference.Inference()` interface with:
+
+..  code-block:: python
+
+    inferer = paddle.inference.Inference(output_layer=[layer1, layer2], parameters=parameters)
+
+* Assign certain fields to output. Take :code:`value` as example, it can be down with following code:
+
+..  code-block:: python
+
+    out = inferer.infer(input=data_batch, field=["value"])
+
+It is important to note that:
+
+* If 2 layers are assigned as output layer, then the output results consists of 2 matrixes.
+* Assume the output of first layer A is a matrix sizes N1 * M1, the output of second layer B is a matrix sizes N2 * M2；
+* By default, paddle.v2 will transverse join A and B, when N1 not equal to N2, it will raise following error:
+
+..      code-block:: python
+
+    ValueError: all the input array dimensions except for the concatenation axis must match exactly
+
+The transverse of different matrixes of multi layers mainly happens when:
+
+* Output sequence layer and non sequence layer;
+* Multiple output layers process multiple sequence with different length;
+
+Such issue can be avoided by calling infer interface and set :code:`flatten_result=False`. Thus, the infer interface returns a python list, in which
+
+* The number of elements equals to the number of output layers in the network;
+* Each element in list is a result matrix of a layer, which type is numpy.ndarray;
+* The height of each matrix outputted by each layer equals to the number of samples under non sequential mode or equals to the number of elements in the input sequence under sequential mode. Their width are both equal to the layer size in configuration.
+
+6.  Fetch the Output of A Certain Layer During Training
+-----------------------------------------------
+
+In event_handler, the interface :code:`event.gm.getLayerOutputs("layer_name")` gives the forward output value organized in :code:`numpy.ndarray` corresponding to :code:`layer_name` in the mini-batch.
+The output can be used in custom measurements in following way:
+
+..      code-block:: python
+
+        def score_diff(right_score, left_score):
+            return np.average(np.abs(right_score - left_score))
+
+        def event_handler(event):
+            if isinstance(event, paddle.event.EndIteration):
+                if event.batch_id % 25 == 0:
+                    diff = score_diff(
+                        event.gm.getLayerOutputs("right_score")["right_score"][
+                            "value"],
+                        event.gm.getLayerOutputs("left_score")["left_score"][
+                            "value"])
+                    logger.info(("Pass %d Batch %d : Cost %.6f, "
+                                "average absolute diff scores: %.6f") %
+                                (event.pass_id, event.batch_id, event.cost, diff))
+
+Note: this function can not get content of :code:`paddle.layer.recurrent_group` step, but output of  :code:`paddle.layer.recurrent_group` can be fetched.
+
+7.  Fetch Parameters’ Weight and Gradient During Training
+-----------------------------------------------
+
+Under certain situations, knowing the weights of currently training mini-batch can provide more inceptions of many problems. Their value can be acquired by printing values in :code:`event_handler` (note that to gain such parameters when training on GPU, you should set :code:`paddle.event.EndForwardBackward`). Detailed code is as following:
+
+..      code-block:: python
+
+        ...
+        parameters = paddle.parameters.create(cost)
+        ...
+        def event_handler(event):
+            if isinstance(event, paddle.event.EndForwardBackward):
+                if event.batch_id % 25 == 0:
+                    for p in parameters.keys():
+                        logger.info("Param %s, Grad %s",
+                            parameters.get(p), parameters.get_grad(p))
+
+Note that “acquire the output of a certain layer during training” or “acquire the weights and gradients of parameters during training ” both needs to copy training data from C++ environment to numpy, which have certain degree of influence on training performance. Don’t use these two functions when the training procedure cares about the performance.

doc/v2/howto/rnn/index_en.rst

 RNN Models
 ==========
+Recurrent neural networks(RNN) are an important tool to model sequential data. PaddlePaddle provides flexible interface for building complex recurrent neural network. We will demonstrate how to use PaddlePaddle to build RNN models in the following 4 parts.
+
+In the first part, we will guide you how to configure recurrent neural network in PaddlePaddle from simple to complex. First, we will use a vanilla recurrent neural network as an example to show how to configure recurrent neural network architecture. Then We will use the sequence to sequence model as an example to demonstrate how you can configure complex recurrent neural network models gradually.
 
 ..  toctree::
   :maxdepth: 1
 
   rnn_config_en.rst
+
+Recurrent Group is the key unit to build complex recurrent neural network models. The second part describes related concepts and Basic principles of Recurrent Group, and give a detailed description of Recurrent Group API interface. In addition, it also introduces Sequence-level RNN(hierarchical sequence as input) and the usage of Recurrent Group in it.
+
+..  toctree::
+  :maxdepth: 1
+  
   recurrent_group_en.md
+  
+In the third part, two-level sequence is demonstrated briefly and then layers supporting two-level sequence as input are listed and described respectively.
+
+..  toctree::
+  :maxdepth: 1
+  
   hierarchical_layer_en.rst
+
+In the last part, the unit test of hierarchical RNN is presented as an example to explain how to use hierarchical RNN. We will use two-level sequence RNN and single-layer sequence RNN which have same effects with former as the network configuration seperately in unit test.
+
+..  toctree::
+  :maxdepth: 1
+  
   hrnn_rnn_api_compare_en.rst
+

paddle/fluid/operators/detail/simple_block_queue.h → paddle/fluid/framework/blocking_queue.h

@@ -17,36 +17,58 @@ limitations under the License. */
 #include <condition_variable>  // NOLINT
 #include <deque>
 #include <mutex>  // NOLINT
+#include <utility>
 
 namespace paddle {
-namespace operators {
-namespace detail {
+namespace framework {
 
 template <typename T>
-class SimpleBlockQueue {
- private:
-  std::mutex mutex_;
-  std::condition_variable condition_;
-  std::deque<T> queue_;
-
+class BlockingQueue {
  public:
-  void Push(T const& value) {
+  void Push(const T &item) {
+    {
+      std::lock_guard<std::mutex> g(mutex_);
+      q_.emplace_back(item);
+    }
+    cv_.notify_one();
+  }
+
+  template <typename U>
+  void Extend(const U &items) {
     {
-      std::unique_lock<std::mutex> lock(this->mutex_);
-      queue_.push_front(value);
+      std::lock_guard<std::mutex> g(mutex_);
+      for (auto &item : items) {
+        q_.emplace_back(item);
+      }
     }
-    this->condition_.notify_one();
+    cv_.notify_all();
+  }
+
+  std::deque<T> PopAll(size_t ms, bool *timeout) {
+    auto time =
+        std::chrono::system_clock::now() + std::chrono::milliseconds(ms);
+    std::unique_lock<std::mutex> lock(mutex_);
+    *timeout = !cv_.wait_until(lock, time, [this] { return !q_.empty(); });
+    std::deque<T> ret;
+    if (!*timeout) {
+      std::swap(ret, q_);
+    }
+    return ret;
   }
 
   T Pop() {
-    std::unique_lock<std::mutex> lock(this->mutex_);
-    this->condition_.wait(lock, [=] { return !this->queue_.empty(); });
-    T rc(std::move(this->queue_.back()));
-    this->queue_.pop_back();
+    std::unique_lock<std::mutex> lock(mutex_);
+    cv_.wait(lock, [=] { return !q_.empty(); });
+    T rc(std::move(q_.front()));
+    q_.pop_front();
     return rc;
   }
+
+ private:
+  std::mutex mutex_;
+  std::condition_variable cv_;
+  std::deque<T> q_;
 };
 
-}  // namespace detail
-}  // namespace operators
+}  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/data_transform.cc

@@ -63,16 +63,16 @@ void DataTransform(const OpKernelType& expected_kernel_type,
 }
 
 void CopyVariableWithTensor(const Variable& in_var, const Tensor& tensor,
-                            Variable& out_var) {
+                            Variable* out_var) {
   if (in_var.IsType<LoDTensor>()) {
     auto& in_lod_tensor = in_var.Get<LoDTensor>();
-    auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
+    auto* tran_lod_tensor = out_var->GetMutable<LoDTensor>();
     tran_lod_tensor->set_lod(in_lod_tensor.lod());
     tran_lod_tensor->set_layout(in_lod_tensor.layout());
     tran_lod_tensor->ShareDataWith(tensor);
   } else if (in_var.IsType<SelectedRows>()) {
     auto& in_selected_rows = in_var.Get<SelectedRows>();
-    auto* trans_selected_rows = out_var.GetMutable<SelectedRows>();
+    auto* trans_selected_rows = out_var->GetMutable<SelectedRows>();
     trans_selected_rows->set_height(in_selected_rows.height());
     trans_selected_rows->set_rows(in_selected_rows.rows());
     trans_selected_rows->mutable_value()->ShareDataWith(tensor);

paddle/fluid/framework/data_transform.h

@@ -35,7 +35,7 @@ void DataTransform(const OpKernelType& expected_kernel_type,
                    const Tensor& input_tensor, Tensor* out);
 
 void CopyVariableWithTensor(const Variable& in_var, const Tensor& tensor,
-                            Variable& out_var);
+                            Variable* out_var);
 
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/data_type.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
 #include <typeindex>
 #include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -22,18 +23,21 @@ namespace paddle {
 namespace framework {
 
 inline proto::VarType::Type ToDataType(std::type_index type) {
-  using namespace paddle::framework::proto;
   if (typeid(platform::float16).hash_code() == type.hash_code()) {
     return proto::VarType::FP16;
-  } else if (typeid(float).hash_code() == type.hash_code()) {
+  } else if (typeid(const float).hash_code() == type.hash_code()) {
+    // CPPLint complains Using C-style cast.  Use static_cast<float>() instead
+    // One fix to this is to replace float with const float because
+    // typeid(T) == typeid(const T)
+    // http://en.cppreference.com/w/cpp/language/typeid
     return proto::VarType::FP32;
-  } else if (typeid(double).hash_code() == type.hash_code()) {
+  } else if (typeid(const double).hash_code() == type.hash_code()) {
     return proto::VarType::FP64;
-  } else if (typeid(int).hash_code() == type.hash_code()) {
+  } else if (typeid(const int).hash_code() == type.hash_code()) {
     return proto::VarType::INT32;
-  } else if (typeid(int64_t).hash_code() == type.hash_code()) {
+  } else if (typeid(const int64_t).hash_code() == type.hash_code()) {
     return proto::VarType::INT64;
-  } else if (typeid(bool).hash_code() == type.hash_code()) {
+  } else if (typeid(const bool).hash_code() == type.hash_code()) {
     return proto::VarType::BOOL;
   } else {
     PADDLE_THROW("Not supported");
@@ -41,7 +45,6 @@ inline proto::VarType::Type ToDataType(std::type_index type) {
 }
 
 inline std::type_index ToTypeIndex(proto::VarType::Type type) {
-  using namespace paddle::framework::proto;
   switch (type) {
     case proto::VarType::FP16:
       return typeid(platform::float16);
@@ -62,7 +65,6 @@ inline std::type_index ToTypeIndex(proto::VarType::Type type) {
 
 template <typename Visitor>
 inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
-  using namespace paddle::framework::proto;
   switch (type) {
     case proto::VarType::FP16:
       visitor.template operator()<platform::float16>();
@@ -88,7 +90,6 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
 }
 
 inline std::string DataTypeToString(const proto::VarType::Type type) {
-  using namespace paddle::framework::proto;
   switch (type) {
     case proto::VarType::FP16:
       return "float16";

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -66,7 +66,7 @@ void FetchOpHandle::RunImpl() {
     auto &t = var->Get<framework::LoDTensor>();
     if (platform::is_gpu_place(t.place())) {
 #ifdef PADDLE_WITH_CUDA
-      TensorCopy(t, cpu, *dev_ctxes_[t.place()], &tensors_[i]);
+      TensorCopy(t, cpu, *dev_ctxes_[t.place()], &tensors_[i], true);
       dev_ctxes_.at(t.place())->Wait();
 #endif
     } else {

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -78,6 +78,33 @@ void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
   }
 }
 
+bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
+                                            OpDesc *send_op) const {
+  if (send_op == nullptr) {
+    return false;
+  }
+
+  auto checker = [&](const std::vector<std::string> opvars,
+                     const std::vector<std::string> sendvars) -> bool {
+    bool is_dist_train_op = false;
+    for (auto &var : opvars) {
+      if (var.find(".block") != std::string::npos &&
+          std::find(sendvars.begin(), sendvars.end(), var) != sendvars.end()) {
+        is_dist_train_op = true;
+        break;
+      }
+    }
+    return is_dist_train_op;
+  };
+
+  if (op.Type() == "split") {
+    return checker(op.OutputArgumentNames(), send_op->InputArgumentNames());
+  } else if (op.Type() == "concat") {
+    return checker(op.InputArgumentNames(), send_op->OutputArgumentNames());
+  }
+  return false;
+}
+
 std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
     const ProgramDesc &program) const {
   auto graph = new SSAGraph();
@@ -89,19 +116,30 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
       std::unordered_map<std::string, std::vector<std::unique_ptr<VarHandle>>>>(
       places_.size());
 
+  // Find "send" op first for split is in front of send.
+  OpDesc *send_op = nullptr;
+  for (auto *op : program.Block(0).AllOps()) {
+    if (op->Type() == "send") {
+      send_op = op;
+      break;
+    }
+  }
+
   bool is_forwarding = true;
   for (auto *op : program.Block(0).AllOps()) {
     if (op->Type() == "send") {
       // append send op if program is distributed trainer main program.
       // always use the first device
       CreateSendOp(&result, *op);
+    } else if (IsDistTrainOp(*op, send_op)) {
+      CreateComputationalOps(&result, *op, 1);
     } else if (IsScaleLossOp(*op)) {
       if (!skip_scale_loss_) {
         CreateScaleLossGradOp(&result);
       }
       is_forwarding = false;
     } else {
-      CreateComputationalOps(&result, *op);
+      CreateComputationalOps(&result, *op, places_.size());
       if (!is_forwarding) {
         // Currently, we assume that once gradient is generated, it can be
         // broadcast, and each gradient is only broadcast once. But there are no
@@ -199,8 +237,9 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(SSAGraph *result) const {
 }
 
 void MultiDevSSAGraphBuilder::CreateComputationalOps(SSAGraph *result,
-                                                     const OpDesc &op) const {
-  for (size_t scope_idx = 0; scope_idx < places_.size(); ++scope_idx) {
+                                                     const OpDesc &op,
+                                                     size_t num_places) const {
+  for (size_t scope_idx = 0; scope_idx < num_places; ++scope_idx) {
     auto p = places_[scope_idx];
     auto s = local_scopes_[scope_idx];
     result->ops_.emplace_back(new ComputationOpHandle(op, s, p));

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -65,7 +65,10 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
 
   void CreateSendOp(SSAGraph *result, const OpDesc &op) const;
 
-  void CreateComputationalOps(SSAGraph *result, const OpDesc &op) const;
+  bool IsDistTrainOp(const OpDesc &op, OpDesc *send_op) const;
+
+  void CreateComputationalOps(SSAGraph *result, const OpDesc &op,
+                              size_t num_places) const;
 
   void CreateScaleLossGradOp(SSAGraph *result) const;
 

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -140,7 +140,9 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
 
     if (timeout) {
       if (exception_) {
-        throw * exception_;
+        auto exp = *exception_;
+        exception_.reset();
+        throw exp;
       } else {
         continue;
       }

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -22,6 +22,7 @@
 
 #include <functional>
 #include "ThreadPool.h"  // ThreadPool in thrird party
+#include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/details/ssa_graph_executor.h"
 
 namespace paddle {
@@ -30,46 +31,6 @@ class Scope;
 
 namespace details {
 
-template <typename T>
-class BlockingQueue {
- public:
-  void Push(const T &item) {
-    {
-      std::lock_guard<std::mutex> g(mutex_);
-      q_.emplace_back(item);
-    }
-    cv_.notify_one();
-  }
-
-  template <typename U>
-  void Extend(const U &items) {
-    {
-      std::lock_guard<std::mutex> g(mutex_);
-      for (auto &item : items) {
-        q_.emplace_back(item);
-      }
-    }
-    cv_.notify_all();
-  }
-
-  std::deque<T> PopAll(size_t ms, bool *timeout) {
-    auto time =
-        std::chrono::system_clock::now() + std::chrono::milliseconds(ms);
-    std::unique_lock<std::mutex> lock(mutex_);
-    *timeout = !cv_.wait_until(lock, time, [this] { return !q_.empty(); });
-    std::deque<T> ret;
-    if (!*timeout) {
-      std::swap(ret, q_);
-    }
-    return ret;
-  }
-
- private:
-  std::mutex mutex_;
-  std::condition_variable cv_;
-  std::deque<T> q_;
-};
-
 class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
  public:
   ThreadedSSAGraphExecutor(size_t num_threads, bool use_event,

paddle/fluid/framework/feed_fetch_type.h

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include <string>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"
 
@@ -22,7 +21,8 @@ namespace framework {
 using FeedFetchType = LoDTensor;
 using FeedFetchList = std::vector<FeedFetchType>;
 
-static const std::string kFeedOpType = "feed";
-static const std::string kFetchOpType = "fetch";
+static const char kFeedOpType[] = "feed";
+static const char kFetchOpType[] = "fetch";
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/init.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include <algorithm>
 #include <stdexcept>
 #include <string>
+#include <vector>
 
 #include "paddle/fluid/framework/init.h"
 #include "paddle/fluid/framework/operator.h"
@@ -28,7 +29,7 @@ namespace framework {
 std::once_flag gflags_init_flag;
 std::once_flag p2p_init_flag;
 
-void InitGflags(std::vector<std::string> &argv) {
+void InitGflags(std::vector<std::string> argv) {
   std::call_once(gflags_init_flag, [&]() {
     int argc = argv.size();
     char **arr = new char *[argv.size()];
@@ -65,7 +66,7 @@ void InitP2P(int count) {
 }
 
 void InitDevices(bool init_p2p) {
-  /*Init all avaiable devices by default */
+  /*Init all available devices by default */
 
   std::vector<platform::Place> places;
   places.emplace_back(platform::CPUPlace());

paddle/fluid/framework/init.h

@@ -12,7 +12,9 @@ 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 <mutex>
+#include <mutex>  // NOLINT
+#include <string>
+#include <vector>
 
 #include "gflags/gflags.h"
 #include "glog/logging.h"
@@ -20,7 +22,7 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-void InitGflags(std::vector<std::string> &argv);
+void InitGflags(std::vector<std::string> argv);
 
 void InitGLOG(const std::string &prog_name);
 

paddle/fluid/framework/library_type.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 #include <cctype>
+#include <string>
 
 namespace paddle {
 namespace framework {
@@ -67,5 +68,5 @@ inline std::ostream& operator<<(std::ostream& out, LibraryType l) {
   return out;
 }
 
-}  // namespace
-}  // framework
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/op_desc.cc

@@ -205,8 +205,8 @@ void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
   need_update_ = true;
 }
 
-void OpDesc::SetBlockAttr(const std::string &name, BlockDesc &block) {
-  this->attrs_[name] = █
+void OpDesc::SetBlockAttr(const std::string &name, BlockDesc *block) {
+  this->attrs_[name] = block;
   need_update_ = true;
 }
 

paddle/fluid/framework/op_desc.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/attribute.h"
@@ -73,7 +74,7 @@ class OpDesc {
 
   void SetAttr(const std::string &name, const Attribute &v);
 
-  void SetBlockAttr(const std::string &name, BlockDesc &block);
+  void SetBlockAttr(const std::string &name, BlockDesc *block);
 
   Attribute GetAttr(const std::string &name) const;
 

paddle/fluid/framework/operator.cc

@@ -171,17 +171,6 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
   return ss.str();
 }
 
-void OperatorBase::Rename(const std::string& old_name,
-                          const std::string& new_name) {
-  for (auto& input : inputs_) {
-    std::replace(input.second.begin(), input.second.end(), old_name, new_name);
-  }
-  for (auto& output : outputs_) {
-    std::replace(output.second.begin(), output.second.end(), old_name,
-                 new_name);
-  }
-}
-
 OperatorBase::OperatorBase(const std::string& type,
                            const VariableNameMap& inputs,
                            const VariableNameMap& outputs,
@@ -327,7 +316,6 @@ bool OpSupportGPU(const std::string& op_type) {
   auto it = all_kernels.find(op_type);
   if (it == all_kernels.end()) {
     // All control operator must support GPU
-
     return true;
   }
   for (auto& kern_pair : it->second) {
@@ -554,7 +542,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
             std::shared_ptr<Tensor> out(new Tensor);
             DataTransform(expected_kernel_key, kernel_type_for_var, *tensor_in,
                           out.get());
-            CopyVariableWithTensor(*var, *(out.get()), *trans_var);
+            CopyVariableWithTensor(*var, *(out.get()), trans_var);
           }
         }
       }

paddle/fluid/framework/operator.h

@@ -79,31 +79,28 @@ class OperatorBase {
 
   virtual ~OperatorBase() {}
 
-  template <typename T>
-  inline const T& Attr(const std::string& name) const {
-    PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
-                   name);
-    return boost::get<T>(attrs_.at(name));
-  }
-
-  /// if scope is not null, also show dimensions of arguments
-  virtual std::string DebugStringEx(const Scope* scope) const;
-
-  std::string DebugString() const { return DebugStringEx(nullptr); }
-
-  /// Net will call this interface function to Run an op.
+  /// Executor will call this interface function to Run an op.
   //  The implementation should be written at RunImpl
   void Run(const Scope& scope, const platform::Place& place);
 
   // FIXME(typhoonzero): this is only used for recv_op to stop event_loop.
   virtual void Stop() {}
 
-  virtual bool IsNetOp() const { return false; }
+  /// if scope is not null, also show dimensions of arguments
+  virtual std::string DebugStringEx(const Scope* scope) const;
+  std::string DebugString() const { return DebugStringEx(nullptr); }
 
   virtual bool SupportGPU() const { return false; }
 
-  /// rename inputs outputs name
-  void Rename(const std::string& old_name, const std::string& new_name);
+  const std::string& Type() const { return type_; }
+
+  template <typename T>
+  inline const T& Attr(const std::string& name) const {
+    PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
+                   name);
+    return boost::get<T>(attrs_.at(name));
+  }
+  const AttributeMap& Attrs() const { return attrs_; }
 
   const VariableNameMap& Inputs() const { return inputs_; }
   const VariableNameMap& Outputs() const { return outputs_; }
@@ -112,7 +109,7 @@ class OperatorBase {
   std::string Input(const std::string& name) const;
   //! Get a input which has multiple variables.
   const std::vector<std::string>& Inputs(const std::string& name) const;
-
+  //! Get all inputs variable names
   std::vector<std::string> InputVars() const;
 
   //! Get a output with argument's name described in `op_proto`
@@ -120,13 +117,9 @@ class OperatorBase {
   //! Get an output which has multiple variables.
   //! TODO add a vector_view to prevent memory copy.
   const std::vector<std::string>& Outputs(const std::string& name) const;
-
+  //! Get all outputs variable names
   virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
 
-  const std::string& Type() const { return type_; }
-  void SetType(const std::string& type) { type_ = type; }
-  const AttributeMap& Attrs() const { return attrs_; }
-
   // Return a new operator instance, which is as same as this.
   // Use unique_ptr to prevent caller forget to delete this pointer.
   virtual std::unique_ptr<OperatorBase> Clone() const = 0;
@@ -278,20 +271,6 @@ class ExecutionContext {
     return res;
   }
 
