Merge branch 'develop' into add_sequence_slice_layer

.clang_format.hook

+#!/bin/bash
+set -e
+
+readonly VERSION="3.8"
+
+version=$(clang-format -version)
+
+if ! [[ $version == *"$VERSION"* ]]; then
+    echo "clang-format version check failed."
+    echo "a version contains '$VERSION' is needed, but get '$version'"
+    echo "you can install the right version, and make an soft-link to '\$PATH' env"
+    exit -1
+fi
+
+clang-format $@

.pre-commit-config.yaml

@@ -19,10 +19,10 @@
     -   id: end-of-file-fixer
 -   repo: local
     hooks:
-    -   id: clang-format
+    -   id: clang-format-with-version-check
         name: clang-format
         description: Format files with ClangFormat.
-        entry: clang-format -i
+        entry: ./.clang_format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
 -   repo: https://github.com/PaddlePaddle/pre-commit-golang

.travis.yml

@@ -37,8 +37,8 @@ before_install:
   - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
   # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
   # protobuf version.
-  - pip install numpy wheel 'protobuf==3.1' sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit requests==2.9.2 LinkChecker
-  - pip install rarfile nltk==3.2.2 scipy==0.19.0 recordio matplotlib Pillow
+  - pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt
+  - pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker
   - curl https://glide.sh/get | bash
   - eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
   - go get -u github.com/alecthomas/gometalinter

CMakeLists.txt

@@ -36,8 +36,8 @@ include(simd)
 ################################ Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
-option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    OFF)
-option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      OFF)
+option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    ${AVX_FOUND})
+option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)
@@ -55,6 +55,7 @@ option(WITH_C_API       "Compile PaddlePaddle with C-API(Prediction)"   OFF)
 option(WITH_GOLANG      "Compile PaddlePaddle with GOLANG"              OFF)
 option(GLIDE_INSTALL    "Download and install go dependencies "         ON)
 option(USE_NNPACK       "Compile PaddlePaddle with NNPACK library"      OFF)
+option(USE_EIGEN_FOR_BLAS   "Use matrix multiplication in Eigen"        OFF)
 
 # CMAKE_BUILD_TYPE
 if(NOT CMAKE_BUILD_TYPE)
@@ -137,9 +138,9 @@ set(EXTERNAL_LIBS
 )
 
 if(WITH_GPU)
-    list(APPEND EXTERNAL_LIB ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
+    list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
     if(NOT WITH_DSO)
-        list(APPEND EXTERNAL_LIB ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY})
+        list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY})
     endif(NOT WITH_DSO)
 endif(WITH_GPU)
 

Dockerfile

@@ -34,9 +34,6 @@ RUN apt-get update && \
     net-tools && \
     apt-get clean -y
 
-# paddle is using numpy.flip, which is introduced since 1.12.0
-RUN pip --no-cache-dir install 'numpy>=1.12.0'
-
 # Install Go and glide
 RUN wget -qO- https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
     tar -xz -C /usr/local && \
@@ -58,33 +55,22 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 # FIXME: due to temporary ipykernel dependency issue, specify ipykernel jupyter
 # version util jupyter fixes this issue.
 RUN pip install --upgrade pip && \
-    pip install -U 'protobuf==3.1.0' && \
-    pip install -U wheel pillow BeautifulSoup && \
+    pip install -U wheel && \
     pip install -U docopt PyYAML sphinx && \
-    pip install -U sphinx-rtd-theme==0.1.9 recommonmark && \
-    pip install pre-commit 'requests==2.9.2' 'ipython==5.3.0' && \
+    pip install -U sphinx-rtd-theme==0.1.9 recommonmark
+
+RUN pip install pre-commit 'ipython==5.3.0' && \
     pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
-    pip install opencv-python rarfile 'scipy>=0.19.0' 'nltk>=3.2.2'
+    pip install opencv-python
+
+COPY ./python/requirements.txt /root/
+RUN pip install -r /root/requirements.txt
 
 # To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
 # the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
 RUN apt-get install -y libssl-dev libffi-dev
 RUN pip install certifi urllib3[secure]
 
-# TODO(qijun) The template library Eigen doesn't work well with GCC 5 
-# coming with the default Docker image, so we switch to use GCC 4.8 
-# by default. And I will check Eigen library later.
-
-RUN ln -sf gcc-4.8 /usr/bin/gcc && \
-    ln -sf gcc-ar-4.8 /usr/bin/gcc-ar && \
-    ln -sf gcc-nm-4.8 /usr/bin/gcc-nm && \
-    ln -sf gcc-ranlib-4.8 /usr/bin/gcc-ranlib && \
-    ln -sf gcc-4.8 /usr/bin/x86_64-linux-gnu-gcc && \
-    ln -sf gcc-ar-4.8 /usr/bin/x86_64-linux-gnu-gcc-ar && \
-    ln -sf gcc-nm-4.8 /usr/bin/x86_64-linux-gnu-gcc-nm && \
-    ln -sf gcc-ranlib-4.8 /usr/bin/x86_64-linux-gnu-gcc-ranlib && \
-    ln -sf g++-4.8 /usr/bin/g++ && \
-    ln -sf g++-4.8 /usr/bin/x86_64-linux-gnu-g++ 
 
 # Install woboq_codebrowser to /woboq
 RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \

cmake/configure.cmake

@@ -28,6 +28,10 @@ if(NOT WITH_TIMER)
     add_definitions(-DPADDLE_DISABLE_TIMER)
 endif(NOT WITH_TIMER)
 
+if(USE_EIGEN_FOR_BLAS)
+    add_definitions(-DPADDLE_USE_EIGEN_FOR_BLAS)
+endif(USE_EIGEN_FOR_BLAS)
+
 if(NOT WITH_PROFILER)
     add_definitions(-DPADDLE_DISABLE_PROFILER)
 endif(NOT WITH_PROFILER)

cmake/cudnn.cmake

@@ -2,7 +2,7 @@ if(NOT WITH_GPU)
     return()
 endif()
 
-set(CUDNN_ROOT "" CACHE PATH "CUDNN ROOT")
+set(CUDNN_ROOT "/usr" CACHE PATH "CUDNN ROOT")
 find_path(CUDNN_INCLUDE_DIR cudnn.h
     PATHS ${CUDNN_ROOT} ${CUDNN_ROOT}/include
     $ENV{CUDNN_ROOT} $ENV{CUDNN_ROOT}/include ${CUDA_TOOLKIT_INCLUDE}

cmake/external/openblas.cmake

@@ -73,10 +73,18 @@ INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
 # linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
 SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
 FILE(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
-ADD_LIBRARY(cblas STATIC ${dummyfile})
+IF(${CBLAS_PROVIDER} MATCHES MKL)
+    ADD_LIBRARY(cblas SHARED ${dummyfile})
+ELSE()
+    ADD_LIBRARY(cblas STATIC ${dummyfile})
+ENDIF()
 TARGET_LINK_LIBRARIES(cblas ${CBLAS_LIBRARIES})
 
 IF(NOT ${CBLAS_FOUND})
     ADD_DEPENDENCIES(cblas extern_openblas)
     LIST(APPEND external_project_dependencies cblas)
+ELSE()
+    IF("${CBLAS_PROVIDER}" STREQUAL "MKLML")
+        ADD_DEPENDENCIES(cblas mklml)
+    ENDIF()
 ENDIF(NOT ${CBLAS_FOUND})

cmake/flags.cmake

@@ -9,13 +9,6 @@ function(CheckCompilerCXX11Flag)
         if(${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 4.8)
             message(FATAL_ERROR "Unsupported GCC version. GCC >= 4.8 required.")
         endif()
-        if(NOT ANDROID)
-            # TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem.
-            # Use Debug mode instead for now.
-            if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9)
-                set(CMAKE_BUILD_TYPE "Debug" CACHE STRING "" FORCE)
-            endif()
-        endif()
     elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
         # cmake >= 3.0 compiler id "AppleClang" on Mac OS X, otherwise "Clang"
         # Apple Clang is a different compiler than upstream Clang which havs different version numbers.
@@ -160,7 +153,7 @@ set(CUDA_PROPAGATE_HOST_FLAGS OFF)
 
 # Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
 # So, don't set these flags here.
-LIST(APPEND CUDA_NVCC_FLAGS -std=c++11 --default-stream per-thread)
+LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
 LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math)
 
 if(CMAKE_BUILD_TYPE  STREQUAL "Debug")

doc/api/v2/config/layer.rst

@@ -367,6 +367,11 @@ trans
 ..  autoclass:: paddle.v2.layer.trans
     :noindex:
 
+scale_shift
+-----------
+..  autoclass:: paddle.v2.layer.scale_shift
+    :noindex:
+
 Sampling Layers
 ===============
 

doc/design/auto_gradient_check.md

+## Auto Gradient Checker Design
+
+## Backgraound：
+- Operator forward computing is easy to check if the result is right because it has a clear definition. **But** backpropagation is a notoriously difficult algorithm to debug and get right:
+  - 1. you should get the right backpropagation formula according to the forward computation.
+  - 2. you should implement it right in CPP.
+  - 3. it's difficult to prepare test data.
+
+- Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
+  - 1. numeric gradient checker only need forward operator.
+  - 2. user only need to prepare the input data for forward Operator.
+
+## Mathematical Theory
+The following two document from stanford has a detailed explanation of how to get numeric gradient and why it's useful.
+
+- [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
+- [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
+
+
+## Numeric Gradient Implementation
+### Python Interface
+```python
+def get_numeric_gradient(op,
+                         input_values,
+                         output_name,
+                         input_to_check,
+                         delta=0.005,
+                         local_scope=None):
+    """
+    Get Numeric Gradient for an operator's input.
+
+    :param op: C++ operator instance, could be an network
+    :param input_values: The input variables. Should be an dictionary, key is
+    variable name. Value is numpy array.
+    :param output_name: The final output variable name.
+    :param input_to_check: The input variable need to get gradient.
+    :param delta: The perturbation value for numeric gradient method. The
+    smaller delta is, the more accurate result will get. But if that delta is
+     too small, it could occur numerical stability problem.
+    :param local_scope: The local scope used for get_numeric_gradient.
+    :return: The gradient array in numpy format.
+    """
+```
+
+### Explaination:
+
+- Why need `output_name`
+  - One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.
+
+- Why need `input_to_check`
+  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
+
+
+### Core Algorithm Implementation
+
+
+```python
+    # we only compute gradient of one element each time.
+    # we use a for loop to compute the gradient of every element.
+    for i in xrange(tensor_size):
+        # get one input element throw it's index i.
+        origin = tensor_to_check.get_float_element(i)
+
+        # add delta to it, run op and then get the sum of the result tensor.
+        x_pos = origin + delta
+        tensor_to_check.set_float_element(i, x_pos)
+        y_pos = get_output()
+
+        # plus delta to this element, run op and get the sum of the result tensor.
+        x_neg = origin - delta
+        tensor_to_check.set_float_element(i, x_neg)
+        y_neg = get_output()
+
+        # restore old value
+        tensor_to_check.set_float_element(i, origin)
+
+        # compute the gradient of this element and store it into a numpy array.
+        gradient_flat[i] = (y_pos - y_neg) / delta / 2
+
+    # reshape the gradient result to the shape of the source tensor.
+    return gradient_flat.reshape(tensor_to_check.get_dims())
+```
+
+## Auto Graident Checker Framework
+
+Each Operator Kernel has three kinds of Gradient:
+
+- 1. Numeric Gradient
+- 2. CPU Operator Gradient
+- 3. GPU Operator Gradient(if supported)
+
+Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.
+
+- 1. calculate the numeric gradient.
+- 2. calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.
+- 3. calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)
+
+#### Python Interface
+
+```python
+    def check_grad(self,
+                   forward_op,
+                   input_vars,
+                   inputs_to_check,
+                   output_name,
+                   no_grad_set=None,
+                   only_cpu=False,
+                   max_relative_error=0.005):
+        """
+        :param forward_op: used to create backward_op
+        :param input_vars: numpy value of input variable. The following
+            computation will use these variables.
+        :param inputs_to_check: inputs var names that should check gradient.
+        :param output_name: output name that used to
+        :param max_relative_error: The relative tolerance parameter.
+        :param no_grad_set: used when create backward ops
+        :param only_cpu: only compute and check gradient on cpu kernel.
+        :return:
+        """
+```
+
+### How to check if two numpy array is close enough?
+if `abs_numeric_grad` is nearly zero, then use abs error for numeric_grad, not relative
+
+```python
+numeric_grad = ...
+operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
+
+abs_numeric_grad = numpy.abs(numeric_grad)
+# if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative
+# error.
+abs_numeric_grad[abs_numeric_grad < 1e-3] = 1
+
+diff_mat = numpy.abs(abs_numeric_grad - operator_grad) / abs_numeric_grad
+max_diff = numpy.max(diff_mat)
+```
+
+
+#### Notes：
+1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.
+
+
+#### Refs:
+
+- [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
+- [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)

doc/design/cluster_train/large_model_dist_train.md

+# Alalysis of large model distributed training in Paddle
+
+***NOTE: This is only some note for how we implemeted this scheme in V1, not a new design.***
+
+## What is it
+
+We often encounter cases that the embedding layer parameters(sparse) are so large that we can not store it in the trainer's memory when training. So we need to put them to several servers, and fetch them row by row instead of fetch all of the parameters.
+
+## How to use
+
+Specify command-line argument like  `--loadsave_parameters_in_pserver=true --ports_num_for_sparse=1  --use_old_updater=1` when starting the paddle trainer. And also add something like `--ports_num_for_sparse=1 --pserver_num_threads=5` when starting pserver processes.
+
+Accrodingly, configure your embedding layers like:
+
+```python
+SPARSE_REMOTE=True
+
+w1 = data_layer(name="w1", size=dict_size)
+emb1 = embedding_layer(input=w1, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
+w2 = data_layer(name="w2", size=dict_size)
+emb2 = embedding_layer(input=w2, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
+...
+```
+
+## Implementation details
+
+```c++
+enum MatType {
+  MAT_NORMAL,
+  MAT_NORMAL_SHARED,
+  MAT_VALUE_SHARED,
+  MAT_SPARSE_ROW_IDS,
+  MAT_SPARSE_ROW_AUTO_GROW,
+  MAT_CACHE_ROW,
+  MAT_SPARSE_ROW,
+  MAT_SPARSE_ROW_PREFETCH,
+  MAT_SPARSE_ROW_PREFETCH_FULL_SIZE,
+};
+```
+
+`MAT_SPARSE_ROW_PREFETCH` is what we use when configured to fetch only row of matrix when training.
+
+In `trainer_internal.cpp:L93 trainOneBatch`:
+
+```c++
+  if (config_->getOptConfig().use_sparse_remote_updater()) {
+    REGISTER_TIMER("prefetch");
+    gradientMachine_->prefetch(inArgs);
+    parameterUpdater_->getParametersRemote();
+  }
+```
+
+When doing actual network forward and backward, at the beginning of each batch, the trainer will try to download one row of data from pserver.
+
+In `trainer/RemoteParameterUpdater.cpp`: `parameterUpdater_->getParametersRemote();`:
+
+```c++
+if (fullSize) {
+    ...
+} else {
+getParams = [&] {
+    parameterClient_->getParameterSparse(
+        /* recvParameterType= */ PARAMETER_VALUE, sendBackParameterType);
+};
+applyL1 = [](Parameter& para, real decayRate) {
+    para.getMat(PARAMETER_VALUE)->applyL1(/*lr=*/1.0f, decayRate);
+};
+}
+```
+
+Calling `parameterClient_->getParameterSparse` will do remote call to pserver's `getParameterSparse`:
+
+```c++
+void ParameterServer2::getParameterSparse(const SendParameterRequest& request,
+                                          std::vector<Buffer>& inputBuffers,
+                                          SendParameterResponse* response,
+                                          std::vector<Buffer>* outputBuffers) {
+  (void)inputBuffers;
+  auto& buffer = *readWriteBuffer_;
+  size_t numReals = 0;
+  for (const auto& block : request.blocks()) {
+    numReals += getParameterConfig(block).dims(1);
+  }
+  buffer.resize(numReals);
+
+  VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals;
+
+  ReadLockGuard guard(parameterMutex_);
+  size_t offset = 0;
+  for (const auto& block : request.blocks()) {
+    size_t width = getParameterConfig(block).dims(1);
+    Buffer buf = {buffer.data() + offset, width};
+    int type = request.send_back_parameter_type();
+    sendBackParameterSparse(block, type, response, &buf, width, outputBuffers);
+    offset += width;
+  }
+}
+```
+
+`getParameterConfig(block).dims(1)` returns the width of the current "parameter block"(a shard of parameter object),
+then `getParameterSparse` remote call returns only one row of data to the client.

doc/design/mkldnn/README.MD

@@ -101,6 +101,7 @@ if use_mkldnn
 5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。
 6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。
 7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
+8. 关于MKLDNN参数的保存。由于MKLDNN参数的格式与PaddlePaddle原有的格式存在不一样的情况，所以需要在保存参数时同时保存该格式信息。目前准备扩展[Header](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/parameter/Parameter.h#L247)里面的`int32_t version`。这个值不管是在v1还是在v2里面，一直保存的是0，所以可以充分利用这个信息，定义一个枚举处理所有MKLDNN的参数格式，从而`MKLDNNLayer`就可以从输入的参数中获取需要的格式信息。
 
 ## References
 

doc/getstarted/build_and_install/build_from_source_en.md

@@ -68,7 +68,7 @@ As a simple example, consider the following:
 
 1. **BLAS Dependencies(optional)**
   
-    CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically.
+    CMake will search BLAS libraries from the system. If not found, OpenBLAS will be downloaded, built and installed automatically.
     To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`.
 
     ```bash
@@ -131,9 +131,9 @@ As a simple example, consider the following:
     To build GPU version, you will need the following installed:
 
         1. a CUDA-capable GPU
-        2. A supported version of Linux with a gcc compiler and toolchain
+        2. A supported version of Linux with a GCC compiler and toolchain
         3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
-        4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
+        4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
 
     The CUDA development environment relies on tight integration with the host development environment,
     including the host compiler and C runtime libraries, and is therefore only supported on
@@ -172,6 +172,7 @@ export PATH=<path to install>/bin:$PATH
 # install PaddlePaddle Python modules.
 sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
 ```
+
 ## <span id="centos">Build on Centos 7</span>
 
 ### Install Dependencies
@@ -192,9 +193,9 @@ sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
     To build GPU version, you will need the following installed:
 
         1. a CUDA-capable GPU
-        2. A supported version of Linux with a gcc compiler and toolchain
+        2. A supported version of Linux with a GCC compiler and toolchain
         3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
-        4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
+        4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
 
     The CUDA development environment relies on tight integration with the host development environment,
     including the host compiler and C runtime libraries, and is therefore only supported on
@@ -222,7 +223,7 @@ mkdir build && cd build
 ``` 
 