-  void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
-                size_t j = 0) const {
-    PADDLE_ENFORCE_LT(i, InputSize(in));
-    PADDLE_ENFORCE_LT(j, OutputSize(out));
-    auto* in_var = MultiInputVar(in)[i];
-    auto* out_var = MultiOutputVar(out)[j];
-    if (!in_var->IsType<LoDTensor>()) return;
-    PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
-                   "The %d-th output of Output(%s) must be LoDTensor.", j, out);
-    auto in_tensor = in_var->Get<LoDTensor>();
-    auto* out_tensor = out_var->GetMutable<LoDTensor>();
-    out_tensor->set_lod(in_tensor.lod());
-  }
-
   platform::Place GetPlace() const { return device_context_.GetPlace(); }
 
   template <typename DeviceContextType>

paddle/fluid/framework/parallel_executor.cc

@@ -74,7 +74,7 @@ ParallelExecutor::ParallelExecutor(
     member_->own_local_scope = false;
     PADDLE_ENFORCE_EQ(member_->places_.size(), local_scopes.size());
     for (size_t i = 0; i < member_->places_.size(); ++i) {
-      member_->local_scopes_.emplace_back(local_scopes[i]);
+      member_->local_scopes_.emplace_back(&local_scopes[i]->NewScope());
     }
   }
 

paddle/fluid/framework/program_desc.cc

@@ -56,7 +56,7 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
       for (const auto &attr : op->Proto()->attrs()) {
         if (attr.type() == proto::AttrType::BLOCK) {
           size_t blk_idx = attr.block_idx();
-          op->SetBlockAttr(attr.name(), *this->MutableBlock(blk_idx));
+          op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
         }
       }
     }
@@ -73,7 +73,7 @@ ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
       for (const auto &attr : op->Proto()->attrs()) {
         if (attr.type() == proto::AttrType::BLOCK) {
           size_t blk_idx = attr.block_idx();
-          op->SetBlockAttr(attr.name(), *this->MutableBlock(blk_idx));
+          op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
         }
       }
     }

paddle/fluid/framework/prune.cc

@@ -14,19 +14,19 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/prune.h"
 
+#include <glog/logging.h>
+
 #include <algorithm>
 #include <set>
 #include <string>
 #include <unordered_map>
 #include <vector>
 
-#include <glog/logging.h>
-
 namespace paddle {
 namespace framework {
 
-const std::string kFeedOpType = "feed";
-const std::string kFetchOpType = "fetch";
+const char kFeedOpType[] = "feed";
+const char kFetchOpType[] = "fetch";
 
 bool HasDependentVar(const proto::OpDesc& op_desc,
                      const std::set<std::string>& dependent_vars) {
@@ -68,7 +68,7 @@ bool HasSubBlock(const proto::OpDesc& op_desc) {
 // the child block to help pruning
 void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
                 int block_id, int parent_block_id,
-                std::set<std::string>& dependent_vars) {
+                std::set<std::string>* dependent_vars) {
   auto& block = input.blocks(block_id);
   auto& ops = block.ops();
 
@@ -90,11 +90,11 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
   std::vector<bool> should_run;
   for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
     auto& op_desc = *op_iter;
-    if (IsTarget(op_desc) || HasDependentVar(op_desc, dependent_vars)) {
+    if (IsTarget(op_desc) || HasDependentVar(op_desc, *dependent_vars)) {
       // insert its input to the dependency graph
       for (auto& var : op_desc.inputs()) {
         for (auto& argu : var.arguments()) {
-          dependent_vars.insert(argu);
+          dependent_vars->insert(argu);
         }
       }
       should_run.push_back(true);
@@ -138,7 +138,7 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
         // GetSubBlockIndex(*op) is the idx of the sub_block in the input desc
         // output_block_id is the idx of the current block in the output desc
         prune_impl(input, output, GetSubBlockIndex(*op), output_block_id,
-                   sub_block_dependent_vars);
+                   &sub_block_dependent_vars);
       }
     }
   }
@@ -181,7 +181,7 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
 void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output) {
   std::set<std::string> dependent_vars;
   output->clear_blocks();
-  prune_impl(input, output, 0, -1, dependent_vars);
+  prune_impl(input, output, 0, -1, &dependent_vars);
 }
 
 void inference_optimize_impl(proto::ProgramDesc* input, int block_id) {

paddle/fluid/framework/tensor_util.cc

@@ -20,7 +20,7 @@ namespace paddle {
 namespace framework {
 
 void TensorCopy(const Tensor& src, const platform::Place& dst_place,
-                const platform::DeviceContext& ctx, Tensor* dst) {
+                const platform::DeviceContext& ctx, Tensor* dst, bool sync) {
   VLOG(3) << "TensorCopy " << src.dims() << " from " << src.place() << " to "
           << dst_place;
   src.check_memory_size();
@@ -47,9 +47,11 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
     PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
     auto ctx_gpu_place = boost::get<platform::CUDAPlace>(ctx_place);
     PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place);
-    memory::Copy(
-        dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size,
-        reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());
+    auto stream =
+        sync ? nullptr
+             : reinterpret_cast<const platform::CUDADeviceContext&>(ctx)
+                   .stream();
+    memory::Copy(dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size, stream);
   } else if (platform::is_cpu_place(src_place) &&
              platform::is_gpu_place(dst_place)) {
     auto src_cpu_place = boost::get<platform::CPUPlace>(src_place);
@@ -58,18 +60,22 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
     PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
     auto ctx_gpu_place = boost::get<platform::CUDAPlace>(ctx_place);
     PADDLE_ENFORCE_EQ(dst_gpu_place, ctx_gpu_place);
-    memory::Copy(
-        dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size,
-        reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());
+    auto stream =
+        sync ? nullptr
+             : reinterpret_cast<const platform::CUDADeviceContext&>(ctx)
+                   .stream();
+    memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
   } else if (platform::is_gpu_place(src_place) &&
              platform::is_gpu_place(dst_place)) {
     auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
     auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
     auto ctx_place = ctx.GetPlace();
     PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
-    memory::Copy(
-        dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
-        reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());
+    auto stream =
+        sync ? nullptr
+             : reinterpret_cast<const platform::CUDADeviceContext&>(ctx)
+                   .stream();
+    memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, stream);
   }
 #endif
 }

paddle/fluid/framework/tensor_util.h

@@ -24,7 +24,8 @@ namespace paddle {
 namespace framework {
 
 void TensorCopy(const Tensor& src, const platform::Place& dst_place,
-                const platform::DeviceContext& ctx, Tensor* dst);
+                const platform::DeviceContext& ctx, Tensor* dst,
+                bool sync = false);
 void TensorCopy(const Tensor& src, const platform::Place& dst_place,
                 Tensor* dst);
 

paddle/fluid/inference/CMakeLists.txt

@@ -21,7 +21,8 @@ endif()
 
 if(WITH_TESTING)
   add_subdirectory(tests/book)
-  if (TENSORRT_FOUND)
-    add_subdirectory(tensorrt)
-  endif()
+endif()
+
+if (TENSORRT_FOUND)
+  add_subdirectory(tensorrt)
 endif()

paddle/fluid/memory/memcpy.cc

@@ -32,7 +32,11 @@ void Copy<platform::CPUPlace, platform::CUDAPlace>(
     platform::CPUPlace dst_place, void* dst, platform::CUDAPlace src_place,
     const void* src, size_t num, cudaStream_t stream) {
   platform::SetDeviceId(src_place.device);
-  platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
+  if (stream) {
+    platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
+  } else {
+    platform::GpuMemcpySync(dst, src, num, cudaMemcpyDeviceToHost);
+  }
 }
 
 template <>
@@ -40,7 +44,11 @@ void Copy<platform::CUDAPlace, platform::CPUPlace>(
     platform::CUDAPlace dst_place, void* dst, platform::CPUPlace src_place,
     const void* src, size_t num, cudaStream_t stream) {
   platform::SetDeviceId(dst_place.device);
-  platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
+  if (stream) {
+    platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
+  } else {
+    platform::GpuMemcpySync(dst, src, num, cudaMemcpyHostToDevice);
+  }
 }
 
 template <>
@@ -49,10 +57,19 @@ void Copy<platform::CUDAPlace, platform::CUDAPlace>(
     const void* src, size_t num, cudaStream_t stream) {
   if (dst_place == src_place) {
     platform::SetDeviceId(src_place.device);
-    platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToDevice, stream);
+    if (stream) {
+      platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToDevice, stream);
+    } else {
+      platform::GpuMemcpySync(dst, src, num, cudaMemcpyDeviceToDevice);
+    }
   } else {
-    platform::GpuMemcpyPeer(dst, dst_place.device, src, src_place.device, num,
-                            stream);
+    if (stream) {
+      platform::GpuMemcpyPeerAsync(dst, dst_place.device, src, src_place.device,
+                                   num, stream);
+    } else {
+      platform::GpuMemcpyPeerSync(dst, dst_place.device, src, src_place.device,
+                                  num);
+    }
   }
 }
 
@@ -83,7 +100,11 @@ void Copy<platform::CUDAPinnedPlace, platform::CUDAPlace>(
     platform::CUDAPlace src_place, const void* src, size_t num,
     cudaStream_t stream) {
   platform::SetDeviceId(src_place.device);
-  platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
+  if (stream) {
+    platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
+  } else {
+    platform::GpuMemcpySync(dst, src, num, cudaMemcpyDeviceToHost);
+  }
 }
 
 template <>
@@ -92,7 +113,11 @@ void Copy<platform::CUDAPlace, platform::CUDAPinnedPlace>(
     platform::CUDAPinnedPlace src_place, const void* src, size_t num,
     cudaStream_t stream) {
   platform::SetDeviceId(dst_place.device);
-  platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
+  if (stream) {
+    platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
+  } else {
+    platform::GpuMemcpySync(dst, src, num, cudaMemcpyHostToDevice);
+  }
 }
 
 #endif

paddle/fluid/operators/bilinear_interp_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+   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/bilinear_interp_op.h"
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class BilinearInterpOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of BilinearInterOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of BilinearInterOp should not be null.");
+
+    auto dim_x = ctx->GetInputDim("X");  // NCHW format
+    int out_h = ctx->Attrs().Get<int>("out_h");
+    int out_w = ctx->Attrs().Get<int>("out_w");
+    PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
+
+    std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
+    ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
+  }
+};
+
+class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  BilinearInterpOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(Tensor) The input tensor of bilinear interpolation, "
+             "This is a 4-D tensor with shape of (N x C x h x w)");
+    AddOutput("Out",
+              "(Tensor) The dimension of output is (N x C x out_h x out_w]");
+
+    AddAttr<int>("out_h", "(int) output height of bilinear interpolation op.");
+    AddAttr<int>("out_w", "(int) output width of bilinear interpolation op.");
+    AddComment(R"DOC(
+          Bilinear interpolation is an extension of linear interpolation for 
+          interpolating functions of two variables (e.g. H-direction and 
+          W-direction in this op) on a rectilinear 2D grid. 
+          
+          The key idea is to perform linear interpolation first in one 
+          direction, and then again in the other direction.
+            
+          For details, please refer to Wikipedia: 
+          https://en.wikipedia.org/wiki/Bilinear_interpolation
+         )DOC");
+  }
+};
+
+class BilinearInterpOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) should not be null");
+    auto dim_x = ctx->GetInputDim("X");
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(bilinear_interp, ops::BilinearInterpOp,
+                  ops::BilinearInterpOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(bilinear_interp_grad, ops::BilinearInterpOpGrad);
+REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>);
+REGISTER_OP_CPU_KERNEL(bilinear_interp_grad,
+                       ops::BilinearInterpGradKernel<float>);