 Finally, you can build and install PaddlePaddle:
-
+  
 ```bash
 # you can add build option here, such as:    
 cmake3 .. -DCMAKE_INSTALL_PREFIX=<path to install>

doc/getstarted/build_and_install/docker_install_cn.rst

@@ -74,13 +74,13 @@ PaddlePaddle发布新版本的时候都会发布对应版本的生产镜像以
 
    .. code-block:: bash
 
-      docker run -it --rm paddlepaddle/paddle:0.10.0-dev /bin/bash
+      docker run -it --rm -v $(pwd):/paddle  paddlepaddle/paddle:0.10.0-dev /bin/bash
 
    或者，可以以后台进程方式运行容器：
 
    .. code-block:: bash
 
-      docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0-dev
+      docker run -d -p 2202:22 -p 8888:8888 -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /usr/sbin/sshd -D
 
    然后用密码 :code:`root` SSH进入容器：
 

paddle/capi/gradient_machine.cpp

@@ -146,3 +146,19 @@ paddle_error paddle_gradient_machine_randomize_param(
   m->machine->randParameters();
   return kPD_NO_ERROR;
 }
+
+paddle_error paddle_gradient_machine_get_layer_output(
+    paddle_gradient_machine machine,
+    const char* layerName,
+    paddle_arguments args) {
+  auto m = cast(machine);
+  auto out = paddle::capi::cast<paddle::capi::CArguments>(args);
+  if (m == nullptr || layerName == nullptr || out == nullptr ||
+      m->machine == nullptr) {
+    return kPD_NULLPTR;
+  }
+
+  auto layerOutput = m->machine->getLayerOutput(layerName);
+  out->args.push_back(layerOutput);
+  return kPD_NO_ERROR;
+}

paddle/capi/gradient_machine.h

@@ -39,7 +39,11 @@ PD_API paddle_error paddle_gradient_machine_create_for_inference(
 /**
  * @brief Create a gradient machine used for model inference, using config with
  *        parameters which is generated by `paddle merge_model`.
- * @param [out] machine that used for model inference.
+ *        Example:
+ *          paddle merge_model \
+ *                 --model_dir="pass-00000" \
+ *                 --model_file="merged_model.paddle"
+ * @param [out] machine that used for model inference
  * @param [in] mergedModel
  * @param [in] size
  * @return paddle_error
@@ -97,6 +101,18 @@ paddle_gradient_machine_randomize_param(paddle_gradient_machine machine);
 PD_API paddle_error
 paddle_gradient_machine_destroy(paddle_gradient_machine machine);
 
+/**
+ * @brief Get the output of the layer named `layerName`.
+ * @param [in] gradient machine that have run a inference
+ * @param [in] layerName name of specified layer
+ * @param [out] args output of the specified layer
+ * @return paddle_error
+ */
+PD_API paddle_error
+paddle_gradient_machine_get_layer_output(paddle_gradient_machine machine,
+                                         const char* layerName,
+                                         paddle_arguments args);
+
 #ifdef __cplusplus
 }
 #endif

paddle/framework/CMakeLists.txt

@@ -7,7 +7,7 @@ cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory device_context)
 cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
 cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
 
-cc_library(lod_tensor SRCS lod_tensor.cc details/lod_tensor.cc DEPS ddim place tensor)
+cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
 cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
 
 cc_test(variable_test SRCS variable_test.cc)
@@ -15,42 +15,41 @@ cc_test(variable_test SRCS variable_test.cc)
 cc_library(scope SRCS scope.cc)
 cc_test(scope_test SRCS scope_test.cc DEPS scope)
 
-proto_library(attribute_proto SRCS attribute.proto)
-proto_library(op_proto SRCS op_proto.proto DEPS attribute_proto)
-proto_library(op_desc SRCS op_desc.proto DEPS attribute_proto)
-cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
-cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
+proto_library(framework_proto SRCS framework.proto)
 
-cc_library(attribute SRCS attribute.cc DEPS op_desc op_proto)
+cc_library(attribute SRCS attribute.cc DEPS framework_proto)
 
-cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope attribute)
+cc_library(operator SRCS operator.cc DEPS framework_proto device_context tensor scope attribute)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
-cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)
-cc_library(op_registry SRCS op_registry.cc DEPS op_desc grad_op_builder)
+cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
+cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder)
 cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
 cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
 
-py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.proto)
+py_proto_compile(framework_py_proto SRCS framework.proto)
 # Generate an empty __init__.py to make framework_py_proto as a valid python module.
 add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(framework_py_proto framework_py_proto_init)
 add_custom_command(TARGET framework_py_proto POST_BUILD
-    COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJ_ROOT}/python/paddle/v2/framework/proto
-    COMMAND cp *.py ${PROJ_ROOT}/python/paddle/v2/framework/proto/
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto
+    COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto/
     COMMENT "Copy generated python proto into directory paddle/v2/framework/proto."
     WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
 cc_library(backward SRCS backward.cc DEPS net_op)
-cc_test(backward_test SRCS backward_test.cc DEPS backward)
+cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
 
 if(WITH_PYTHON)
 cc_library(paddle_pybind SHARED
     SRCS pybind.cc
     DEPS pybind python backward
-    fc_op
     sgd_op
     add_op
+    mul_op
+    rowwise_add_op
+    sigmoid_op
+    softmax_op
     mean_op
     cross_entropy_op
     recurrent_op

paddle/framework/attribute.cc

@@ -44,7 +44,7 @@ AttrType AttrTypeID<std::vector<std::string>>() {
   return STRINGS;
 }
 
-Attribute GetAttrValue(const AttrDesc& attr_desc) {
+Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
   switch (attr_desc.type()) {
     case paddle::framework::AttrType::INT: {
       return attr_desc.i();

paddle/framework/attribute.h

@@ -20,8 +20,7 @@ limitations under the License. */
 #include <unordered_set>
 #include <vector>
 
-#include "paddle/framework/attribute.pb.h"
-#include "paddle/framework/op_desc.pb.h"
+#include "paddle/framework/framework.pb.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/variant.h"
 
@@ -37,7 +36,7 @@ typedef std::unordered_map<std::string, Attribute> AttributeMap;
 template <typename T>
 AttrType AttrTypeID();
 
-Attribute GetAttrValue(const AttrDesc& attr_desc);
+Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
 
 // check whether a value(attribute) fit a certain limit
 template <typename T>

paddle/framework/backward.cc

@@ -15,31 +15,44 @@
 #include "paddle/framework/backward.h"
 
 #include <list>
+#include <memory>
+
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/net_op.h"
+#include "paddle/operators/recurrent_op.h"
 
 namespace paddle {
 namespace framework {
 
-static bool AllInSet(const std::vector<std::string>& names,
-                     const std::string& suffix,
-                     const std::unordered_set<std::string>& set) {
+template <typename Map, typename T>
+static void ForEachVarName(const Map& names, T callback) {
   for (auto& name : names) {
-    if (set.find(name + suffix) == set.end()) {
-      return false;
+    for (auto& n : name.second) {
+      if (callback(n)) return;
     }
   }
-  return true;
 }
 
-static std::shared_ptr<OperatorBase> NOP() {
-  auto net_op = std::make_shared<operators::NetOp>();
-  net_op->type_ = "@NOP@";
+// return whether all the names + suffixes in the set
+static bool AllInSet(
+    const std::map<std::string, std::vector<std::string>>& names,
+    const std::string& suffix, const std::unordered_set<std::string>& set) {
+  bool all_in_set = true;
+  ForEachVarName(names, [&all_in_set, &set, &suffix](const std::string& n) {
+    all_in_set = set.find(n + suffix) != set.end();
+    return !all_in_set;
+  });
+  return all_in_set;
+}
+
+static std::unique_ptr<OperatorBase> NOP() {
+  auto net_op = new operators::NetOp();
+  net_op->SetType("@NOP@");
   net_op->CompleteAddOp();
-  return net_op;
+  return std::unique_ptr<OperatorBase>(net_op);
 }
 
-//  Get backward operator from a forward operator, recursively implementation.
+//  Get backward operator from a forward operator, a recursive implementation.
 //
 //  no_grad_names the gradient variable names without gradient calculating.
 //
@@ -47,122 +60,152 @@ static std::shared_ptr<OperatorBase> NOP() {
 //  BackwardRecursive. use `uid = uniq_id++;` to get the unique index, and
 //  pass `uniq_id` through recursive calling.
 //
-//  returns The backward operator. For simple situation, it is a simple
-//  operator. For complex situation, it is a NetOp.
+//  returns The backward operator. In a simple situation, it may be a simple
+//  operator, in a complex situation, it maybe a NetOp.
 //
 //  See Backward.h for details
-static std::shared_ptr<OperatorBase> BackwardRecursive(
-    const OperatorBase& forwardOp,
-    std::unordered_set<std::string>& no_grad_names, size_t& uniq_id);
-std::shared_ptr<OperatorBase> BackwardRecursive(
+static std::unique_ptr<OperatorBase> BackwardRecursive(
     const OperatorBase& forwardOp,
     std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
   //  If all input gradients of forwarding operator do not need to calculate,
   //  just return an NOP. Not return null ptr because NOP does not take
   //  too much time for calculation, but it is useful for simplifying logic.
-  if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
+  if (AllInSet(forwardOp.Inputs() /*names*/, kGradVarSuffix /*suffix*/,
+               no_grad_names /*set*/)) {
     return NOP();
   }
 
   //  All output gradients of forwarding operator do not need to calculate.
   //  Then all input gradients cannot be computed at all, and we put them into
   //  `no_grad_names` set. Return an NOP.
-  if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
-    for (auto& name : forwardOp.inputs_) {
-      // Mark all input is not need
-      no_grad_names.insert(name + kGradVarSuffix);
-    }
+  if (AllInSet(forwardOp.Outputs() /*names*/, kGradVarSuffix /*suffix*/,
+               no_grad_names /*set*/)) {
+    ForEachVarName(forwardOp.Inputs(),
+                   [&no_grad_names](const std::string& name) -> bool {
+                     no_grad_names.insert(GradVarName(name));
+                     return false;
+                   });
     return NOP();
   }
 
   // Returned gradient network
-  auto net = std::make_shared<operators::NetOp>();
+  auto net = std::unique_ptr<operators::NetOp>(new operators::NetOp());
 
   if (forwardOp.IsNetOp()) {
     // Because forwardOp is a net op, it can static_cast.
     auto& forwardNet = static_cast<const operators::NetOp&>(forwardOp);
 
     // Map from output gradient variable name to operator's indices in
-    // backward net. That operator generates that variable.
+    // backward net's ops_. That operator generates that variable.
     std::unordered_map<std::string, std::vector<size_t>> dup_output_ops;
 
     size_t local_op_id = 0;
-    // reversely travel forwardNet
+    // reversely travel forwardNet and collect all duplicate outputs.
     for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
          ++it, ++local_op_id) {
-      auto fwd = *it;
+      auto& fwd = *it;
       auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
-      net->AddOp(bwd);
-      for (auto& out : bwd->outputs_) {
-        dup_output_ops[out].emplace_back(local_op_id);
-      }
+      ForEachVarName(bwd->Outputs(),
+                     [&dup_output_ops, local_op_id](const std::string& out) {
+                       dup_output_ops[out].emplace_back(local_op_id);
+                       return false;
+                     });
+      net->AppendOp(std::move(bwd));
     }
     // Get unique ID for this method.
     auto uid = uniq_id++;
     // TODO(dzh): more comment
-    using Pos = std::pair<size_t, std::shared_ptr<OperatorBase>>;
+    // multiple operators which have the same output (y for example) may
+    // overwrite the same y variable when backward, special operations are token
+    // to handle this case. For each duplicate output, rename it to an alias
+    // (original name with a offset), append an `add` op for its operator,
+    // and finally sum all the alias variable to the final output variable y.
+    using Pos = std::pair<size_t, std::unique_ptr<OperatorBase>>;
     std::list<Pos> insert_position;
     for (auto& dup_output_op : dup_output_ops) {
       const std::string& name = dup_output_op.first;
       auto& dup_op = dup_output_op.second;
+      // no duplicate output
       if (dup_op.size() == 1) continue;
-      std::vector<std::string> dup_outputs;
 
+      // process the duplicate outputs
+      std::vector<std::string> dup_outputs;
       for (size_t i = 0; i < dup_op.size(); ++i) {
+        // rename each duplicate output to an alias
         auto op_offset = dup_op[i];
         dup_outputs.push_back(name + "@RENAME@" + std::to_string(uid) + "@" +
                               std::to_string(i));
         net->ops_[op_offset]->Rename(name, dup_outputs.back());
       }
+      // collect all the offset to append `add` op for each alias
       insert_position.push_back(
-          {dup_op.back(),
-           OpRegistry::CreateOp(
-               "add", {dup_outputs}, {name},
-               {{"input_format",
-                 std::vector<int>{0, static_cast<int>(dup_outputs.size())}}})});
+          {dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}},
+                                               {{"Out", {name}}}, {})});
     }
 
+    // make sure the inserted `add` ops follow the BFS order.
     insert_position.sort(
         [](const Pos& l, const Pos& r) { return l.first > r.first; });
 
     for (auto& pos : insert_position) {
-      net->InsertOp(pos.first + 1, pos.second);
+      net->InsertOp(pos.first + 1, std::move(pos.second));
     }
-
   } else {
-    std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
-    for (std::string& grad_input : grad_op->inputs_) {
+    std::unique_ptr<OperatorBase> grad_op(OpRegistry::CreateGradOp(forwardOp));
+
+    ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
+                                          const std::string& grad_input) {
       if (no_grad_names.count(grad_input)) {
         // +1 for \0
         std::string prefix = grad_input.substr(
             0, grad_input.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
-        grad_input = prefix + kZeroVarSuffix;
+        grad_op->Rename(grad_input, prefix + kZeroVarSuffix);
 
         // If part of input gradient of that operator is not calculated, fill
         // zero variables to that input gradient.
-        net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {prefix},
-                                        {grad_input}, {}));
-      }
-    }
-
-    for (std::string& grad_output : grad_op->outputs_) {
-      if (no_grad_names.count(grad_output)) {
-        grad_output = kEmptyVarName;
+        net->AppendOp(OpRegistry::CreateOp("fill_zeros_like",
+                                           {{"Src", {prefix}}},
+                                           {{"Dst", {grad_input}}}, {}));
       }
+      return false;
+    });
+
+    ForEachVarName(grad_op->Outputs(),
+                   [&no_grad_names, &grad_op](const std::string& grad_output) {
+                     if (no_grad_names.count(grad_output)) {
+                       grad_op->Rename(grad_output, kEmptyVarName);
+                     }
+                     return false;
+                   });
+
+    // process recurrent gradient op as a special operator.
+    if (forwardOp.Type() == "recurrent_op") {
+      // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or
+      // this will result in infinite loop.
+      const auto& rnnop =
+          *static_cast<const operators::RecurrentOp*>(&forwardOp);
+      auto rnn_grad_op =
+          static_cast<operators::RecurrentGradientOp*>(grad_op.get());
+      const auto& stepnet_op =
+          *static_cast<const OperatorBase*>(&rnnop.stepnet());
+      // create stepnet's gradient op
+      rnn_grad_op->set_stepnet(
+          BackwardRecursive(stepnet_op, no_grad_names, uniq_id));
     }
 
     if (net->ops_.empty()) {  // Current no aux op is added to network
       return grad_op;
     }
-    net->AddOp(grad_op);
+    net->AppendOp(std::move(grad_op));
   }
-  net->type_ = "@GENERATED_BACKWARD@";
+  net->SetType("@GENERATED_BACKWARD@");
   net->CompleteAddOp();
-  return net;
+  return std::unique_ptr<OperatorBase>(
+      static_cast<OperatorBase*>(net.release()));
 }
 
 // See header for comments
-std::shared_ptr<OperatorBase> Backward(
+std::unique_ptr<OperatorBase> Backward(
     const OperatorBase& forwardOp,
     const std::unordered_set<std::string>& no_grad_vars) {
   std::unordered_set<std::string> no_grad_names;

paddle/framework/backward.h

@@ -20,7 +20,7 @@ namespace framework {
 
 // Create the backward operator from a forward operator.
 // TODO(yuyang18): Add more API reference comment.
-extern std::shared_ptr<OperatorBase> Backward(
+extern std::unique_ptr<OperatorBase> Backward(
     const OperatorBase& forwardOp,
     const std::unordered_set<std::string>& no_grad_vars);
 }  // namespace framework

paddle/framework/backward_test.cc

@@ -28,19 +28,13 @@ using OpAttrChecker = framework::OpAttrChecker;
 using Scope = framework::Scope;
 using DeviceContext = platform::DeviceContext;
 
-class EmptyOp : public OperatorBase {
- public:
-  void InferShape(const Scope &scope) const override {}
-  void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
-};
-
 class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
  public:
   RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "Input X of Add").IgnoreGradient();
-    AddInput("b", "Bias of Add").IgnoreGradient();
-    AddOutput("Out", "Out of Add").IgnoreGradient();
+    AddInput("X", "Input X of Add").NotInGradient();
+    AddInput("b", "Bias of Add").NotInGradient();
+    AddOutput("Out", "Out of Add").NotInGradient();
     AddComment("Add Op");
   }
 };
@@ -49,8 +43,8 @@ class MulOpMaker : public OpProtoAndCheckerMaker {
  public:
   MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("A", "A");
-    AddInput("B", "B");
+    AddInput("X", "A");
+    AddInput("Y", "B");
     AddOutput("Out", "Out");
     AddComment("Mul");
   }
@@ -61,7 +55,7 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker {
   SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "X");
-    AddOutput("Y", "Y");
+    AddOutput("Out", "Y");
     AddComment("Sigmoid");
   }
 };
@@ -71,21 +65,25 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
   NoGradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "X input");
-    AddOutput("Y", "Y output");
+    AddOutput("Out", "Y output");
     AddComment("NoGradOp, same input output. no Grad");
   }
 };
 
 class FcOp : public operators::NetOp {
  public:
-  void Init() override {
-    AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
-                               {Output("mul_result")}, {}));
-    auto b_name = Input("b");
+  FcOp(const std::string &type, const VarNameMap &inputs,
+       const VarNameMap &outputs, const AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    AppendOp(OpRegistry::CreateOp("mul",
+                                  {{"X", {Input("X")}}, {"Y", {Input("W")}}},
+                                  {{"Out", {Output("mul_result")}}}, {}));
+    auto input_b = Inputs("b");
     std::string before_act = "mul_result";
-    if (b_name != kEmptyVarName) {
-      AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
-                                 {Output("add_result")}, {}));
+    if (input_b.size() != 0) {
+      AppendOp(OpRegistry::CreateOp(
+          "rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
+          {{"Out", {Output("add_result")}}}, {}));
       before_act = "add_result";
     } else {
       auto out_varname = Output("add_result");
@@ -94,8 +92,8 @@ class FcOp : public operators::NetOp {
       }
     }
 
-    AddOp(OpRegistry::CreateOp("sigmoid", {Output(before_act)}, {Output("Out")},
-                               {}));
+    AppendOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
+                                  {{"Out", {Output("Out")}}}, {}));
     CompleteAddOp(false);
   }
 };
@@ -107,8 +105,8 @@ class FcOpMaker : public OpProtoAndCheckerMaker {
     AddInput("X", "x");
     AddInput("W", "w");
     AddInput("b", "b");
-    AddOutput("mul_result", "").SetTemporary();
-    AddOutput("add_result", "").SetTemporary();
+    AddOutput("mul_result", "").AsIntermediate();
+    AddOutput("add_result", "").AsIntermediate();
     AddOutput("Out", "");
     AddComment("");
   }
@@ -139,7 +137,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
  public:
   AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "x").SetMultiple();
+    AddInput("X", "x").AsDuplicable();
     AddOutput("Y", "y");
     AddComment("");
   }
@@ -150,51 +148,48 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
 namespace f = paddle::framework;
 namespace ops = paddle::operators;
 using EnforceNotMet = paddle::platform::EnforceNotMet;
-REGISTER_OP(rowwise_add, f::EmptyOp, f::RowWiseAddOpMaker);
-REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, f::EmptyOp);
-REGISTER_OP(mul, f::EmptyOp, f::MulOpMaker);
-REGISTER_GRADIENT_OP(mul, mul_grad, f::EmptyOp);
-REGISTER_OP(sigmoid, f::EmptyOp, f::SigmoidOpMaker);
-REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, f::EmptyOp);
-REGISTER_OP(nograd, f::EmptyOp, f::NoGradOpMaker);
-REGISTER_OP(fill_zeros_like, f::EmptyOp, f::FillZeroOpMaker);
-REGISTER_OP(add, f::EmptyOp, f::AddOpMaker);
-REGISTER_GRADIENT_OP(add, add_grad, f::EmptyOp);
-REGISTER_OP(fc, f::FcOp, f::FcOpMaker);
-REGISTER_OP(many_output_op, f::EmptyOp, f::ManyOutputOpMaker);
-REGISTER_GRADIENT_OP(many_output_op, many_output_op_grad, f::EmptyOp);
+REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
+            f::NOP);
+REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
+REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
+REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
+REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
+REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
+REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
+            f::NOP);
 
 TEST(Backward, simple_op_grad) {
-  auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
+  auto fwd = f::OpRegistry::CreateOp(
+      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
   ASSERT_NE(fwd, nullptr);
   auto gop = f::OpRegistry::CreateGradOp(*fwd);
-  ASSERT_EQ(4UL, gop->inputs_.size());
-  ASSERT_EQ(f::kEmptyVarName, gop->inputs_[0]);
-  ASSERT_EQ("rowwise_add_grad", gop->type_);
-  ASSERT_EQ(f::GradVarName("X"), gop->outputs_[0]);
-  ASSERT_EQ(f::GradVarName("b"), gop->outputs_[1]);
-
-  ASSERT_EQ(f::GradVarName("X"), gop->Output(f::GradVarName("X")));
+  ASSERT_EQ(1UL, gop->Inputs().size());
+  ASSERT_EQ("rowwise_add_grad", gop->Type());
+  ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
+  ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
 }
 
 TEST(Backward, simple_op_not_need_grad) {
-  auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
+  auto fwd = f::OpRegistry::CreateOp(
+      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
   ASSERT_NE(fwd, nullptr);
-  auto gop = f::Backward(*fwd, {"X"});
-  ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
-                      f::GradVarName("X")),
-            gop->outputs_.end());
+  auto gop = f::Backward(*fwd, {"x"});
+  ASSERT_EQ(gop->Output(f::GradVarName("X")), f::kEmptyVarName);
 
-  auto no_input_gop = f::Backward(*fwd, {"X", "b"});
+  auto no_input_gop = f::Backward(*fwd, {"x", "b"});
   ASSERT_NE(no_input_gop, nullptr);
   ASSERT_TRUE(no_input_gop->IsNetOp());
-  ASSERT_EQ(0UL,
-            std::static_pointer_cast<ops::NetOp>(no_input_gop)->ops_.size());
+  ASSERT_EQ(0UL, static_cast<ops::NetOp *>(no_input_gop.get())->ops_.size());
 }
 
 TEST(Backward, net_fc_backward_normal) {
-  std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
-      "fc", {"X", "w", "b"}, {"mul_result", "add_result", "out"}, {});
+  std::shared_ptr<f::OperatorBase> fwd =
+      f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
+                              {{"mul_result", {"mul_res"}},
+                               {"add_result", {"add_re"}},
+                               {"Out", {"out"}}},
+                              {});
   ASSERT_NE(fwd, nullptr);
   std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
   ASSERT_TRUE(gop->IsNetOp());
@@ -205,19 +200,22 @@ TEST(Backward, net_fc_backward_normal) {
   ASSERT_EQ(3UL, net->ops_.size());
 
   f::OperatorBase &d_sigmoid = *net->ops_[0];
-  ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
+  ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
 
   f::OperatorBase &d_add = *net->ops_[1];
-  ASSERT_EQ("rowwise_add_grad", d_add.type_);
+  ASSERT_EQ("rowwise_add_grad", d_add.Type());
 
   f::OperatorBase &d_mul = *net->ops_[2];
-  ASSERT_EQ("mul_grad", d_mul.type_);
+  ASSERT_EQ("mul_grad", d_mul.Type());
 }
 
 TEST(Backward, net_fc_backward_not_have_b) {
   std::shared_ptr<f::OperatorBase> fwd =
-      f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
-                              {"mul_result", "add_result", "tmp"}, {});
+      f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {}}},
+                              {{"mul_result", {"mul_res"}},
+                               {"add_result", {"add_res"}},
+                               {"Out", {"tmp"}}},
+                              {});
   ASSERT_NE(fwd, nullptr);
   std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
   ASSERT_TRUE(gop->IsNetOp());
@@ -228,96 +226,113 @@ TEST(Backward, net_fc_backward_not_have_b) {
   ASSERT_EQ(2UL, net->ops_.size());
 
   f::OperatorBase &d_sigmoid = *net->ops_[0];
-  ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
+  ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
 
   f::OperatorBase &d_mul = *net->ops_[1];
-  ASSERT_EQ("mul_grad", d_mul.type_);
+  ASSERT_EQ("mul_grad", d_mul.Type());
 }
 
 TEST(Backward, net_input_of_network_not_need_grad) {
   ops::NetOp net;
-  net.AddOp(f::OpRegistry::CreateOp("fc", {"X", "W1", "b1"},
-                                    {"mul_tmp_0", "add_tmp_0", "hidden0"}, {}));
-  net.AddOp(f::OpRegistry::CreateOp("fc", {"hidden0", "W2", "b2"},
-                                    {"mul_tmp_1", "add_tmp_1", "hidden1"}, {}));
+  net.AppendOp(f::OpRegistry::CreateOp(
+      "fc", {{"X", {"x"}}, {"W", {"W1"}}, {"b", {"b1"}}},
+      {{"mul_result", {"mul_tmp_0"}},
+       {"add_result", {"add_tmp_0"}},
+       {"Out", {"hidden0"}}},
+      {}));
+  net.AppendOp(f::OpRegistry::CreateOp(
+      "fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
+      {{"mul_result", {"mul_tmp_1"}},
+       {"add_result", {"add_tmp_1"}},
+       {"Out", {"hidden1"}}},
+      {}));
   net.CompleteAddOp();
-  auto bwd = Backward(net, {"X"});  // X@GRAD is not need.
+  auto bwd = Backward(net, {"x"});  // x@GRAD is not need.
   ASSERT_TRUE(bwd->IsNetOp());
   auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
 
-  std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
-      bwd_net->outputs_.begin(), bwd_net->outputs_.end());
-  all_output.erase(f::kEmptyVarName);
+  auto output_vars = bwd_net->OutputVars(true);
+  std::unordered_set<std::string> all_outputs =
+      std::unordered_set<std::string>(output_vars.begin(), output_vars.end());
+  all_outputs.erase(f::kEmptyVarName);
 
   for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
-    ASSERT_NE(all_output.find(f::GradVarName(out)), all_output.end());
+    ASSERT_NE(all_outputs.find(f::GradVarName(out)), all_outputs.end());
   }
 
   // Not Generated X
-  ASSERT_EQ(all_output.find(f::GradVarName("X")), all_output.end());
+  ASSERT_EQ(all_outputs.find(f::GradVarName("X")), all_outputs.end());
 
   ASSERT_EQ(2UL, bwd_net->ops_.size());
   ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
   auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
   ASSERT_EQ(3UL, first_fc_grad->ops_.size());
   ASSERT_EQ(f::kEmptyVarName,
-            first_fc_grad->ops_[2]->Output(f::GradVarName("A")));
+            first_fc_grad->ops_[2]->Output(f::GradVarName("X")));
 }
 
 TEST(Backward, net_shared_weight) {
   ops::NetOp net;
-  net.AddOp(f::OpRegistry::CreateOp("mul", {"X", "W"}, {"Out"}, {}));
-  net.AddOp(f::OpRegistry::CreateOp("mul", {"Out", "W"}, {"FinalOut"}, {}));
+  net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
+                                       {{"Out", {"out"}}}, {}));
+  net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
+                                       {{"Out", {"FinalOut"}}}, {}));
   net.CompleteAddOp();
 
   auto bwd = f::Backward(net, {});
   ASSERT_TRUE(bwd->IsNetOp());
   auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
   ASSERT_EQ(3UL, bwd_net->ops_.size());
-  ASSERT_EQ("add", bwd_net->ops_[2]->type_);
+  ASSERT_EQ("add", bwd_net->ops_[2]->Type());
 }
 
 TEST(Backward, op_register_grad_not_for_network) {
-  auto fwd = f::OpRegistry::CreateOp(
-      "fc", {"X", "W", "b"}, {"mul_out", "add_out", "out1"},
-      {{"temporary_index", std::vector<int>{0, 1}}});
+  auto fwd =
+      f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
+                              {{"mul_result", {"mul_out"}},
+                               {"add_result", {"add_out"}},
+                               {"Out", {"out1"}}},
+                              {{"temporary_index", std::vector<int>{0, 1}}});
 
   ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet);
 }
 
 TEST(Backward, op_all_input_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
-  auto backward = f::Backward(*fwd, {"X", "b"});
+  auto fwd = f::OpRegistry::CreateOp(
+      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
+  auto backward = f::Backward(*fwd, {"x", "b"});
   ASSERT_TRUE(backward->IsNetOp());
   auto net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_TRUE(net->ops_.empty());
 }
 
 TEST(Backward, op_all_output_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
-  auto backward = f::Backward(*fwd, {"Out"});
+  auto fwd = f::OpRegistry::CreateOp(
+      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
+  auto backward = f::Backward(*fwd, {"out"});
   ASSERT_TRUE(backward->IsNetOp());
   auto net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_TRUE(net->ops_.empty());
 }
 
 TEST(Backward, op_part_of_output_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp("many_output_op", {"X"}, {"Y", "Z"}, {});
+  auto fwd = f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
+                                     {{"y", {"Y"}}, {"z", {"Z"}}}, {});
   auto backward = f::Backward(*fwd, {"Z"});
   ASSERT_TRUE(backward->IsNetOp());
   auto net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_EQ(net->ops_.size(), 2UL);
 
   auto &fill_zero = *net->ops_[0];
-  ASSERT_EQ("fill_zeros_like", fill_zero.type_);
-  ASSERT_EQ(1UL, fill_zero.inputs_.size());
-  ASSERT_EQ("Z", fill_zero.inputs_[0]);
-  ASSERT_EQ(1UL, fill_zero.outputs_.size());
-  ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.outputs_[0]);
+  ASSERT_EQ("fill_zeros_like", fill_zero.Type());
+  ASSERT_EQ(1UL, fill_zero.Inputs("Src").size());
+  ASSERT_EQ("Z", fill_zero.Input("Src"));
+  ASSERT_EQ(1UL, fill_zero.Outputs("Dst").size());
+  ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.Output("Dst"));
 
   auto &d_many_out = *net->ops_[1];
-  ASSERT_EQ("many_output_op_grad", d_many_out.type_);
-  ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size());  // I/O/OG
+  ASSERT_EQ("many_output_op_grad", d_many_out.Type());
+  ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.Inputs().size());  // I/O/OG
   ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix,
             d_many_out.Input(f::GradVarName("z")));
   ASSERT_EQ(f::GradVarName("Y"), d_many_out.Input(f::GradVarName("y")));
@@ -325,44 +340,62 @@ TEST(Backward, op_part_of_output_are_not_need) {
 }
 
 TEST(Backward, op_part_of_input_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp("mul", {"a", "b"}, {"out"}, {});
+  auto fwd = f::OpRegistry::CreateOp("mul", {{"X", {"a"}}, {"Y", {"b"}}},
+                                     {{"Out", {"out"}}}, {});
   auto backward = f::Backward(*fwd, {"a"});
   auto &grad_mul = *backward;
-  ASSERT_EQ(grad_mul.type_, "mul_grad");
-  ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
-  ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
-  ASSERT_EQ(grad_mul.Output(f::GradVarName("A")), f::kEmptyVarName);
-  ASSERT_EQ(grad_mul.Output(f::GradVarName("B")), f::GradVarName("b"));
+  ASSERT_EQ(grad_mul.Type(), "mul_grad");
+  ASSERT_EQ(grad_mul.Inputs().size(), 2UL + 1UL + 1UL);
+  ASSERT_EQ(grad_mul.Outputs().size(), 2UL);
+  ASSERT_EQ(grad_mul.Output(f::GradVarName("X")), f::kEmptyVarName);
+  ASSERT_EQ(grad_mul.Output(f::GradVarName("Y")), f::GradVarName("b"));
   ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
-  ASSERT_EQ(grad_mul.Input("A"), "a");
-  ASSERT_EQ(grad_mul.Input("B"), "b");
+  ASSERT_EQ(grad_mul.Input("X"), "a");
+  ASSERT_EQ(grad_mul.Input("Y"), "b");
   ASSERT_EQ(grad_mul.Input("Out"), "out");
 }
 
 TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
   ops::NetOp net;
-  net.AddOp(f::OpRegistry::CreateOp("fc", {"x1", "w1", "b1"},
-                                    {"mul_out1", "add_out1", "out1"}, {}));
-  net.AddOp(f::OpRegistry::CreateOp("fc", {"out1", "w2", "b2"},
-                                    {"mul_out2", "tmp_out2", "out2"}, {}));
-  net.AddOp(f::OpRegistry::CreateOp("fc", {"out2", "w3", "b3"},
-                                    {"mul_out3", "tmp_out3", "out3"}, {}));
+  net.AppendOp(f::OpRegistry::CreateOp(
+      "fc", {{"X", {"x1"}}, {"W", {"w1"}}, {"b", {"b1"}}},
+      {{"mul_result", {"mul_out1"}},
+       {"add_result", {"add_out1"}},
+       {"Out", {"out1"}}},
+      {}));
+  net.AppendOp(f::OpRegistry::CreateOp(
+      "fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
+      {{"mul_result", {"mul_out2"}},
+       {"add_result", {"tmp_out2"}},
+       {"Out", {"out2"}}},
+      {}));
+  net.AppendOp(f::OpRegistry::CreateOp(
+      "fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
+      {{"mul_result", {"mul_out3"}},
+       {"add_result", {"tmp_out3"}},
+       {"Out", {"out3"}}},
+      {}));
   net.CompleteAddOp();
+
   auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
   ASSERT_TRUE(backward->IsNetOp());
   auto bwd_net = static_cast<ops::NetOp *>(backward.get());
   ASSERT_EQ(bwd_net->ops_.size(), 3UL);
   auto &grad_fc = *bwd_net->ops_[0];
-  EXPECT_EQ(grad_fc.inputs_.size(),
-            3UL       /* external input number */
+
+  const char *all = paddle::operators::NetOp::kAll;
+  EXPECT_EQ(grad_fc.Inputs(all).size(),
+            2UL       /* external input number */
                 + 1UL /* external output number*/
                 + 1UL /* number of gradient of external output*/
                 + 2U /* internal variable number*/);
-  EXPECT_EQ(grad_fc.outputs_.size(), 2UL       /* input number of mul*/
-                                         + 2UL /* input number of rowwise_add */
-                                         + 1UL /* input number of sigmod */);
-  EXPECT_EQ(bwd_net->ops_[1]->inputs_.size(), 0UL);
-  EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
-  EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
-  EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
+  EXPECT_EQ(grad_fc.Outputs(all).size(),
+            2UL       /* input number of mul*/
+                + 2UL /* input number of rowwise_add
+                       */
+                + 1UL /* input number of sigmod */);
+  EXPECT_EQ(bwd_net->ops_[1]->Inputs(all).size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[1]->Outputs(all).size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL);
 }

paddle/framework/ddim.cc

@@ -283,6 +283,5 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
 DDim::DDim(std::initializer_list<int> init_list) {
   *this = make_ddim(init_list);
 }
-
 }  // namespace framework
 }  // namespace paddle

paddle/framework/ddim.h

@@ -20,7 +20,6 @@ limitations under the License. */
 #include "paddle/framework/dim.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/variant.h"
-#include "unsupported/Eigen/CXX11/Tensor"
 
 namespace paddle {
 namespace framework {

paddle/framework/details/lod_tensor.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/framework/lod_tensor.h"
-
-#include <memory>
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-using LOD = LODTensor::LOD;
-
-std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level_begin,
-                              size_t level_end) {
-  auto new_lod = std::make_shared<LOD>();
-  new_lod->reserve(level_end - level_begin);
-  for (size_t i = level_begin; i < level_end; i++) {
-    new_lod->emplace_back(lod[i]);
-  }
-  return new_lod;
-}
-
-std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level, size_t elem_begin,
-                              size_t elem_end, bool tensor_shared) {
-  // slice the lod.
-  auto new_lod = std::make_shared<LOD>();
-  new_lod->reserve(lod.size() - level);
-  auto start = lod.at(level)[elem_begin];
-  auto end = lod.at(level)[elem_end];
-
-  for (auto it = lod.begin() + level; it != lod.end(); it++) {
-    auto it_begin = std::find(it->begin(), it->end(), start);
-    auto it_end = std::find(it_begin, it->end(), end);
-    PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
-    PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
-    new_lod->emplace_back(it_begin, it_end + 1);
-    if (!tensor_shared) {
-      // reset offset if tensor is copyed and sliced.
-      std::transform(new_lod->back().begin(), new_lod->back().end(),
-                     new_lod->back().begin(),
-                     [start](int v) { return v - start; });
-      PADDLE_ENFORCE(new_lod->back().front() == 0, "error in slice LOD");
-    }
-  }
-  return new_lod;
-}
-
-}  // namespace details
-}  // namespace framework
-}  // namespace paddle

paddle/framework/details/lod_tensor.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 <memory>
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-/*
- * Slice levels from LOD.
- *
- * @lod: LOD to slice.
- * @level_begin: level to begin slice.
- * @level_end: level to end slice.
- */
-std::shared_ptr<LODTensor::LOD> SliceLOD(const LODTensor::LOD &lod,
-                                         size_t level_begin, size_t level_end);
-
-/*
- * Slice elements from a level of LOD.
- *
- * @lod: LOD to slice.
- * @level: which level to slice.
- * @elem_begin: element's index to begin slice.
- * @elem_end: element's index to end slice.
- */
-std::shared_ptr<LODTensor::LOD> SliceLOD(const LODTensor::LOD &lod,
-                                         size_t level, size_t elem_begin,
-                                         size_t elem_end, bool tensor_shared);
-}  // namespace details
-}  // namespace framework
-}  // namespace paddle

paddle/framework/framework.proto

+/* 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. */
+
+syntax = "proto2";
+package paddle.framework;
+
+enum AttrType {
+  INT = 0;
+  FLOAT = 1;
+  STRING = 2;
+  INTS = 3;
+  FLOATS = 4;
+  STRINGS = 5;
+}
+
+// OpDesc describes an instance of a C++ framework::OperatorBase
+// derived class type.
+message OpDesc {
+
+  message Attr {
+    required string name = 1;
+    required AttrType type = 2;
+    optional int32 i = 3;
+    optional float f = 4;
+    optional string s = 5;
+    repeated int32 ints = 6;
+    repeated float floats = 7;
+    repeated string strings = 8;
+  };
+
+  message Var {
+    required string parameter = 1;
+    repeated string arguments = 2;
+  };
+
+  required string type = 3;
+  repeated Var inputs = 1;
+  repeated Var outputs = 2;
+  repeated Attr attrs = 4;
+};
+
+// OpProto describes a C++ framework::OperatorBase derived class.
+message OpProto {
+
+  // VarProto describes the C++ type framework::Variable.
+  message Var {
+    required string name = 1;
+    required string comment = 2;
+
+    optional bool duplicable = 3 [ default = false ];
+    optional bool intermediate = 4 [ default = false ];
+    optional bool not_in_gradient = 5 [ default = false ];
+  }
+
+  // AttrProto describes the C++ type Attribute.
+  message Attr {
+    required string name = 1;
+    required AttrType type = 2;
+    required string comment = 3;
+    // If that attribute is generated, it means the Paddle third
+    // language binding has responsibility to fill that
+    // attribute. End-User should not set that attribute.
+    optional bool generated = 4 [ default = false ];
+  }
+
+  required string type = 1;
+  repeated Var inputs = 2;
+  repeated Var outputs = 3;
+  repeated Attr attrs = 4;
+  required string comment = 5;
+}

paddle/framework/grad_op_builder.cc

@@ -13,90 +13,53 @@ express or implied. See the License for the specific language governing
 permissions and limitations under the License. */
 
 #include "paddle/framework/grad_op_builder.h"
-#include "paddle/framework/op_proto.pb.h"
 #include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace framework {
-
-class OpRegistry;
-
-using VarIndexMap = std::unordered_map<std::string, int>;
-
 enum class OpArgType { IN, OUT };
 
-static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
-  std::string key = type == OpArgType::IN ? "input_format" : "output_format";
-  return op->attrs_.count(key)
-             ? &boost::get<std::vector<int>>(op->attrs_.at(key))
-             : nullptr;
-}
-
-static const std::vector<int>* GetOpFormat(const OperatorBase* op,
-                                           const OpArgType& type) {
-  std::string key = type == OpArgType::IN ? "input_format" : "output_format";
-  return op->attrs_.count(key)
-             ? &boost::get<std::vector<int>>(op->attrs_.at(key))
-             : nullptr;
-}
-
-static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
-                       const OpArgType& src_type, const OpArgType& dst_type,
-                       int& idx, bool is_grad) {
-  const std::vector<std::string>& src_inout =
-      src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
-  const std::vector<int>* src_format = GetOpFormat(src_op, src_type);
-
-  std::vector<std::string>& dst_inout =
-      dst_type == OpArgType::IN ? dst_op->inputs_ : dst_op->outputs_;
-  std::vector<int>* dst_format = GetOpFormat(dst_op, dst_type);
-  const OpProto& proto = OpRegistry::protos().at(src_op->type_);
+static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
+                       bool is_grad, OperatorBase::VarNameMap* vars) {
+  const auto& src_inout =
+      src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
+  auto& dst_inout = *vars;
+  const OpProto* proto = OpRegistry::op_info_map().at(src_op->Type()).proto_;
   const auto& src_arg_list =
-      src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
-
+      src_type == OpArgType::IN ? proto->inputs() : proto->outputs();
   for (const auto& arg : src_arg_list) {
-    std::string src_name = arg.name();
-    std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
-    (*dst_op->in_out_idxs_)[dst_name] = idx++;
-    int src_arg_idx = src_op->in_out_idxs_->at(src_name);
-    int src_begin =
-        src_format == nullptr ? src_arg_idx : src_format->at(src_arg_idx);
-    int src_end = src_format == nullptr ? src_arg_idx + 1
-                                        : src_format->at(src_arg_idx + 1);
-    for (int i = src_begin; i < src_end; ++i) {
-      std::string s =
-          is_grad ? src_inout[i] + kGradVarSuffix
-                  : (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
-      dst_inout.emplace_back(s);
-    }
-    if (dst_format != nullptr) {
-      dst_format->push_back(dst_inout.size());
+    if (arg.not_in_gradient() && !is_grad) continue;
+    const std::string src_name = arg.name();
+    std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
+    dst_inout[dst_name].reserve(src_inout.at(src_name).size());
+    for (auto& var_name : src_inout.at(src_name)) {
+      std::string s = is_grad ? GradVarName(var_name) : var_name;
+      dst_inout[dst_name].emplace_back(s);
     }
   }
 }
 
 OperatorBase* BuildGradOp(const OperatorBase* op) {
-  std::string grad_op_type = OpRegistry::grad_ops().at(op->type_);
-  OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
-  grad_op->type_ = grad_op_type;
-  grad_op->attrs_ = op->attrs_;
-  grad_op->attrs_.erase("input_format");
-  grad_op->attrs_.erase("output_format");
-  if (GetOpFormat(op, OpArgType::IN) != nullptr) {
-    grad_op->attrs_["output_format"] = std::vector<int>({0});
-  }
-  if (GetOpFormat(op, OpArgType::IN) != nullptr ||
-      GetOpFormat(op, OpArgType::OUT) != nullptr) {
-    grad_op->attrs_["input_format"] = std::vector<int>({0});
-  }
-  grad_op->in_out_idxs_.reset(new VarIndexMap());
-  int in_idx = 0;
-  int out_idx = 0;
-  TransOpArg(op, grad_op, OpArgType::IN, OpArgType::IN, in_idx, false);   // I
-  TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, false);  // G
-  TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, true);   // OG
-  TransOpArg(op, grad_op, OpArgType::IN, OpArgType::OUT, out_idx, true);  // IG
-  return grad_op;
+  auto it = OpRegistry::op_info_map().find(op->Type());
+  PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(),
+                 "'%s' has not been registered.", op->Type());
+  PADDLE_ENFORCE(it->second.proto_ != nullptr, "'%s' has no OpProto.",
+                 op->Type());
+  std::string grad_op_type = it->second.grad_op_type_;
+  PADDLE_ENFORCE(!grad_op_type.empty(), "'%s' has no gradient operator.",
+                 op->Type());
+
+  OperatorBase::VarNameMap inputs;
+  OperatorBase::VarNameMap outputs;
+  TransOpArg(op, OpArgType::IN, false, &inputs);   // I
+  TransOpArg(op, OpArgType::OUT, false, &inputs);  // O
+  TransOpArg(op, OpArgType::OUT, true, &inputs);   // OG
+  TransOpArg(op, OpArgType::IN, true, &outputs);   // IG
+
+  it = OpRegistry::op_info_map().find(grad_op_type);
+  PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(),
+                 "'%s' has not been registered.", grad_op_type);
+  return it->second.creator_(grad_op_type, inputs, outputs, op->Attrs());
 }
 
 }  // namespace framework

paddle/framework/grad_op_builder_test.cc

@@ -8,22 +8,15 @@ USE_OP(add_two);
 namespace paddle {
 namespace framework {
 
-class NOP : public OperatorBase {
- public:
-  void InferShape(const Scope &scope) const override {}
-  void Run(const Scope &scope,
-           const platform::DeviceContext &dev_ctx) const override {}
-};
-
 class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
  public:
   MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("In1", "a single input");
-    AddInput("In2_mult", "a multiple input").SetMultiple();
+    AddInput("In2_mult", "a multiple input").AsDuplicable();
     AddInput("In3", "another single input");
     AddOutput("Out1", "a single output");
-    AddOutput("Out2_mult", "a multiple output").SetMultiple();
+    AddOutput("Out2_mult", "a multiple output").AsDuplicable();
     AddComment("test op with multiple inputs and outputs");
   }
 };
@@ -33,10 +26,10 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
   IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("In1", "a single input");
-    AddInput("In2_mult", "a multiple input").SetMultiple().IgnoreGradient();
-    AddInput("In3_mult", "another multiple input").SetMultiple();
-    AddOutput("Out1_mult", "a multiple output").SetMultiple();
-    AddOutput("Out2", "a single output").IgnoreGradient();
+    AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient();
+    AddInput("In3_mult", "another multiple input").AsDuplicable();
+    AddOutput("Out1_mult", "a multiple output").AsDuplicable();
+    AddOutput("Out2", "a single output").NotInGradient();
     AddComment("op with inputs and outputs ignored in gradient calculating");
   }
 };
@@ -47,35 +40,33 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
 namespace f = paddle::framework;
 
 TEST(GradOpBuilder, AddTwo) {
-  std::shared_ptr<f::OperatorBase> add_op(
-      f::OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
+  std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
+      "add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
   std::shared_ptr<f::OperatorBase> grad_add_op =
       f::OpRegistry::CreateGradOp(*add_op);
-  EXPECT_EQ(static_cast<int>(grad_add_op->inputs_.size()), 4);
-  EXPECT_EQ(static_cast<int>(grad_add_op->outputs_.size()), 2);
+  EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
+  EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
   EXPECT_EQ(grad_add_op->Input("X"), "x");
   EXPECT_EQ(grad_add_op->Input("Y"), "y");
   EXPECT_EQ(grad_add_op->Input("Out"), "out");
-  EXPECT_EQ(grad_add_op->Input("Out@GRAD"), "out@GRAD");
-  EXPECT_EQ(grad_add_op->Output("X@GRAD"), "x@GRAD");
-  EXPECT_EQ(grad_add_op->Output("Y@GRAD"), "y@GRAD");
+  EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
+  EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
+  EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
 }
 
-REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker);
-REGISTER_GRADIENT_OP(mult_io, mult_io_grad, f::NOP);
-REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
-REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
+REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
+REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
 
 TEST(GradOpBuilder, MutiInOut) {
-  f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 4, 5}},
-                        {"output_format", std::vector<int>{0, 1, 3}}};
   std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
-      "mult_io", {"in1", "in2_1", "in2_2", "in2_3", "in3"},
-      {"out1", "out2_1", "out2_2"}, attrs));
+      "mult_io", {{"In1", {"in1"}},
+                  {"In2_mult", {"in2_1", "in2_2", "in2_3"}},
+                  {"In3", {"in3"}}},
+      {{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
   std::shared_ptr<f::OperatorBase> grad_test_op =
       f::OpRegistry::CreateGradOp(*test_op);
 
-  ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
+  ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
   EXPECT_EQ(grad_test_op->Input("In1"), "in1");
   EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
             std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
@@ -89,7 +80,7 @@ TEST(GradOpBuilder, MutiInOut) {
             std::vector<std::string>(
                 {f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
 
-  ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
+  ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
   EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
   EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
             std::vector<std::string>({f::GradVarName("in2_1"),
@@ -99,31 +90,28 @@ TEST(GradOpBuilder, MutiInOut) {
 }
 
 TEST(GradOpBuilder, IOIgnoredInGradient) {
-  f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 3, 5}},
-                        {"output_format", std::vector<int>{0, 2, 3}}};
   std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
-      "io_ignored", {"in1", "in2_1", "in2_2", "in3_1", "in3_2"},
-      {"out1_1", "out1_2", "out2"}, attrs));
+      "io_ignored", {{"In1", {"in1"}},
+                     {"In2_mult", {"in2_1", "in2_2"}},
+                     {"In3_mult", {"in3_1", "in3_2"}}},
+      {{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
   std::shared_ptr<f::OperatorBase> grad_test_op =
       f::OpRegistry::CreateGradOp(*test_op);
 
   // 'In2' and 'Out2' are ignored in gradient calculating
-  ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
+  ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
   EXPECT_EQ(grad_test_op->Input("In1"), "in1");
-  EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
-            std::vector<std::string>({f::kEmptyVarName, f::kEmptyVarName}));
   EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
             std::vector<std::string>({"in3_1", "in3_2"}));
   EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
             std::vector<std::string>({"out1_1", "out1_2"}));
-  EXPECT_EQ(grad_test_op->Input("Out2"), f::kEmptyVarName);
   EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
             std::vector<std::string>(
                 {f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
   EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
             f::GradVarName("out2"));
 
-  ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
+  ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
   EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
   EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
             std::vector<std::string>(

paddle/framework/lod_tensor.cc

@@ -19,32 +19,59 @@
 namespace paddle {
 namespace framework {
 
-LODTensor LODTensor::SliceShared(size_t level_begin, size_t level_end) const {
-  PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
-  auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
-  // slice levels just need to update LOD info, each level will contains the
-  // whole tensor_, so no need to modify tensor_.
-  return LODTensor(tensor_, new_lod);
+LODTensor::LOD LODTensor::LOD::SliceLevels(size_t level_begin,
+                                           size_t level_end) const {
+  LOD new_lod;
+  new_lod.reserve(level_end - level_begin);
+  for (size_t i = level_begin; i < level_end; i++) {
+    new_lod.emplace_back(at(i));
+  }
+  return new_lod;
 }
 
-LODTensor LODTensor::SliceShared(size_t level, size_t elem_begin,
-                                 size_t elem_end) const {
-  PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
-  PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
-                 NumLevels());
-  PADDLE_ENFORCE(elem_begin < NumElements(level),
-                 "element begin [%d] out of range [%d]", elem_begin,
-                 NumElements(level));
-  PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
-                 "element end [%d] out of range [%d]", elem_end,
-                 NumElements(level));
-
-  auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
-                                   true /*tensor_shared*/);
-
-  // slice elements just need to update LOD info, because offsets are not
-  // changed, so the original tensor_ can be reused.
-  return LODTensor(tensor_, new_lod);
+LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
+                                            size_t elem_end) const {
+  // slice the lod.
+  LOD new_lod;
+  new_lod.reserve(size() - level);
+  auto start = this->at(level)[elem_begin];
+  auto end = this->at(level)[elem_end];
+
+  for (auto it = this->begin() + level; it != this->end(); it++) {
+    auto it_begin = std::find(it->begin(), it->end(), start);
+    auto it_end = std::find(it_begin, it->end(), end);
+    PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
+    PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
+    new_lod.emplace_back(it_begin, it_end + 1);
+    // reset offset if tensor is copyed and sliced.
+    std::transform(new_lod.back().begin(), new_lod.back().end(),
+                   new_lod.back().begin(),
+                   [start](int v) { return v - start; });
+    PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD");
+  }
+  PADDLE_ENFORCE_LE(new_lod.size(), this->size());
+  return new_lod;
+}
+
+bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
+  if (a.size() != b.size()) {
+    return false;
+  }
+
+  for (size_t i = 0; i < a.size(); i++) {
+    const auto& a_level = a[i];
+    const auto& b_level = b[i];
+    if (a_level.size() != b_level.size()) {
+      return false;
+    }
+    for (size_t j = 0; j < a_level.size(); j++) {
+      if (a_level[j] != b_level[j]) {
+        return false;
+      }
+    }
+  }
+
+  return true;
 }
 
 }  // namespace framework

paddle/framework/lod_tensor.h

@@ -15,7 +15,7 @@
 #pragma once
 
 #include <memory>
-#if (!PADDLE_ONLY_CPU)
+#if !defined(PADDLE_ONLY_CPU)
 #include <thrust/device_vector.h>
 #include <thrust/host_vector.h>
 #endif
@@ -31,30 +31,29 @@ namespace framework {
  * LODTensor (Level of details Tensor)
  * see https://en.wikipedia.org/wiki/Level_of_details for reference.
  */
-class LODTensor {
+class LODTensor : public Tensor {
  public:
 // Level save offsets of each unit.
 #ifdef PADDLE_ONLY_CPU
-  using Level = std::vector<size_t>;
+  template <typename T>
+  using Vector = std::vector<T>;
 #else
-  using Level = thrust::device_vector<size_t>;
+  template <typename T>
+  using Vector = thrust::host_vector<T>;
 #endif
-  // LOD stores offsets of each level of units, the largest units level first,
+  // LoD stores offsets of each level of units, the largest units level first,
   // then the smaller units level. Each Level stores the offsets of units in
   // Tesor.
-  typedef std::vector<Level> LOD;
+  class LOD : public std::vector<Vector<size_t>> {
+   public:
+    LOD SliceLevels(size_t level_begin, size_t level_end) const;
+    LOD SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) const;
+  };
 
   LODTensor() {}
-  LODTensor(const std::shared_ptr<Tensor> &tensor,
-            const std::shared_ptr<LOD> &lod) {
-    Reset(tensor, lod);
-  }
+  explicit LODTensor(const LOD &lod) : lod_(lod) {}
 
-  void Reset(const std::shared_ptr<Tensor> &tensor,
-             const std::shared_ptr<LOD> &lod) {
-    tensor_ = tensor;
-    lod_start_pos_ = lod;
-  }
+  virtual Tensor *Clone() const { return new LODTensor(lod_); }
 
   /*
    * Get a element from LOD.
@@ -65,16 +64,14 @@ class LODTensor {
     PADDLE_ENFORCE(elem < NumElements(level),
                    "element begin [%d] out of range [%d]", elem,
                    NumElements(level));
-    return (*lod_start_pos_)[level][elem];
+    return (lod_)[level][elem];
   }
 
   /*
    * Number of LODTensor's levels, each level has units of data, for example,
    * in the sentence's view, article, paragraph, sentence are 3 levels.
    */
-  size_t NumLevels() const {
-    return lod_start_pos_ ? lod_start_pos_->size() : 0UL;
-  }
+  size_t NumLevels() const { return lod_.size(); }
   /*
    * Number of elements in a level.
    */
@@ -82,64 +79,71 @@ class LODTensor {
     PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
                    NumLevels());
     // the last offset is the end of last element
-    return lod_start_pos_->at(level).size() - 1;
+    return lod_[level].size() - 1;
   }
 
-  /*
-   * Slice of levels[level_begin:level_end], with tensor copied.
-   */
-  template <typename T>
-  LODTensor SliceCopied(size_t level_begin, size_t level_end,
-                        const platform::Place &dst_place) const;
-
   /*
    * Slice of levels[level_begin:level_end], with tensor shared.
    */
-  LODTensor SliceShared(size_t level_begin, size_t level_end) const;
-
-  /*
-   * Slice of elements of a level, [elem_begin: elem_end], with tensor copied.
-   * @note: low performance in slice lod_start_pos_.
-   */
   template <typename T>
-  LODTensor SliceCopied(size_t level, size_t elem_begin, size_t elem_end,
-                        const platform::Place &dst_place) const;
+  LODTensor SliceLevels(size_t level_begin, size_t level_end) const;
 
   /*
    * Slice of elements of a level, [elem_begin: elem_end], with tensor shared.
-   * @note: low performance in slice lod_start_pos_.
-   */
-  LODTensor SliceShared(size_t level, size_t elem_begin, size_t elem_end) const;
-
-  /*
-   * Copy other's lod_start_pos_, to share LOD info.
-   * @note: the LOD info should not be changed.
+   * @note: low performance in slice lod_.
    */
-  void ShareLOD(const LODTensor &other) {
-    lod_start_pos_ = other.lod_start_pos_;
-  }
+  template <typename T>
+  LODTensor SliceInLevel(size_t level, size_t elem_begin,
+                         size_t elem_end) const;
 
   /*
-   * Copy other's lod_start_pos_'s content, free to mutate.
+   * Copy other's lod_'s content, free to mutate.
    */
-  void CopyLOD(const LODTensor &other) {
-    lod_start_pos_ = std::make_shared<LOD>(*other.lod_start_pos_);
-  }
+  void CopyLOD(const LODTensor &other) { lod_ = other.lod_; }
   /*
    * Determine whether LODTensor has a valid LOD info.
    */
-  bool HasLOD() const { return bool(lod_start_pos_); }
-  LOD *lod() const { return lod_start_pos_.get(); }
+  const LOD &lod() const { return lod_; }
+  LOD *mutable_lod() { return &lod_; }
 
-  std::shared_ptr<Tensor> &tensor() { return tensor_; }
-  Tensor *raw_tensor() { return tensor_.get(); }
+  virtual ~LODTensor() {}
 
  private:
-  std::shared_ptr<LOD> lod_start_pos_;
-  std::shared_ptr<Tensor> tensor_;
+  LOD lod_;
 };
 
+bool operator==(const LODTensor::LOD &a, const LODTensor::LOD &b);
+
+template <typename T>
+LODTensor LODTensor::SliceLevels(size_t level_begin, size_t level_end) const {
+  auto new_lod = lod_.SliceLevels(level_begin, level_end);
+  // slice levels just need to update LOD info, each level will contains the
+  // whole tensor_, so no need to modify tensor_.
+  LODTensor new_tensor(new_lod);
+  new_tensor.ShareDataWith<T>(*this);
+  return new_tensor;
+}
+
+template <typename T>
+LODTensor LODTensor::SliceInLevel(size_t level, size_t elem_begin,
+                                  size_t elem_end) const {
+  PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
+                 NumLevels());
+  PADDLE_ENFORCE(elem_begin < NumElements(level),
+                 "element begin [%d] out of range [%d]", elem_begin,
+                 NumElements(level));
+  PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
+                 "element end [%d] out of range [%d]", elem_end,
+                 NumElements(level));
+
+  auto new_lod = lod_.SliceInLevel(level, elem_begin, elem_end);
+
+  // slice elements just need to update LOD info, because offsets are not
+  // changed, so the original tensor_ can be reused.
+  LODTensor new_tensor(new_lod);
+  new_tensor.ShareDataWith<T>(*this);
+  return new_tensor;
+}
+
 }  // namespace framework
 }  // namespace paddle
-
-#include "paddle/framework/lod_tensor_impl.h"

paddle/framework/lod_tensor_impl.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/framework/details/lod_tensor.h"
-
-namespace paddle {
-namespace framework {
-
-template <typename T>
-LODTensor LODTensor::SliceCopied(size_t level_begin, size_t level_end,
-                                 const platform::Place &dst_place) const {
-  PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
-  auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
-  auto new_tensor = std::make_shared<Tensor>();
-  new_tensor->CopyFrom<T>(*tensor_, dst_place);
-
-  return LODTensor(new_tensor, new_lod);
-}
-
-template <typename T>
-LODTensor LODTensor::SliceCopied(size_t level, size_t elem_begin,
-                                 size_t elem_end,
-                                 const platform::Place &dst_place) const {
-  PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
-  PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
-                 NumLevels());
-  PADDLE_ENFORCE(elem_begin < NumElements(level),
-                 "element begin [%d] out of range [%d]", elem_begin,
-                 NumElements(level));
-  PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
-                 "element end [%d] out of range [%d]", elem_end,
-                 NumElements(level));
-
-  auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
-                                   false /*tensor_shared*/);
-
-  auto start_idx = new_lod->front().front();
-  auto end_idx = new_lod->front().back() - 1 /*the next element's start*/;
-  auto sliced_tensor = tensor_->Slice<T>(start_idx, end_idx);
-  auto new_tensor = std::make_shared<Tensor>();
-  new_tensor->CopyFrom<T>(sliced_tensor, dst_place);
-
-  return LODTensor(new_tensor, new_lod);
-}
-
-}  // namespace framework
-}  // namespace paddle

paddle/framework/lod_tensor_test.cc

@@ -15,6 +15,7 @@
 
 #include <glog/logging.h>
 #include <gtest/gtest.h>
+#include <algorithm>
 #include <memory>
 
 namespace paddle {
@@ -29,22 +30,28 @@ class LODTensorTester : public ::testing::Test {
     // 0 10 20
     // 0 5 10 15 20
     // 0 2 5 7 10 12 15 20
-    auto lod = std::make_shared<LODTensor::LOD>();
-    lod->push_back(std::vector<size_t>{0, 10, 20});
-    lod->push_back(std::vector<size_t>{0, 5, 10, 15, 20});
-    lod->push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
+    LODTensor::LOD lod;
+    lod.push_back(std::vector<size_t>{0, 10, 20});
+    lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
+    lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
 
-    auto tensor = std::make_shared<Tensor>();
-    tensor->Resize({20 /*batch size*/, 128 /*dim*/});
+    ASSERT_EQ(lod.size(), 3UL);
+
+    tensor.Resize({20 /*batch size*/, 128 /*dim*/});
     // malloc memory
-    tensor->mutable_data<float>(place);
+    tensor.mutable_data<float>(place);
+
+    lod_tensor.reset(new LODTensor(lod));
+    lod_tensor->Resize({20 /*batch size*/, 128 /*dim*/});
 
-    lod_tensor->Reset(tensor, lod);
+    lod_tensor->ShareDataWith<float>(tensor);
+    // lod_tensor->ShareDataWith<Tensor>(tensor);
   }
 
  protected:
   std::unique_ptr<LODTensor> lod_tensor;
   platform::CPUPlace place;
+  Tensor tensor;
 };
 
 TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); }
@@ -55,110 +62,54 @@ TEST_F(LODTensorTester, NumElements) {
   ASSERT_EQ(lod_tensor->NumElements(2), 8UL);
 }
 
-TEST_F(LODTensorTester, SliceShared_Level) {
-  // slice 1 level
-  for (size_t level = 0; level < 3UL; ++level) {
-    auto new_lod_tensor = lod_tensor->SliceShared(level, level + 1);
-    ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
-    ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
-    ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
-  }
-  // slice 2 level
-  for (size_t level = 0; level < 2UL; ++level) {
-    auto new_lod_tensor = lod_tensor->SliceShared(level, level + 2);
-    ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
-    ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
-    ASSERT_EQ(new_lod_tensor.NumElements(1),
-              lod_tensor->NumElements(level + 1));
-    ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
-  }
-}
-
-TEST_F(LODTensorTester, SliceCopied_Level) {
+TEST_F(LODTensorTester, SliceLevels) {
   // slice 1 level
   for (size_t level = 0; level < 3UL; ++level) {
-    auto new_lod_tensor =
-        lod_tensor->SliceCopied<float>(level, level + 1, place);
+    auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 1);
     ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
     ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
-    // ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
-    // TODO(superjom) add tensor comparation here.
+    // ASSERT_EQ(new_lod_tensor, *lod_tensor);
   }
   // slice 2 level
   for (size_t level = 0; level < 2UL; ++level) {
-    auto new_lod_tensor =
-        lod_tensor->SliceCopied<float>(level, level + 2, place);
+    auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 2);
     ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
     ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
     ASSERT_EQ(new_lod_tensor.NumElements(1),
               lod_tensor->NumElements(level + 1));
-    // ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
-    // TODO(superjom) add tensor comparation here.
+    ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
   }
 }
 
-TEST_F(LODTensorTester, SliceShared_Element) {
-  size_t level = 0;
-  auto new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
-  ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
-  ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
-
-  level = 1;
-  new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
-  ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
-  ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
-}
-
-TEST_F(LODTensorTester, SliceCopied_Element) {
+TEST_F(LODTensorTester, SliceInLevel) {
   size_t level = 0;
-  auto new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
-  ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
-  ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
+  auto new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
+  EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
+  EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
+  EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
+  EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
+  ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
 
   level = 1;
-  new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
+  new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
   ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
   ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
   ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
-  ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
-
-  level = 1;
-  // LOD is
-  //    0 5 10
-  //    0 2 5 7 10
-  new_lod_tensor = lod_tensor->SliceCopied<float>(level, 1, 3, place);
-  ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
-  ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
-
-  ASSERT_EQ(new_lod_tensor.lod_element(0, 0), 0UL);
-  ASSERT_EQ(new_lod_tensor.lod_element(0, 1), 5UL);
-  ASSERT_EQ(new_lod_tensor.lod_element(1, 0), 0UL);
-  ASSERT_EQ(new_lod_tensor.lod_element(1, 1), 2UL);
-  ASSERT_EQ(new_lod_tensor.lod_element(1, 2), 5UL);
-  ASSERT_EQ(new_lod_tensor.lod_element(1, 3), 7UL);
-
-  // TODO(superjom) compare the content of these tensors
+  ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
 }
 
 TEST_F(LODTensorTester, ShareLOD) {
   LODTensor new_lod_tensor;
-  new_lod_tensor.ShareLOD(*lod_tensor);
+  new_lod_tensor.CopyLOD(*lod_tensor);
   ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod());
 }
 
 TEST_F(LODTensorTester, CopyLOD) {
   LODTensor new_lod_tensor;
   new_lod_tensor.CopyLOD(*lod_tensor);
-  ASSERT_NE(new_lod_tensor.lod(), lod_tensor->lod());
+  bool equals = std::equal(lod_tensor->lod().begin(), lod_tensor->lod().end(),
+                           new_lod_tensor.lod().begin());
+  ASSERT_TRUE(equals);
 }
 
 }  // namespace framework

paddle/framework/op_proto.proto

-/* 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. */
-
-// Protocol Message for 3rd-party language binding.
-//
-// Paddle Python package will use `OpProto` to generate op creation methods.
-// The op creation methods take user's input and generate `OpDesc` proto
-// message,
-// then pass `OpDesc` to C++ side and create Op pointer.
-//
-syntax = "proto2";
-package paddle.framework;
-
-import "attribute.proto";
-
-// Attribute protocol message for 3rd-party language binding.
-// It will store the Op support what attribute and what type.
-message AttrProto {
-  // Supported attribute name. e.g. `scale` for cosine op.
-  required string name = 1;
-
-  // Supported attribute type.
-  required AttrType type = 2;
-
-  // Supported attribute comments. It helps 3rd-party language generate
-  // doc-string.
-  required string comment = 3;
-
-  // If that attribute is generated, it means the Paddle third language
-  // binding has responsibility to fill that attribute. End-User should
-  // not set that attribute.
-  optional bool generated = 4 [ default = false ];
-}
-
-// Input or output message for 3rd-party language binding.
-// It contains parameter name and its comments.
-message VarProto {
-  // Input or output name in that op creation function.
-  // e.g. `cos(a, b, output, ...)`, "a", "b", "output" are names.
-  required string name = 1;
-
-  // The comment for that input. It helps 3rd-party language generate
-  // doc-string.
-  required string comment = 2;
-
-  // Is that input/output could be a list or not.
-  // If so, that Op should write a attributed named `input_format` or
-  // `output_format`.
-  //
-  // e.g.
-  //   If the op is a fc op, the inputs are `X`, `W`, `b`. The `X` and `W`
-  //   could be multiple, so the multiple of `X` and `W` is True, and OpDesc
-  //   will hold a attribute of them.
-  //
-  //   The Op desc of same fc could be
-  //   {
-  //      "type": "fc",
-  //      "input": ["X1", "X2", "W1", "W2", "b"],
-  //      "output": "fc.out",
-  //      "attrs" : {
-  //        "input_format": [0, 2, 4, 5]
-  //      }
-  //   }
-  //
-  optional bool multiple = 3 [ default = false ];
-
-  // It marks that output is a temporary output. That output is not used by
-  // user, but used by other op internally as input. If other op is not use
-  // that output, it could be optimized early.
-  //
-  // Attribute temporary_index will be set in OpDesc if there is some
-  // outputs are temporary.
-  //
-  // output = [ "xxx.out1", "xxx.tmp", "xxx.out2"],
-  // attrs = {
-  //   "temporary_index": [1]
-  // }