paddle/fluid/operators/bilinear_interp_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+   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/bilinear_interp_op.h"
+#include "paddle/fluid/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+template <typename T>
+__global__ void KeBilinearInterpFw(
+    const T* in, const size_t in_img_h, const size_t in_img_w,
+    const size_t input_h, const size_t input_w, T* out, const size_t out_img_h,
+    const size_t out_img_w, const size_t output_h, const size_t output_w,
+    const size_t num_channels, const T ratio_h, const T ratioW) {
+  int nthreads = output_h * output_w;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  if (tid < nthreads) {
+    int out_id_h = tid / output_w;
+    int out_id_w = tid % output_w;
+    int in_img_size = input_w / num_channels;
+    int out_img_size = output_w / num_channels;
+    int channel_id = out_id_w / out_img_size;
+
+    int out_img_idy = (out_id_w % out_img_size) / out_img_w;
+    int in_img_idy = ratio_h * out_img_idy;
+    int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
+    T h1lambda = ratio_h * out_img_idy - in_img_idy;
+    T h2lambda = 1.f - h1lambda;
+
+    int out_img_idx = tid % out_img_w;
+    int in_img_idx = ratioW * out_img_idx;
+    int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
+    T w1lambda = ratioW * out_img_idx - in_img_idx;
+    T w2lambda = 1.f - w1lambda;
+
+    const T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
+                          in_img_idy * in_img_w + in_img_idx];
+
+    // bilinear interpolation
+    out[out_id_h * output_w + out_id_w] =
+        h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[w_id]) +
+        h1lambda * (w2lambda * in_pos[h_id * in_img_w] +
+                    w1lambda * in_pos[h_id * in_img_w + w_id]);
+  }
+}
+
+template <typename T>
+__global__ void KeBilinearInterpBw(
+    T* in, const size_t in_img_h, const size_t in_img_w, const size_t input_h,
+    const size_t input_w, const T* out, const size_t out_img_h,
+    const size_t out_img_w, const size_t output_h, const size_t output_w,
+    const size_t num_channels, const T ratio_h, const T ratioW) {
+  int nthreads = output_h * output_w;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  if (tid < nthreads) {
+    int out_id_h = tid / output_w;
+    int out_id_w = tid % output_w;
+    int in_img_size = input_w / num_channels;
+    int out_img_size = output_w / num_channels;
+    int channel_id = out_id_w / out_img_size;
+
+    int out_img_idy = (out_id_w % out_img_size) / out_img_w;
+    int in_img_idy = ratio_h * out_img_idy;
+    int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
+    T h1lambda = ratio_h * out_img_idy - in_img_idy;
+    T h2lambda = 1.f - h1lambda;
+
+    int out_img_idx = tid % out_img_w;
+    int in_img_idx = ratioW * out_img_idx;
+    int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
+    T w1lambda = ratioW * out_img_idx - in_img_idx;
+    T w2lambda = 1.f - w1lambda;
+
+    T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
+                    in_img_idy * in_img_w + in_img_idx];
+    const T* out_pos = &out[out_id_h * output_w + out_id_w];
+    atomicAdd(&in_pos[0], h2lambda * w2lambda * out_pos[0]);
+    atomicAdd(&in_pos[w_id], h2lambda * w1lambda * out_pos[0]);
+    atomicAdd(&in_pos[h_id * in_img_w], h1lambda * w2lambda * out_pos[0]);
+    atomicAdd(&in_pos[h_id * in_img_w + w_id],
+              h1lambda * w1lambda * out_pos[0]);
+  }
+}
+
+template <typename T>
+class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "This kernel only runs on GPU device.");
+    auto* input_t = ctx.Input<Tensor>("X");      // float tensor
+    auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
+    auto* input = input_t->data<T>();
+    auto* output = output_t->mutable_data<T>(ctx.GetPlace());
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    int batch_size = input_t->dims()[0];
+    int channels = input_t->dims()[1];
+    int in_h = input_t->dims()[2];
+    int in_w = input_t->dims()[3];
+
+    int in_hw = in_h * in_w;
+    int out_hw = out_h * out_w;
+    int in_chw = channels * in_hw;
+    int out_chw = channels * out_hw;
+
+    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
+    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+
+    if (in_h == out_h && in_w == out_w) {
+      memcpy(output, input, input_t->numel() * sizeof(T));
+    } else {
+      int threadNum = batch_size * out_chw;
+      int blocks = (threadNum + 1024 - 1) / 1024;
+
+      KeBilinearInterpFw<
+          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
+          input, in_h, in_w, batch_size, in_chw, output, out_h, out_w,
+          batch_size, out_chw, channels, ratio_h, ratio_w);
+    }
+  }
+};
+
+template <typename T>
+class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
+    auto* d_output = d_output_t->data<T>();
+
+    auto& device_ctx =
+        ctx.template device_context<platform::CUDADeviceContext>();
+    math::SetConstant<platform::CUDADeviceContext, T> zero;
+    zero(device_ctx, d_input_t, static_cast<T>(0.0));
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    int batch_size = d_input_t->dims()[0];
+    int channels = d_input_t->dims()[1];
+    int in_h = d_input_t->dims()[2];
+    int in_w = d_input_t->dims()[3];
+
+    int in_hw = in_h * in_w;
+    int out_hw = out_h * out_w;
+    int in_chw = channels * in_hw;
+    int out_chw = channels * out_hw;
+
+    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
+    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+
+    if (in_h == out_h && in_w == out_w) {
+      memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
+    } else {
+      int threadNum = batch_size * out_chw;
+      int blocks = (threadNum + 1024 - 1) / 1024;
+
+      KeBilinearInterpBw<
+          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
+          d_input, in_h, in_w, batch_size, in_chw, d_output, out_h, out_w,
+          batch_size, out_chw, channels, ratio_h, ratio_w);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(bilinear_interp,
+                        ops::BilinearInterpOpCUDAKernel<float>);
+REGISTER_OP_CUDA_KERNEL(bilinear_interp_grad,
+                        ops::BilinearInterpGradOpCUDAKernel<float>);

paddle/fluid/operators/bilinear_interp_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+   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 "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class BilinearInterpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input_t = ctx.Input<Tensor>("X");      // float tensor
+    auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
+    auto* input = input_t->data<T>();
+    auto* output = output_t->mutable_data<T>(ctx.GetPlace());
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    int batch_size = input_t->dims()[0];
+    int channels = input_t->dims()[1];
+    int in_h = input_t->dims()[2];
+    int in_w = input_t->dims()[3];
+
+    int in_hw = in_h * in_w;
+    int out_hw = out_h * out_w;
+    int in_chw = channels * in_hw;
+    int out_chw = channels * out_hw;
+
+    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
+    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+
+    if (in_h == out_h && in_w == out_w) {
+      memcpy(output, input, input_t->numel() * sizeof(T));
+    } else {
+      for (int k = 0; k < batch_size; ++k) {  // loop for batches
+        for (int i = 0; i < out_h; ++i) {     // loop for images
+          int h = ratio_h * i;
+          int hid = (h < in_h - 1) ? 1 : 0;
+          T h1lambda = ratio_h * i - h;
+          T h2lambda = 1 - h1lambda;
+
+          for (int j = 0; j < out_w; ++j) {
+            int w = ratio_w * j;
+            int wid = (w < in_w - 1) ? 1 : 0;
+            T w1lambda = ratio_w * j - w;
+            T w2lambda = 1 - w1lambda;
+            // calculate four position for bilinear interpolation
+            const T* in_pos = &input[k * in_chw + h * in_w + w];
+            T* out_pos = &output[k * out_chw + i * out_w + j];
+
+            for (int c = 0; c < channels; ++c) {  // loop for channels
+              // bilinear interpolation
+              out_pos[0] =
+                  h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[wid]) +
+                  h1lambda * (w2lambda * in_pos[hid * in_w] +
+                              w1lambda * in_pos[hid * in_w + wid]);
+              in_pos += in_hw;
+              out_pos += out_hw;
+            }
+          }
+        }
+      }
+    }
+  }
+};
+
+template <typename T>
+class BilinearInterpGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
+    auto* d_output = d_output_t->data<T>();
+
+    auto& device_ctx =
+        ctx.template device_context<platform::CPUDeviceContext>();
+    math::SetConstant<platform::CPUDeviceContext, T> zero;
+    zero(device_ctx, d_input_t, static_cast<T>(0.0));
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    int batch_size = d_input_t->dims()[0];
+    int channels = d_input_t->dims()[1];
+    int in_h = d_input_t->dims()[2];
+    int in_w = d_input_t->dims()[3];
+
+    int in_hw = in_h * in_w;
+    int out_hw = out_h * out_w;
+    int in_chw = channels * in_hw;
+    int out_chw = channels * out_hw;
+
+    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
+    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+
+    if (in_h == out_h && in_w == out_w) {
+      memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
+    } else {
+      for (int k = 0; k < batch_size; ++k) {  // loop for batches
+        for (int i = 0; i < out_h; ++i) {     // loop for images
+          int h = ratio_h * i;
+          int hid = (h < in_h - 1) ? 1 : 0;
+          T h1lambda = ratio_h * i - h;
+          T h2lambda = 1 - h1lambda;
+
+          for (int j = 0; j < out_w; ++j) {
+            int w = ratio_w * j;
+            int wid = (w < in_w - 1) ? 1 : 0;
+            T w1lambda = ratio_w * j - w;
+            T w2lambda = 1 - w1lambda;
+            T* in_pos = &d_input[k * in_chw + h * in_w + w];
+            const T* out_pos = &d_output[k * out_chw + i * out_w + j];
+
+            for (int c = 0; c < channels; ++c) {  // loop for channels
+              in_pos[0] += h2lambda * w2lambda * out_pos[0];
+              in_pos[wid] += h2lambda * w1lambda * out_pos[0];
+              in_pos[hid * in_w] += h1lambda * w2lambda * out_pos[0];
+              in_pos[hid * in_w + wid] += h1lambda * w1lambda * out_pos[0];
+              in_pos += in_hw;
+              out_pos += out_hw;
+            }
+          }
+        }
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/conditional_block_op.cc

@@ -227,7 +227,7 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
     grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
     grad_op->SetOutput(framework::GradVarName("Params"),
                        InputGrad("Params", false));
-    grad_op->SetBlockAttr("sub_block", *this->grad_block_[0]);
+    grad_op->SetBlockAttr("sub_block", this->grad_block_[0]);
     grad_op->SetAttr("is_scalar_condition", GetAttr("is_scalar_condition"));
     return std::unique_ptr<framework::OpDesc>(grad_op);
   }

paddle/fluid/operators/detail/grpc_client.h

@@ -29,12 +29,12 @@ limitations under the License. */
 #include "grpc++/support/byte_buffer.h"
 #include "grpc++/support/slice.h"
 #include "grpc/support/log.h"
+#include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/operators/detail/sendrecvop_utils.h"
-#include "paddle/fluid/operators/detail/simple_block_queue.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/detail/grpc_server.cc

@@ -101,7 +101,7 @@ class RequestGet final : public RequestBase {
                       ::grpc::ServerCompletionQueue* cq, bool sync_mode,
                       framework::Scope* scope,
                       const platform::DeviceContext* dev_ctx,
-                      SimpleBlockQueue<MessageWithName>* queue)
+                      framework::BlockingQueue<MessageWithName>* queue)
       : RequestBase(service, cq, sync_mode, dev_ctx),
         responder_(&ctx_),
         scope_(scope),
@@ -139,7 +139,7 @@ class RequestGet final : public RequestBase {
   sendrecv::VariableMessage request_;
   ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
   framework::Scope* scope_;
-  SimpleBlockQueue<MessageWithName>* queue_;
+  framework::BlockingQueue<MessageWithName>* queue_;
 };
 
 class RequestPrefetch final : public RequestBase {

paddle/fluid/operators/detail/grpc_server.h

@@ -19,6 +19,7 @@ limitations under the License. */
 #include <utility>
 
 #include "grpc++/grpc++.h"
+#include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/program_desc.h"
@@ -29,7 +30,6 @@ limitations under the License. */
 #include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
 #include "paddle/fluid/operators/detail/send_recv.pb.h"
 #include "paddle/fluid/operators/detail/sendrecvop_utils.h"
-#include "paddle/fluid/operators/detail/simple_block_queue.h"
 
 namespace paddle {
 namespace operators {
@@ -37,7 +37,7 @@ namespace detail {
 
 typedef std::pair<std::string, std::shared_ptr<VariableResponse>>
     ReceivedMessage;
-typedef SimpleBlockQueue<ReceivedMessage> ReceivedQueue;
+typedef framework::BlockingQueue<ReceivedMessage> ReceivedQueue;
 
 typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
 class RequestBase;
@@ -101,7 +101,7 @@ class AsyncGRPCServer final {
   const platform::DeviceContext *dev_ctx_;
 
   // received variable from RPC, operators fetch variable from this queue.
-  SimpleBlockQueue<MessageWithName> var_get_queue_;
+  framework::BlockingQueue<MessageWithName> var_get_queue_;
   // client send variable to this queue.
   ReceivedQueue var_recv_queue_;
 

paddle/fluid/operators/detail/sendrecvop_utils.cc

@@ -39,7 +39,9 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   // parallelism execution, need to know when to free the tensor.
   DestroyCallback destroy_callback = [](void* backing) {};
 