-  optional bool temporary = 4 [ default = false ];
-
-  // The gradient of operator can be ignored immediately
-  // e.g. operator AddOp, y = x1 + x2, the gradient of dy/dx1, dy/dx2
-  // can be ignored for the future optimized on graph.
-  optional bool ignore_gradient = 6;
-}
-
-// Op protocol message for 3rd-party language binding.
-// It contains all information for generating op creation method.
-message OpProto {
-  // The input information to generate op creation method.
-  repeated VarProto inputs = 1;
-
-  // The output information to generate op creation method.
-  repeated VarProto outputs = 2;
-
-  // The attribute information to generate op creation method.
-  repeated AttrProto attrs = 3;
-
-  // The comments for that Op. It helps 3rd-party language generate
-  // doc-string. The whole documentation of that Op is generated by comment,
-  // inputs, outputs, attrs together.
-  required string comment = 4;
-
-  // The type of that Op.
-  required string type = 5;
-}

paddle/framework/op_proto_test.cc

-#include <gtest/gtest.h>
-#include <paddle/framework/op_proto.pb.h>
-
-TEST(TestOpProto, ALL) {
-  paddle::framework::OpProto proto;
-  {
-    auto ipt = proto.mutable_inputs()->Add();
-    *ipt->mutable_name() = "a";
-    *ipt->mutable_comment() = "the one input of cosine op";
-  }
-  {
-    auto ipt = proto.mutable_inputs()->Add();
-    *ipt->mutable_name() = "b";
-    *ipt->mutable_comment() = "the other input of cosine op";
-  }
-  {
-    auto opt = proto.mutable_outputs()->Add();
-    *opt->mutable_name() = "output";
-    *opt->mutable_comment() = "the output of cosine op";
-  }
-  {
-    auto attr = proto.mutable_attrs()->Add();
-    *attr->mutable_name() = "scale";
-    attr->set_type(paddle::framework::AttrType::FLOAT);
-    *attr->mutable_comment() = "the scale attribute of cosine op";
-  }
-  proto.set_type("cos");
-  *proto.mutable_comment() = "cosine op, output = scale * cos(a, b)";
-
-  ASSERT_TRUE(proto.IsInitialized());
-}
\ No newline at end of file

paddle/framework/op_registry.cc

@@ -17,5 +17,48 @@ limitations under the License. */
 #include <vector>
 
 namespace paddle {
-namespace framework {}  // namespace framework
+namespace framework {
+
+std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const std::string& type,
+                                                   const VarNameMap& inputs,
+                                                   const VarNameMap& outputs,
+                                                   AttributeMap attrs) {
+  auto it = op_info_map().find(type);
+  PADDLE_ENFORCE(it != op_info_map().end(),
+                 "Operator '%s' has not been registered.", type);
+  it->second.checker_->Check(attrs);
+  auto op = it->second.creator_(type, inputs, outputs, attrs);
+  return std::unique_ptr<OperatorBase>(op);
+}
+
+std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
+  VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
+  VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
+  AttributeMap attrs;
+  for (auto& attr : op_desc.attrs()) {
+    attrs[attr.name()] = GetAttrValue(attr);
+  }
+
+  return CreateOp(op_desc.type(), inputs, outputs, attrs);
+}
+
+OperatorBase::VarNameMap OpRegistry::ConvertOpDescVarsToVarNameMap(
+    const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars) {
+  VarNameMap ret_val;
+  for (auto& var : op_desc_vars) {
+    auto& var_names = ret_val[var.parameter()];
+    auto& var_names_in_proto = var.arguments();
+    var_names.reserve(static_cast<size_t>(var_names_in_proto.size()));
+    std::copy(var_names_in_proto.begin(), var_names_in_proto.end(),
+              std::back_inserter(var_names));
+  }
+  return ret_val;
+}
+
+std::unique_ptr<OperatorBase> OpRegistry::CreateGradOp(const OperatorBase& op) {
+  PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops");
+  return std::unique_ptr<OperatorBase>(BuildGradOp(&op));
+}
+
+}  // namespace framework
 }  // namespace paddle

paddle/framework/op_registry.h

@@ -17,309 +17,114 @@ limitations under the License. */
 #include <algorithm>
 #include <atomic>
 #include <type_traits>
+#include <typeinfo>
 #include <unordered_map>
 #include <unordered_set>
 #include "paddle/framework/attribute.h"
+#include "paddle/framework/framework.pb.h"
 #include "paddle/framework/grad_op_builder.h"
-#include "paddle/framework/op_desc.pb.h"
+#include "paddle/framework/operator.h"
 #include "paddle/framework/scope.h"
 
 namespace paddle {
 namespace framework {
 
-// this class not only make proto but also init attribute checkers.
-class OpProtoAndCheckerMaker {
- public:
-  OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
-      : proto_(proto), op_checker_(op_checker) {}
-
-  ~OpProtoAndCheckerMaker() {
-    PADDLE_ENFORCE(validated_, "should call Validate after build");
-  }
-
-  void Validate() {
-    validated_ = true;
-    CheckNoDuplicatedInOutAttrs();
-  }
-
- protected:
-  struct VariableBuilder {
-    VarProto* var_;
-    std::function<void()> on_multiple_;
-    std::function<void()> on_temporary_;
-
-    VariableBuilder& SetMultiple() {
-      var_->set_multiple(true);
-      on_multiple_();
-      return *this;
-    }
-
-    VariableBuilder& SetTemporary() {
-      PADDLE_ENFORCE(bool(on_temporary_), "Cannot set temporary");
-      var_->set_temporary(true);
-      on_temporary_();
-      return *this;
-    }
-
-    VariableBuilder& IgnoreGradient() {
-      var_->set_ignore_gradient(true);
-      return *this;
-    }
-  };
-
-  VariableBuilder AddInput(const std::string& name,
-                           const std::string& comment) {
-    auto input = proto_->mutable_inputs()->Add();
-    *input->mutable_name() = name;
-    *input->mutable_comment() = comment;
-    return VariableBuilder{input, [=] { this->SetHasMultipleInput(); },
-                           nullptr};
-  }
-
-  VariableBuilder AddOutput(const std::string& name,
-                            const std::string& comment) {
-    auto output = proto_->mutable_outputs()->Add();
-    *output->mutable_name() = name;
-    *output->mutable_comment() = comment;
-    return VariableBuilder{output, [=] { this->SetHasMultipleOutput(); },
-                           [=] { this->SetHasTemporaryOutput(); }};
-  }
-
-  template <typename T>
-  TypedAttrChecker<T>& AddAttr(const std::string& name,
-                               const std::string& comment,
-                               bool generated = false) {
-    auto attr = proto_->mutable_attrs()->Add();
-    *attr->mutable_name() = name;
-    *attr->mutable_comment() = comment;
-    attr->set_generated(generated);
-    attr->set_type(AttrTypeID<T>());
-    return op_checker_->AddAttrChecker<T>(name);
-  }
-
-  void AddComment(const std::string& comment) {
-    *(proto_->mutable_comment()) = comment;
-  }
-
- private:
-  void SetHasMultiple(const std::string& in_out, bool* flag) {
-    if (!*flag) {
-      AddAttr<std::vector<int>>(in_out + "_format",
-                                "The multiple index of " + in_out +
-                                    "\n"
-                                    R"DOC(
-This attribute is used by Paddle core framework. Paddle's Op support each input
-or output could be a list of variable. This attribute is used to show how that
-list organized.
-
-e.g.
-  input = ["a", "b", "c", "d", "e", "f"]
-  input_format = [0, 4, 5, 6]
-
-means
-  The number of all input variables this op is six, and they are segmented into
-  three inputs.
-
-  The first input is input[0:4], second is input[4:5], third is input[5:6].
-)DOC",
-                                /*generated*/ true);
-      *flag = true;
-    }
-  }
-
-  void SetHasMultipleInput() { SetHasMultiple("input", &has_multiple_input_); }
-  void SetHasMultipleOutput() {
-    SetHasMultiple("output", &has_multiple_output_);
-  }
-
-  void SetHasTemporaryOutput() {
-    if (!has_temporary_output_) {
-      AddAttr<std::vector<int>>("temporary_index",
-                                R"DOC(The temporary index of output.
-
-Not all output of Paddle Op is used by user. For faster computation, each op
-could output some its internal state to other op, other op could take that
-output to make compute faster.
-
-Add a mark to which output is temporary is helpful for future optimization.
-)DOC",
-                                /*generated*/ true)
-          .SetDefault(std::vector<int>());
-      has_temporary_output_ = true;
-    }
-  }
-
-  void CheckNoDuplicatedInOutAttrs() {
-    std::unordered_set<std::string> names;
-    auto checker = [&](const std::string& name) {
-      PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name);
-      names.insert(name);
-    };
-    for (auto& attr : proto_->attrs()) {
-      checker(attr.name());
-    }
-    for (auto& input : proto_->inputs()) {
-      checker(input.name());
-    }
-    for (auto& output : proto_->outputs()) {
-      checker(output.name());
-    }
-  }
-
-  OpProto* proto_;
-  OpAttrChecker* op_checker_;
-  bool validated_{false};
-  bool has_multiple_input_{false};
-  bool has_multiple_output_{false};
-  bool has_temporary_output_{false};
-};
-
 class OpRegistry {
-  using OpCreator = std::function<OperatorBase*()>;
-  using VarIndexMap = std::unordered_map<std::string, int>;
-  using VarNameList = std::vector<std::string>;
+  using VarNameMap = OperatorBase::VarNameMap;
+  using OpCreator = std::function<OperatorBase*(
+      const std::string& /*type*/, const VarNameMap& /*inputs*/,
+      const VarNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
 
  public:
-  template <typename OpType, typename ProtoMakerType>
-  static void RegisterOp(const std::string& op_type) {
-    op_creators()[op_type] = [] { return new OpType; };
-    OpAttrChecker& op_checker = op_checkers()[op_type];
-    OpProto& op_proto = protos()[op_type];
-    auto maker = ProtoMakerType(&op_proto, &op_checker);
-    maker.Validate();
-    *op_proto.mutable_type() = op_type;
-    PADDLE_ENFORCE(
-        op_proto.IsInitialized(),
-        "Fail to initialize %s's OpProto, because %s is not initialized",
-        op_type, op_proto.InitializationErrorString());
-
-    VarIndexMaps()[op_type].reset(new VarIndexMap());
-    auto& varmap = *VarIndexMaps()[op_type];
-    int idx = 0;
-    for (auto& var : op_proto.inputs()) {
-      varmap[var.name()] = idx++;
-    }
-    idx = 0;
-    for (auto& var : op_proto.outputs()) {
-      varmap[var.name()] = idx++;
-    }
-  }
-
-  template <typename GradOpType>
-  static void RegisterGradOp(const std::string& op_type,
-                             const std::string& grad_op_type) {
-    op_creators()[grad_op_type] = [] { return new GradOpType; };
-    grad_ops()[op_type] = grad_op_type;
-  }
-
-  static std::shared_ptr<OperatorBase> CreateOp(const std::string& type,
-                                                const VarNameList& inputs,
-                                                const VarNameList& outputs,
-                                                const AttributeMap& attrs) {
-    auto op_create_it = op_creators().find(type);
-    PADDLE_ENFORCE(op_create_it != op_creators().end(),
-                   "Operator %s cannot be found.", type);
-
-    auto op = op_create_it->second();
-    op->type_ = type;
-    op->inputs_ = inputs;
-    op->outputs_ = outputs;
-
-    op->attrs_ = attrs;
-    op_checkers().at(type).Check(op->attrs_);
-
-    GenerateTempVariableName(op);
+  struct OpInfo {
+    OpCreator creator_;
+    std::string grad_op_type_;
+    OpProto* proto_;
+    OpAttrChecker* checker_;
+  };
 
-    {
-      auto var_index_it = VarIndexMaps().find(type);
-      if (var_index_it != VarIndexMaps().end()) {
-        op->in_out_idxs_ = var_index_it->second;
-      }
+  template <typename OpType, typename ProtoMakerType, typename GradOpType>
+  static void RegisterOp(const std::string& op_type,
+                         const std::string& grad_op_type) {
+    PADDLE_ENFORCE(op_info_map().count(op_type) == 0,
+                   "'%s' is registered more than once.", op_type);
+    OpInfo op_info;
+    op_info.creator_ = [](const std::string& type, const VarNameMap& inputs,
+                          const VarNameMap& outputs,
+                          const AttributeMap& attrs) {
+      return new OpType(type, inputs, outputs, attrs);
+    };
+    op_info.grad_op_type_ = grad_op_type;
+    if (std::type_index(typeid(ProtoMakerType)) !=
+        std::type_index(typeid(NOPMaker))) {
+      op_info.proto_ = new OpProto;
+      op_info.checker_ = new OpAttrChecker;
+      auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
+      maker.Validate();
+      op_info.proto_->set_type(op_type);
+      PADDLE_ENFORCE(
+          op_info.proto_->IsInitialized(),
+          "Fail to initialize %s's OpProto, because %s is not initialized",
+          op_type, op_info.proto_->InitializationErrorString());
+    } else {
+      op_info.proto_ = nullptr;
+      op_info.checker_ = nullptr;
     }
-
-    op->Init();
-    return std::shared_ptr<OperatorBase>(op);
-  }
-
-  static std::shared_ptr<OperatorBase> CreateOp(const OpDesc& op_desc) {
-    std::vector<std::string> inputs;
-    inputs.reserve((size_t)op_desc.inputs_size());
-    std::copy(op_desc.inputs().begin(), op_desc.inputs().end(),
-              std::back_inserter(inputs));
-
-    std::vector<std::string> outputs;
-    outputs.reserve((size_t)op_desc.outputs_size());
-    std::copy(op_desc.outputs().begin(), op_desc.outputs().end(),
-              std::back_inserter(outputs));
-
-    AttributeMap attrs;
-    for (auto& attr : op_desc.attrs()) {
-      attrs[attr.name()] = GetAttrValue(attr);
+    op_info_map().insert(std::make_pair(op_type, op_info));
+    // register gradient op
+    if (!grad_op_type.empty()) {
+      RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
     }
-
-    return CreateOp(op_desc.type(), inputs, outputs, attrs);
-  }
-
-  static std::shared_ptr<OperatorBase> CreateGradOp(const OperatorBase& op) {
-    PADDLE_ENFORCE(!op.IsNetOp(),
-                   "Use framework::Backward to get backward ops");
-    std::shared_ptr<OperatorBase> grad_op(BuildGradOp(&op));
-    grad_op->Init();
-    return grad_op;
   }
 
-  static std::unordered_map<std::string, OpProto>& protos() {
-    static std::unordered_map<std::string, OpProto> protos_;
-    return protos_;
-  }
+  static std::unique_ptr<OperatorBase> CreateOp(const std::string& type,
+                                                const VarNameMap& inputs,
+                                                const VarNameMap& outputs,
+                                                AttributeMap attrs);
 
-  static std::unordered_map<std::string, std::string>& grad_ops() {
-    static std::unordered_map<std::string, std::string> grad_ops_;
-    return grad_ops_;
-  }
+  static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
 
-  static std::unordered_map<std::string, std::shared_ptr<VarIndexMap>>&
-  VarIndexMaps() {
-    static std::unordered_map<std::string, std::shared_ptr<VarIndexMap>> maps_;
-    return maps_;
-  }
+  static VarNameMap ConvertOpDescVarsToVarNameMap(
+      const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars);
 
-  static std::unordered_map<std::string, OpCreator>& op_creators() {
-    static std::unordered_map<std::string, OpCreator> op_creators_;
-    return op_creators_;
-  }
+  static std::unique_ptr<OperatorBase> CreateGradOp(const OperatorBase& op);
 
- private:
-  static std::unordered_map<std::string, OpAttrChecker>& op_checkers() {
-    static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
-    return op_checkers_;
+  static std::unordered_map<std::string, const OpInfo>& op_info_map() {
+    static std::unordered_map<std::string, const OpInfo> op_info_map_;
+    return op_info_map_;
   }
+};
 
-  static void GenerateTempVariableName(OperatorBase* op) {
-    static std::atomic<size_t> gUniqId(0UL);
-    for (auto& outname : op->outputs_) {
-      if (outname == kTempVarName) {
-        outname += op->type_;
-        outname += "@";
-        outname += std::to_string(gUniqId.fetch_add(1));
-      }
-    }
-  }
+class Registrar {
+ public:
+  // In our design, various kinds of classes, e.g., operators and kernels,
+  // have their corresponding registry and registrar. The action of
+  // registration is in the constructor of a global registrar variable, which,
+  // however, are not used in the code that calls package framework, and would
+  // be removed from the generated binary file by the linker. To avoid such
+  // removal, we add Touch to all registrar classes and make USE_OP macros to
+  // call this method. So, as long as the callee code calls USE_OP, the global
+  // registrar variable won't be removed by the linker.
+  void Touch() {}
 };
 
-template <typename OpType, typename ProtoMakerType>
-class OpRegisterHelper {
+template <typename OpType, typename ProtoMakerType, typename GradOpType>
+class OpRegistrar : public Registrar {
  public:
-  explicit OpRegisterHelper(const char* op_type) {
-    OpRegistry::RegisterOp<OpType, ProtoMakerType>(op_type);
+  explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
+  OpRegistrar(const char* op_type, const char* grad_op_type) {
+    OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
+                                                               grad_op_type);
   }
 };
 
-template <typename GradOpType>
-class GradOpRegisterHelper {
+template <typename PlaceType, typename KernelType>
+class OpKernelRegistrar : public Registrar {
  public:
-  GradOpRegisterHelper(const char* op_type, const char* grad_op_type) {
-    OpRegistry::RegisterGradOp<GradOpType>(op_type, grad_op_type);
+  explicit OpKernelRegistrar(const char* op_type) {
+    OperatorWithKernel::OpKernelKey key;
+    key.place_ = PlaceType();
+    OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KernelType);
   }
 };
 
@@ -333,97 +138,93 @@ class GradOpRegisterHelper {
                 msg)
 
 /**
- * Macro to Register Operator.
+ * Macro to register Operator.
  */
-#define REGISTER_OP(__op_type, __op_class, __op_maker_class)                 \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(__reg_op__##__op_type,                      \
-                                 "REGISTER_OP must be in global namespace"); \
-  static ::paddle::framework::OpRegisterHelper<__op_class, __op_maker_class> \
-      __op_register_##__op_type##__(#__op_type);                             \
-  int __op_register_##__op_type##_handle__() { return 0; }
+#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type,          \
+                    grad_op_class)                                            \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                             \
+      __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
+  class _OpClass_##op_type##_ : public op_class {                             \
+   public:                                                                    \
+    DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_);                            \
+    DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class);                   \
+  };                                                                          \
+  class _OpGradClass_##op_type##_ : public grad_op_class {                    \
+   public:                                                                    \
+    DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_);                        \
+    DEFINE_OP_CONSTRUCTOR(_OpGradClass_##op_type##_, grad_op_class);          \
+  };                                                                          \
+  static ::paddle::framework::OpRegistrar<                                    \
+      _OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_>       \
+      __op_registrar_##op_type##__(#op_type, #grad_op_type);                  \
+  int TouchOpRegistrar_##op_type() {                                          \
+    __op_registrar_##op_type##__.Touch();                                     \
+    return 0;                                                                 \
+  }
+
+#define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
+  REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
 
 /**
- * Macro to Register Gradient Operator.
+ * Macro to register OperatorKernel.
  */
-#define REGISTER_GRADIENT_OP(__op_type, __grad_op_type, __grad_op_class)       \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(                                              \
-      __reg_gradient_op__##__op_type##__grad_op_type,                          \
-      "REGISTER_GRADIENT_OP must be in global namespace");                     \
-  static ::paddle::framework::GradOpRegisterHelper<__grad_op_class>            \
-      __op_gradient_register_##__op_type##__grad_op_type##__(#__op_type,       \
-                                                             #__grad_op_type); \
-  int __op_gradient_register_##__op_type##__grad_op_type##_handle__() {        \
-    return 0;                                                                  \
-  }
-
-/**
- * Macro to Forbid user register Gradient Operator.
- */
-#define NO_GRADIENT(__op_type)                          \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(                       \
-      __reg_gradient_op__##__op_type##__op_type##_grad, \
-      "NO_GRADIENT must be in global namespace")
-
-/**
- * Macro to Register OperatorKernel.
- */
-#define REGISTER_OP_KERNEL(type, DEVICE_TYPE, PlaceType, ...)             \
+#define REGISTER_OP_KERNEL(op_type, DEVICE_TYPE, place_class, ...)        \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                         \
-      __reg_op_kernel_##type##_##DEVICE_TYPE##__,                         \
-      "REGISTER_OP_KERNEL must be in global namespace");                  \
-  struct __op_kernel_register__##type##__##DEVICE_TYPE##__ {              \
-    __op_kernel_register__##type##__##DEVICE_TYPE##__() {                 \
-      ::paddle::framework::OperatorWithKernel::OpKernelKey key;           \
-      key.place_ = PlaceType();                                           \
-      ::paddle::framework::OperatorWithKernel::AllOpKernels()[#type][key] \
-          .reset(new __VA_ARGS__());                                      \
-    }                                                                     \
-  };                                                                      \
-  static __op_kernel_register__##type##__##DEVICE_TYPE##__                \
-      __reg_kernel_##type##__##DEVICE_TYPE##__;                           \
-  int __op_kernel_register_##type##_handle_##DEVICE_TYPE##__() { return 0; }
+      __reg_op_kernel_##op_type##_##DEVICE_TYPE##__,                      \
+      "REGISTER_OP_KERNEL must be called in global namespace");           \
+  static ::paddle::framework::OpKernelRegistrar<place_class, __VA_ARGS__> \
+      __op_kernel_registrar_##op_type##_##DEVICE_TYPE##__(#op_type);      \
+  int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE() {                \
+    __op_kernel_registrar_##op_type##_##DEVICE_TYPE##__.Touch();          \
+    return 0;                                                             \
+  }
 
-// (type, KernelType)
-#define REGISTER_OP_GPU_KERNEL(type, ...) \
-  REGISTER_OP_KERNEL(type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
+#define REGISTER_OP_GPU_KERNEL(op_type, ...) \
+  REGISTER_OP_KERNEL(op_type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
 
-// (type, KernelType)
-#define REGISTER_OP_CPU_KERNEL(type, ...) \
-  REGISTER_OP_KERNEL(type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
+#define REGISTER_OP_CPU_KERNEL(op_type, ...) \
+  REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
 
 /**
- * Macro to mark what Operator and Kernel we will use and tell the compiler to
+ * Macro to mark what Operator and Kernel
+ * we will use and tell the compiler to
  * link them into target.
  */
-#define USE_OP_WITHOUT_KERNEL(op_type)                      \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(                           \
-      __use_op_without_kernel_##op_type,                    \
-      "USE_OP_WITHOUT_KERNEL must be in global namespace"); \
-  extern int __op_register_##op_type##_handle__();          \
-  static int __use_op_ptr_##op_type##_without_kernel__      \
-      __attribute__((unused)) = __op_register_##op_type##_handle__()
-
-#define USE_OP_KERNEL(op_type, DEVICE_TYPE)                               \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(                                         \
-      __use_op_kernel_##op_type##_##DEVICE_TYPE##__,                      \
-      "USE_OP_KERNEL must be in global namespace");                       \
-  extern int __op_kernel_register_##op_type##_handle_##DEVICE_TYPE##__(); \
-  static int __use_op_ptr_##op_type##_##DEVICE_TYPE##_kernel__            \
-      __attribute__((unused)) =                                           \
-          __op_kernel_register_##op_type##_handle_##DEVICE_TYPE##__()
-
-// use Operator with only cpu kernel.
-#define USE_OP_CPU(op_type)       \
-  USE_OP_WITHOUT_KERNEL(op_type); \
-  USE_OP_KERNEL(op_type, CPU)
+#define USE_OP_ITSELF(op_type)                                    \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                 \
+      __use_op_itself_##op_type,                                  \
+      "USE_OP_ITSELF must be called in global namespace");        \
+  extern int TouchOpRegistrar_##op_type();                        \
+  static int use_op_itself_##op_type##_ __attribute__((unused)) = \
+      TouchOpRegistrar_##op_type()
+
+#define USE_OP_DEVICE_KERNEL(op_type, DEVICE_TYPE)               \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                \
+      __use_op_kernel_##op_type##_##DEVICE_TYPE##__,             \
+      "USE_OP_DEVICE_KERNEL must be in global namespace");       \
+  extern int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE(); \
+  static int use_op_kernel_##op_type##_##DEVICE_TYPE##_          \
+      __attribute__((unused)) =                                  \
+          TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE()
+
+// TODO(fengjiayi): The following macros
+// seems ugly, do we have better method?
 
 #ifdef PADDLE_ONLY_CPU
-#define USE_OP(op_type) USE_OP_CPU(op_type)
+#define USE_OP_KERNEL(op_type) USE_OP_DEVICE_KERNEL(op_type, CPU)
 #else
-#define USE_OP(op_type) \
-  USE_OP_CPU(op_type);  \
-  USE_OP_KERNEL(op_type, GPU)
+#define USE_OP_KERNEL(op_type)        \
+  USE_OP_DEVICE_KERNEL(op_type, CPU); \
+  USE_OP_DEVICE_KERNEL(op_type, GPU)
 #endif
 
+#define USE_CPU_ONLY_OP(op_type) \
+  USE_OP_ITSELF(op_type);        \
+  USE_OP_DEVICE_KERNEL(op_type, CPU);
+
+#define USE_OP(op_type)   \
+  USE_OP_ITSELF(op_type); \
+  USE_OP_KERNEL(op_type)
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_registry_test.cc

@@ -7,6 +7,7 @@ namespace paddle {
 namespace framework {
 class CosineOp : public OperatorBase {
  public:
+  using OperatorBase::OperatorBase;
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}
   void InferShape(const Scope& scope) const override {}
@@ -27,6 +28,7 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 
 class MyTestOp : public OperatorBase {
  public:
+  using OperatorBase::OperatorBase;
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}
@@ -36,8 +38,8 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
  public:
   MyTestOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("input", "input of cosine op").SetMultiple();
-    AddOutput("output", "output of cosine op").SetTemporary();
+    AddInput("input", "input of cosine op").AsDuplicable();
+    AddOutput("output", "output of cosine op").AsIntermediate();
     auto my_checker = [](int i) {
       PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
     };
@@ -49,16 +51,24 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_OP(cos_sim, paddle::framework::CosineOp,
-            paddle::framework::CosineOpProtoAndCheckerMaker);
-REGISTER_OP(my_test_op, paddle::framework::MyTestOp,
-            paddle::framework::MyTestOpProtoAndCheckerMaker);
+static void BuildVar(const std::string& param_name,
+                     std::initializer_list<const char*> arguments,
+                     paddle::framework::OpDesc::Var* var) {
+  var->set_parameter(param_name);
+  for (auto& arg_name : arguments) {
+    var->add_arguments(arg_name);
+  }
+}
+REGISTER_OP_WITHOUT_GRADIENT(cos_sim, paddle::framework::CosineOp,
+                             paddle::framework::CosineOpProtoAndCheckerMaker);
+REGISTER_OP_WITHOUT_GRADIENT(my_test_op, paddle::framework::MyTestOp,
+                             paddle::framework::MyTestOpProtoAndCheckerMaker);
 
 TEST(OpRegistry, CreateOp) {
   paddle::framework::OpDesc op_desc;
   op_desc.set_type("cos_sim");
-  op_desc.add_inputs("aa");
-  op_desc.add_outputs("bb");
+  BuildVar("input", {"aa"}, op_desc.add_inputs());
+  BuildVar("output", {"bb"}, op_desc.add_outputs());
 
   float scale = 3.3;
   auto attr = op_desc.mutable_attrs()->Add();
@@ -66,8 +76,7 @@ TEST(OpRegistry, CreateOp) {
   attr->set_type(paddle::framework::AttrType::FLOAT);
   attr->set_f(scale);
 
-  std::shared_ptr<paddle::framework::OperatorBase> op =
-      paddle::framework::OpRegistry::CreateOp(op_desc);
+  auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   paddle::framework::Scope scope;
   paddle::platform::CPUDeviceContext dev_ctx;
   op->Run(scope, dev_ctx);
@@ -78,8 +87,8 @@ TEST(OpRegistry, CreateOp) {
 TEST(OpRegistry, IllegalAttr) {
   paddle::framework::OpDesc op_desc;
   op_desc.set_type("cos_sim");
-  op_desc.add_inputs("aa");
-  op_desc.add_outputs("bb");
+  BuildVar("input", {"aa"}, op_desc.add_inputs());
+  BuildVar("output", {"bb"}, op_desc.add_outputs());
 
   auto attr = op_desc.mutable_attrs()->Add();
   attr->set_name("scale");
@@ -103,33 +112,23 @@ TEST(OpRegistry, IllegalAttr) {
 TEST(OpRegistry, DefaultValue) {
   paddle::framework::OpDesc op_desc;
   op_desc.set_type("cos_sim");
-  op_desc.add_inputs("aa");
-  op_desc.add_outputs("bb");
+  BuildVar("input", {"aa"}, op_desc.add_inputs());
+  BuildVar("output", {"bb"}, op_desc.add_outputs());
 
   ASSERT_TRUE(op_desc.IsInitialized());
 
-  std::shared_ptr<paddle::framework::OperatorBase> op =
-      paddle::framework::OpRegistry::CreateOp(op_desc);
+  auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   paddle::framework::Scope scope;
   paddle::platform::CPUDeviceContext dev_ctx;
   op->Run(scope, dev_ctx);
   ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
 }
 
-static void SetInputFormat(paddle::framework::OpDesc* desc) {
-  auto attr = desc->add_attrs();
-  attr->set_name("input_format");
-  attr->set_type(paddle::framework::INTS);
-  attr->mutable_ints()->Add(0);
-  attr->mutable_ints()->Add(1);
-}
-
 TEST(OpRegistry, CustomChecker) {
   paddle::framework::OpDesc op_desc;
   op_desc.set_type("my_test_op");
-  op_desc.add_inputs("ii");
-  op_desc.add_outputs("oo");
-  SetInputFormat(&op_desc);
+  BuildVar("input", {"ii"}, op_desc.add_inputs());
+  BuildVar("output", {"oo"}, op_desc.add_outputs());
 
   // attr 'test_attr' is not set
   bool caught = false;
@@ -169,7 +168,6 @@ TEST(OpRegistry, CustomChecker) {
   attr->set_name("test_attr");
   attr->set_type(paddle::framework::AttrType::INT);
   attr->set_i(4);
-  SetInputFormat(&op_desc);
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   paddle::platform::CPUDeviceContext dev_ctx;
   paddle::framework::Scope scope;

paddle/framework/operator.cc

@@ -12,9 +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. */
 
-#include <algorithm>
-
 #include "paddle/framework/operator.h"
+#include <algorithm>
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -34,83 +34,172 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
 #endif
 
 const std::string& OperatorBase::Input(const std::string& name) const {
-  PADDLE_ENFORCE_NOT_NULL(in_out_idxs_,
-                          "Input Output Indices could not be nullptr");
-  auto it = in_out_idxs_->find(name);
-  PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
-                 name);
-  if (attrs_.count("input_format") == 0) {
-    return inputs_.at((size_t)it->second);
-  } else {
-    const auto& input_format = GetAttr<std::vector<int>>("input_format");
-    int idx = input_format[it->second];
-    return inputs_.at((size_t)idx);
-  }
+  auto& ins = Inputs(name);
+  PADDLE_ENFORCE_EQ(ins.size(), 1UL,
+                    "Op %s input %s should contain only one variable", type_,
+                    name);
+  return ins[0];
 }
 
-std::vector<std::string> OperatorBase::Inputs(const std::string& name) const {
-  PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "IO Idx could not be nullptr");
-  auto input_format = GetAttr<std::vector<int>>("input_format");
-  auto offset = in_out_idxs_->at(name);
-  PADDLE_ENFORCE(input_format.at(static_cast<size_t>(offset) + 1) <=
-                     static_cast<int>(inputs_.size()),
-                 "Input Out Of Range");
-
-  return std::vector<std::string>{
-      inputs_.begin() + input_format.at(offset),
-      inputs_.begin() + input_format.at(offset + 1)};
+const std::vector<std::string>& OperatorBase::Inputs(
+    const std::string& name) const {
+  auto it = inputs_.find(name);
+  PADDLE_ENFORCE(it != inputs_.end(), "Op %s do not have input %s", type_,
+                 name);
+  return it->second;
 }
 
 const std::string& OperatorBase::Output(const std::string& name) const {
-  PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "InOut Indice could not be nullptr");
-  auto it = in_out_idxs_->find(name);
-  PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
-                 name);
-  if (attrs_.count("output_format") == 0) {
-    return outputs_.at((size_t)it->second);
-  } else {
-    const auto& output_format = GetAttr<std::vector<int>>("output_format");
-    int idx = output_format[it->second];
-    return outputs_.at((size_t)idx);
-  }
+  auto& outs = Outputs(name);
+  PADDLE_ENFORCE_EQ(outs.size(), 1UL,
+                    "Op %s output %s should contain only one variable", type_,
+                    name);
+  return outs[0];
 }
 
-std::vector<std::string> OperatorBase::Outputs(const std::string& name) const {
-  PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "InOut Indice could not be nullptr");
-  auto output_format = GetAttr<std::vector<int>>("output_format");
-  auto offset = in_out_idxs_->at(name);
-  PADDLE_ENFORCE(output_format.at(static_cast<size_t>(offset) + 1) <=
-                     static_cast<int>(outputs_.size()),
-                 "Output Out of Range");
-  return std::vector<std::string>{
-      outputs_.begin() + output_format.at(offset),
-      outputs_.begin() + output_format.at(offset + 1)};
+const std::vector<std::string>& OperatorBase::Outputs(
+    const std::string& name) const {
+  auto it = outputs_.find(name);
+  PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output %s", type_,
+                 name);
+  return it->second;
 }
 
 std::string OperatorBase::DebugString() const {
   std::stringstream ss;
-  ss << "Op(" << type_ << "), inputs:(";
-  for (size_t i = 0; i < inputs_.size(); ++i) {
-    ss << inputs_[i];
-    if (i != inputs_.size() - 1) {
+  ss << "Op(" << type_ << "), inputs:{";
+  for (auto it = inputs_.begin(); it != inputs_.end();) {
+    auto& input = *it;
+    ss << input.first << "[";
+    for (size_t i = 0; i < input.second.size(); ++i) {
+      ss << input.second[i];
+      if (i != input.second.size() - 1) {
+        ss << ", ";
+      }
+    }
+    ss << "]";
+    ++it;
+    if (it != inputs_.end()) {
       ss << ", ";
     }
   }
-  ss << "), outputs:(";
-  for (size_t i = 0; i < outputs_.size(); ++i) {
-    ss << outputs_[i];
-    if (i != outputs_.size() - 1) {
+  ss << "}, outputs:{";
+  for (auto it = outputs_.begin(); it != outputs_.end();) {
+    auto& output = *it;
+    ss << output.first << "[";
+    for (size_t i = 0; i < output.second.size(); ++i) {
+      ss << output.second[i];
+      if (i != output.second.size() - 1) {
+        ss << ", ";
+      }
+    }
+    ss << "]";
+    ++it;
+    if (it != outputs_.end()) {
       ss << ", ";
     }
   }
-  ss << ").";
+  ss << "}.";
   return ss.str();
 }
 
 void OperatorBase::Rename(const std::string& old_name,
                           const std::string& new_name) {
-  std::replace(inputs_.begin(), inputs_.end(), old_name, new_name);
-  std::replace(outputs_.begin(), outputs_.end(), old_name, 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 OperatorBase::VarNameMap& inputs,
+                           const OperatorBase::VarNameMap& outputs,
+                           const AttributeMap& attrs)
+    : type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs) {
+  static std::atomic<size_t> gUniqId(0UL);
+  for (auto& output : outputs_) {
+    for (auto& output_name : output.second) {
+      if (output_name == kTempVarName) {
+        output_name += type_;
+        output_name += "@";
+        output_name += std::to_string(gUniqId.fetch_add(1));
+      }
+    }
+  }
+}
+
+std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
+  std::vector<std::string> ret_val;
+  if (has_intermediate) {
+    // push all outputs into ret_val
+    for (auto& o : outputs_) {
+      ret_val.reserve(ret_val.size() + o.second.size());
+      ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
+    }
+    return ret_val;
+  }
+  auto it = OpRegistry::op_info_map().find(type_);
+  PADDLE_ENFORCE(
+      it != OpRegistry::op_info_map().end(),
+      "Operator %s not registered, cannot figure out intermediate outputs",
+      type_);
+  PADDLE_ENFORCE(
+      it->second.proto_ != nullptr,
+      "Operator %s has no OpProto, cannot figure out intermediate outputs",
+      type_);
+
+  // get all OpProto::Var for outputs
+  for (auto& o : it->second.proto_->outputs()) {
+    // ignore all intermediate output
+    if (o.intermediate()) continue;
+    auto out = outputs_.find(o.name());
+    if (out != outputs_.end()) {
+      ret_val.reserve(ret_val.size() + out->second.size());
+      ret_val.insert(ret_val.end(), out->second.begin(), out->second.end());
+    }
+  }
+  return ret_val;
+}
+
+void OpProtoAndCheckerMaker::Validate() {
+  validated_ = true;
+  CheckNoDuplicatedInOutAttrs();
+}
+
+OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
+    const std::string& name, const std::string& comment) {
+  auto* input = proto_->add_inputs();
+  input->set_name(name);
+  input->set_comment(comment);
+  return OpProtoAndCheckerMaker::VariableBuilder{input};
+}
+
+OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
+    const std::string& name, const std::string& comment) {
+  auto* output = proto_->add_outputs();
+  output->set_name(name);
+  output->set_comment(comment);
+  return OpProtoAndCheckerMaker::VariableBuilder{output};
+}
+
+void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
+  std::unordered_set<std::string> names;
+  auto checker = [&](const std::string& name) {
+    PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name);
+    names.insert(name);
+  };
+  for (auto& attr : proto_->attrs()) {
+    checker(attr.name());
+  }
+  for (auto& input : proto_->inputs()) {
+    checker(input.name());
+  }
+  for (auto& output : proto_->outputs()) {
+    checker(output.name());
+  }
 }
 
 }  // namespace framework

paddle/framework/operator.h

@@ -20,8 +20,7 @@ limitations under the License. */
 #include <vector>
 
 #include "paddle/framework/attribute.h"
-#include "paddle/framework/op_desc.pb.h"
-#include "paddle/framework/op_proto.pb.h"
+#include "paddle/framework/framework.pb.h"
 #include "paddle/framework/scope.h"
 #include "paddle/framework/tensor.h"
 #include "paddle/platform/device_context.h"
@@ -63,6 +62,11 @@ class ExecutionContext;
  */
 class OperatorBase {
  public:
+  using VarNameMap = std::map<std::string, std::vector<std::string>>;
+
+  OperatorBase(const std::string& type, const VarNameMap& inputs,
+               const VarNameMap& outputs, const AttributeMap& attrs);
+
   virtual ~OperatorBase() {}
 
   template <typename T>
@@ -74,10 +78,6 @@ class OperatorBase {
 
   virtual std::string DebugString() const;
 
-  /// Init will be called after CreateOperator, you can put some initialization
-  /// logic here.
-  virtual void Init() {}
-
   /// InferShape infer the size of Variables used by this Operator with
   /// information inside scope
   virtual void InferShape(const Scope& scope) const = 0;
@@ -93,31 +93,134 @@ class OperatorBase {
   /// rename inputs outputs name
   void Rename(const std::string& old_name, const std::string& new_name);
 
+  const VarNameMap& Inputs() const { return inputs_; }
+  const VarNameMap& Outputs() const { return outputs_; }
   //! Get a input with argument's name described in `op_proto`
   const std::string& Input(const std::string& name) const;
-
   //! Get a input which has multiple variables.
-  //! TODO add a vector_view to prevent memory copy.
-  std::vector<std::string> Inputs(const std::string& name) const;
+  const std::vector<std::string>& Inputs(const std::string& name) const;
+
   //! Get a output with argument's name described in `op_proto`
   const std::string& Output(const std::string& name) const;
   //! Get an output which has multiple variables.
   //! TODO add a vector_view to prevent memory copy.
-  std::vector<std::string> Outputs(const std::string& name) const;
+  const std::vector<std::string>& Outputs(const std::string& name) const;
 
- public:
+  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;
+
+ protected:
   std::string type_;
   // NOTE: in case of OpGrad, inputs_ contains:
-  // I (Inputs)
+  // I (Inputs)opear
   // O (Outputs)
   // OG (Output Gradients)
-  std::vector<std::string> inputs_;
+  VarNameMap inputs_;
+
   // NOTE: in case of OpGrad, outputs_ contains
   // IG (Inputs Gradients)
-  std::vector<std::string> outputs_;
+  VarNameMap outputs_;
   AttributeMap attrs_;
-  // store the arguments' offset described in op_desc.
-  std::shared_ptr<std::unordered_map<std::string, int>> in_out_idxs_;
+};
+
+// Macro for define a clone method.
+// If you are writing an kernel operator, `Clone` will be defined when you
+// register it. i.e. `Clone` method is not needed to define by yourself.
+#define DEFINE_OP_CLONE_METHOD(CLS)                       \
+  std::unique_ptr<OperatorBase> Clone() const final {     \
+    return std::unique_ptr<OperatorBase>(new CLS(*this)); \
+  }
+
+// Macro for define a default constructor for Operator.
+// You can also use
+//   using PARENT_CLASS::PARENT_CLASS;
+// to use parent's constructor.
+#define DEFINE_OP_CONSTRUCTOR(CLS, PARENT_CLS)                                 \
+  CLS(const std::string& type, const VarNameMap& inputs,                       \
+      const VarNameMap& outputs, const paddle::framework::AttributeMap& attrs) \
+      : PARENT_CLS(type, inputs, outputs, attrs) {}
+
+class NOP : public OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+  void InferShape(const Scope& scope) const override {}
+  void Run(const Scope& scope,
+           const platform::DeviceContext& dev_ctx) const override {}
+  std::unique_ptr<OperatorBase> Clone() const override {
+    return std::unique_ptr<OperatorBase>(new NOP(*this));
+  }
+};
+
+// this class not only make proto but also init attribute checkers.
+class OpProtoAndCheckerMaker {
+ public:
+  OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : proto_(proto), op_checker_(op_checker) {}
+
+  ~OpProtoAndCheckerMaker() {
+    PADDLE_ENFORCE(validated_, "should call Validate after build");
+  }
+
+  void Validate();
+
+ protected:
+  struct VariableBuilder {
+    OpProto::Var* var_;
+
+    VariableBuilder& AsDuplicable() {
+      var_->set_duplicable(true);
+      return *this;
+    }
+
+    VariableBuilder& AsIntermediate() {
+      var_->set_intermediate(true);
+      return *this;
+    }
+
+    VariableBuilder& NotInGradient() {
+      var_->set_not_in_gradient(true);
+      return *this;
+    }
+  };
+
+  VariableBuilder AddInput(const std::string& name, const std::string& comment);
+
+  VariableBuilder AddOutput(const std::string& name,
+                            const std::string& comment);
+
+  template <typename T>
+  TypedAttrChecker<T>& AddAttr(const std::string& name,
+                               const std::string& comment,
+                               bool generated = false) {
+    auto* attr = proto_->add_attrs();
+    attr->set_name(name);
+    attr->set_comment(comment);
+    attr->set_generated(generated);
+    attr->set_type(AttrTypeID<T>());
+    return op_checker_->AddAttrChecker<T>(name);
+  }
+
+  void AddComment(const std::string& comment) { proto_->set_comment(comment); }
+
+ private:
+  void CheckNoDuplicatedInOutAttrs();
+
+  OpProto* proto_;
+  OpAttrChecker* op_checker_;
+  bool validated_{false};
+};
+
+class NOPMaker : public OpProtoAndCheckerMaker {
+ public:
+  NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {}
 };
 
 class InferShapeContext {
@@ -125,16 +228,12 @@ class InferShapeContext {
   InferShapeContext(const OperatorBase& op, const Scope& scope)
       : op_(op), scope_(scope) {}
 
-  size_t InputSize() const { return op_.inputs_.size(); }
-
-  size_t OutputSize() const { return op_.outputs_.size(); }
-
-  const Variable* InputVar(const size_t index) const {
-    return scope_.FindVar(op_.inputs_.at(index));
+  size_t InputSize(const std::string& name) const {
+    return op_.Inputs(name).size();
   }
 
-  Variable* OutputVar(const size_t index) const {
-    return scope_.FindVar(op_.outputs_.at(index));
+  size_t OutputSize(const std::string& name) const {
+    return op_.Outputs(name).size();
   }
 
   const Variable* InputVar(const std::string& name) const {
@@ -166,27 +265,9 @@ class InferShapeContext {
     return res;
   }
 
-  template <typename T>
-  const T* Input(const size_t index) const {
-    auto var = InputVar(index);
-    PADDLE_ENFORCE_NOT_NULL(var, "Input(%d) should not be nullptr", index);
-    return &var->Get<T>();
-  }
-
-  template <typename T>
-  T* Output(const size_t index) const {
-    auto var = OutputVar(index);
-    PADDLE_ENFORCE_NOT_NULL(
-        var,
-        "Output(%d) not be nullptr, which means variable [%s] does not "
-        "exist in scope",
-        index, op_.outputs_[index]);
-    return var->GetMutable<T>();
-  }
-
   template <typename T>
   const T* Input(const std::string& name) const {
-    auto var = InputVar(name);
+    auto* var = InputVar(name);
     PADDLE_ENFORCE_NOT_NULL(var, "Input(%s) should not be nullptr", name);
     return &var->Get<T>();
   }
@@ -223,7 +304,7 @@ class InferShapeContext {
                    [&](const std::string& sub_name) {
                      auto var = scope_.FindVar(sub_name);
                      PADDLE_ENFORCE_NOT_NULL(
-                         var, "MultiOutput(%s:%s) should not be nullptr", name,
+                         var, "MultiOutput(%s:%s) should not be nullptr.", name,
                          sub_name);
                      return var->GetMutable<T>();
                    });
@@ -262,6 +343,10 @@ class ExecutionContext : public InferShapeContext {
 
   platform::Place GetPlace() const { return device_context_->GetPlace(); }
 
+  const platform::DeviceContext* device_context() const {
+    return device_context_;
+  }
+
   const platform::DeviceContext* device_context_;
 };
 
@@ -304,6 +389,10 @@ class OperatorWithKernel : public OperatorBase {
   using OpKernelMap =
       std::unordered_map<OpKernelKey, std::unique_ptr<OpKernel>, OpKernelHash>;
 
+  OperatorWithKernel(const std::string& type, const VarNameMap& inputs,
+                     const VarNameMap& outputs, const AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
   void InferShape(const Scope& scope) const override {
     InferShape(InferShapeContext(*this, scope));
   }

paddle/framework/operator_test.cc

@@ -23,20 +23,22 @@ static int op_run_num = 0;
 
 class OpWithoutKernelTest : public OperatorBase {
  public:
-  void Init() override { x = 1; }
+  OpWithoutKernelTest(const std::string& type, const VarNameMap& inputs,
+                      const VarNameMap& outputs, const AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs), x(1) {}
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
-    op_run_num++;
-    ASSERT_EQ((int)inputs_.size(), 1);
-    ASSERT_EQ((int)outputs_.size(), 1);
-    ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
+    ++op_run_num;
+    ASSERT_EQ(static_cast<int>(inputs_.size()), 1);
+    ASSERT_EQ(static_cast<int>(outputs_.size()), 1);
+    ASSERT_EQ(scope.FindVar(inputs_.at("input")[0]), nullptr);
     ASSERT_EQ(x, 1);
-    ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
+    ASSERT_NE(scope.FindVar(outputs_.at("output")[0]), nullptr);
   }
 
  public:
-  float x = 0;
+  int x{0};
 };
 
 class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
@@ -54,14 +56,25 @@ class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_OP(test_operator, paddle::framework::OpWithoutKernelTest,
-            paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
+static void BuildVar(const std::string& param_name,
+                     std::initializer_list<const char*> arguments,
+                     paddle::framework::OpDesc::Var* var) {
+  var->set_parameter(param_name);
+  for (auto& arg_name : arguments) {
+    *var->mutable_arguments()->Add() = arg_name;
+  }
+}
+
+REGISTER_OP_WITHOUT_GRADIENT(
+    test_operator, paddle::framework::OpWithoutKernelTest,
+    paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
 
 TEST(OperatorBase, all) {
   paddle::framework::OpDesc op_desc;
   op_desc.set_type("test_operator");
-  *op_desc.mutable_inputs()->Add() = "IN1";
-  *op_desc.mutable_outputs()->Add() = "OUT1";
+  BuildVar("input", {"IN1"}, op_desc.add_inputs());
+  BuildVar("output", {"OUT1"}, op_desc.add_outputs());
+
   auto attr = op_desc.mutable_attrs()->Add();
   attr->set_name("scale");
   attr->set_type(paddle::framework::AttrType::FLOAT);
@@ -97,6 +110,9 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 static int cpu_kernel_run_num = 0;
 
 class OpWithKernelTest : public OperatorWithKernel {
+ public:
+  using OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext& ctx) const override {}
 };
@@ -113,33 +129,15 @@ class CPUKernelTest : public OpKernel {
   }
 };
 
-// multiple inputs test
-class OperatorMultiInputsTest : public OperatorBase {
- public:
-  void Init() override { x = 1; }
-  void InferShape(const Scope& scope) const override {}
-  void Run(const Scope& scope,
-           const platform::DeviceContext& dev_ctx) const override {
-    ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
-    ASSERT_EQ(x, 1);
-    ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
-    ASSERT_EQ(Input("x"), "IN1");
-    ASSERT_EQ(Input("y"), "OUT1");
-  }
-
- public:
-  float x = 0;
-};
-
 class OpKernelTestMultiInputsProtoAndCheckerMaker
     : public OpProtoAndCheckerMaker {
  public:
   OpKernelTestMultiInputsProtoAndCheckerMaker(OpProto* proto,
                                               OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("xs", "inputs of test op").SetMultiple();
+    AddInput("xs", "inputs of test op").AsDuplicable();
     AddInput("k", "input of test op");
-    AddOutput("ys", "outputs of test op").SetMultiple();
+    AddOutput("ys", "outputs of test op").AsDuplicable();
     AddAttr<float>("scale", "scale of cosine op")
         .SetDefault(1.0)
         .LargerThan(0.0);
@@ -187,8 +185,9 @@ class CPUKernalMultiInputsTest : public OpKernel {
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_OP(op_with_kernel, paddle::framework::OpWithKernelTest,
-            paddle::framework::OpKernelTestProtoAndCheckerMaker);
+REGISTER_OP_WITHOUT_GRADIENT(
+    op_with_kernel, paddle::framework::OpWithKernelTest,
+    paddle::framework::OpKernelTestProtoAndCheckerMaker);
 REGISTER_OP_CPU_KERNEL(op_with_kernel,
                        paddle::framework::CPUKernelTest<float, float>);
 
@@ -196,8 +195,9 @@ REGISTER_OP_CPU_KERNEL(op_with_kernel,
 TEST(OpKernel, all) {
   paddle::framework::OpDesc op_desc;
   op_desc.set_type("op_with_kernel");
-  *op_desc.mutable_inputs()->Add() = "IN1";
-  *op_desc.mutable_outputs()->Add() = "OUT1";
+  BuildVar("x", {"IN1"}, op_desc.add_inputs());
+  BuildVar("y", {"OUT1"}, op_desc.add_outputs());
+
   auto attr = op_desc.mutable_attrs()->Add();
   attr->set_name("scale");
   attr->set_type(paddle::framework::AttrType::FLOAT);
@@ -212,8 +212,9 @@ TEST(OpKernel, all) {
   ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
 }
 
-REGISTER_OP(op_multi_inputs_with_kernel, paddle::framework::OpWithKernelTest,
-            paddle::framework::OpKernelTestMultiInputsProtoAndCheckerMaker);
+REGISTER_OP_WITHOUT_GRADIENT(
+    op_multi_inputs_with_kernel, paddle::framework::OpWithKernelTest,
+    paddle::framework::OpKernelTestMultiInputsProtoAndCheckerMaker);
 REGISTER_OP_CPU_KERNEL(op_multi_inputs_with_kernel,
                        paddle::framework::CPUKernalMultiInputsTest);
 
@@ -223,32 +224,15 @@ TEST(OpKernel, multi_inputs) {
 
   OpDesc op_desc;
   op_desc.set_type("op_multi_inputs_with_kernel");
-  *op_desc.mutable_inputs()->Add() = "x0";
-  *op_desc.mutable_inputs()->Add() = "x1";
-  *op_desc.mutable_inputs()->Add() = "x2";
-  *op_desc.mutable_inputs()->Add() = "k0";
-  *op_desc.mutable_outputs()->Add() = "y0";
-  *op_desc.mutable_outputs()->Add() = "y1";
+  BuildVar("xs", {"x0", "x1", "x2"}, op_desc.add_inputs());
+  BuildVar("k", {"k0"}, op_desc.add_inputs());
+  BuildVar("ys", {"y0", "y1"}, op_desc.add_outputs());
+
   auto attr = op_desc.mutable_attrs()->Add();
   attr->set_name("scale");
   attr->set_type(paddle::framework::AttrType::FLOAT);
   attr->set_f(3.14);
 
-  auto attr0 = op_desc.mutable_attrs()->Add();
-  attr0->set_name("input_format");
-  attr0->set_type(paddle::framework::AttrType::INTS);
-  auto input_format = attr0->mutable_ints();
-  input_format->Add(0);  // x0
-  input_format->Add(3);  // k
-  input_format->Add(4);  // end
-
-  auto attr1 = op_desc.mutable_attrs()->Add();
-  attr1->set_name("output_format");
-  attr1->set_type(paddle::framework::AttrType::INTS);
-  auto output_format = attr1->mutable_ints();
-  output_format->Add(0);  // y0
-  output_format->Add(2);  // y1
-
   paddle::platform::CPUDeviceContext cpu_device_context;
   paddle::framework::Scope scope;
   scope.NewVar("x0")->GetMutable<Tensor>();
@@ -261,3 +245,21 @@ TEST(OpKernel, multi_inputs) {
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   op->Run(scope, cpu_device_context);
 }
+
+class OperatorClone : public paddle::framework::OperatorBase {
+ public:
+  DEFINE_OP_CLONE_METHOD(OperatorClone);
+  OperatorClone(const std::string& type, const VarNameMap& inputs,
+                const VarNameMap& outputs,
+                const paddle::framework::AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void InferShape(const paddle::framework::Scope& scope) const override {}
+  void Run(const paddle::framework::Scope& scope,
+           const paddle::platform::DeviceContext& dev_ctx) const override {}
+};
+
+TEST(Operator, Clone) {
+  OperatorClone a("ABC", {}, {}, {});
+  auto b = a.Clone();
+  ASSERT_EQ(a.Type(), b->Type());
+}
\ No newline at end of file

paddle/framework/pybind.cc

@@ -20,6 +20,7 @@ limitations under the License. */
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/tensor_py.h"
 #include "paddle/operators/net_op.h"
+#include "paddle/operators/recurrent_op.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/place.h"
 #include "paddle/string/to_string.h"
@@ -30,8 +31,7 @@ limitations under the License. */
 namespace py = pybind11;
 
 USE_OP(add_two);
-USE_OP_CPU(onehot_cross_entropy);
-USE_OP_WITHOUT_KERNEL(fc);
+USE_OP(onehot_cross_entropy);
 USE_OP(sgd);
 USE_OP(mul);
 USE_OP(mean);
@@ -39,7 +39,7 @@ USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP(fill_zeros_like);
-USE_OP_WITHOUT_KERNEL(recurrent_op);
+USE_OP_ITSELF(recurrent_op);
 USE_OP(gaussian_random);
 USE_OP(uniform_random);
 
@@ -48,41 +48,6 @@ namespace framework {
 
 using Tensor = framework::Tensor;
 
-template <typename ClassType>
-void ExposeOperator(ClassType &m) {
-  m.def("infer_shape", &ClassType::type::InferShape)
-      .def("run", &ClassType::type::Run)
-      .def("type",
-           [](const typename ClassType::type &op) -> std::string {
-             return op.type_;
-           })
-      .def("outputs",
-           [](const typename ClassType::type &op) -> std::vector<std::string> {
-             return op.outputs_;
-           })
-      .def("inputs",
-           [](const typename ClassType::type &op) -> std::vector<std::string> {
-             return op.inputs_;
-           })
-      .def("support_gpu", &ClassType::type::SupportGPU)
-      .def("temp_outputs",
-           [](const typename ClassType::type &op) -> std::vector<std::string> {
-             auto iter = op.attrs_.find("temporary_index");
-             std::vector<std::string> ret;
-             if (iter == op.attrs_.end()) {
-               return ret;
-             } else {
-               auto tmp_idx = boost::get<std::vector<int>>(iter->second);
-               ret.reserve(tmp_idx.size());
-               for (auto &index : tmp_idx) {
-                 ret.push_back(op.outputs_.at(index));
-               }
-               return ret;
-             }
-           })
-      .def("__str__", &ClassType::type::DebugString);
-}
-
 static size_t UniqueIntegerGenerator() {
   static std::atomic<size_t> generator;
   return generator.fetch_add(1);
@@ -173,13 +138,16 @@ All parameter, weight, gradient are variables in Paddle.
   //! @note: Be careful! PyBind will return std::string as an unicode, not
   //! Python str. If you want a str object, you should cast them in Python.
   m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
-    auto &protos = OpRegistry::protos();
+    auto &op_info_map = OpRegistry::op_info_map();
     std::vector<py::bytes> ret_values;
-    for (auto it = protos.begin(); it != protos.end(); ++it) {
-      PADDLE_ENFORCE(it->second.IsInitialized(),
-                     "OpProto must all be initialized");
+    for (auto it = op_info_map.begin(); it != op_info_map.end(); ++it) {
+      const OpProto *proto = it->second.proto_;
+      if (proto == nullptr) {
+        continue;
+      }
+      PADDLE_ENFORCE(proto->IsInitialized(), "OpProto must all be initialized");
       std::string str;
-      PADDLE_ENFORCE(it->second.SerializeToString(&str),
+      PADDLE_ENFORCE(proto->SerializeToString(&str),
                      "Serialize OpProto Error. This could be a bug of Paddle.");
       ret_values.push_back(py::bytes(str));
     }
@@ -216,47 +184,69 @@ All parameter, weight, gradient are variables in Paddle.
       .def(py::init<>())
       .def("__str__", string::to_string<const platform::CPUPlace &>);
 
-  py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
-      m, "Operator");
-
-  operator_base.def_static("create", [](py::bytes protobin) {
-    OpDesc desc;
-    PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
-                   "Cannot parse user input to OpDesc");
-    PADDLE_ENFORCE(desc.IsInitialized(),
-                   "User OpDesc is not initialized, reason %s",
-                   desc.InitializationErrorString());
-    return OpRegistry::CreateOp(desc);
-  });
-
-  operator_base.def("backward",
-                    [](const OperatorBase &forwardOp,
-                       const std::unordered_set<std::string> &no_grad_vars) {
-                      return Backward(forwardOp, no_grad_vars);
-                    });
-
-  ExposeOperator(operator_base);
-
-  py::class_<operators::NetOp, std::shared_ptr<operators::NetOp>> net(m, "Net");
-
-  net.def_static("create",
-                 []() -> std::shared_ptr<operators::NetOp> {
-                   auto retv = std::make_shared<operators::NetOp>();
-                   retv->type_ = "plain_net";
-                   return retv;
-                 })
-      .def("add_op", &operators::NetOp::AddOp)
-      .def("add_op",
-           [](operators::NetOp &self,
-              const std::shared_ptr<operators::NetOp> &net) -> void {
-             self.AddOp(std::static_pointer_cast<OperatorBase>(net));
+  py::class_<OperatorBase>(m, "Operator")
+      .def_static("create",
+                  [](py::bytes protobin) {
+                    OpDesc desc;
+                    PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
+                                   "Cannot parse user input to OpDesc");
+                    PADDLE_ENFORCE(desc.IsInitialized(),
+                                   "User OpDesc is not initialized, reason %s",
+                                   desc.InitializationErrorString());
+                    return OpRegistry::CreateOp(desc);
+                  })
+      .def("backward",
+           [](const OperatorBase &forwardOp,
+              const std::unordered_set<std::string> &no_grad_vars) {
+             return Backward(forwardOp, no_grad_vars).release();
            })
+      .def("infer_shape", &OperatorBase::InferShape)
+      .def("run", &OperatorBase::Run)
+      .def("type",
+           [](const OperatorBase &op) -> std::string { return op.Type(); })
+      .def("outputs",
+           [](const OperatorBase &op)
+               -> std::map<std::string, std::vector<std::string>> {
+                 return op.Outputs();
+               })
+      .def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
+      .def("__str__", &OperatorBase::DebugString)
+      .def("no_intermediate_outputs",
+           [](const OperatorBase &op) { return op.OutputVars(false); })
+      .def("support_gpu", &OperatorBase::SupportGPU);
+
+  py::class_<operators::NetOp, OperatorBase>(m, "Net")
+      .def_static("create",
+                  []() -> operators::NetOp * {
+                    auto *retv = new operators::NetOp;
+                    retv->SetType("plain_net");
+                    return retv;
+                  })
+      .def("append_op", [](operators::NetOp &self,
+                           const OperatorBase &op) { self.AppendOp(op); })
       .def("complete_add_op", &operators::NetOp::CompleteAddOp)
       .def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
         self->CompleteAddOp();
       });
 
-  ExposeOperator(net);
+  // recurrent_op
+  py::class_<operators::RecurrentOp, OperatorBase>(m, "RecurrentOp")
+      .def_static(
+          "create",
+          [](py::bytes protobin) -> operators::RecurrentOp * {
+            OpDesc desc;
+            PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
+                           "Cannot parse user input to OpDesc");
+            PADDLE_ENFORCE(desc.IsInitialized(),
+                           "User OpDesc is not initialized, reason %s",
+                           desc.InitializationErrorString());
+            auto rnn_op = OpRegistry::CreateOp(desc);
+            return static_cast<operators::RecurrentOp *>(rnn_op.release());
+          })
+      .def("set_stepnet", [](operators::RecurrentOp &self,
+                             const operators::NetOp &net) -> void {
+        self.set_stepnet(net.Clone());
+      });
 
   m.def("unique_integer", UniqueIntegerGenerator);
 

paddle/framework/tensor.h

@@ -105,6 +105,8 @@ class Tensor {
   template <typename T>
   inline Tensor Slice(const int& begin_idx, const int& end_idx) const;
 
+  platform::Place place() const { return holder_->place(); }
+
  private:
   template <typename T>
   inline void check_memory_size() const;

paddle/function/CMakeLists.txt

@@ -4,6 +4,10 @@ file(GLOB cpp_files . *Op.cpp)
 list(APPEND h_files Function.h)
 list(APPEND cpp_files Function.cpp)
 list(APPEND cpp_files BufferArg.cpp)
+list(APPEND cpp_files GemmFunctor.cpp)
+if(USE_EIGEN_FOR_BLAS)
+  list(APPEND cpp_files EigenGemm.cpp)
+endif(USE_EIGEN_FOR_BLAS)
 
 if(WITH_GPU)
     file(GLOB cu_files . *OpGpu.cu)
@@ -38,10 +42,11 @@ if(WITH_GPU)
     add_simple_unittest(RowConvOpTest)
     add_simple_unittest(BlockExpandOpTest)
     add_simple_unittest(CropOpTest)
+    add_simple_unittest(DepthwiseConvOpTest)
 endif()
 
-add_simple_unittest(ConvOpTest)
 add_simple_unittest(Im2ColTest)
+add_simple_unittest(GemmConvOpTest)
 endif()
 
 add_style_check_target(paddle_function ${h_files})

paddle/function/ConvOpTest.cpp

-/* 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 <gtest/gtest.h>
-#include <memory>
-#include "Function.h"
-#include "FunctionTest.h"
-
-namespace paddle {
-
-enum TestType {
-  kForwardTest = 0,
-  kBackwardInputTest = 1,
-  kBackwardFilterTest = 2,
-};
-
-template <DeviceType DType1, DeviceType DType2>
-class ConvolutionTest {
-public:
-  ConvolutionTest(const std::string& conv1,
-                  const std::string& conv2,
-                  TestType type,
-                  bool useGroups = true,
-                  std::string algo = "auto") {
-    for (size_t batchSize : {1, 32}) {
-      for (size_t inputSize : {7, 14, 54}) {
-        for (size_t filterSize : {1, 3, 5}) {
-          for (size_t inputChannels : {3, 64}) {
-            for (size_t outputChannels : {3, 64}) {
-              if (inputChannels > outputChannels) break;
-              size_t groups;
-              if (!useGroups) {
-                groups = 1;
-              } else {
-                if (outputChannels % inputChannels != 0) continue;
-                groups = inputChannels;
-              }
-
-              for (size_t stride : {1, 2}) {
-                for (size_t padding : {0, 1}) {
-                  if (padding >= filterSize) break;
-                  size_t outputSize =
-                      (inputSize - filterSize + 2 * padding + stride) / stride;
-                  VLOG(3) << " batchSize=" << batchSize
-                          << " inputChannels=" << inputChannels
-                          << " inputHeight=" << inputSize
-                          << " inputWidth=" << inputSize
-                          << " outputChannels=" << outputChannels
-                          << " filterHeight=" << filterSize
-                          << " filterWidth=" << filterSize
-                          << " outputHeight=" << outputSize
-                          << " outputWidth=" << outputSize
-                          << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DType1, DType2> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", groups)
-                          .set("algo", algo));
-
-                  TensorShape input{
-                      batchSize, inputChannels, inputSize, inputSize};
-
-                  TensorShape filter;
-                  if (groups > 1)
-                    filter = TensorShape({groups,
-                                          outputChannels / groups,
-                                          inputChannels / groups,
-                                          filterSize,
-                                          filterSize});
-                  else
-                    filter = TensorShape({outputChannels,
-                                          inputChannels,
-                                          filterSize,
-                                          filterSize});
-                  TensorShape output{
-                      batchSize, outputChannels, outputSize, outputSize};
-
-                  if (type == kForwardTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.run();
-                  } else if (type == kBackwardInputTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
-                    test.run();
-                  } else if (type == kBackwardFilterTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
-                                    ADD_TO);
-                    test.run();
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-// Mainly used to test cases where the height and width (input, filter)
-// are not equal.
-template <DeviceType DType1, DeviceType DType2>
-class ConvolutionTest2 {
-public:
-  ConvolutionTest2(const std::string& conv1,
-                   const std::string& conv2,
-                   TestType type,
-                   bool useGroups = true,
-                   std::string algo = "auto") {
-    for (size_t batchSize : {16}) {
-      for (size_t inputHeight : {7, 31}) {
-        for (size_t inputWidth : {10, 54}) {
-          for (size_t filterHeight : {1, 5}) {
-            for (size_t filterWidth : {3, 7}) {
-              for (size_t inputChannels : {7}) {
-                for (size_t outputChannels : {7}) {
-                  size_t groups;
-                  if (!useGroups) {
-                    groups = 1;
-                  } else {
-                    if (outputChannels % inputChannels != 0) continue;
-                    groups = inputChannels;
-                  }
-
-                  size_t stride = 1;
-                  size_t padding = 0;
-                  size_t outputHeight =
-                      (inputHeight - filterHeight + 2 * padding + stride) /
-                      stride;
-                  size_t outputWidth =
-                      (inputWidth - filterWidth + 2 * padding + stride) /
-                      stride;
-                  VLOG(3) << " batchSize=" << batchSize
-                          << " inputChannels=" << inputChannels
-                          << " inputHeight=" << inputHeight
-                          << " inputWidth=" << inputWidth
-                          << " outputChannels=" << outputChannels
-                          << " filterHeight=" << filterHeight
-                          << " filterWidth=" << filterWidth
-                          << " outputHeight=" << outputHeight
-                          << " outputWidth=" << outputWidth
-                          << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DType1, DType2> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", groups)
-                          .set("algo", algo));
-
-                  TensorShape input{
-                      batchSize, inputChannels, inputHeight, inputWidth};
-
-                  TensorShape filter;
-                  if (groups > 1)
-                    filter = TensorShape({groups,
-                                          outputChannels / groups,
-                                          inputChannels / groups,
-                                          filterHeight,
-                                          filterWidth});
-                  else
-                    filter = TensorShape({outputChannels,
-                                          inputChannels,
-                                          filterHeight,
-                                          filterWidth});
-                  TensorShape output{
-                      batchSize, outputChannels, outputHeight, outputWidth};
-
-                  if (type == kForwardTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.run();
-                  } else if (type == kBackwardInputTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
-                    test.run();
-                  } else if (type == kBackwardFilterTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
-                                    ADD_TO);
-                    test.run();
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-// ======Start Convolution TEST======
-
-TEST(Forward, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
-      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
-      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
-}
-
-#ifndef PADDLE_ONLY_CPU
-TEST(Forward, GEMM2) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
-}
-
-TEST(BackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      kBackwardInputTest,
-      false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      kBackwardInputTest,
-      false);
-}
-
-TEST(BackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      kBackwardFilterTest,
-      false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      kBackwardFilterTest,
-      false);
-}
-#endif
-// ======End Convolution TEST======
-
-// ======Start DepthwiseConvolution TEST======
-
-// TODO(zhaolong) The depthwise convolution cpu test will be added when the cpu
-// version of depthwiseConv is implemented.
-
-#ifndef PADDLE_ONLY_CPU
-
-TEST(DepthwiseConvForward, GEMM2) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
-}
-
-TEST(DepthwiseConvBackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "DepthwiseConvGradInput-GPU",
-      kBackwardInputTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "DepthwiseConvGradInput-GPU",
-      kBackwardInputTest);
-}
-
-TEST(DepthwiseConvBackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "DepthwiseConvGradFilter-GPU",
-      kBackwardFilterTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "DepthwiseConvGradFilter-GPU",
-      kBackwardFilterTest);
-}
-
-#endif
-// ======End DepthwiseConvolution TEST======
-
-}  // namespace paddle

paddle/function/ConvOpTest.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. */
+
+#include "FunctionTest.h"