-  void* buf = malloc(1024);
+  auto buffer = std::unique_ptr<char[]>(new char[1024]);
+  void* buf = buffer.get();
+
   void* payload = nullptr;
   size_t payload_size;
   ProtoEncodeHelper e(static_cast<char*>(buf), 1024);

paddle/fluid/operators/elementwise_op_function.h

@@ -356,8 +356,8 @@ __device__ T reduceSum(T val, int tid, int len) {
   // I use Warp-Level Parallelism and assume the Warp size
   // is 32 which may be different for different GPU,
   // but most card's warp size is 32.
-  __shared__ T shm[32];
   const int warpSize = 32;
+  __shared__ T shm[warpSize];
   unsigned mask = 0u;
   CREATE_SHFL_MASK(mask, tid < len);
 
@@ -371,6 +371,7 @@ __device__ T reduceSum(T val, int tid, int len) {
   if (tid % warpSize == 0) {
     shm[tid / warpSize] = val;
   }
+  __syncthreads();
 
   CREATE_SHFL_MASK(mask, tid < warpSize);
 

paddle/fluid/operators/gru_op.h

@@ -56,8 +56,6 @@ class GRUKernel : public framework::OpKernel<T> {
     auto* hidden = context.Output<LoDTensor>("Hidden");
     hidden->mutable_data<T>(context.GetPlace());
 
-    context.ShareLoD("Input", "Hidden");
-
     auto hidden_dims = hidden->dims();
 
     bool is_reverse = context.Attr<bool>("is_reverse");

paddle/fluid/operators/mul_mkldnn_op.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 "mkldnn.hpp"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/mul_op.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using paddle::framework::Tensor;
+using paddle::platform::MKLDNNDeviceContext;
+
+template <typename Format = mkldnn::memory::format>
+mkldnn::memory::desc type(const std::vector<int>& dims, Format&& f) {
+  return platform::MKLDNNMemDesc(dims, mkldnn::memory::data_type::f32, f);
+}
+
+template <typename T>
+class MulMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
+    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+    auto mkldnn_engine = dev_ctx.GetEngine();
+
+    auto input = ctx.Input<Tensor>("X");
+    auto weight = ctx.Input<Tensor>("Y");
+
+    PADDLE_ENFORCE(input->dims().size() & (2 | 4),
+                   "Input must be with 2 or 4 dimensions, i.e. NC or NCHW");
+    PADDLE_ENFORCE(weight->dims().size() & (2 | 4),
+                   "Weights must be with 2 or 4 dimensions, i.e. OI or OIHW");
+
+    std::vector<int> w_tz = paddle::framework::vectorize2int(weight->dims());
+    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
+
+    auto src_md =
+        src_tz.size() != 2
+            ? type(src_tz, mkldnn::memory::format::nchw)
+            : type({src_tz[0], src_tz[1]}, mkldnn::memory::format::nc);
+
+    auto dst_md = type({src_tz[0], w_tz[1]}, mkldnn::memory::format::nc);
+
+    auto weights_md =
+        src_tz.size() != 2
+            ? type({w_tz[1], src_tz[1], src_tz[2], src_tz[3]},
+                   mkldnn::memory::format::oihw)
+            : type({w_tz[1], src_tz[1]}, mkldnn::memory::format::oi);
+
+    auto output = ctx.Output<Tensor>("Out");
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    const std::string key = ctx.op().Output("Out");
+    const std::string key_fc_pd = key + "@mul_pd";
+
+    const T* input_data = input->data<T>();
+    const T* w_data = weight->data<T>();
+
+    auto dst_memory = mkldnn::memory({dst_md, mkldnn_engine}, output_data);
+
+    auto src_memory = mkldnn::memory({src_md, mkldnn_engine},
+                                     platform::to_void_cast(input_data));
+
+    auto weights_memory = mkldnn::memory({weights_md, mkldnn_engine},
+                                         platform::to_void_cast(w_data));
+
+    auto pd = platform::MKLDNNFwdPrimitiveDesc<mkldnn::inner_product_forward>(
+        mkldnn_engine, src_md, weights_md, dst_md);
+
+    dev_ctx.SetBlob(key_fc_pd, pd);
+
+    auto forward = mkldnn::inner_product_forward(*pd, src_memory,
+                                                 weights_memory, dst_memory);
+
+    std::vector<mkldnn::primitive> pipeline = {forward};
+    mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+  }
+};
+
+template <typename T>
+class MulMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
+ public:
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
+    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+    auto mkldnn_engine = dev_ctx.GetEngine();
+
+    const Tensor* input = ctx.Input<Tensor>("X");
+    const Tensor* w = ctx.Input<Tensor>("Y");
+
+    const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    Tensor* w_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+
+    const std::string key = ctx.op().Input("Out");
+    const std::string key_fc_pd = key + "@mul_pd";
+
+    const T* input_data = input->data<T>();
+    const T* w_data = w->data<T>();
+    const T* out_grad_data = out_grad->data<T>();
+    T* input_grad_data = nullptr;
+    T* w_grad_data = nullptr;
+
+    if (input_grad) {
+      input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+    }
+    if (w_grad) {
+      w_grad_data = w_grad->mutable_data<T>(ctx.GetPlace());
+    }
+
+    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
+    std::vector<int> w_tz = paddle::framework::vectorize2int(w->dims());
+
+    auto src_md =
+        src_tz.size() != 2
+            ? type(src_tz, mkldnn::memory::format::nchw)
+            : type({src_tz[0], src_tz[1]}, mkldnn::memory::format::nc);
+
+    auto dst_md = type({src_tz[0], w_tz[1]}, mkldnn::memory::format::nc);
+
+    auto weights_md =
+        src_tz.size() != 2
+            ? type({w_tz[1], src_tz[1], src_tz[2], src_tz[3]},
+                   mkldnn::memory::format::oihw)
+            : type({w_tz[1], src_tz[1]}, mkldnn::memory::format::oi);
+
+    auto src_memory = mkldnn::memory({src_md, mkldnn_engine},
+                                     platform::to_void_cast(input_data));
+
+    auto dst_memory = mkldnn::memory({dst_md, mkldnn_engine},
+                                     platform::to_void_cast(out_grad_data));
+
+    auto weight_memory = mkldnn::memory({weights_md, mkldnn_engine},
+                                        platform::to_void_cast(w_data));
+
+    auto pd =
+        std::static_pointer_cast<mkldnn::inner_product_forward::primitive_desc>(
+            dev_ctx.GetBlob(key_fc_pd));
+
+    PADDLE_ENFORCE(pd != nullptr, "Fail to find pd in device context");
+
+    if (w_grad) {
+      auto weights_grad_memory = mkldnn::memory(
+          {weights_md, mkldnn_engine}, platform::to_void_cast(w_grad_data));
+
+      auto bwd_weight_pd = platform::MKLDNNBwdPrimitiveDesc<
+          mkldnn::inner_product_backward_weights>(mkldnn_engine, *pd, src_md,
+                                                  weights_md, dst_md);
+
+      auto bwd_weights_prim = mkldnn::inner_product_backward_weights(
+          bwd_weight_pd, src_memory, dst_memory, weights_grad_memory);
+
+      std::vector<mkldnn::primitive> pipeline{bwd_weights_prim};
+      mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+    }
+
+    if (input_grad) {
+      auto src_grad_memory = mkldnn::memory(
+          {src_md, mkldnn_engine}, platform::to_void_cast(input_grad_data));
+
+      auto bwd_data_pd =
+          platform::MKLDNNBwdPrimitiveDesc<mkldnn::inner_product_backward_data>(
+              mkldnn_engine, *pd, src_md, weights_md, dst_md);
+
+      auto bwd_data_prim = mkldnn::inner_product_backward_data(
+          bwd_data_pd, dst_memory, weight_memory, src_grad_memory);
+
+      std::vector<mkldnn::primitive> pipeline{bwd_data_prim};
+      mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_KERNEL(mul, MKLDNN, ::paddle::platform::CPUPlace,
+                   paddle::operators::MulMKLDNNOpKernel<float>);
+
+REGISTER_OP_KERNEL(mul_grad, MKLDNN, ::paddle::platform::CPUPlace,
+                   paddle::operators::MulMKLDNNGradOpKernel<float>);

paddle/fluid/operators/mul_op.cc

@@ -13,8 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/mul_op.h"
+#include <string>
 #include <vector>
 
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
 namespace paddle {
 namespace operators {
 
@@ -71,6 +76,22 @@ class MulOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
     ctx->ShareLoD("X", /*->*/ "Out");
   }
+
+ private:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_MKLDNN
+    if (library == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library = framework::LibraryType::kMKLDNN;
+    }
+#endif
+    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
+        layout, library);
+  }
 };
 
 class MulOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -100,6 +121,9 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker {
         )DOC")
         .SetDefault(1)
         .EqualGreaterThan(1);
+    AddAttr<bool>("use_mkldnn",
+                  "(bool, default false) Only used in mkldnn kernel")
+        .SetDefault(false);
     AddAttr<int>(
         "y_num_col_dims",
         R"DOC((int, default 1), The mul_op can take tensors with more than two,
@@ -154,6 +178,22 @@ class MulGradOp : public framework::OperatorWithKernel {
       ctx->SetOutputDim(y_grad_name, y_dims);
     }
   }
+
+ private:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_MKLDNN
+    if (library == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library = framework::LibraryType::kMKLDNN;
+    }
+#endif
+    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
+        layout, library);
+  }
 };
 
 }  // namespace operators

paddle/fluid/operators/parallel_do_op.cc

@@ -364,7 +364,7 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
       }
     }
     grad->SetAttrMap(this->Attrs());
-    grad->SetBlockAttr(kParallelBlock, *grad_block_[0]);
+    grad->SetBlockAttr(kParallelBlock, grad_block_[0]);
 
     return std::unique_ptr<framework::OpDesc>(grad);
   }

paddle/fluid/operators/reader/create_double_buffer_reader_op.cc

@@ -180,7 +180,8 @@ void DoubleBufferReader::PrefetchThreadFunc() {
       auto* gpu_ctx = ctxs_[cached_tensor_id].get();
       gpu_batch.resize(cpu_batch.size());
       for (size_t i = 0; i < cpu_batch.size(); ++i) {
-        framework::TensorCopy(cpu_batch[i], place_, *gpu_ctx, &gpu_batch[i]);
+        framework::TensorCopy(cpu_batch[i], place_, *gpu_ctx, &gpu_batch[i],
+                              true);
         gpu_batch[i].set_lod(cpu_batch[i].lod());
       }
     }

paddle/fluid/operators/reader/create_threaded_reader_op.cc

@@ -21,26 +21,16 @@ namespace reader {
 
 class ThreadedReader : public framework::DecoratedReader {
  public:
-  ThreadedReader(ReaderBase* reader, bool safe_mode)
-      : DecoratedReader(reader), safe_mode_(safe_mode) {}
+  explicit ThreadedReader(ReaderBase* reader) : DecoratedReader(reader) {}
 
   void ReadNext(std::vector<framework::LoDTensor>* out) override {
     std::lock_guard<std::mutex> lock(mutex_);
     reader_->ReadNext(out);
   }
 
-  void ReInit() override {
-    if (safe_mode_) {
-      PADDLE_THROW(
-          "ThreadedReader::ReInit() is disabled when 'safe_mode' is true.");
-    }
-    VLOG(5) << "ThreadedReader::ReInit() is invoked! It might be buggy in "
-               "multi-thread environment.";
-    reader_->ReInit();
-  }
+  void ReInit() override { reader_->ReInit(); }
 
  private:
-  bool safe_mode_;
   std::mutex mutex_;
 };
 
@@ -58,8 +48,7 @@ class CreateThreadedReaderOp : public framework::OperatorBase {
     }
     const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
                                         ->Get<framework::ReaderHolder>();
-    bool safe_mode = Attr<bool>("safe_mode");
-    out->Reset(new ThreadedReader(underlying_reader.Get(), safe_mode));
+    out->Reset(new ThreadedReader(underlying_reader.Get()));
   }
 };
 