+
+namespace paddle {
+
+template <DeviceType DType1, DeviceType DType2>
+void forward(Compare2Function<DType1, DType2>& test,
+             const TensorShape& input,
+             const TensorShape& filter,
+             const TensorShape& output) {
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
+  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
+  test.run();
+}
+
+template <DeviceType DType1, DeviceType DType2>
+void backward_input(Compare2Function<DType1, DType2>& test,
+                    const TensorShape& input,
+                    const TensorShape& filter,
+                    const TensorShape& output) {
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
+  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
+  test.run();
+}
+
+template <DeviceType DType1, DeviceType DType2>
+void backward_filter(Compare2Function<DType1, DType2>& test,
+                     const TensorShape& input,
+                     const TensorShape& filter,
+                     const TensorShape& output) {
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
+  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter), ADD_TO);
+  test.run();
+}
+
+template <DeviceType DType1, DeviceType DType2>
+using Function = void (*)(Compare2Function<DType1, DType2>& test,
+                          const TensorShape& input,
+                          const TensorShape& filter,
+                          const TensorShape& output);
+
+/**
+ * \brief A basic convolution function test interface.
+ *
+ * \param conv1         type name of convolution function 1.
+ * \param conv2         type name of convolution function 2.
+ * \param function      test function, can be one of the forward, backward_input
+ *                      backward_filter function.
+ * Example:
+ * 1. Compare GemmConv's CPU and GPU implementation:
+ *   Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+ *      "GemmConv-CPU", "GemmConv-GPU", forward);
+ */
+template <DeviceType DType1, DeviceType DType2>
+void Convolution(const std::string& conv1,
+                 const std::string& conv2,
+                 Function<DType1, DType2> function) {
+  for (size_t batchSize : {1, 5}) {
+    for (size_t inputSize : {7, 14, 31}) {
+      for (size_t filterSize : {1, 3, 5}) {
+        for (size_t inputChannels : {3, 16}) {
+          for (size_t outputChannels : {3, 16}) {
+            if (outputChannels < inputChannels) continue;
+            for (size_t stride : {1, 2}) {
+              for (size_t padding : {0, 1}) {
+                if (padding >= filterSize) break;
+
+                // NNPACK only supports stride = 1 if batchSize > 1
+                if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
+                    batchSize > 1 && stride > 1)
+                  break;
+
+                size_t outputSize =
+                    (inputSize - filterSize + 2 * padding + stride) / stride;
+                VLOG(3) << " batchSize=" << batchSize
+                        << " inputChannels=" << inputChannels
+                        << " inputHeight=" << inputSize
+                        << " inputWidth=" << inputSize
+                        << " outputChannels=" << outputChannels
+                        << " filterHeight=" << filterSize
+                        << " filterWidth=" << filterSize
+                        << " outputHeight=" << outputSize
+                        << " outputWidth=" << outputSize << " stride=" << stride
+                        << " padding=" << padding;
+
+                std::vector<size_t> paddings = {padding, padding};
+                std::vector<size_t> strides = {stride, stride};
+                Compare2Function<DType1, DType2> test(
+                    conv1,
+                    conv2,
+                    FuncConfig()
+                        .set("paddings", paddings)
+                        .set("strides", strides)
+                        .set("groups", (size_t)1)
+                        .set("algo", (std::string) "auto"));
+
+                TensorShape input{
+                    batchSize, inputChannels, inputSize, inputSize};
+                TensorShape filter{
+                    outputChannels, inputChannels, filterSize, filterSize};
+                TensorShape output{
+                    batchSize, outputChannels, outputSize, outputSize};
+
+                function(test, input, filter, output);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+/**
+ * \brief A convolution function test interface for
+ *        image height is not equal image width.
+ */
+template <DeviceType DType1, DeviceType DType2>
+void Convolution2(const std::string& conv1,
+                  const std::string& conv2,
+                  Function<DType1, DType2> function) {
+  for (size_t batchSize : {4}) {
+    for (size_t inputHeight : {7, 31}) {
+      for (size_t inputWidth : {10, 54}) {
+        for (size_t filterHeight : {1, 5}) {
+          for (size_t filterWidth : {3, 7}) {
+            for (size_t inputChannels : {7}) {
+              for (size_t outputChannels : {7}) {
+                size_t stride = 1;
+                size_t padding = 0;
+                size_t outputHeight =
+                    (inputHeight - filterHeight + 2 * padding + stride) /
+                    stride;
+                size_t outputWidth =
+                    (inputWidth - filterWidth + 2 * padding + stride) / stride;
+                VLOG(3) << " batchSize=" << batchSize
+                        << " inputChannels=" << inputChannels
+                        << " inputHeight=" << inputHeight
+                        << " inputWidth=" << inputWidth
+                        << " outputChannels=" << outputChannels
+                        << " filterHeight=" << filterHeight
+                        << " filterWidth=" << filterWidth
+                        << " outputHeight=" << outputHeight
+                        << " outputWidth=" << outputWidth
+                        << " stride=" << stride << " padding=" << padding;
+
+                std::vector<size_t> paddings = {padding, padding};
+                std::vector<size_t> strides = {stride, stride};
+                Compare2Function<DType1, DType2> test(
+                    conv1,
+                    conv2,
+                    FuncConfig()
+                        .set("paddings", paddings)
+                        .set("strides", strides)
+                        .set("groups", (size_t)1)
+                        .set("algo", (std::string) "auto"));
+
+                TensorShape input{
+                    batchSize, inputChannels, inputHeight, inputWidth};
+                TensorShape filter{
+                    outputChannels, inputChannels, filterHeight, filterWidth};
+                TensorShape output{
+                    batchSize, outputChannels, outputHeight, outputWidth};
+
+                function(test, input, filter, output);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+/**
+ * \brief A convolution function test interface for depthwise convolution.
+ */
+template <DeviceType DType1, DeviceType DType2>
+void DepthwiseConvolution(const std::string& conv1,
+                          const std::string& conv2,
+                          Function<DType1, DType2> function) {
+  for (size_t batchSize : {1, 32}) {
+    for (size_t inputSize : {7, 14, 54}) {
+      for (size_t filterSize : {3, 4}) {
+        for (size_t inputChannels : {32}) {
+          for (size_t outputChannels : {32, 64}) {
+            for (size_t stride : {1, 2}) {
+              for (size_t padding : {0, 1}) {
+                // NNPACK only supports stride = 1 if batchSize > 1,
+                // and there has some bug when batchSize > 1 and groups != 1
+                if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
+                    batchSize > 1)
+                  break;
+
+                size_t outputSize =
+                    (inputSize - filterSize + 2 * padding + stride) / stride;
+                VLOG(3) << " batchSize=" << batchSize
+                        << " inputChannels=" << inputChannels
+                        << " inputHeight=" << inputSize
+                        << " inputWidth=" << inputSize
+                        << " outputChannels=" << outputChannels
+                        << " filterHeight=" << filterSize
+                        << " filterWidth=" << filterSize
+                        << " outputHeight=" << outputSize
+                        << " outputWidth=" << outputSize << " stride=" << stride
+                        << " padding=" << padding;
+
+                std::vector<size_t> paddings = {padding, padding};
+                std::vector<size_t> strides = {stride, stride};
+                size_t groups = inputChannels;
+                Compare2Function<DType1, DType2> test(
+                    conv1,
+                    conv2,
+                    FuncConfig()
+                        .set("paddings", paddings)
+                        .set("strides", strides)
+                        .set("groups", groups)
+                        .set("algo", (std::string) "auto"));
+
+                TensorShape input{
+                    batchSize, inputChannels, inputSize, inputSize};
+                TensorShape filter{groups,
+                                   outputChannels / groups,
+                                   inputChannels / groups,
+                                   filterSize,
+                                   filterSize};
+                TensorShape output{
+                    batchSize, outputChannels, outputSize, outputSize};
+
+                function(test, input, filter, output);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+}  // namespace paddle

paddle/function/DepthwiseConvOp.cpp

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #include "DepthwiseConvOp.h"
 #include "ConvOp.h"
-#include "GemmFunctor.h"
 
 namespace paddle {
 

paddle/function/DepthwiseConvOpGpu.cu

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "DepthwiseConvOp.h"
-#include "GemmFunctor.h"
 #include "paddle/math/BaseMatrix.h"
 
 namespace paddle {

paddle/framework/op_desc_test.cc → paddle/function/DepthwiseConvOpTest.cpp

@@ -13,23 +13,25 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include <paddle/framework/op_desc.pb.h>
-
-TEST(OpDesc, Create) {
-  paddle::framework::OpDesc op_desc;
-  op_desc.set_type("add");
-  op_desc.add_inputs("X");
-  op_desc.add_inputs("Y");
-  op_desc.add_outputs("Z");
-
-  auto attr = op_desc.mutable_attrs()->Add();
-  attr->set_type(paddle::framework::AttrType::FLOAT);
-  attr->set_f(3.14);
-
-  // required field name is not set, so IsInitialized should be false.
-  ASSERT_FALSE(op_desc.IsInitialized());
-
-  attr->set_name("add");
-  // after all required fields are set, IsInitialized should be true now.
-  ASSERT_TRUE(op_desc.IsInitialized());
-}
\ No newline at end of file
+#include "ConvOpTest.h"
+
+namespace paddle {
+
+#ifndef PADDLE_ONLY_CPU
+TEST(DepthwiseConv, Forward) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConv-CPU", "DepthwiseConv-GPU", forward);
+}
+
+TEST(DepthwiseConv, BackwardInput) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradInput-CPU", "DepthwiseConvGradInput-GPU", backward_input);
+}
+
+TEST(DepthwiseConv, BackwardFilter) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradFilter-CPU", "DepthwiseConvGradFilter-GPU", backward_filter);
+}
+#endif
+
+}  // namespace paddle

paddle/function/EigenGemm.cpp

+/* 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 <glog/logging.h>
+#include "unsupported/Eigen/CXX11/Tensor"
+
+namespace paddle {
+
+template <class T>
+struct EigenBlasGemm {
+  typedef Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, int>,
+                           Eigen::Aligned>
+      Matrix;
+
+  static void compute(const bool transA,
+                      const bool transB,
+                      const int M,
+                      const int N,
+                      const int K,
+                      const T alpha,
+                      const T* A,
+                      const int lda,
+                      const T* B,
+                      const int ldb,
+                      const T beta,
+                      T* C,
+                      const int ldc) {
+    Eigen::array<int, 2> sizeA;
+    if (transA) {
+      sizeA[0] = K;
+      sizeA[1] = M;
+      CHECK_EQ(M, lda);
+    } else {
+      sizeA[0] = M;
+      sizeA[1] = K;
+      CHECK_EQ(K, lda);
+    }
+    Eigen::array<int, 2> sizeB;
+    if (transB) {
+      sizeB[0] = N;
+      sizeB[1] = K;
+      CHECK_EQ(K, ldb);
+    } else {
+      sizeB[0] = K;
+      sizeB[1] = N;
+      CHECK_EQ(N, ldb);
+    }
+    Eigen::array<int, 2> sizeC;
+    sizeC[0] = M;
+    sizeC[1] = N;
+    CHECK_EQ(N, ldc);
+
+    const Matrix a(const_cast<T*>(A), sizeA);
+    const Matrix b(const_cast<T*>(B), sizeB);
+    Matrix c(C, sizeC);
+
+    typedef typename Eigen::Tensor<T, 2>::DimensionPair DimPair;
+    Eigen::array<DimPair, 1> dims;
+    dims[0] = DimPair(1, 0);
+    dims[0].first = transA ? 0 : 1;
+    dims[0].second = transB ? 1 : 0;
+
+    Eigen::DefaultDevice device;
+    if (alpha == T(1) && beta == T(0)) {
+      c.device(device) = a.contract(b, dims);
+    } else if (alpha == T(1) && beta == T(1)) {
+      c.device(device) += a.contract(b, dims);
+    } else {
+      c.device(device) = alpha * a.contract(b, dims) + beta * c;
+    }
+  }
+};
+
+#ifdef PADDLE_TYPE_DOUBLE
+template class EigenBlasGemm<double>;
+#else
+template class EigenBlasGemm<float>;
+#endif
+
+}  // namespace paddle

paddle/function/GemmConvOp.cpp

@@ -85,7 +85,6 @@ public:
     }
 
     Im2ColFunctor<kCFO, Device, real> im2col;
-    GemmFunctor<Device, real> gemm;
     size_t inputOffset = imShape.getElements();
     size_t outputOffset =
         (outputChannels / groups_) * outputHeight * outputWidth;
@@ -108,19 +107,19 @@ public:
         int M = outputChannels / groups_;
         int N = outputHeight * outputWidth;
         int K = inputChannels / groups_ * filterHeight * filterWidth;
-        gemm(CblasNoTrans,
-             CblasNoTrans,
-             M,
-             N,
-             K,
-             1.0f,
-             filterData + g * filterOffset,
-             K,
-             colData,
-             N,
-             beta,
-             outputData + g * outputOffset,
-             N);
+        BlasGemm<Device, real>::compute(false,
+                                        false,
+                                        M,
+                                        N,
+                                        K,
+                                        1.0f,
+                                        filterData + g * filterOffset,
+                                        K,
+                                        colData,
+                                        N,
+                                        beta,
+                                        outputData + g * outputOffset,
+                                        N);
       }
       inputData += inputChannels * inputHeight * inputWidth;
       outputData += outputChannels * outputHeight * outputWidth;
@@ -188,8 +187,6 @@ public:
     }
 
     Col2ImFunctor<kCFO, Device, real> col2im;
-    GemmFunctor<Device, real> gemm;
-
     size_t inputOffset = imShape.getElements();
     size_t outputOffset =
         (outputChannels / groups_) * outputHeight * outputWidth;
@@ -205,19 +202,19 @@ public:
           colData = inputGrad + g * inputOffset;
           scale = 1.0f;
         }
-        gemm(CblasTrans,
-             CblasNoTrans,
-             M,
-             N,
-             K,
-             1.0f,
-             filterData + g * filterOffset,
-             M,
-             outputGrad + g * outputOffset,
-             N,
-             scale,
-             colData,
-             N);
+        BlasGemm<Device, real>::compute(true,
+                                        false,
+                                        M,
+                                        N,
+                                        K,
+                                        1.0f,
+                                        filterData + g * filterOffset,
+                                        M,
+                                        outputGrad + g * outputOffset,
+                                        N,
+                                        scale,
+                                        colData,
+                                        N);
         if (needIm2col) {
           col2im(inputGrad + g * inputOffset,
                  imShape,
@@ -299,7 +296,6 @@ public:
     }
 
     Im2ColFunctor<kCFO, Device, real> im2col;
-    GemmFunctor<Device, real> gemm;
     size_t inputOffset = imShape.getElements();
     size_t outputOffset =
         (outputChannels / groups_) * outputHeight * outputWidth;
@@ -321,19 +317,19 @@ public:
         int M = outputChannels / groups_;
         int K = outputHeight * outputWidth;
         int N = inputChannels / groups_ * filterHeight * filterWidth;
-        gemm(CblasNoTrans,
-             CblasTrans,
-             M,
-             N,
-             K,
-             1.0f,
-             outputGrad + g * outputOffset,
-             K,
-             colData,
-             K,
-             i == 0 ? beta : 1.0f,
-             filterGrad + g * filterOffset,
-             N);
+        BlasGemm<Device, real>::compute(false,
+                                        true,
+                                        M,
+                                        N,
+                                        K,
+                                        1.0f,
+                                        outputGrad + g * outputOffset,
+                                        K,
+                                        colData,
+                                        K,
+                                        i == 0 ? beta : 1.0f,
+                                        filterGrad + g * filterOffset,
+                                        N);
       }
       inputData += inputChannels * inputHeight * inputWidth;
       outputGrad += outputChannels * outputHeight * outputWidth;

paddle/framework/attribute.proto → paddle/function/GemmConvOpTest.cpp

@@ -12,17 +12,39 @@ 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. */
 
-syntax = "proto2";
-package paddle.framework;
-
-// Attribute Type for paddle's Op.
-// Op contains many attributes. Each type of attributes could be different.
-// The AttrType will be shared between AttrDesc and AttrProto.
-enum AttrType {
-  INT = 0;
-  FLOAT = 1;
-  STRING = 2;
-  INTS = 3;
-  FLOATS = 4;
-  STRINGS = 5;
-}
\ No newline at end of file
+#include <gtest/gtest.h>
+#include "ConvOpTest.h"
+
+namespace paddle {
+
+TEST(GemmConv, NaiveConv) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "NaiveConv-CPU", "GemmConv-CPU", forward);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "NaiveConv-CPU", "GemmConv-CPU", forward);
+}
+
+#ifndef PADDLE_ONLY_CPU
+TEST(GemmConv, Forward) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConv-CPU", "GemmConv-GPU", forward);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConv-CPU", "GemmConv-GPU", forward);
+}
+
+TEST(GemmConv, BackwardInput) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradInput-CPU", "GemmConvGradInput-GPU", backward_input);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradInput-CPU", "GemmConvGradInput-GPU", backward_input);
+}
+
+TEST(GemmConv, BackwardFilter) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", backward_filter);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", backward_filter);
+}
+#endif
+
+}  // namespace paddle

paddle/function/GemmFunctor.cpp

+/* 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 "GemmFunctor.h"
+#include "paddle/math/MathFunctions.h"
+
+namespace paddle {
+
+template <class T>
+struct BlasGemm<DEVICE_TYPE_CPU, T> {
+  static void compute(const bool transA,
+                      const bool transB,
+                      const int M,
+                      const int N,
+                      const int K,
+                      const T alpha,
+                      const T* A,
+                      const int lda,
+                      const T* B,
+                      const int ldb,
+                      const T beta,
+                      T* C,
+                      const int ldc) {
+#ifdef PADDLE_USE_EIGEN_FOR_BLAS
+    EigenBlasGemm<T>::compute(
+        transA, transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
+#else
+    gemm<T>(transA == false ? CblasNoTrans : CblasTrans,
+            transB == false ? CblasNoTrans : CblasTrans,
+            M,
+            N,
+            K,
+            alpha,
+            A,
+            lda,
+            B,
+            ldb,
+            beta,
+            C,
+            ldc);
+#endif
+  }
+};
+
+template <class T>
+struct BlasGemm<DEVICE_TYPE_GPU, T> {
+  static void compute(const bool transA,
+                      const bool transB,
+                      const int M,
+                      const int N,
+                      const int K,
+                      const T alpha,
+                      const T* A,
+                      const int lda,
+                      const T* B,
+                      const int ldb,
+                      const T beta,
+                      T* C,
+                      const int ldc) {
+    hl_matrix_mul((T*)A,
+                  transA == false ? HPPL_OP_N : HPPL_OP_T,
+                  (T*)B,
+                  transB == false ? HPPL_OP_N : HPPL_OP_T,
+                  C,
+                  M,
+                  N,
+                  K,
+                  alpha,
+                  beta,
+                  lda,
+                  ldb,
+                  ldc);
+  }
+};
+
+template struct BlasGemm<DEVICE_TYPE_CPU, real>;
+template struct BlasGemm<DEVICE_TYPE_GPU, real>;
+
+}  // namespace paddle

paddle/function/GemmFunctor.h

@@ -14,7 +14,7 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/math/MathFunctions.h"
+#include "TensorType.h"
 
 namespace paddle {
 
@@ -24,73 +24,42 @@ namespace paddle {
 // of MatMulFunction, we need to consider the reconstruction of hl_matrix_mul
 // interface.
 template <DeviceType Device, class T>
-class GemmFunctor {
-public:
-  void operator()(const CBLAS_TRANSPOSE transA,
-                  const CBLAS_TRANSPOSE TransB,
-                  const int M,
-                  const int N,
-                  const int K,
-                  const T alpha,
-                  const T* A,
-                  const int lda,
-                  const T* B,
-                  const int ldb,
-                  const T beta,
-                  T* C,
-                  const int ldc);
+struct BlasGemm {
+  static void compute(const bool transA,
+                      const bool transB,
+                      const int M,
+                      const int N,
+                      const int K,
+                      const T alpha,
+                      const T* A,
+                      const int lda,
+                      const T* B,
+                      const int ldb,
+                      const T beta,
+                      T* C,
+                      const int ldc);
 };
 
+// TODO(hedaoyuan): Since the definition of the real type in the Paddle
+// conflicts with the Eigen library, so compile the Eigen code can not
+// include the Paddle header file. And need an EigenBlasGemm template class
+// that does not contain the DeviceType parameter.
+// I will fix this problem and merge BlasGemm and EigenBlasGemm into one.
 template <class T>
-class GemmFunctor<DEVICE_TYPE_CPU, T> {
-public:
-  void operator()(const CBLAS_TRANSPOSE transA,
-                  const CBLAS_TRANSPOSE TransB,
-                  const int M,
-                  const int N,
-                  const int K,
-                  const T alpha,
-                  const T* A,
-                  const int lda,
-                  const T* B,
-                  const int ldb,
-                  const T beta,
-                  T* C,
-                  const int ldc) {
-    gemm<T>(transA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
-  }
-};
-
-template <class T>
-class GemmFunctor<DEVICE_TYPE_GPU, T> {
-public:
-  void operator()(const CBLAS_TRANSPOSE transA,
-                  const CBLAS_TRANSPOSE TransB,
-                  const int M,
-                  const int N,
-                  const int K,
-                  const T alpha,
-                  const T* A,
-                  const int lda,
-                  const T* B,
-                  const int ldb,
-                  const T beta,
-                  T* C,
-                  const int ldc) {
-    hl_matrix_mul((T*)A,
-                  transA == CblasNoTrans ? HPPL_OP_N : HPPL_OP_T,
-                  (T*)B,
-                  TransB == CblasNoTrans ? HPPL_OP_N : HPPL_OP_T,
-                  C,
-                  M,
-                  N,
-                  K,
-                  alpha,
-                  beta,
-                  lda,
-                  ldb,
-                  ldc);
-  }
+struct EigenBlasGemm {
+  static void compute(const bool transA,
+                      const bool transB,
+                      const int M,
+                      const int N,
+                      const int K,
+                      const T alpha,
+                      const T* A,
+                      const int lda,
+                      const T* B,
+                      const int ldb,
+                      const T beta,
+                      T* C,
+                      const int ldc);
 };
 
 }  // namespace paddle

paddle/function/nnpack/NNPACKConvOp.cpp

@@ -196,30 +196,30 @@ public:
         CHECK_EQ(status, nnp_status_success);
       }
     } else {
-      for (size_t g = 0; g < groups_; g++) {
-        // only supports stride = 1
-        CHECK_EQ(strideH(), 1);
-        CHECK_EQ(strideW(), 1);
-        nnp_status status =
-            nnp_convolution_output(algorithm_,
-                                   batchSize,
-                                   inputChannels / groups_,
-                                   outputChannels / groups_,
-                                   inputSize,
-                                   padding,
-                                   kernelSize,
-                                   inputData + inputOffset * g,
-                                   filterData + filterOffset * g,
-                                   nullptr, /* bias */
-                                   outputData + outputOffset * g,
-                                   bufferPtr,
-                                   sizePtr,
-                                   nnp_activation_identity,
-                                   nullptr,
-                                   threadpool_, /* threadpool */
-                                   nullptr);
-        CHECK_EQ(status, nnp_status_success);
-      }
+      // only supports stride = 1
+      CHECK_EQ(strideH(), 1);
+      CHECK_EQ(strideW(), 1);
+
+      // TODO(hedaoyuan): There has some bug when batchSize > 1 and groups_ > 1.
+      CHECK_EQ(groups_, static_cast<size_t>(1));
+      nnp_status status = nnp_convolution_output(algorithm_,
+                                                 batchSize,
+                                                 inputChannels,
+                                                 outputChannels,
+                                                 inputSize,
+                                                 padding,
+                                                 kernelSize,
+                                                 inputData,
+                                                 filterData,
+                                                 nullptr, /* bias */
+                                                 outputData,
+                                                 bufferPtr,
+                                                 sizePtr,
+                                                 nnp_activation_identity,
+                                                 nullptr,
+                                                 threadpool_, /* threadpool */
+                                                 nullptr);
+      CHECK_EQ(status, nnp_status_success);
     }
   }
 

paddle/function/nnpack/NNPACKConvOpTest.cpp

@@ -13,87 +13,18 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include "paddle/function/Function.h"
-#include "paddle/function/FunctionTest.h"
-
-DEFINE_string(algo,
-              "auto",
-              "The algorithm (auto, ft8x8, ft16x16, wt8x8, "
-              "implicit-gemm, or direct) for computing convolution of NNPACK.");
+#include "paddle/function/ConvOpTest.h"
 
 namespace paddle {
 
-#define IS_NNPACK_SUPPORT(algo, filterSize, stride)        \
-  if (algo == "direct" && filterSize != 1) continue;       \
-  if (algo == "direct" && batchSize != 1) continue;        \
-  if (algo == "wt8x8" && filterSize != 3) continue;        \
-  if (algo == "implicit-gemm" && batchSize != 1) continue; \
-  if (algo != "auto" && algo != "implicit-gemm" && stride > 1) continue;
-
-class ConvolutionTest {
-public:
-  ConvolutionTest(const std::string& conv1,
-                  const std::string& conv2,
-                  std::string algo = "auto") {
-    for (size_t batchSize : {1, 32}) {
-      for (size_t inputSize : {7, 14, 54}) {
-        for (size_t filterSize : {1, 3, 5}) {
-          for (size_t inputChannels : {3, 64}) {
-            for (size_t outputChannels : {3, 64, 128}) {
-              if (inputChannels < outputChannels) break;
-              for (size_t stride : {1, 2}) {
-                // if batchSize > 1 NNPACKConv only supports stride = 1
-                if (batchSize > 1 && stride > 1) break;
-                for (size_t padding : {0, 1}) {
-                  if (padding >= filterSize) break;
-                  size_t outputSize =
-                      (inputSize - filterSize + 2 * padding + stride) / stride;
-                  IS_NNPACK_SUPPORT(algo, filterSize, stride);
-                  LOG(INFO) << " batchSize=" << batchSize
-                            << " inputChannels=" << inputChannels
-                            << " inputHeight=" << inputSize
-                            << " inputWidth=" << inputSize
-                            << " outputChannels=" << outputChannels
-                            << " filterHeight=" << filterSize
-                            << " filterWidth=" << filterSize
-                            << " outputHeight=" << outputSize
-                            << " outputWidth=" << outputSize
-                            << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", (size_t)1)
-                          .set("algo", algo));
-
-                  TensorShape shape0{
-                      batchSize, inputChannels, inputSize, inputSize};
-                  TensorShape shape1{
-                      outputChannels, inputChannels, filterSize, filterSize};
-                  TensorShape shape2{
-                      batchSize, outputChannels, outputSize, outputSize};
-                  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape0));
-                  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape1));
-                  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape2));
-                  test.run();
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
+TEST(NNPACK, Forward) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "GemmConv-CPU", "NNPACKConv-CPU", forward);
+}
 
-TEST(Convolution, NNPACK) {
-  // NNPACK only supports stride = 1
-  ConvolutionTest test("GemmConv-CPU", "NNPACKConv-CPU", FLAGS_algo);
+TEST(NNPACK, Depthwise) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "GemmConv-CPU", "NNPACKConv-CPU", forward);
 }
 
 }  // namespace paddle

paddle/gserver/activations/ActivationFunction.cpp

@@ -112,7 +112,6 @@ BEGIN_DEFINE_ACTIVATION(softmax)
 private:
 MatrixPtr sftMaxSum_;
 MatrixPtr sftMaxDot_;
-MatrixPtr one_;
 
 public:
 Error __must_check forward(Argument& act) {
@@ -138,14 +137,6 @@ Error __must_check backward(Argument& act) {
                            1,
                            /* trans */ false,
                            useGpu(act.deviceId));
-    if (!one_ || one_->getWidth() != outputG->getWidth()) {
-      Matrix::resizeOrCreate(one_,
-                             1,
-                             outputG->getWidth(),
-                             /* trans */ false,
-                             useGpu(act.deviceId));
-      one_->one();
-    }
 
     sftMaxDot_->dotMul(*outputG, *outputV);
     sftMaxSum_->colMerge(*sftMaxDot_);

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -202,7 +202,7 @@ void NeuralNetwork::prefetch(const std::vector<Argument>& inArgs) {
         auto mat = dynamic_cast<SparsePrefetchRowCpuMatrix*>(
             para->getMat(PARAMETER_VALUE).get());
         para->clearGradient();
-        mat->clearIndices();
+        if (mat) mat->clearIndices();
       }
     }
   }

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -184,7 +184,7 @@ public:
   }
 
   void backward(const UpdateCallback& callback) override {
-    if (biases_) {
+    if (biases_ && biases_->getWGrad()) {
       backwardActivation();
       biases_->getWGrad()->collectBias(*getOutputGrad(), 1);
       biases_->getParameterPtr()->incUpdate(callback);

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -57,11 +57,14 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
 }
 
 void MKLDNNFcLayer::convertWeightsFromPaddle() {
-  if (FLAGS_use_mkldnn_wgt) {
+  if (hasInitedWgt_) {
     return;
   }
 
-  if (hasInitedWgt_) {
+  // TODO(TJ): dst format should get from wgtVal_
+  int dstFmt = PARAM_FORMAT_MKLDNN_OI;
+  int srcFmt = weight_->getParameterPtr()->getHeaderFormat();
+  if (srcFmt == dstFmt) {
     return;
   }
 
@@ -78,6 +81,7 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
   MatrixPtr paddleWgtT;
   paddleWgt->transpose(paddleWgtT, true);
   weight_->getW()->copyFrom(*paddleWgtT);
+  weight_->getParameterPtr()->setHeaderFormat(dstFmt);
   hasInitedWgt_ = true;
 }
 

paddle/gserver/layers/ScaleShiftLayer.cpp

+/* 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 "Layer.h"
+
+namespace paddle {
+
+/**
+ * A layer applies a linear transformation to each element in each row of
+ * the input matrix. For each element, the layer first re-scale it and then
+ * adds a bias to it.
+ *
+ * \f[
+ *    y = wx + b
+ * \f]
+ *
+ * Here, w is the scale and b is the bias. Both w and b are trainable scalars.
+ *
+ */
+
+class ScaleShiftLayer : public Layer {
+protected:
+  std::unique_ptr<Weight> scale_;
+  std::unique_ptr<Weight> offset_;
+
+public:
+  explicit ScaleShiftLayer(const LayerConfig& config) : Layer(config) {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+REGISTER_LAYER(scale_shift, ScaleShiftLayer);
+
+bool ScaleShiftLayer::init(const LayerMap& layerMap,
+                           const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+  CHECK_EQ(inputLayers_.size(), 1U);
+  scale_.reset(new Weight(1, 1, parameters_[0]));
+  if (biasParameter_.get() != NULL) {
+    offset_ = std::unique_ptr<Weight>(new Weight(1, 1, biasParameter_));
+  }
+  return true;
+}
+
+void ScaleShiftLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  MatrixPtr inV = getInputValue(0);
+  resetOutput(inV->getHeight(), inV->getWidth());
+  MatrixPtr outV = getOutputValue();
+  real scaleValue = scale_->getW()->getElement(0, 0);
+  outV->mulScalar(*inV, scaleValue);
+  if (offset_) {
+    real offsetValue = offset_->getW()->getElement(0, 0);
+    outV->add(offsetValue);
+  }
+}
+
+void ScaleShiftLayer::backward(const UpdateCallback& callback) {
+  MatrixPtr inV = getInputValue(0);
+  MatrixPtr inG = getInputGrad(0);
+  MatrixPtr outV = getOutputValue();
+  MatrixPtr outG = getOutputGrad();
+
+  /* Calculate the parameter gradient for the current layer */
+  if (scale_->getWGrad()) {
+    MatrixPtr rowSumMtx;
+    Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_);
+    // this_i = scaleDest * this_i + scaleSum * \sum_j b_{ij} * c_{ij}
+    rowSumMtx->sumOfProducts(
+        /* b= */ *inV, /* c= */ *outG, /* scaleSum= */ 1, /* scaleDest= */ 0.);
+    // this_i = scaleDest * this_i + scaleSum * \sum_j b_{ji}
+    scale_->getWGrad()->sumCols(
+        /* b= */ *rowSumMtx, /* scaleSum= */ 1., /* scaleDest= */ 1.);
+    scale_->getParameterPtr()->incUpdate(callback);
+  }