@@ -67,10 +56,6 @@ class CreateThreadedReaderOpMaker : public DecoratedReaderMakerBase {
  public:
   CreateThreadedReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
       : DecoratedReaderMakerBase(op_proto, op_checker) {
-    AddAttr<bool>("safe_mode",
-                  "When 'safe_mode' is true, 'ReInit()' is disabled to avoid "
-                  "unexpected bugs in multi-thread environment.")
-        .SetDefault(true);
     AddComment(R"DOC(
       CreateThreadedReader Operator
 

paddle/fluid/operators/recurrent_op.cc

@@ -596,7 +596,7 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
       }
     }
     grad->SetAttrMap(this->Attrs());
-    grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
+    grad->SetBlockAttr(kStepBlock, grad_block_[0]);
 
     return std::unique_ptr<framework::OpDesc>(grad);
   }

paddle/fluid/operators/sequence_conv_op.h

@@ -33,7 +33,6 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     auto filter = *context.Input<Tensor>("Filter");
 
     out->mutable_data<T>(context.GetPlace());
-    context.ShareLoD("X", "Out");
 
     int context_start = context.Attr<int>("contextStart");
     int context_length = context.Attr<int>("contextLength");

paddle/fluid/operators/while_op.cc

@@ -288,7 +288,7 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
     while_grad->SetInput(framework::GradVarName(kOutputs), output_grads_list);
 
     while_grad->SetAttrMap(this->Attrs());
-    while_grad->SetBlockAttr(kStepBlock, *grad_block);
+    while_grad->SetBlockAttr(kStepBlock, grad_block);
     // record the original output gradient names, since the gradient name of
     // while operator could be renamed.
     while_grad->SetAttr("original_output_grad", output_grads_list);

paddle/fluid/platform/CMakeLists.txt

@@ -12,7 +12,7 @@ add_custom_command(TARGET profiler_py_proto POST_BUILD
         WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
 if(WITH_GPU)
-  cc_library(enforce SRCS enforce.cc DEPS)
+  nv_library(enforce SRCS enforce.cc)
 else()
   cc_library(enforce SRCS enforce.cc)
 endif()

paddle/fluid/platform/gpu_info.cc

@@ -127,11 +127,24 @@ void GpuMemcpyAsync(void *dst, const void *src, size_t count,
                  "cudaMemcpyAsync failed in paddle::platform::GpuMemcpyAsync");
 }
 
-void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device,
-                   size_t count, cudaStream_t stream) {
+void GpuMemcpySync(void *dst, const void *src, size_t count,
+                   enum cudaMemcpyKind kind) {
+  PADDLE_ENFORCE(cudaMemcpy(dst, src, count, kind),
+                 "cudaMemcpy failed in paddle::platform::GpuMemcpySync");
+}
+
+void GpuMemcpyPeerAsync(void *dst, int dst_device, const void *src,
+                        int src_device, size_t count, cudaStream_t stream) {
   PADDLE_ENFORCE(
       cudaMemcpyPeerAsync(dst, dst_device, src, src_device, count, stream),
-      "cudaMemcpyPeerAsync failed in paddle::platform::GpuMemcpyPeer");
+      "cudaMemcpyPeerAsync failed in paddle::platform::GpuMemcpyPeerAsync");
+}
+
+void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
+                       int src_device, size_t count) {
+  PADDLE_ENFORCE(
+      cudaMemcpyPeer(dst, dst_device, src, src_device, count),
+      "cudaMemcpyPeer failed in paddle::platform::GpuMemcpyPeerSync");
 }
 
 void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream) {

paddle/fluid/platform/gpu_info.h

@@ -57,9 +57,17 @@ size_t GpuMaxChunkSize();
 void GpuMemcpyAsync(void *dst, const void *src, size_t count,
                     enum cudaMemcpyKind kind, cudaStream_t stream);
 
-//! Copy memory from one device to another device.
-void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device,
-                   size_t count, cudaStream_t stream);
+//! Copy memory from address src to dst synchronously.
+void GpuMemcpySync(void *dst, const void *src, size_t count,
+                   enum cudaMemcpyKind kind);
+
+//! Copy memory from one device to another device asynchronously.
+void GpuMemcpyPeerAsync(void *dst, int dst_device, const void *src,
+                        int src_device, size_t count, cudaStream_t stream);
+
+//! Copy memory from one device to another device synchronously.
+void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
+                       int src_device, size_t count);
 
 //! Set memory dst with value count size asynchronously
 void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream);

paddle/fluid/platform/mkldnn_helper.h

@@ -13,9 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #pragma once
 
+#include <mkldnn.h>
 #include <vector>
-
-#include "mkldnn/include/mkldnn.hpp"
 #include "paddle/fluid/framework/operator.h"
 
 namespace paddle {
@@ -34,6 +33,32 @@ typedef std::unique_ptr<MKLDNNMemory> MKLDNNMemoryPtr;
 typedef std::unique_ptr<MKLDNNPrimitive> MKLDNNPrimitivePtr;
 typedef std::unique_ptr<MKLDNNPrimitiveDesc> MKLDNNPrimitiveDescPtr;
 
+template <typename Type>
+void* to_void_cast(const Type* t) {
+  return static_cast<void*>(const_cast<Type*>(t));
+}
+
+template <class Type>
+using tf_desc = typename Type::desc;
+
+template <class Type>
+using tf_pd = typename Type::primitive_desc;
+
+template <typename Type, typename Engine, typename... Args>
+std::shared_ptr<tf_pd<Type>> MKLDNNFwdPrimitiveDesc(const Engine& e,
+                                                    Args&&... args) {
+  auto desc = tf_desc<Type>(mkldnn::prop_kind::forward, (args)...);
+  auto pd = new tf_pd<Type>(desc, e);
+  return std::shared_ptr<tf_pd<Type>>(pd);
+}
+
+template <typename Type, typename Engine, typename Primitive, typename... Args>
+tf_pd<Type> MKLDNNBwdPrimitiveDesc(const Engine& e, const Primitive& p,
+                                   Args&&... args) {
+  auto desc = tf_desc<Type>(args...);
+  return tf_pd<Type>(desc, e, p);
+}
+
 inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int>& dims,
                                           mkldnn::memory::data_type data_type,
                                           mkldnn::memory::format format) {

paddle/fluid/recordio/README.md

+## Background
+
+The RecordIO file format is a container for records.  This package is a C++ implementation of https://github.com/paddlepaddle/recordio, which originates from https://github.com/wangkuiyi/recordio.
+
+## Fault-tolerant Writing
+
+For the initial design purpose of RecordIO within Google, which was logging, RecordIO groups record into *chunks*, whose header contains an MD5 hash of the chunk.  A process that writes logs is supposed to call the Writer interface to add records.  Once the writer accumulates a handful of them, it groups a chunk, put the MD5 into the chunk header, and appends the chunk to the file.  In the event the process crashes unexpected, the last chunk in the RecordIO file could be incomplete/corrupt. The RecordIO reader is able to recover from these errors when the process restarts by identifying incomplete chucks and skipping over them.
+
+## Reading Ranges
+
+A side-effect of chunks is to make it easy to indexing records while reading, thus allows us to read a range of successive records.  This is good for distributed log process, where each MapReduce task handles only part of records in a big RecordIO file.
+
+The procedure that creates the index starts from reading the header of the first chunk. It indexes the offset (0) and the size of the chunk, and skips to the header of the next chunk by calling the `fseek` API. Please be aware that most distributed filesystems and all POSIX-compatible local filesystem provides `fseek`, and makes sure that `fseek` runs much faster than `fread`.  This procedure generates a map from chunks to their offsets, which allows the readers is to locate and read a range of records.

paddle/gserver/dataproviders/PyDataProvider2.cpp

@@ -390,9 +390,7 @@ private:
 
       if (this->loadThread_) {  // wait poolActualSize < poolSize;
         std::unique_lock<std::mutex> l(mtx_);
-        pushCV_.wait(l, [this, additionalBatchSize] {
-          return this->poolActualSize_ < poolSize_;
-        });
+        pushCV_.wait(l, [this] { return this->poolActualSize_ < poolSize_; });
       }
 
       {

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -52,7 +52,7 @@ MultiGradientMachine::MultiGradientMachine(const ModelConfig& config,
   } else {
     numDevices_ = 0;
   }
-  ParamInitCallback mainParamInitCb = [this](int paramId, Parameter* para) {
+  ParamInitCallback mainParamInitCb = [](int paramId, Parameter* para) {
     // only create buf for CPU parameters
     // GPU parameters will be created in each thread
     if (para->useGpu()) return;

paddle/gserver/layers/RecurrentLayerGroup.cpp

@@ -72,7 +72,7 @@ void RecurrentLayerGroup::initSubNetwork(
   setNeedGradient(true);
 
   network_.reset(new RecurrentGradientMachine(config_.name(), rootNetwork));
-  ParamInitCallback cb = [this, rootNetwork](int paramId, Parameter* para) {
+  ParamInitCallback cb = [rootNetwork](int paramId, Parameter* para) {
     para->enableSharedType(
         PARAMETER_VALUE,
         rootNetwork->getParameters()[paramId]->getBuf(PARAMETER_VALUE),

paddle/parameter/Argument.cpp

@@ -325,12 +325,12 @@ void Argument::concat(const std::vector<Argument>& args,
         ->copyFrom(*src->subVec(srcStartRow, size), stream);
   };
 
-  auto copyStrs = [batchSize, stream](SVectorPtr& dst,
-                                      const SVectorPtr& src,
-                                      int desStartRow,
-                                      int srcStartRow,
-                                      int size,
-                                      bool useGpu) {
+  auto copyStrs = [batchSize](SVectorPtr& dst,
+                              const SVectorPtr& src,
+                              int desStartRow,
+                              int srcStartRow,
+                              int size,
+                              bool useGpu) {
     if (!src) {
       dst.reset();
       return;
@@ -413,7 +413,7 @@ void Argument::concat(const std::vector<Argument>& args,
     dst->subVec(startRow, src->getSize())->copyFrom(*src, stream);
   };
 
-  auto copyStrs = [batchSize, stream](
+  auto copyStrs = [batchSize](
       SVectorPtr& dst, const SVectorPtr& src, int startRow, bool useGpu) {
     if (!src) {
       dst.reset();

paddle/parameter/AverageOptimizer.cpp

@@ -81,9 +81,9 @@ ParameterOptimizer::TraverseCallback AverageOptimizer::needSpecialTraversal(
   if (numUpdates_ % kMaxNumAccumulates == 0) {
     // Move the sum to a different buffer to avoid loss of precision
     // due to too many sums.
-    callbacks.emplace_back([this](const VectorPtr vecs[],
-                                  const ParameterConfig& config,
-                                  size_t sparseId) {
+    callbacks.emplace_back([](const VectorPtr vecs[],
+                              const ParameterConfig& config,
+                              size_t sparseId) {
       vecs[PARAMETER_SUM2]->add(*vecs[PARAMETER_SUM1]);
       vecs[PARAMETER_SUM1]->zeroMem();
     });
@@ -94,9 +94,9 @@ ParameterOptimizer::TraverseCallback AverageOptimizer::needSpecialTraversal(
     if (auto callback = this->startCatchUpWith()) {
       callbacks.emplace_back(callback);
     }
-    callbacks.emplace_back([this](const VectorPtr vecs[],
-                                  const ParameterConfig& config,
-                                  size_t sparseId) {
+    callbacks.emplace_back([](const VectorPtr vecs[],
+                              const ParameterConfig& config,
+                              size_t sparseId) {
       vecs[PARAMETER_SUM3]->add(*vecs[PARAMETER_SUM1], *vecs[PARAMETER_SUM2]);
       vecs[PARAMETER_SUM1]->zeroMem();
       vecs[PARAMETER_SUM2]->zeroMem();

paddle/parameter/FirstOrderOptimizer.cpp

@@ -145,9 +145,9 @@ AdagradParameterOptimizer::needSpecialTraversal(
   if (numUpdates_ % kMaxNumAccumulates == 0) {
     // Move the sum to a different buffer to avoid loss of precision
     // due to too many sums.
-    return [this](const VectorPtr vecs[],
-                  const ParameterConfig& config,
-                  size_t sparseId) {
+    return [](const VectorPtr vecs[],
+              const ParameterConfig& config,
+              size_t sparseId) {
       vecs[PARAMETER_GRADIENT_SQURESUM]->add(
           *vecs[PARAMETER_GRADIENT_SQURESUM1]);
       vecs[PARAMETER_GRADIENT_SQURESUM1]->zeroMem();

python/paddle/dataset/uci_housing.py

@@ -19,7 +19,11 @@ https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and
 parse training set and test set into paddle reader creators.
 """
 
+import os
+
 import numpy as np
+import tempfile
+import tarfile
 import os
 import paddle.dataset.common
 
@@ -34,8 +38,9 @@ feature_names = [
 
 UCI_TRAIN_DATA = None
 UCI_TEST_DATA = None
-URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fit_a_line.tar'
-MD5_MODEL = '52fc3da8ef3937822fcdd87ee05c0c9b'
+
+FLUID_URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fluid/fit_a_line.fluid.tar'
+FLUID_MD5_MODEL = '6e6dd637ccd5993961f68bfbde46090b'
 
 
 def feature_range(maximums, minimums):
@@ -113,6 +118,29 @@ def test():
     return reader
 
 
+def fluid_model():
+    parameter_tar = paddle.dataset.common.download(
+        FLUID_URL_MODEL, 'uci_housing', FLUID_MD5_MODEL, 'fit_a_line.fluid.tar')
+
+    tar = tarfile.TarFile(parameter_tar, mode='r')
+    dirpath = tempfile.mkdtemp()
+    tar.extractall(path=dirpath)
+
+    return dirpath
+
+
+def predict_reader():
+    """
+    It returns just one tuple data to do inference.
+
+    :return: one tuple data
+    :rtype: tuple 
+    """
+    global UCI_TEST_DATA
+    load_data(paddle.dataset.common.download(URL, 'uci_housing', MD5))
+    return (UCI_TEST_DATA[0][:-1], )
+
+
 def fetch():
     paddle.dataset.common.download(URL, 'uci_housing', MD5)
 

python/paddle/fluid/layers/io.py

@@ -457,8 +457,8 @@ def __create_shared_decorated_reader__(op_type, reader, attrs):
     return monkey_patch_reader_methods(main_prog_var)
 
 
-def __create_unshared_decorated_reader__(op_type, reader, attrs):
-    new_reader_name = unique_name(op_type)
+def __create_unshared_decorated_reader__(op_type, reader, attrs, name=None):
+    new_reader_name = name if name is not None else unique_name(op_type)
     main_blk = default_main_program().current_block()
     new_reader = main_blk.create_var(name=new_reader_name)
     main_blk.append_op(
@@ -481,12 +481,12 @@ def batch(reader, batch_size):
         'create_batch_reader', reader, {'batch_size': int(batch_size)})
 
 
-def double_buffer(reader, place=None):
+def double_buffer(reader, place=None, name=None):
     attrs = dict()
     if place is not None:
         attrs['place'] = str(place).upper()
-    return __create_unshared_decorated_reader__('create_double_buffer_reader',
-                                                reader, attrs)
+    return __create_unshared_decorated_reader__(
+        'create_double_buffer_reader', reader, attrs, name=name)
 
 
 def multi_pass(reader, pass_num):

python/paddle/fluid/layers/nn.py

@@ -159,67 +159,37 @@ def fc(input,
     dtype = helper.input_dtype()
 
     mul_results = []
-    if use_mkldnn:
-        tmp = helper.create_tmp_variable(dtype)
-        input_shape = input.shape
+    for input_var, param_attr in helper.iter_inputs_and_params():
+        input_shape = input_var.shape
         param_shape = [
             reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
         ] + [size]
 
         w = helper.create_parameter(
-            attr=helper.param_attr,
-            shape=param_shape,
-            dtype=dtype,
-            is_bias=False)
-        if bias_attr is None or bias_attr is False:
-            bias_attr = False
-        else:
-            bias_attr = True
+            attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
+        tmp = helper.create_tmp_variable(dtype)
         helper.append_op(
-            type="fc",
-            inputs={"Input": input,
-                    "W": w},
+            type="mul",
+            inputs={"X": input_var,
+                    "Y": w},
             outputs={"Out": tmp},
             attrs={
-                "use_mkldnn": use_mkldnn,
-                "is_test": is_test,
-                "bias_attr": bias_attr
+                "x_num_col_dims": num_flatten_dims,
+                "y_num_col_dims": 1,
+                "use_mkldnn": use_mkldnn
             })
-        return helper.append_activation(tmp)
+        mul_results.append(tmp)
+
+    if len(mul_results) == 1:
+        pre_bias = mul_results[0]
     else:
-        for input_var, param_attr in helper.iter_inputs_and_params():
-            input_shape = input_var.shape
-            param_shape = [
-                reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
-            ] + [size]
-
-            w = helper.create_parameter(
-                attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
-            tmp = helper.create_tmp_variable(dtype)
-            helper.append_op(
-                type="mul",
-                inputs={"X": input_var,
-                        "Y": w},
-                outputs={"Out": tmp},
-                attrs={
-                    "x_num_col_dims": num_flatten_dims,
-                    "y_num_col_dims": 1,
-                })
-            mul_results.append(tmp)
-
-        if len(mul_results) == 1:
-            pre_bias = mul_results[0]
-        else:
-            pre_bias = helper.create_tmp_variable(dtype)
-            helper.append_op(
-                type="sum",
-                inputs={"X": mul_results},
-                outputs={"Out": pre_bias})
-        # add bias
-        pre_activation = helper.append_bias_op(
-            pre_bias, dim_start=num_flatten_dims)
-        # add activation
-        return helper.append_activation(pre_activation)
+        pre_bias = helper.create_tmp_variable(dtype)
+        helper.append_op(
+            type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
+    # add bias
+    pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
+    # add activation
+    return helper.append_activation(pre_activation)
 
 
 def embedding(input,
@@ -3733,8 +3703,8 @@ def label_smooth(label,
                  name=None):
     """
     Label smoothing is a mechanism to regularize the classifier layer and is
-    called label-smoothing regularization (LSR). 
-    
+    called label-smoothing regularization (LSR).
+
     Label smoothing is proposed to encourage the model to be less confident,
     since optimizing the log-likelihood of the correct label directly may
     cause overfitting and reduce the ability of the model to adapt. Label
@@ -3758,10 +3728,10 @@ def label_smooth(label,
         prior_dist(Variable): The prior distribution to be used to smooth
                               labels. If not provided, an uniform distribution
                               is used. The shape of :attr:`prior_dist` should
-                              be :math:`(1, class\_num)`. 
+                              be :math:`(1, class\_num)`.
         epsilon(float): The weight used to mix up the original ground-truth
                         distribution and the fixed distribution.
-        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, 
+        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
                                                   float_64, int etc.
         name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.

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

@@ -244,7 +244,7 @@ def infer(use_cuda, save_dirname=None):
         assert len(results[0]) == len(transpiler_results[0])
         for i in range(len(results[0])):
             np.testing.assert_almost_equal(
-                results[0][i], transpiler_results[0][i], decimal=6)
+                results[0][i], transpiler_results[0][i], decimal=5)
 
         print("infer results: ", results[0])
 

python/paddle/fluid/tests/demo/text_classification/.gitignore

+*.recordio

python/paddle/fluid/tests/demo/text_classification/convert_data_to_recordio.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 sys
+import paddle.fluid as fluid
+import paddle.v2 as paddle
+
+
+def load_vocab(filename):
+    """
+    load vocabulary
+    """
+    vocab = {}
+    with open(filename) as f:
+        wid = 0
+        for line in f:
+            vocab[line.strip()] = wid
+            wid += 1
+    return vocab
+
+
+# load word dict with paddle inner function
+word_dict = load_vocab(sys.argv[1])
+word_dict["<unk>"] = len(word_dict)
+print "Dict dim = ", len(word_dict)
+
+# input text data
+data = fluid.layers.data(name="words", shape=[1], dtype="int64", lod_level=1)
+
+# label data
+label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+# like placeholder
+feeder = fluid.DataFeeder(feed_list=[data, label], place=fluid.CPUPlace())
+
+# train data set
+BATCH_SIZE = 128
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.imdb.train(word_dict), buf_size=10000),
+    batch_size=BATCH_SIZE)
+
+test_reader = paddle.batch(
+    paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
+
+fluid.recordio_writer.convert_reader_to_recordio_file(
+    "train.recordio", feeder=feeder, reader_creator=train_reader)
+fluid.recordio_writer.convert_reader_to_recordio_file(
+    "test.recordio", feeder=feeder, reader_creator=test_reader)

python/paddle/fluid/tests/demo/text_classification/train.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 paddle.fluid as fluid
+import numpy
+import sys
+
+TRAIN_FILES = ['train.recordio']
+TEST_FILES = ['test.recordio']
+
+DICT_DIM = 89528
+
+# embedding dim
+emb_dim = 128
+
+# hidden dim
+hid_dim = 128
+
+# hidden dim2
+hid_dim2 = 96
+
+# class num
+class_dim = 2
+
+
+def network_cfg(is_train, pass_num=100):
+    with fluid.unique_name.guard():
+        train_file_obj = fluid.layers.open_files(
+            filenames=TRAIN_FILES,
+            pass_num=pass_num,
+            shapes=[[-1, 1], [-1, 1]],
+            lod_levels=[1, 0],
+            dtypes=['int64', 'int64'],
+            thread_num=1)
+
+        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)
+
+        if is_train:
+            file_obj = fluid.layers.shuffle(train_file_obj, buffer_size=1000)
+        else:
+            file_obj = test_file_obj
+
+        file_obj = fluid.layers.double_buffer(
+            file_obj,
+            name="train_double_buffer" if is_train else 'test_double_buffer')
+
+        data, label = fluid.layers.read_file(file_obj)
+
+        emb = fluid.layers.embedding(input=data, size=[DICT_DIM, emb_dim])
+
+        # sequence conv with window size = 3
+        win_size = 3
+        conv_3 = fluid.nets.sequence_conv_pool(
+            input=emb,
+            num_filters=hid_dim,
+            filter_size=win_size,
+            act="tanh",
+            pool_type="max")
+
+        # fc layer after conv
+        fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
+
+        # probability of each class
+        prediction = fluid.layers.fc(input=[fc_1],
+                                     size=class_dim,
+                                     act="softmax")
+        # cross entropy loss
+        cost = fluid.layers.cross_entropy(input=prediction, label=label)
+
+        # mean loss
+        avg_cost = fluid.layers.mean(x=cost)
+        acc = fluid.layers.accuracy(input=prediction, label=label)
+
+        if is_train:
+            # SGD optimizer
+            sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.01)
+            sgd_optimizer.minimize(avg_cost)
+
+        return {
+            'loss': avg_cost,
+            'log': [avg_cost, acc],
+            'file': train_file_obj if is_train else test_file_obj
+        }
+
+
+def main():
+    train = fluid.Program()
+    startup = fluid.Program()
+
+    with fluid.program_guard(train, startup):
+        train_args = network_cfg(is_train=True)
+
+    test = fluid.Program()
+
+    with fluid.program_guard(test, fluid.Program()):
+        test_args = network_cfg(is_train=False)
+
+    # startup
+    place = fluid.CUDAPlace(0)
+    exe = fluid.Executor(place=place)
+    exe.run(startup)
+
+    train_exe = fluid.ParallelExecutor(
+        use_cuda=True, loss_name=train_args['loss'].name, main_program=train)
+
+    fetch_var_list = [var.name for var in train_args['log']]
+    for i in xrange(sys.maxint):
+        result = map(numpy.array,
+                     train_exe.run(fetch_list=fetch_var_list
+                                   if i % 1000 == 0 else []))
+        if len(result) != 0:
+            print 'Train: ', result
+
+        if i % 1000 == 0:
+            test_exe = fluid.ParallelExecutor(
+                use_cuda=True, main_program=test, share_vars_from=train_exe)
+            loss = []
+            acc = []
+            try:
+                while True:
+                    loss_np, acc_np = map(
+                        numpy.array, test_exe.run(fetch_list=fetch_var_list))
+                    loss.append(loss_np[0])
+                    acc.append(acc_np[0])
+            except:
+                test_args['file'].reset()
+                print 'TEST: ', numpy.mean(loss), numpy.mean(acc)
+
+
+if __name__ == '__main__':
+    main()