+  if (offset_ && offset_->getWGrad()) {
+    MatrixPtr rowSumMtx;
+    Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_);
+    rowSumMtx->sumRows(*outG, 1., 0.);
+    offset_->getWGrad()->sumCols(*rowSumMtx, 1., 1.);
+    offset_->getParameterPtr()->incUpdate(callback);
+  }
+
+  /* Calculate the input layers error */
+  if (inG) {
+    real scaleValue = scale_->getW()->getElement(0, 0);
+    inG->add(*outG, scaleValue);
+  }
+}
+
+}  // namespace paddle

paddle/gserver/tests/LayerGradUtil.cpp

@@ -388,14 +388,23 @@ void initDataLayer(TestConfig testConf,
         data.grad->zeroMem();
         break;
       case INPUT_SELF_DEFINE_DATA: {
-        size_t height = testConf.inputDefs[i].selfDefinedData->getHeight();
-        size_t width = testConf.inputDefs[i].selfDefinedData->getWidth();
-        CHECK_GT(static_cast<int>(height), 0);
-        CHECK_GT(static_cast<int>(width), 0);
-        data.value = Matrix::create(height, width, false, useGpu);
-        data.grad = Matrix::create(height, width, false, useGpu);
-        data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData);
-        data.grad->zeroMem();
+        if (testConf.inputDefs[i].ids.size()) {
+          data.ids = IVector::create(testConf.inputDefs[i].ids.size(), useGpu);
+          data.ids->copyFrom(testConf.inputDefs[i].ids.data(),
+                             testConf.inputDefs[i].ids.size());
+        } else if (testConf.inputDefs[i].selfDefinedData) {
+          size_t height = testConf.inputDefs[i].selfDefinedData->getHeight();
+          size_t width = testConf.inputDefs[i].selfDefinedData->getWidth();
+          CHECK_GT(static_cast<int>(height), 0);
+          CHECK_GT(static_cast<int>(width), 0);
+          data.value = Matrix::create(height, width, false, useGpu);
+          data.grad = Matrix::create(height, width, false, useGpu);
+          data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData);
+          data.grad->zeroMem();
+        } else {
+          LOG(FATAL) << "No self-defined data are given.";
+          return;
+        }
 
         const std::vector<int>& labelSeqStartPositions =
             testConf.inputDefs[i].labelSeqStartPositions;

paddle/gserver/tests/LayerGradUtil.h

@@ -68,6 +68,7 @@ struct InputDef {
   std::vector<int> labelInitValue;
   std::vector<int> labelSeqStartPositions;
   std::vector<int> labelSubSeqStartPositions;
+  std::vector<int> ids;
   MatrixPtr selfDefinedData;
 
   InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) {
@@ -95,6 +96,23 @@ struct InputDef {
     isStatic = false;
   }
 
+  InputDef(InputType type,
+           string nameIn,
+           const std::vector<int>& ids,
+           const std::vector<int>& selfDefinedSeqStartPos = {},
+           const std::vector<int>& selfDefinedSubSeqStartPos = {})
+      : labelSeqStartPositions(selfDefinedSeqStartPos),
+        labelSubSeqStartPositions(selfDefinedSubSeqStartPos),
+        ids(ids) {
+    selfDefinedData = nullptr;
+    inputType = type;
+    name = nameIn;
+    dim = 0;
+    sparse = {""};
+    paraSize = 0;
+    isStatic = false;
+  }
+
   InputDef(InputType type,
            string nameIn,
            size_t dimIn,

paddle/gserver/tests/MKLDNNTester.cpp

@@ -330,9 +330,7 @@ void MKLDNNTester::run(const TestConfig& dnn,
   log_ = log;
   lvl_ = level;
 
-  // Firstly test FLAGS_use_mkldnn_wgt = false
-  FLAGS_use_mkldnn_wgt = false;
-  // reset and run once
+  // Firstly test mkldnn init from PARAM_FORMAT_ORIGINAL weight
   reset(dnn, ref, batchSize);
   randomWgtDatas();
   clearWgtDiffs();
@@ -342,17 +340,32 @@ void MKLDNNTester::run(const TestConfig& dnn,
     runOnce();
   }
 
-  // Then test FLAGS_use_mkldnn_wgt = true
-  FLAGS_use_mkldnn_wgt = true;
-  // after run once the mkldnn weight has been stored in dnnlayer
+  if (parameters_[DNN].empty()) {
+    // has no paramters
+    return;
+  }
+
+  // After run some iterations, the mkldnn weight has been stored in dnnLayer
+  // and we can also get the mkldnn weight parameter header format.
+  // Weight parameter should always be index 0 (and bias index 1).
+  // TODO(TJ): should also consider mean and var format when batchnorm ready
+  int dnnWgtFmt = parameters_[DNN][0]->getHeaderFormat();
+  int refWgtFmt = parameters_[REF][0]->getHeaderFormat();
+  if (dnnWgtFmt == refWgtFmt) {
+    // weight format are equal, so no need check more
+    return;
+  }
+
   // then save the weights and restart again
   vector<VectorPtr> dnnWgts, refWgts;
   CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
   saveWgt(parameters_[DNN], dnnWgts);
   saveWgt(parameters_[REF], refWgts);
 
-  // restart again with flag true
+  // restart again with dnn weight format
   reset(dnn, ref, batchSize);
+  // TODO(TJ): should also considerate mean and var format when batchnorm ready
+  parameters_[DNN][0]->setHeaderFormat(dnnWgtFmt);
 
   // restore wgt
   restoreWgt(dnnWgts, parameters_[DNN]);

paddle/gserver/tests/MKLDNNTester.h

@@ -108,7 +108,7 @@ private:
    * if many(>failRate) wrong(abs(dnn-ref)/abs(ref)>thres) points return the
    * max(diff/ref)
    * else return sum(abs(a-b)) / sum(abs(b))
-   * The return value should smaller than eps when passing.
+   * The return value should be smaller than eps when passing.
    */
   double getDelta(const real* d1,
                   const real* d2,

paddle/gserver/tests/test_LayerGrad.cpp

@@ -2007,6 +2007,21 @@ TEST(Layer, RowL2NormLayer) {
   }
 }
 
+TEST(Layer, ScaleShiftLayer) {
+  const size_t batchSize = 16;
+  const size_t size = 32;
+  TestConfig config;
+  config.layerConfig.set_type("scale_shift");
+  config.layerConfig.set_size(size);
+  config.biasSize = 1;
+  config.inputDefs.push_back(
+      {INPUT_DATA, "input", /* dim= */ size, /* paraSize= */ 1});
+  config.layerConfig.add_inputs();
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "scale_shift", batchSize, false, useGpu, false);
+  }
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);

paddle/gserver/tests/test_NetworkCompare.cpp

@@ -269,7 +269,8 @@ TEST(Compare, img_conv2) {
   bool useGpu = FLAGS_use_gpu;
   double eps = FLAGS_checkgrad_eps;
   FLAGS_use_gpu = true;
-  FLAGS_checkgrad_eps = 1e-2;
+  // Sometimes, this unit test will fail with 1e-2
+  FLAGS_checkgrad_eps = 4e-2;
   compareNetwork(config_file_a, config_file_b);
   FLAGS_use_gpu = useGpu;
   FLAGS_checkgrad_eps = eps;

paddle/memory/CMakeLists.txt

 add_subdirectory(detail)
 
 cc_library(memory SRCS memory.cc)
-cc_library(memcpy SRCS memcpy.cc DEPS device_context)
+cc_library(memcpy SRCS memcpy.cc)
 
 cc_library(paddle_memory
     DEPS

paddle/memory/detail/system_allocator.cc

@@ -27,7 +27,7 @@ limitations under the License. */
 // between host and device.  Allocates too much would reduce the amount
 // of memory available to the system for paging.  So, by default, we
 // should set false to use_pinned_memory.
-DEFINE_bool(use_pinned_memory, false, "If set, allocate cpu pinned memory.");
+DEFINE_bool(use_pinned_memory, true, "If set, allocate cpu pinned memory.");
 
 namespace paddle {
 namespace memory {

paddle/memory/memcpy.cc

@@ -16,8 +16,6 @@ limitations under the License. */
 
 #include <cstring>  // for memcpy
 
-#include "paddle/platform/device_context.h"
-
 namespace paddle {
 namespace memory {
 

paddle/memory/memory.cc

@@ -13,22 +13,38 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/memory/memory.h"
+
+#include <algorithm>  // for transform
+#include <cstring>    // for memcpy
+#include <memory>     // for unique_ptr
+#include <mutex>      // for call_once
+
+#include "glog/logging.h"
+
 #include "paddle/memory/detail/buddy_allocator.h"
 #include "paddle/memory/detail/system_allocator.h"
+#include "paddle/platform/gpu_info.h"
 
-#include <cstring>  // for memcpy
+DECLARE_double(fraction_of_gpu_memory_to_use);
 
 namespace paddle {
 namespace memory {
 
-detail::BuddyAllocator* GetCPUBuddyAllocator() {
-  static detail::BuddyAllocator* a = nullptr;
-  if (a == nullptr) {
-    a = new detail::BuddyAllocator(new detail::CPUAllocator,
-                                   platform::CpuMinChunkSize(),
-                                   platform::CpuMaxChunkSize());
-  }
-  return a;
+using BuddyAllocator = detail::BuddyAllocator;
+
+std::once_flag cpu_allocator_flag;
+std::once_flag gpu_allocator_flag;
+
+BuddyAllocator* GetCPUBuddyAllocator() {
+  static std::unique_ptr<BuddyAllocator> a{nullptr};
+
+  std::call_once(cpu_allocator_flag, [&]() {
+    a.reset(new BuddyAllocator(new detail::CPUAllocator,
+                               platform::CpuMinChunkSize(),
+                               platform::CpuMaxChunkSize()));
+  });
+
+  return a.get();
 }
 
 template <>
@@ -48,20 +64,36 @@ size_t Used<platform::CPUPlace>(platform::CPUPlace place) {
 
 #ifndef PADDLE_ONLY_CPU
 
-detail::BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
-  static detail::BuddyAllocator** as = NULL;
-  if (as == NULL) {
+BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
+  using BuddyAllocVec = std::vector<BuddyAllocator*>;
+  static std::unique_ptr<BuddyAllocVec, void (*)(BuddyAllocVec * p)> as{
+      new BuddyAllocVec, [](BuddyAllocVec* p) {
+        std::for_each(p->begin(), p->end(),
+                      [](BuddyAllocator* p) { delete p; });
+      }};
+
+  // GPU buddy allocators
+  auto& allocators = *as.get();
+
+  // GPU buddy allocator initialization
+  std::call_once(gpu_allocator_flag, [&]() {
     int gpu_num = platform::GetDeviceCount();
-    as = new detail::BuddyAllocator*[gpu_num];
+    allocators.reserve(gpu_num);
     for (int gpu = 0; gpu < gpu_num; gpu++) {
       platform::SetDeviceId(gpu);
-      as[gpu] = new detail::BuddyAllocator(new detail::GPUAllocator,
-                                           platform::GpuMinChunkSize(),
-                                           platform::GpuMaxChunkSize());
+      allocators.emplace_back(new BuddyAllocator(new detail::GPUAllocator,
+                                                 platform::GpuMinChunkSize(),
+                                                 platform::GpuMaxChunkSize()));
     }
-  }
+    VLOG(3) << "\n\nNOTE: each GPU device use "
+            << FLAGS_fraction_of_gpu_memory_to_use * 100 << "% of GPU memory.\n"
+            << "You can set environment variable '"
+            << platform::kEnvFractionGpuMemoryToUse
+            << "' to change the fraction of GPU usage.\n\n";
+  });
+
   platform::SetDeviceId(gpu_id);
-  return as[gpu_id];
+  return allocators[gpu_id];
 }
 
 template <>

paddle/memory/memory.h

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/platform/gpu_info.h"
 #include "paddle/platform/place.h"
 
 namespace paddle {

paddle/operators/CMakeLists.txt

@@ -41,8 +41,11 @@ function(op_library TARGET)
     endif()
 endfunction()
 
+add_subdirectory(math)
 cc_test(gather_test SRCS gather_test.cc DEPS tensor)
 
+cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
+
 cc_library(net_op SRCS net_op.cc DEPS op_registry)
 cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
 
@@ -50,7 +53,7 @@ op_library(add_op SRCS add_op.cc add_op.cu)
 
 op_library(mean_op SRCS mean_op.cc mean_op.cu)
 
-op_library(mul_op SRCS mul_op.cc mul_op.cu)
+op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
 op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
 
 op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu)
@@ -61,11 +64,7 @@ op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
 
 op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 
-op_library(fc_op
-    SRCS fc_op.cc
-    DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
-    DEPS op_desc tensor op_registry operator net_op)
-cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
+    DEPS framework_proto tensor op_registry operator net_op)
 op_library(uniform_random_op
         SRCS uniform_random_op.cc uniform_random_op.cu)

paddle/operators/add_op.cc

@@ -18,16 +18,15 @@ namespace paddle {
 namespace operators {
 
 class AddOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1);
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "Inputs of AddOp must all be set");
-    PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr,
-                   "Outputs of AddOp must all be set");
-    PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims() == ctx.Input<Tensor>(1)->dims(),
-                   "Two input of Add Op's dimension must be same.");
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
+                      ctx.Input<Tensor>("Y")->dims(),
+                      "Two input of Add Op's dimension must be same.");
+    ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
 
@@ -47,6 +46,9 @@ The equation is: Out = X + Y
 };
 
 class AddOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {}
 };
@@ -55,8 +57,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker);
-REGISTER_GRADIENT_OP(add_two, add_two_grad, ops::AddOpGrad);
+REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
 
 REGISTER_OP_CPU_KERNEL(add_two,
                        ops::AddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/add_op.h

@@ -28,9 +28,9 @@ template <typename Place, typename T>
 class AddKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto input0 = context.Input<Tensor>(0);
-    auto input1 = context.Input<Tensor>(1);
-    auto output = context.Output<Tensor>(0);
+    auto* input0 = context.Input<Tensor>("X");
+    auto* input1 = context.Input<Tensor>("Y");
+    auto* output = context.Output<Tensor>("Out");
 
     output->mutable_data<T>(context.GetPlace());
 

paddle/operators/cross_entropy_op.cc

@@ -18,33 +18,31 @@ namespace paddle {
 namespace operators {
 
 class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 2,
-                      "Input size of OnehotCrossEntropyOp must be two");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1,
-                      "Output size of OnehotCrossEntropyOp must be one");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0),
-                            "0-th input of OnehotCrossEntropyOp should be set");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(1),
-                            "1-th input of OnehotCrossEntropyOp should be set");
-    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0),
-                            "Outputs of OnehotCrossEntropyOp must all be set");
-    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>(0)->dims().size(), 2);
-    PADDLE_ENFORCE_EQ(ctx.Output<Tensor>(0)->dims().size(), 1,
-                      "label's dimension must be 1.");
-    ctx.Output<Tensor>(0)->Resize({ctx.Input<Tensor>(0)->dims()[0]});
+    auto *X = ctx.Input<Tensor>("X");
+    auto *label = ctx.Input<Tensor>("label");
+
+    PADDLE_ENFORCE_EQ(X->dims().size(), 2, "X's dimension must be 2.");
+    PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label's dimension must be 1.");
+    PADDLE_ENFORCE_EQ(X->dims()[0], label->dims()[0]);
+    ctx.Output<Tensor>("Y")->Resize({X->dims()[0]});
   }
 };
 
 class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto X = ctx.Input<Tensor>("X");
 
-    // TODO(superjom) add enforce here after helper functions ready
-    X_grad->Resize(X->dims());
+    dX->Resize(X->dims());
   }
 };
 
@@ -69,12 +67,9 @@ OnehotCrossEntropy Operator.
 
 namespace ops = paddle::operators;
 REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
-            ops::OnehotCrossEntropyOpMaker);
-REGISTER_OP_CPU_KERNEL(
-    onehot_cross_entropy,
-    ops::OnehotCrossEntropyOpKernel<paddle::platform::CPUPlace, float>);
-REGISTER_GRADIENT_OP(onehot_cross_entropy, onehot_cross_entropy_grad,
-                     ops::OnehotCrossEntropyGradientOp);
-REGISTER_OP_CPU_KERNEL(
-    onehot_cross_entropy_grad,
-    ops::OnehotCrossEntropyGradientOpKernel<paddle::platform::CPUPlace, float>);
+            ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
+            ops::OnehotCrossEntropyGradientOp);
+REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
+                       ops::OnehotCrossEntropyOpKernel<float>);
+REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,
+                       ops::OnehotCrossEntropyGradientOpKernel<float>);

paddle/operators/cross_entropy_op.cu

@@ -12,10 +12,122 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#define EIGEN_USE_GPU
-#include "paddle/operators/cross_entropy_op.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/assert.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+__host__ __device__ T clipping_log(const T x) {
+  PADDLE_ASSERT(std::is_floating_point<T>::value);
+  const T kApproInf = 1e20;
+  T v = log(x);
+  if (v == INFINITY) {
+    return kApproInf;
+  }
+  if (v == -INFINITY) {
+    return -kApproInf;
+  }
+  return v;
+}
+
+template <typename T>
+__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
+                                   const int N, const int D) {
+  // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
+  // CUDA_1D_KERNEL_LOOP(i, N) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
+       i += blockDim.x * gridDim.x) {
+    PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
+    Y[i] = -clipping_log(X[i * D + label[i]]);
+  }
+}
+
+// TODO(qingqing): make zero setting an common function.
+template <typename T>
+__global__ void zero(T* X, const int N) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
+       i += blockDim.x * gridDim.x) {
+    X[i] = 0.0;
+  }
+}
+
+template <typename T>
+__global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
+                                           const int* label, const int N,
+                                           const int D) {
+  // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
+  // CUDA_1D_KERNEL_LOOP(i, N) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
+       i += blockDim.x * gridDim.x) {
+    int idx = i * D + label[i];
+    dX[idx] = -dY[i] / X[idx];
+  }
+}
+
+template <typename T>
+class OnehotCrossEntropyOpCUDAKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+
+    auto X = ctx.Input<Tensor>("X");
+    const T* Xdata = X->data<T>();
+    const int* label_data = ctx.Input<Tensor>("label")->data<int>();
+    auto Y = ctx.Output<Tensor>("Y");
+    Y->mutable_data<T>(ctx.GetPlace());
+    T* Ydata = Y->data<T>();
+
+    int N = X->dims()[0];
+    int D = X->dims()[1];
+    int block = 512;
+    int grid = (N + block - 1) / block;
+    // TODO(qingqing) launch kernel on specified stream
+    // base on ExecutionContext.
+    CrossEntropyKernel<T><<<grid, block>>>(Ydata, Xdata, label_data, N, D);
+  }
+};
+
+template <typename T>
+class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+
+    auto X = ctx.Input<Tensor>("X");
+    auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    auto label = ctx.Input<Tensor>("label");
+
+    auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
+    auto* dYdata = dY->template data<T>();
+    auto* Xdata = X->template data<T>();
+    auto* label_data = label->data<int>();
+
+    int N = X->dims()[0];
+    int D = X->dims()[1];
+    int block = 512;
+    int grid = (N * D + block - 1) / block;
+    zero<T><<<grid, block>>>(dXdata, N * D);
+
+    grid = (N + block - 1) / block;
+    // TODO(qingqing): launch kernel on specified stream
+    // base on ExecutionContext.
+    CrossEntropyGradientKernel<T><<<grid, block>>>(dXdata, dYdata, Xdata,
+                                                   label_data, N, D);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(
-    onehot_cross_entropy,
-    ops::OnehotCrossEntropyOpKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
+                       ops::OnehotCrossEntropyOpCUDAKernel<float>);
+REGISTER_OP_GPU_KERNEL(onehot_cross_entropy_grad,
+                       ops::OnehotCrossEntropyGradientOpCUDAKernel<float>);

paddle/operators/cross_entropy_op.h

@@ -21,7 +21,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename T>
-T tolerable_value(T x) {
+inline T tolerable_value(const T x) {
   static_assert(std::is_floating_point<T>::value,
                 "tolerable_value works only on float, "
                 "double and double double.");
@@ -39,13 +39,16 @@ T tolerable_value(T x) {
   return x;
 }
 
-template <typename Place, typename T>
+template <typename T>
 class OnehotCrossEntropyOpKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
     auto X = ctx.Input<Tensor>("X");
     const T* Xdata = X->data<T>();
-    const int* label_data = ctx.Input<Tensor>(1)->data<int>();
+    const int* label_data = ctx.Input<Tensor>("label")->data<int>();
     auto Y = ctx.Output<Tensor>("Y");
 
     Y->mutable_data<T>(ctx.GetPlace());
@@ -62,10 +65,13 @@ class OnehotCrossEntropyOpKernel : public framework::OpKernel {
   }
 };
 
-template <typename Place, typename T>
+template <typename T>
 class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
     auto X = ctx.Input<Tensor>("X");
     auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
@@ -79,6 +85,8 @@ class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel {
     const int batch_size = X->dims()[0];
     const int class_num = X->dims()[1];
 
+    // TODO(qingqing): make zero setting an common function.
+    memset(dXdata, 0, sizeof(T) * batch_size * class_num);
     for (int i = 0; i < batch_size; ++i) {
       int index = i * class_num + label_data[i];
       dXdata[index] = -tolerable_value(dYdata[i] / Xdata[index]);

paddle/operators/fc_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/operators/net_op.h"
-
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-using OpRegistry = framework::OpRegistry;
-
-class FullyConnectedOp : public NetOp {
- public:
-  void Init() override {
-    AddOp(OpRegistry::CreateOp("mul",
-                               {
-                                   Input("X"), Input("W"),
-                               },
-                               {Output("before_act")}, {}));
-    auto b = Input("b");
-    if (b != framework::kEmptyVarName) {
-      AddOp(OpRegistry::CreateOp("rowwise_add",
-                                 {Output("before_act"), Input("b")},
-                                 {Output("before_act")}, {}));
-    }
-
-    auto activation = GetAttr<std::string>("activation");
-    AddOp(OpRegistry::CreateOp(activation, {Output("before_act")},
-                               {Output("Y")}, {}));
-    CompleteAddOp(false);
-  }
-};
-
-class FullyConnectedOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  FullyConnectedOpMaker(framework::OpProto *proto,
-                        framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "the input of fc operator");
-    AddInput("W", "the weight of fc operator");
-    AddInput("b", "the bias of fc operator");
-
-    AddOutput("Y", "the output of fc operator");
-    AddOutput("before_act", "the before activation output of fc operator")
-        .SetTemporary();
-    AddAttr<std::string>("activation", "The activation key for fc layer")
-        .SetDefault("sigmoid")
-        .InEnum({"sigmoid", "softmax"});
-
-    //! TODO(yuyang18): Complete comment;
-    AddComment("FullyConnected Operator");
-  }
-};
-}  // namespace operators
-}  // namespace paddle
-
-USE_OP(mul);
-USE_OP(rowwise_add);
-USE_OP(sigmoid);
-USE_OP(softmax);
-
-namespace ops = paddle::operators;
-REGISTER_OP(fc, ops::FullyConnectedOp, ops::FullyConnectedOpMaker);

paddle/operators/fill_zeros_like_op.cc

@@ -18,18 +18,13 @@ namespace paddle {
 namespace operators {
 
 class FillZerosLikeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
-                      "Input size of FillZerosLikeOp must be one.");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
-                      "Output size of AddOp must be one.");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0),
-                            "Input of FillZerosLikeOp must be set.");
-    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0),
-                            "Output of FillZerosLikeOp must be set.");
-    ctx.Output<framework::Tensor>(0)->Resize(
-        ctx.Input<framework::Tensor>(0)->dims());
+    ctx.Output<framework::Tensor>("Dst")->Resize(
+        ctx.Input<framework::Tensor>("Src")->dims());
   }
 };
 
@@ -51,7 +46,8 @@ The output will have the same size with input.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(fill_zeros_like, ops::FillZerosLikeOp, ops::FillZerosLikeOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
+                             ops::FillZerosLikeOpMaker);
 REGISTER_OP_CPU_KERNEL(
     fill_zeros_like,
     ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);

paddle/operators/fill_zeros_like_op.h

@@ -23,7 +23,7 @@ template <typename Place, typename T>
 class FillZerosLikeKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* output = context.Output<framework::Tensor>(0);
+    auto* output = context.Output<framework::Tensor>("Dst");
     output->mutable_data<T>(context.GetPlace());
     auto t = framework::EigenVector<T>::Flatten(*output);
     t.device(context.GetEigenDevice<Place>()) = t.constant(T(0));

paddle/operators/gather.h

@@ -29,7 +29,7 @@ void CPUGather(const T* params, const int* indices, const int slice_size,
                const int index_size, T* output) {
   const size_t slice_bytes = slice_size * sizeof(T);
 
-  for (size_t i = 0; i < index_size; ++i) {
+  for (int i = 0; i < index_size; ++i) {
     int index_ = indices[i];
     memcpy(output + i * slice_size, params + index_ * slice_size, slice_bytes);
   }
@@ -60,7 +60,7 @@ void Gather(const platform::Place& place, const paddle::framework::Tensor* src,
 
   // slice size
   int slice_size = 1;
-  for (size_t i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
+  for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
 
   // Gathering
   if (platform::is_cpu_place(place)) {

paddle/operators/gather_test.cc

@@ -35,7 +35,7 @@ TEST(Gather, GatherData) {
   p_src = src->mutable_data<int>(make_ddim({3, 4}), CPUPlace());
   p_index = index->mutable_data<int>(make_ddim({2}), CPUPlace());
 
-  for (size_t i = 0; i < 12; ++i) p_src[i] = i;
+  for (int i = 0; i < 12; ++i) p_src[i] = i;
   p_index[0] = 1;
   p_index[1] = 0;
 
@@ -43,6 +43,10 @@ TEST(Gather, GatherData) {
 
   Gather<int>(CPUPlace(), src, index, output);
 
-  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4);
-  for (size_t i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4);
+  for (int i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4);
+  for (int i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4);
+
+  delete src;
+  delete index;
+  delete output;
 }

paddle/operators/gaussian_random_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.
@@ -19,33 +16,36 @@ namespace paddle {
 namespace operators {
 
 template <typename T>
-class GaussianRandomKernel : public framework::OpKernel {
+class CPUGaussianRandomKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     float mean = context.op_.GetAttr<float>("mean");
     float std = context.op_.GetAttr<float>("std");
-    auto* tensor = context.Output<framework::Tensor>(0);
+    auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
 
-    // TODO(dzh): attribute does not support unsigned int.
-    // And we need a global random seed configuration.
-    int seed = context.op_.GetAttr<int>("seed");
+    unsigned int seed =
+        static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
+    std::minstd_rand engine;
     if (seed == 0) {
       seed = std::random_device()();
     }
-    std::mt19937 g(seed);
-    std::normal_distribution<T> distribution(mean, std);
+    engine.seed(seed);
+    std::normal_distribution<T> dist(mean, std);
     ssize_t size = framework::product(tensor->dims());
-    for (int i = 0; i < size; ++i) {
-      data[i] = distribution(g);
+    for (ssize_t i = 0; i < size; ++i) {
+      data[i] = dist(engine);
     }
   }
 };
 
 class GaussianRandomOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>(0);
+    auto* tensor = context.Output<framework::Tensor>("Out");
     auto dims = GetAttr<std::vector<int>>("dims");
     PADDLE_ENFORCE(dims.size() > 0UL,
                    "dims can be one int or array. dims must be set.");
@@ -65,8 +65,8 @@ Use to initialize tensor with gaussian random generator.
 )DOC");
 
     AddAttr<std::vector<int>>("dims", "The dimension of random tensor.");
-    AddAttr<float>("mean", "mean value of random.").SetDefault(.0f);
-    AddAttr<float>("std", "minimum value of random value.").SetDefault(1.0f);
+    AddAttr<float>("mean", "mean of random tensor.").SetDefault(.0f);
+    AddAttr<float>("std", "std of random tensor.").SetDefault(1.0f);
     AddAttr<int>("seed",
                  "Random seed of generator."
                  "0 means use system wide seed")
@@ -78,5 +78,6 @@ Use to initialize tensor with gaussian random generator.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker);
-REGISTER_OP_CPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
+REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
+                             ops::GaussianRandomOpMaker);
+REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel<float>);

paddle/operators/gaussian_random_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 <memory>
-#include <random>
-#include "paddle/platform/dynload/curand.h"
-#include "paddle/platform/gpu_info.h"
-
+#include <thrust/device_ptr.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/random.h>
+#include <thrust/transform.h>
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename T>
-class GaussianRandomKernel : public framework::OpKernel {
+struct GaussianGenerator {
+  T mean_, std_;
+  unsigned int seed_;
+
+  __host__ __device__ GaussianGenerator(T mean, T std, int seed)
+      : mean_(mean), std_(std), seed_(seed) {}
+
+  __host__ __device__ T operator()(const unsigned int n) const {
+    thrust::minstd_rand rng;
+    rng.seed(seed_);
+    thrust::normal_distribution<T> dist(mean_, std_);
+    rng.discard(n);
+    return dist(rng);
+  }
+};
+
+template <typename T>
+class GPUGaussianRandomKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    float mean = context.op_.GetAttr<float>("mean");
-    float std = context.op_.GetAttr<float>("std");
-    auto* tensor = context.Output<framework::Tensor>(0);
+    auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
-
-    int seed = context.op_.GetAttr<int>("seed");
+    unsigned int seed =
+        static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
     if (seed == 0) {
       std::random_device rd;
       seed = rd();
     }
-    curandGenerator_t g;
-    PADDLE_ENFORCE(platform::dynload::curandCreateGenerator(
-        &g, CURAND_RNG_PSEUDO_DEFAULT));
-    PADDLE_ENFORCE(
-        platform::dynload::curandSetPseudoRandomGeneratorSeed(g, seed));
-    platform::dynload::curandGenerateNormal(
-        g, data, framework::product(tensor->dims()), mean, std);
+    T mean = static_cast<T>(context.op_.GetAttr<float>("mean"));
+    T std = static_cast<T>(context.op_.GetAttr<float>("std"));
+    thrust::counting_iterator<unsigned int> index_sequence_begin(0);
+    ssize_t N = framework::product(tensor->dims());
+    thrust::transform(index_sequence_begin, index_sequence_begin + N,
+                      thrust::device_ptr<T>(data),
+                      GaussianGenerator<T>(mean, std, seed));
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
+REGISTER_OP_GPU_KERNEL(gaussian_random,
+                       paddle::operators::GPUGaussianRandomKernel<float>);

paddle/operators/math/CMakeLists.txt

+
+if(WITH_GPU)
+    nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context)
+else()
+    cc_library(math_function SRCS math_function.cc DEPS cblas device_context)
+endif()
+
+nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)

paddle/operators/math/math_function.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/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <>
+void gemm<platform::CPUPlace, float>(const CBLAS_TRANSPOSE transA,
+                                     const CBLAS_TRANSPOSE transB, const int M,
+                                     const int N, const int K,
+                                     const float alpha, const float* A,
+                                     const float* B, const float beta, float* C,
+                                     platform::DeviceContext* context) {
+  int lda = (transA == CblasNoTrans) ? K : M;
+  int ldb = (transB == CblasNoTrans) ? N : K;
+  int ldc = N;
+  cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
+              beta, C, ldc);
+}
+
+template <>
+void gemm<platform::CPUPlace, double>(const CBLAS_TRANSPOSE transA,
+                                      const CBLAS_TRANSPOSE transB, const int M,
+                                      const int N, const int K,
+                                      const double alpha, const double* A,
+                                      const double* B, const double beta,
+                                      double* C,
+                                      platform::DeviceContext* context) {
+  int lda = (transA == CblasNoTrans) ? K : M;
+  int ldb = (transB == CblasNoTrans) ? N : K;
+  int ldc = N;
+  cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
+              beta, C, ldc);
+}
+
+template <>
+void matmul<platform::CPUPlace, float>(const framework::Tensor& matrix_a,
+                                       bool trans_a,
+                                       const framework::Tensor& matrix_b,
+                                       bool trans_b, float alpha,
+                                       framework::Tensor* matrix_out,
+                                       float beta,
+                                       platform::DeviceContext* context) {
+  auto dim_a = matrix_a.dims();
+  auto dim_b = matrix_b.dims();
+  auto dim_out = matrix_out->dims();
+  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
+                 "The input and output of matmul be matrix");
+
+  PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
+                     platform::is_cpu_place(matrix_b.place()) &&
+                     platform::is_cpu_place(matrix_out->place()),
+                 "Matrix must all be in CPUPlace");
+
+  int M = dim_out[0];
+  int N = dim_out[1];
+  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
+
+  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
+  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
+
+  gemm<platform::CPUPlace, float>(
+      transA, transB, M, N, K, alpha, matrix_a.data<float>(),
+      matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
+}
+
+template <>
+void matmul<platform::CPUPlace, double>(const framework::Tensor& matrix_a,
+                                        bool trans_a,
+                                        const framework::Tensor& matrix_b,
+                                        bool trans_b, double alpha,
+                                        framework::Tensor* matrix_out,
+                                        double beta,
+                                        platform::DeviceContext* context) {
+  auto dim_a = matrix_a.dims();
+  auto dim_b = matrix_b.dims();
+  auto dim_out = matrix_out->dims();
+  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
+                 "The input and output of matmul be matrix");
+
+  PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
+                     platform::is_cpu_place(matrix_b.place()) &&
+                     platform::is_cpu_place(matrix_out->place()),
+                 "Matrix must all be in CPUPlace");
+
+  int M = dim_out[0];
+  int N = dim_out[1];
+  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
+
+  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
+  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
+
+  gemm<platform::CPUPlace, double>(
+      transA, transB, M, N, K, alpha, matrix_a.data<double>(),
+      matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/math_function.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/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <>
+void gemm<platform::GPUPlace, float>(const CBLAS_TRANSPOSE transA,
+                                     const CBLAS_TRANSPOSE transB, const int M,
+                                     const int N, const int K,
+                                     const float alpha, const float* A,
+                                     const float* B, const float beta, float* C,
+                                     platform::DeviceContext* context) {
+  // Note that cublas follows fortran order, so the order is different from
+  // the cblas convention.
+  int lda = (transA == CblasNoTrans) ? K : M;