python/paddle/fluid/tests/unittests/test_bilinear_interp_op.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 unittest
+import numpy as np
+from op_test import OpTest
+
+
+def bilinear_interp_np(input, out_h, out_w):
+    batch_size, channel, in_h, in_w = input.shape
+    if out_h > 1:
+        ratio_h = (in_h - 1.0) / (out_h - 1.0)
+    else:
+        ratio_h = 0.0
+    if out_w > 1:
+        ratio_w = (in_w - 1.0) / (out_w - 1.0)
+    else:
+        ratio_w = 0.0
+
+    out = np.zeros((batch_size, channel, out_h, out_w))
+    for i in range(out_h):
+        h = int(ratio_h * i)
+        hid = 1 if h < in_h - 1 else 0
+        h1lambda = ratio_h * i - h
+        h2lambda = 1.0 - h1lambda
+        for j in range(out_w):
+            w = int(ratio_w * j)
+            wid = 1 if w < in_w - 1 else 0
+            w1lambda = ratio_w * j - w
+            w2lambda = 1.0 - w1lambda
+
+            out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
+                                        w1lambda*input[:, :, h, w+wid]) + \
+                              h1lambda*(w2lambda*input[:, :, h+hid, w] +
+                                        w1lambda*input[:, :, h+hid, w+wid])
+    return out.astype("float32")
+
+
+class TestBilinearInterpOp(OpTest):
+    def setUp(self):
+        self.init_test_case()
+        self.op_type = "bilinear_interp"
+        input_np = np.random.random(self.input_shape).astype("float32")
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w)
+
+        self.inputs = {'X': input_np}
+        self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.input_shape = [2, 3, 4, 4]
+        self.out_h = 2
+        self.out_w = 2
+
+
+class TestCase1(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+
+
+class TestCase2(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+
+
+class TestCase3(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_elementwise_gradient_op.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 unittest
+import numpy as np
+
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+
+
+class TestElementWiseAddOp(unittest.TestCase):
+    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
+        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
+
+    def check_forward_backward(self):
+        def test_with_place(place):
+            out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
+            x_grad = out_grad
+            sum_axis = range(0, len(self.x.shape))
+            del sum_axis[self.axis]
+            y_grad = np.sum(out_grad, axis=tuple(sum_axis))
+
+            var_dict = locals()
+            var_dict['y'] = self.y
+            var_dict['x'] = self.x
+            var_dict['out'] = self.out
+            var_dict['y@GRAD'] = y_grad
+            var_dict['x@GRAD'] = x_grad
+            var_dict['out@GRAD'] = out_grad
+
+            var_names = ['x', 'y', 'out', 'y@GRAD', 'x@GRAD', 'out@GRAD']
+            ground_truth = {name: var_dict[name] for name in var_names}
+
+            program = fluid.Program()
+            with fluid.program_guard(program):
+                block = program.global_block()
+                for name in ground_truth:
+                    block.create_var(
+                        name=name,
+                        dtype='float32',
+                        shape=ground_truth[name].shape)
+                elementwise_add_op = block.append_op(
+                    type="elementwise_add",
+                    inputs={
+                        "X": block.var('x'),
+                        "Y": block.var('y'),
+                    },
+                    outputs={"Out": block.var('out'), },
+                    attrs={"axis": self.axis, })
+
+                # generate backward op_desc
+                grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
+                    elementwise_add_op.desc, set(), [])
+                grad_op_desc = grad_op_desc_list[0]
+                new_op_desc = block.desc.append_op()
+                new_op_desc.copy_from(grad_op_desc)
+                for var_name in grad_op_desc.output_arg_names():
+                    block.desc.var(var_name.encode("ascii"))
+                grad_op_desc.infer_var_type(block.desc)
+                grad_op_desc.infer_shape(block.desc)
+                for arg in grad_op_desc.output_arg_names():
+                    grad_var = block.desc.find_var(arg.encode("ascii"))
+                    grad_var.set_dtype(core.VarDesc.VarType.FP32)
+
+                exe = fluid.Executor(place)
+                out = exe.run(program,
+                              feed={
+                                  name: var_dict[name]
+                                  for name in ['x', 'y', 'out@GRAD']
+                              },
+                              fetch_list=['x@GRAD', 'y@GRAD'])
+                self.__assert_close(x_grad, out[0], "x@GRAD")
+                self.__assert_close(y_grad, out[1], "y@GRAD", atol=1.4)
+
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu(
+                "elementwise_add"):
+            places.append(core.CUDAPlace(0))
+
+        for place in places:
+            test_with_place(place)
+
+    def test_check_forward_backward_with_scale_and_bias(self):
+        np.random.seed(123)
+        self.x = np.random.random((4, 32, 220, 220)).astype(np.float32)
+        self.y = np.random.random((32)).astype(np.float32)
+        self.out = self.x + self.y.reshape(1, 32, 1, 1)
+        self.axis = 1
+        self.check_forward_backward()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_mul_mkldnn_op.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 unittest
+from test_mul_op import TestMulOp, TestMulOp2, TestFP16MulOp1, TestFP16MulOp2
+
+
+class TestMKLDNNMulOp(TestMulOp):
+    def init_op_test(self):
+        super(TestMKLDNNMulOp, self).setUp()
+        self.attrs = {"use_mkldnn": True}
+
+
+class TestMKLDNNMulOp2(TestMulOp2):
+    def init_op_test(self):
+        super(TestMKLDNNMulOp2, self).setUp()
+        self.attrs = {"use_mkldnn": True}
+
+
+class TestMKLDNNFP16MulOp1(TestFP16MulOp1):
+    def init_op_test(self):
+        super(TestMKLDNNFP16MulOp1, self).setUp()
+        self.attrs = {"use_mkldnn": True}
+
+
+class TestMKLDNNFP16MulOp2(TestFP16MulOp2):
+    def init_op_test(self):
+        super(TestMKLDNNFP16MulOp2, self).setUp()
+        self.attrs = {"use_mkldnn": True}
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -21,10 +21,12 @@ from op_test import OpTest
 class TestMulOp(OpTest):
     def setUp(self):
         self.op_type = "mul"
+        self.use_mkldnn = False
         self.inputs = {
             'X': np.random.random((32, 84)).astype("float32"),
             'Y': np.random.random((84, 100)).astype("float32")
         }
+        self.attrs = {'use_mkldnn': self.use_mkldnn}
         self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
 
     def test_check_output(self):
@@ -45,11 +47,16 @@ class TestMulOp(OpTest):
 class TestMulOp2(OpTest):
     def setUp(self):
         self.op_type = "mul"
+        self.use_mkldnn = False
         self.inputs = {
             'X': np.random.random((15, 4, 12, 10)).astype("float32"),
             'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32")
         }
-        self.attrs = {'x_num_col_dims': 2, 'y_num_col_dims': 2}
+        self.attrs = {
+            'x_num_col_dims': 2,
+            'y_num_col_dims': 2,
+            'use_mkldnn': self.use_mkldnn
+        }
         result = np.dot(self.inputs['X'].reshape(15 * 4, 12 * 10),
                         self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9))
         result = result.reshape(15, 4, 8, 2, 9)
@@ -73,9 +80,11 @@ class TestMulOp2(OpTest):
 class TestFP16MulOp1(OpTest):
     def setUp(self):
         self.op_type = "mul"
+        self.use_mkldnn = False
         x = np.random.random((32, 84)).astype("float16")
         y = np.random.random((84, 100)).astype("float16")
         self.inputs = {'X': x.view(np.uint16), 'Y': y.view(np.uint16)}
+        self.attrs = {'use_mkldnn': self.use_mkldnn}
         self.outputs = {'Out': np.dot(x, y)}
 
     def test_check_output(self):
@@ -88,12 +97,14 @@ class TestFP16MulOp1(OpTest):
 class TestFP16MulOp2(OpTest):
     def setUp(self):
         self.op_type = "mul"
+        self.use_mkldnn = False
         x = np.random.random((15, 4, 12, 10)).astype("float16")
         y = np.random.random((4, 30, 8, 2, 9)).astype("float16")
         self.inputs = {'X': x.view(np.uint16), 'Y': y.view(np.uint16)}
         self.attrs = {
             'x_num_col_dims': 2,
             'y_num_col_dims': 2,
+            'use_mkldnn': self.use_mkldnn
         }
         result = np.dot(
             x.reshape(15 * 4, 12 * 10), y.reshape(4 * 30, 8 * 2 * 9))

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

@@ -62,7 +62,8 @@ class TestOperator(unittest.TestCase):
         self.assertEqual(mul_op.output_names, ["Out"])
         self.assertEqual(mul_op.output("Out"), ["mul.out"])
         self.assertEqual(
-            set(mul_op.attr_names), set(["x_num_col_dims", "y_num_col_dims"]))
+            set(mul_op.attr_names),
+            set(["x_num_col_dims", "y_num_col_dims", "use_mkldnn"]))
         self.assertEqual(mul_op.has_attr("x_num_col_dims"), True)
         self.assertEqual(mul_op.attr_type("x_num_col_dims"), core.AttrType.INT)
         self.assertEqual(mul_op.attr("x_num_col_dims"), 1)

tools/aws_benchmarking/README.md

@@ -77,10 +77,10 @@ Training nodes will run your `ENTRYPOINT` script with the following environment
 Now let's start the training process:
 
 ```bash
-docker run -i -v $HOME/.aws:/root/.aws -v <full path to your pem file>:/root/<key pare name>.pem \
+docker run -i -v $HOME/.aws:/root/.aws -v <full path to your pem file>:/root/<key pair name>.pem \
 putcn/paddle_aws_client \
 --action create \
---key_name <your key pare name> \
+--key_name <your key pair name> \
 --security_group_id <your security group id> \
 --docker_image myreponame/paddle_benchmark \
 --pserver_count 2 \
@@ -154,8 +154,31 @@ Master exposes 4 major services:
 
 ### Parameters
 
-TBD, please refer to client/cluster_launcher.py for now
+ - key_name: required, aws key pair name
+ - security_group_id: required, the security group id associated with your VPC
+ - vpc_id: The VPC in which you wish to run test, if not provided, this tool will use your default VPC.
+ - subnet_id: The Subnet_id in which you wish to run test, if not provided, this tool will create a new sub net to run test.
+ - pserver_instance_type: your pserver instance type, c5.2xlarge by default, which is a memory optimized machine.
+ - trainer_instance_type: your trainer instance type, p2.8xlarge by default, which is a GPU machine with 8 cards.
+ - task_name: the name you want to identify your job, if not provided, this tool will generate one for you.
+ - pserver_image_id: ami id for system image. Please note, although the default one has nvidia-docker installed, pserver is always launched with `docker` instead of `nvidia-docker`, please DO NOT init your training program with GPU place.
+ - pserver_command: pserver start command, format example: python,vgg.py,batch_size:128,is_local:no, which will be translated as `python vgg.py --batch_size 128 --is_local no` when trying to start the training in pserver. "--device CPU" is passed as default.
+ - trainer_image_id: ami id for system image, default one has nvidia-docker ready.
+ - trainer_command: trainer start command. Format is the same as pserver's, "--device GPU" is passed as default.
+ - availability_zone: aws zone id to place ec2 instances, us-east-2a by default.
+ - trainer_count: Trainer count, 1 by default.
+ - pserver_count: Pserver count, 1 by default.
+ - action: create|cleanup|status, "create" by default.
+ - pserver_port: the port for pserver to open service, 5436 by default.
+ - docker_image: the training docker image id.
+ - master_service_port: the port for master to open service, 5436 by default.
+ - master_server_public_ip: the master service ip, this is required when action is not "create"
+ - master_docker_image: master's docker image id, "putcn/paddle_aws_master:latest" by default
+ - no_clean_up: no instance termination when training is finished or failed when this value is set "yes". This is for debug purpose, so that you can inspect into the instances when the process is finished. 
+ 
 
 ### Trouble shooting
 
-TBD
+ 1. How to check logs
+
+    Master log is served at `http://<masterip>:<masterport>/status`, and you can list all the log files from `http://<masterip>:<masterport>/logs`, and access either one of them by `http://<masterip>:<masterport>/log/<logfilename>`