+  int ldb = (transB == CblasNoTrans) ? N : K;
+  cublasOperation_t cuTransA =
+      (transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransB =
+      (transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+
+  PADDLE_ENFORCE(platform::dynload::cublasSgemm(
+      reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
+      cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
+}
+
+template <>
+void gemm<platform::GPUPlace, double>(const CBLAS_TRANSPOSE transA,
+                                      const CBLAS_TRANSPOSE transB, const int M,
+                                      const int N, const int K,
+                                      const double alpha, const double* A,
+                                      const double* B, const double beta,
+                                      double* C,
+                                      platform::DeviceContext* context) {
+  // Note that cublas follows fortran order, so the order is different from
+  // the cblas convention.
+  int lda = (transA == CblasNoTrans) ? K : M;
+  int ldb = (transB == CblasNoTrans) ? N : K;
+  cublasOperation_t cuTransA =
+      (transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransB =
+      (transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  PADDLE_ENFORCE(platform::dynload::cublasDgemm(
+      reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
+      cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
+}
+
+template <>
+void matmul<platform::GPUPlace, float>(const framework::Tensor& matrix_a,
+                                       bool trans_a,
+                                       const framework::Tensor& matrix_b,
+                                       bool trans_b, float alpha,
+                                       framework::Tensor* matrix_out,
+                                       float beta,
+                                       platform::DeviceContext* context) {
+  auto dim_a = matrix_a.dims();
+  auto dim_b = matrix_b.dims();
+  auto dim_out = matrix_out->dims();
+  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
+                 "The input and output of matmul be matrix");
+
+  PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
+                     platform::is_gpu_place(matrix_b.place()) &&
+                     platform::is_gpu_place(matrix_out->place()),
+                 "Matrix must all be in GPUPlace");
+
+  int M = dim_out[0];
+  int N = dim_out[1];
+  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
+
+  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
+  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
+
+  gemm<platform::GPUPlace, float>(
+      transA, transB, M, N, K, alpha, matrix_a.data<float>(),
+      matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
+}
+
+template <>
+void matmul<platform::GPUPlace, double>(const framework::Tensor& matrix_a,
+                                        bool trans_a,
+                                        const framework::Tensor& matrix_b,
+                                        bool trans_b, double alpha,
+                                        framework::Tensor* matrix_out,
+                                        double beta,
+                                        platform::DeviceContext* context) {
+  auto dim_a = matrix_a.dims();
+  auto dim_b = matrix_b.dims();
+  auto dim_out = matrix_out->dims();
+  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
+                 "The input and output of matmul be matrix");
+
+  PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
+                     platform::is_gpu_place(matrix_b.place()) &&
+                     platform::is_gpu_place(matrix_out->place()),
+                 "Matrix must all be in GPUPlace");
+
+  int M = dim_out[0];
+  int N = dim_out[1];
+  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
+
+  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
+  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
+
+  gemm<platform::GPUPlace, double>(
+      transA, transB, M, N, K, alpha, matrix_a.data<double>(),
+      matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/math_function.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
+#ifdef PADDLE_USE_MKLML
+#include <mkl_cblas.h>
+#include <mkl_lapacke.h>
+#include <mkl_vml_functions.h>
+#endif
+
+#ifdef PADDLE_USE_MKL
+#include <mkl.h>
+#include <mkl_lapacke.h>
+#endif
+
+#ifdef PADDLE_USE_ATLAS
+extern "C" {
+#include <cblas.h>
+#include <clapack.h>
+}
+#endif
+
+#ifdef PADDLE_USE_OPENBLAS
+#include <cblas.h>
+#include <lapacke.h>
+#endif
+
+#ifndef LAPACK_FOUND
+extern "C" {
+#include <cblas.h>
+int LAPACKE_sgetrf(int matrix_layout, int m, int n, float* a, int lda,
+                   int* ipiv);
+int LAPACKE_dgetrf(int matrix_layout, int m, int n, double* a, int lda,
+                   int* ipiv);
+int LAPACKE_sgetri(int matrix_layout, int n, float* a, int lda,
+                   const int* ipiv);
+int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda,
+                   const int* ipiv);
+}
+#endif
+
+#include <cmath>
+
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/device_context.h"
+#include "paddle/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+// Support continuous memory now
+// If transA = N, and transB = N
+// Then matrixA: M * K, matrixB: K * N matrixC : M * N
+// For more detailed info, please refer to
+// http://www.netlib.org/lapack/explore-html/d4/de2/sgemm_8f.html
+template <typename Place, typename T>
+void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
+          const int M, const int N, const int K, const T alpha, const T* A,
+          const T* B, const T beta, T* C, platform::DeviceContext* context);
+
+// matrix multiply with continuous memory
+template <typename Place, typename T>
+void matmul(const framework::Tensor& matrix_a, bool trans_a,
+            const framework::Tensor& matrix_b, bool trans_b, T alpha,
+            framework::Tensor* matrix_out, T beta,
+            platform::DeviceContext* context);
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/math_function_test.cc

+#include "paddle/operators/math/math_function.h"
+#include "gtest/gtest.h"
+
+#ifndef PADDLE_ONLY_CPU
+TEST(math_function, notrans_mul_trans) {
+  paddle::framework::Tensor input1;
+  paddle::framework::Tensor input1_gpu;
+  paddle::framework::Tensor input2_gpu;
+  paddle::framework::Tensor out_gpu;
+  paddle::framework::Tensor out;
+
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
+  float arr[6] = {0, 1, 2, 3, 4, 5};
+  memcpy(input1_ptr, arr, 6 * sizeof(float));
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::platform::DeviceContext* context =
+      new paddle::platform::CUDADeviceContext(*gpu_place);
+
+  input1_gpu.CopyFrom<float>(input1, *gpu_place);
+  input2_gpu.CopyFrom<float>(input1, *gpu_place);
+
+  out_gpu.mutable_data<float>({2, 2}, *gpu_place);
+
+  paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
+      input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0, context);
+
+  out.CopyFrom<float>(out_gpu, *cpu_place);
+
+  float* out_ptr = out.data<float>();
+  EXPECT_EQ(out_ptr[0], 5);
+  EXPECT_EQ(out_ptr[1], 14);
+  EXPECT_EQ(out_ptr[2], 14);
+  EXPECT_EQ(out_ptr[3], 50);
+}
+
+TEST(math_function, trans_mul_notrans) {
+  paddle::framework::Tensor input1;
+  paddle::framework::Tensor input1_gpu;
+  paddle::framework::Tensor input2_gpu;
+  paddle::framework::Tensor out_gpu;
+  paddle::framework::Tensor out;
+
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
+  float arr[6] = {0, 1, 2, 3, 4, 5};
+  memcpy(input1_ptr, arr, 6 * sizeof(float));
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::platform::DeviceContext* context =
+      new paddle::platform::CUDADeviceContext(*gpu_place);
+
+  input1_gpu.CopyFrom<float>(input1, *gpu_place);
+  input2_gpu.CopyFrom<float>(input1, *gpu_place);
+
+  out_gpu.mutable_data<float>({3, 3}, *gpu_place);
+
+  paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
+      input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0, context);
+
+  out.CopyFrom<float>(out_gpu, *cpu_place);
+
+  float* out_ptr = out.data<float>();
+  EXPECT_EQ(out_ptr[0], 9);
+  EXPECT_EQ(out_ptr[1], 12);
+  EXPECT_EQ(out_ptr[2], 15);
+  EXPECT_EQ(out_ptr[3], 12);
+  EXPECT_EQ(out_ptr[4], 17);
+  EXPECT_EQ(out_ptr[5], 22);
+  EXPECT_EQ(out_ptr[6], 15);
+  EXPECT_EQ(out_ptr[7], 22);
+  EXPECT_EQ(out_ptr[8], 29);
+}
+#endif

paddle/operators/mean_op.cc

@@ -18,13 +18,14 @@ namespace paddle {
 namespace operators {
 
 class MeanOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 1, "Input size of AddOp must be one");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of AddOp must be one");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "input should be set");
-    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0), "output should be set");
-    ctx.Output<Tensor>(0)->Resize(framework::make_ddim({1}));
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input of MeanOp must be initialized.");
+    ctx.Output<Tensor>("Out")->Resize({1});
   }
 };
 
@@ -33,12 +34,15 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
   MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The input of mean op");
-    AddOutput("Out", "The output of mean op").IgnoreGradient();
+    AddOutput("Out", "The output of mean op").NotInGradient();
     AddComment("Mean Operator");
   }
 };
 
 class MeanGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     ctx.Output<Tensor>(framework::GradVarName("X"))
@@ -50,9 +54,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
+REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
 REGISTER_OP_CPU_KERNEL(mean,
                        ops::MeanKernel<paddle::platform::CPUPlace, float>);
-REGISTER_GRADIENT_OP(mean, mean_grad, ops::MeanGradOp);
 REGISTER_OP_CPU_KERNEL(mean_grad,
                        ops::MeanGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mean_op.h

@@ -31,14 +31,14 @@ template <typename Place, typename T>
 class MeanKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto input = context.Input<Tensor>(0);
-    auto output = context.Output<Tensor>(0);
+    auto* input = context.Input<Tensor>("X");
+    auto* output = context.Output<Tensor>("Out");
 
     output->mutable_data<T>(context.GetPlace());
 
     auto X = EigenVector<T>::Flatten(*input);
     auto y = EigenScalar<T>::From(*output);
-    auto place = context.GetEigenDevice<Place>();
+    auto& place = context.GetEigenDevice<Place>();
 
     y.device(place) = X.mean();
   }
@@ -55,9 +55,10 @@ class MeanGradKernel : public framework::OpKernel {
     IG->mutable_data<T>(context.GetPlace());
 
     T ig_size = (T)framework::product(IG->dims());
+    Eigen::DSizes<int, 1> bcast(ig_size);
 
     EigenVector<T>::Flatten(*IG).device(context.GetEigenDevice<Place>()) =
-        EigenScalar<T>::From(*OG) / ig_size;
+        (EigenVector<T>::From(*OG) / ig_size).broadcast(bcast);
   }
 };
 

paddle/operators/mul_op.cc

@@ -17,12 +17,16 @@
 namespace paddle {
 namespace operators {
 
+using framework::Tensor;
+
 class MulOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
-    auto dim0 = ctx.Input<Tensor>(0)->dims();
-    auto dim1 = ctx.Input<Tensor>(1)->dims();
+    auto dim0 = ctx.Input<Tensor>("X")->dims();
+    auto dim1 = ctx.Input<Tensor>("Y")->dims();
     PADDLE_ENFORCE_EQ(dim0.size(), 2,
                       "input X(%s) should be a tensor with 2 dims, a matrix",
                       ctx.op_.Input("X"));
@@ -32,8 +36,7 @@ class MulOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(
         dim0[1], dim1[0],
         "First matrix's width must be equal with second matrix's height.");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "The mul op takes only one output");
-    ctx.Output<Tensor>(0)->Resize({dim0[0], dim1[1]});
+    ctx.Output<Tensor>("Out")->Resize({dim0[0], dim1[1]});
   }
 };
 
@@ -53,11 +56,27 @@ The equation is: Out = X * Y
 };
 
 class MulOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {}
-  std::string DebugString() const override {
-    LOG(INFO) << "MulGrad";
-    return "";
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    PADDLE_ENFORCE(x_dims[0] == out_dims[0],
+                   "Out@GRAD M X N must equal to X dims 0, M ");
+    PADDLE_ENFORCE(y_dims[1] == out_dims[1],
+                   "Out@GRAD M X N must equal to Y dims 1, N ");
+
+    x_grad->Resize(x_dims);
+    y_grad->Resize(y_dims);
   }
 };
 
@@ -65,7 +84,7 @@ class MulOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker);
-REGISTER_GRADIENT_OP(mul, mul_grad, ops::MulOpGrad);
-
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
 REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(mul_grad,
+                       ops::MulGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mul_op.cu

@@ -16,5 +16,6 @@
 #include "paddle/operators/mul_op.h"
 
 namespace ops = paddle::operators;
-
 REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(mul_grad,
+                       ops::MulGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/mul_op.h

@@ -13,6 +13,9 @@
    limitations under the License. */
 
 #pragma once
+
+#include "paddle/operators/math/math_function.h"
+
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
 
@@ -28,21 +31,34 @@ template <typename Place, typename T>
 class MulKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
-        {Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
-
-    auto input0 = context.Input<Tensor>("X");
-    auto input1 = context.Input<Tensor>("Y");
-    auto output = context.Output<Tensor>(0);
-
-    output->mutable_data<T>(context.GetPlace());
+    auto* X = context.Input<Tensor>("X");
+    auto* Y = context.Input<Tensor>("Y");
+    auto* Z = context.Output<Tensor>("Out");
+    Z->mutable_data<T>(context.GetPlace());
+    auto* device_context =
+        const_cast<platform::DeviceContext*>(context.device_context_);
+    math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
+  }
+};
 
-    auto X = EigenMatrix<T>::From(*input0);
-    auto Y = EigenMatrix<T>::From(*input1);
-    auto Z = EigenMatrix<T>::From(*output);
-    auto place = context.GetEigenDevice<Place>();
+template <typename Place, typename T>
+class MulGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* X = ctx.Input<Tensor>("X");
+    auto* Y = ctx.Input<Tensor>("Y");
+    auto* dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
 
-    Z.device(place) = X.contract(Y, dim_pair);
+    auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dY = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    dX->mutable_data<T>(ctx.GetPlace());
+    dY->mutable_data<T>(ctx.GetPlace());
+    auto* device_context =
+        const_cast<platform::DeviceContext*>(ctx.device_context_);
+    // dX = dOut * Y'. dX: M x K, dOut : M x N, Y : K x N
+    math::matmul<Place, T>(*dOut, false, *Y, true, 1, dX, 0, device_context);
+    // dY = X' * dOut. dY: K x N, dOut : M x N, X : M x K
+    math::matmul<Place, T>(*X, true, *dOut, false, 1, dY, 0, device_context);
   }
 };
 

paddle/operators/net_op.cc

@@ -15,48 +15,42 @@
 */
 
 #include "paddle/operators/net_op.h"
+#include <set>
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+const char NetOp::kAll[] = "all";
+
 void NetOp::CompleteAddOp(bool calc) {
   add_op_done_ = true;
   if (!calc) return;
-  std::unordered_set<std::string> input_set;
-  std::unordered_set<std::string> output_set;
-  std::unordered_set<std::string> temp_output;
+  std::set<std::string> input_set;
+  std::set<std::string> output_set;
   for (auto& op : ops_) {
-    for (auto& ipt : op->inputs_) {
-      if (!Contains(output_set, ipt)) {  // Not other op's output
-        input_set.insert(ipt);
-      } else {
-        temp_output.insert(ipt);
+    for (auto& ipt : op->Inputs()) {
+      for (auto& var_name : ipt.second) {
+        if (!Contains(output_set, var_name)) {  // Not other op's output
+          input_set.insert(var_name);
+        } else {
+          intermediate_outputs_.insert(var_name);
+        }
       }
     }
 
-    for (auto& opt : op->outputs_) {
-      output_set.insert(opt);
-    }
-  }
-
-  inputs_.reserve(input_set.size());
-  std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs_));
-  std::sort(inputs_.begin(), inputs_.end());
-
-  outputs_.reserve(output_set.size());
-  std::copy(output_set.begin(), output_set.end(), std::back_inserter(outputs_));
-  std::sort(outputs_.begin(), outputs_.end());
-
-  std::vector<int> tmp_index;
-  tmp_index.reserve(temp_output.size());
-  int output_len = static_cast<int>(outputs_.size());
-  for (int i = 0; i < output_len; ++i) {
-    if (Contains(temp_output, outputs_[i])) {
-      tmp_index.push_back(i);
+    for (auto& opt : op->Outputs()) {
+      for (auto& var_name : opt.second) {
+        output_set.insert(var_name);
+      }
     }
   }
-
-  attrs_["temporary_index"] = tmp_index;
+  auto& inputs = inputs_[kAll];
+  inputs.reserve(input_set.size());
+  std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs));
+  auto& outputs = outputs_[kAll];
+  outputs.reserve(output_set.size());
+  std::copy(output_set.begin(), output_set.end(), std::back_inserter(outputs));
 }
 
 std::string NetOp::DebugString() const {
@@ -73,5 +67,32 @@ std::string NetOp::DebugString() const {
 
 bool NetOp::IsNetOp() const { return true; }
 
+std::vector<std::string> NetOp::OutputVars(bool has_intermediate) const {
+  if (has_intermediate) {
+    return this->outputs_.at(kAll);
+  }
+  auto& all = this->outputs_.at(kAll);
+  std::vector<std::string> ret_val;
+  for (auto& each : all) {
+    if (!Contains(intermediate_outputs_, each)) {
+      ret_val.push_back(each);
+    }
+  }
+  return ret_val;
+}
+
+NetOp::NetOp(const std::string& type,
+             const framework::OperatorBase::VarNameMap& inputs,
+             const framework::OperatorBase::VarNameMap& outputs,
+             const framework::AttributeMap& attrs)
+    : framework::OperatorBase(type, inputs, outputs, attrs) {}
+
+std::unique_ptr<framework::OperatorBase> NetOp::Clone() const {
+  PADDLE_ENFORCE(
+      add_op_done_,
+      "Must clone a sealed NetOp, invoke Net::CompleteAddOp before clone");
+  return std::unique_ptr<OperatorBase>(new NetOp(*this));
+}
+
 }  // namespace operators
 }  // namespace paddle

paddle/operators/net_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/framework/framework.pb.h"
 #include "paddle/framework/op_registry.h"
 
 namespace paddle {
@@ -35,6 +36,21 @@ namespace operators {
  */
 class NetOp : public framework::OperatorBase {
  public:
+  static const char kAll[];
+  NetOp() : framework::OperatorBase("plain_net", {}, {}, {}) {}
+  NetOp(const std::string& type, const VarNameMap& inputs,
+        const VarNameMap& outputs, const framework::AttributeMap& attrs);
+
+  NetOp(const NetOp& o) : framework::OperatorBase(o.type_, {}, {}, o.attrs_) {
+    this->ops_.reserve(o.ops_.size());
+    std::transform(
+        o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_),
+        [](const std::unique_ptr<framework::OperatorBase>& op) {
+          return std::unique_ptr<framework::OperatorBase>(op->Clone());
+        });
+    this->CompleteAddOp();
+  }
+
   /**
    * Infer all the operators' input and output variables' shapes, will be called
    * before every mini-batch
@@ -68,21 +84,28 @@ class NetOp : public framework::OperatorBase {
     return true;
   }
 
+  void AppendOp(const framework::OperatorBase& op) { AppendOp(op.Clone()); }
+
   /**
    * @brief Add an operator by ptr
    */
-  void AddOp(const std::shared_ptr<OperatorBase>& op) {
-    PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed");
+  void AppendOp(std::unique_ptr<framework::OperatorBase> op) {
+    PADDLE_ENFORCE(!add_op_done_,
+                   "Cannot AppendOp when this network is sealed");
     PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
-    ops_.push_back(op);
+    ops_.push_back(std::move(op));
   }
 
-  void InsertOp(size_t pos, const std::shared_ptr<OperatorBase>& op) {
+  void InsertOp(size_t pos, std::unique_ptr<framework::OperatorBase> op) {
     PADDLE_ENFORCE(!add_op_done_,
                    "Cannot InsertOp when this network is sealed");
     PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
     PADDLE_ENFORCE_LE(pos, ops_.size(), "Out of range");
-    ops_.insert(ops_.begin() + pos, op);
+    ops_.insert(ops_.begin() + pos, std::move(op));
+  }
+
+  void InsertOp(size_t pos, const framework::OperatorBase& op) {
+    InsertOp(pos, op.Clone());
   }
 
   void CompleteAddOp(bool calculate = true);
@@ -90,11 +113,15 @@ class NetOp : public framework::OperatorBase {
   std::string DebugString() const override;
 
   bool IsNetOp() const override;
+  std::vector<std::string> OutputVars(bool has_intermediate) const override;
+
+  std::unique_ptr<framework::OperatorBase> Clone() const override;
 
-  std::vector<std::shared_ptr<OperatorBase>> ops_;
+  std::vector<std::unique_ptr<framework::OperatorBase>> ops_;
 
  private:
   bool add_op_done_{false};
+  std::set<std::string> intermediate_outputs_;
 
   template <typename T, typename KeyType>
   static bool Contains(T container, KeyType key) {

paddle/operators/net_op_test.cc

@@ -12,6 +12,8 @@ static int run_cnt = 0;
 
 class TestOp : public framework::OperatorBase {
  public:
+  using framework::OperatorBase::OperatorBase;
+  DEFINE_OP_CLONE_METHOD(TestOp);
   void InferShape(const Scope& scope) const override { ++infer_shape_cnt; }
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
@@ -19,12 +21,6 @@ class TestOp : public framework::OperatorBase {
   }
 };
 
-class EmptyOp : public framework::OperatorBase {
- public:
-  void InferShape(const Scope& scope) const override {}
-  void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {}
-};
-
 template <typename T>
 void AssertSameVectorWithoutOrder(const std::vector<T>& expected,
                                   const std::vector<T>& actual) {
@@ -42,46 +38,51 @@ TEST(OpKernel, all) {
   auto net = std::make_shared<NetOp>();
   ASSERT_NE(net, nullptr);
 
-  auto op1 = std::make_shared<TestOp>();
-  op1->inputs_ = {"x", "w1", "b1"};
-  op1->outputs_ = {"y"};
-  net->AddOp(op1);
-
-  auto op2 = std::make_shared<TestOp>();
-  op2->inputs_ = {"y", "w2", "b2"};
-  op2->outputs_ = {"z"};
-  net->AddOp(op2);
+  net->AppendOp(std::unique_ptr<TestOp>(
+      new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
+                 {{"Out", {"y"}}}, {})));
+  net->AppendOp(std::unique_ptr<TestOp>(
+      new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}},
+                 {{"Out", {"z"}}}, {})));
 
   net->CompleteAddOp();
-  AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"}, net->inputs_);
-  AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_);
-  auto tmp_idx_iter = net->attrs_.find("temporary_index");
-  ASSERT_NE(net->attrs_.end(), tmp_idx_iter);
-  auto& tmp_idx = boost::get<std::vector<int>>(tmp_idx_iter->second);
-  ASSERT_EQ(1UL, tmp_idx.size());
-  ASSERT_EQ("y", net->outputs_[tmp_idx[0]]);
+  AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
+                               net->Inputs(NetOp::kAll));
+  AssertSameVectorWithoutOrder({"y", "z"}, net->Outputs(NetOp::kAll));
 
-  Scope scope;
-  platform::CPUDeviceContext dev_ctx;
+  auto final_outs = net->OutputVars(false);
 
-  net->InferShape(scope);
-  net->Run(scope, dev_ctx);
-  ASSERT_EQ(2, infer_shape_cnt);
-  ASSERT_EQ(2, run_cnt);
-  ASSERT_THROW(net->AddOp(op2), platform::EnforceNotMet);
+  ASSERT_EQ(final_outs.size(), 1UL);
+  ASSERT_EQ(final_outs[0], "z");
 }
 
 TEST(NetOp, insert_op) {
   NetOp net;
-  auto op1 = std::make_shared<EmptyOp>();
-  op1->inputs_ = {"x", "w1", "b1"};
-  op1->outputs_ = {"y"};
-  net.AddOp(op1);
-  net.InsertOp(0, op1);
+  auto op1 = std::unique_ptr<framework::NOP>(
+      new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
+                         {{"Out", {"y"}}}, {}));
+  net.AppendOp(*op1);
+  net.InsertOp(0, *op1);
   ASSERT_EQ(2UL, net.ops_.size());
-  net.InsertOp(2, op1);
+  net.InsertOp(2, std::move(op1));
   ASSERT_EQ(3UL, net.ops_.size());
 }
 
+TEST(NetOp, Clone) {
+  NetOp net;
+  net.AppendOp(
+      std::unique_ptr<framework::NOP>(new framework::NOP{"empty", {}, {}, {}}));
+  net.AppendOp(std::unique_ptr<framework::NOP>(
+      new framework::NOP{"empty2", {}, {}, {}}));
+  net.CompleteAddOp(true);
+  auto new_net_op = net.Clone();
+  ASSERT_NE(new_net_op, nullptr);
+  ASSERT_TRUE(new_net_op->IsNetOp());
+  auto* new_net = static_cast<NetOp*>(new_net_op.get());
+  ASSERT_EQ(2, new_net->ops_.size());
+  ASSERT_EQ(new_net->ops_[0]->Type(), "empty");
+  ASSERT_EQ(new_net->ops_[1]->Type(), "empty2");
+}
+
 }  // namespace operators
 }  // namespace paddle

paddle/operators/recurrent_op.cc

@@ -36,15 +36,13 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      true /*infer_shape_mode*/);
   InitMemories(step_scopes[0], true /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(arg_->step_net);
-  PADDLE_ENFORCE(net != nullptr, "failed to get step net");
 
   for (size_t i = 0; i < seq_len_; i++) {
     if (i > 0) {
       rnn::LinkMemories(step_scopes, arg_->memories, i, -1,
                         true /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
+    (*stepnet_)->InferShape(*step_scopes[i]);
   }
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
                      true /*infer_shape_mode*/);
@@ -56,7 +54,6 @@ void RecurrentAlgorithm::Run(const Scope& scope,
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      false /*infer_shape_mode*/);
   InitMemories(step_scopes[0], false /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(arg_->step_net);
 
   for (size_t step_id = 0; step_id < seq_len_; step_id++) {
     // create output alias variables
@@ -64,7 +61,7 @@ void RecurrentAlgorithm::Run(const Scope& scope,
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
                         false /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
+    (*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
   }
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
                      false /*infer_shape_mode*/);
@@ -78,25 +75,28 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
   auto step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
 
   // Now all variables in scope must be created outside of op.
-  auto net_var = scope.FindVar(arg_->step_net);
-  PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope",
-                 arg_->step_net);
-  auto net_op = net_var->GetMutable<NetOp>();
-  PADDLE_ENFORCE(!net_op->outputs_.empty(), "net_op has no outputs");
+  PADDLE_ENFORCE_NOT_NULL(stepnet_);
+  PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs");
+  PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs");
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
       auto& step_scope = scope.NewScope();
 
       // create step net's temp inputs
-      for (auto& input : net_op->inputs_) {
+      for (auto& input : (*stepnet_)->Inputs()) {
         // the weight are located in parent scope
-        if (!step_scope.FindVar(input))
-          step_scope.NewVar(input)->GetMutable<Tensor>();
+        for (auto& var_name : input.second) {
+          if (!step_scope.FindVar(var_name)) {
+            step_scope.NewVar(var_name)->GetMutable<Tensor>();
+          }
+        }
       }
       // create stepnet's outputs
-      for (const auto& output : net_op->outputs_) {
-        step_scope.NewVar(output);
+      for (const auto& output : (*stepnet_)->Outputs()) {
+        for (auto& var_name : output.second) {
+          step_scope.NewVar(var_name);
+        }
       }
       step_scopes->emplace_back(&step_scope);
     }
@@ -130,11 +130,13 @@ const rnn::ArgumentName RecurrentGradientOp::kArgName{
     "inlink@grad", "inlink_alias", "outlink_alias",
     "memories",    "pre_memories", "boot_memories@grad"};
 
-void RecurrentOp::Init() {
-  OperatorBase::Init();
-  std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
-  rnn::InitArgument(kArgName, arg.get(), *this);
-  alg_.Init(std::move(arg));
+RecurrentOp::RecurrentOp(const std::string& type,
+                         const framework::OperatorBase::VarNameMap& inputs,
+                         const framework::OperatorBase::VarNameMap& outputs,
+                         const framework::AttributeMap& attrs)
+    : OperatorBase(type, inputs, outputs, attrs) {
+  rnn::InitArgument(kArgName, &arg_, *this);
+  alg_.Init(&arg_, &stepnet_);
 }
 
 class RecurrentAlgorithmProtoAndCheckerMaker
@@ -147,13 +149,12 @@ class RecurrentAlgorithmProtoAndCheckerMaker
     // inputs and outputs stored in proto
     AddInput(name.inlinks,
              "the inputs that need to be segmented for each step.")
-        .SetMultiple();
+        .AsDuplicable();
     AddInput(name.boot_memories, "variables to initialize memories.")
-        .SetMultiple();
-    AddInput(name.step_net, "network shared by all steps.");
+        .AsDuplicable();
 
     AddOutput(name.outlinks, "the outputs that need to concated for all steps.")
-        .SetMultiple();
+        .AsDuplicable();
     AddOutput(name.step_scopes, "step scopes");
 
     // Attributes stored in AttributeMap
@@ -172,14 +173,12 @@ void RecurrentGradientAlgorithm::Run(
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      false /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(arg_->step_net);
-  PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
                         false /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
+    (*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
   }
   LinkBootMemoryGradients(step_scopes[0], false);
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
@@ -211,29 +210,30 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      true /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(arg_->step_net);
-  PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
                         true /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
+    (*stepnet_)->InferShape(*step_scopes[step_id]);
   }
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
                      true /*infer_shape_mode*/);
   LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
 }
 
-void RecurrentGradientOp::Init() {
-  OperatorBase::Init();
-  std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
-  rnn::InitArgument(kArgName, arg.get(), *this);
-  alg_.Init(std::move(arg));
+RecurrentGradientOp::RecurrentGradientOp(
+    const std::string& type, const framework::OperatorBase::VarNameMap& inputs,
+    const framework::OperatorBase::VarNameMap& outputs,
+    const framework::AttributeMap& attrs)
+    : OperatorBase(type, inputs, outputs, attrs) {
+  rnn::InitArgument(kArgName, &arg_, *this);
+  alg_.Init(&arg_, &stepnet_);
 }
 
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(recurrent_op, paddle::operators::RecurrentOp,
-            paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
+REGISTER_OP_WITHOUT_GRADIENT(
+    recurrent_op, paddle::operators::RecurrentOp,
+    paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);

paddle/operators/recurrent_op.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include "paddle/framework/operator.h"
+#include "paddle/operators/net_op.h"
 #include "paddle/operators/rnn/recurrent_op_utils.h"
 
 namespace paddle {
@@ -33,7 +34,12 @@ class RecurrentAlgorithm {
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const;
 
-  void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
+  void Init(rnn::Argument* arg,
+            std::unique_ptr<framework::OperatorBase>* stepnet) {
+    PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
+    arg_ = arg;
+    stepnet_ = stepnet;
+  }
 
   /**
    * InferShape must be called before Run.
@@ -58,7 +64,8 @@ class RecurrentAlgorithm {
   void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
 
  private:
-  std::unique_ptr<rnn::Argument> arg_;
+  std::unique_ptr<framework::OperatorBase>* stepnet_;
+  rnn::Argument* arg_;
   mutable size_t seq_len_;
 };
 
@@ -74,7 +81,12 @@ class RecurrentGradientAlgorithm {
    * operator.
    */
  public:
-  void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
+  void Init(rnn::Argument* arg,
+            std::unique_ptr<framework::OperatorBase>* stepnet) {
+    PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
+    arg_ = std::move(arg);
+    stepnet_ = stepnet;
+  }
 
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const;
@@ -95,14 +107,22 @@ class RecurrentGradientAlgorithm {
   }
 
  private:
-  std::unique_ptr<rnn::Argument> arg_;
+  rnn::Argument* arg_;
   mutable size_t seq_len_;
+  std::unique_ptr<framework::OperatorBase>* stepnet_;
 };
 
-class RecurrentOp final : public framework::OperatorBase {
+class RecurrentOp : public framework::OperatorBase {
  public:
-  void Init() override;
-
+  RecurrentOp(const std::string& type, const VarNameMap& inputs,
+              const VarNameMap& outputs, const framework::AttributeMap& attrs);
+
+  RecurrentOp(const RecurrentOp& o)
+      : framework::OperatorBase(
+            static_cast<const framework::OperatorBase&>(o)) {
+    // TODO(yuyang18): Implement copy ctor well.
+    PADDLE_THROW("Not implemented");
+  }
   /**
    * InferShape must be called before Run.
    */
@@ -115,15 +135,31 @@ class RecurrentOp final : public framework::OperatorBase {
     alg_.Run(scope, dev_ctx);
   }
 
+  void set_stepnet(std::unique_ptr<OperatorBase> net) {
+    stepnet_ = std::move(net);
+  }
+  const OperatorBase& stepnet() const { return *stepnet_; }
+
   static const rnn::ArgumentName kArgName;
 
  private:
   RecurrentAlgorithm alg_;
+  rnn::Argument arg_;
+  std::unique_ptr<OperatorBase> stepnet_;
 };
 
-class RecurrentGradientOp final : public framework::OperatorBase {
+class RecurrentGradientOp : public framework::OperatorBase {
  public:
-  void Init() override;
+  RecurrentGradientOp(const std::string& type, const VarNameMap& inputs,
+                      const VarNameMap& outputs,
+                      const framework::AttributeMap& attrs);
+
+  RecurrentGradientOp(const RecurrentGradientOp& o)
+      : framework::OperatorBase(
+            static_cast<const framework::OperatorBase&>(o)) {
+    // TODO(yuyang18): Implement Copy ctor.
+    PADDLE_THROW("Not Implemented");
+  }
 
   /**
    * InferShape must be called before Run.
@@ -139,8 +175,15 @@ class RecurrentGradientOp final : public framework::OperatorBase {
 
   static const rnn::ArgumentName kArgName;
 
+  void set_stepnet(std::unique_ptr<OperatorBase> net) {
+    stepnet_ = std::move(net);
+  }
+  const OperatorBase& stepnet() const { return *stepnet_; }
+
  private:
   RecurrentGradientAlgorithm alg_;
+  std::unique_ptr<OperatorBase> stepnet_;
+  rnn::Argument arg_;
 };
 
 }  // namespace operators

paddle/operators/recurrent_op_test.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/operators/recurrent_op.h"
-
-#include <glog/logging.h>
-#include <gtest/gtest.h>
-
-#include "paddle/framework/ddim.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
-#include "paddle/framework/tensor.h"
-#include "paddle/operators/net_op.h"
-
-namespace paddle {
-namespace operators {
-
-using framework::make_ddim;
-using framework::DDim;
-using framework::Tensor;
-using framework::Variable;
-using framework::Scope;
-using framework::OpRegistry;
-
-class RecurrentOpTest : public ::testing::Test {
- protected:
-  virtual void SetUp() override {
-    CreateGlobalVariables();
-    CreateStepNet();
-    CreateRNNOp();
-  }
-
-  virtual void TearDown() override {}
-
-  void CreateGlobalVariables() {
-    // create input, and init content
-    LOG(INFO) << "create global variable x";
-    for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
-      Variable* x = scope_.NewVar(inlink);
-      DDim dims = make_ddim(std::vector<int>{
-          10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
-      x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
-    }
-    // create output alias just for test
-    for (auto inlink : std::vector<std::string>{"h@alias"}) {
-      Variable* x = scope_.NewVar(inlink);
-      DDim dims =
-          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
-      x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
-    }
-
-    LOG(INFO) << "create global variable w";
-    Variable* w = scope_.NewVar("rnn/w");
-    w->GetMutable<Tensor>()->mutable_data<float>(
-        make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
-
-    for (auto boot : std::vector<std::string>{"h_boot"}) {
-      LOG(INFO) << "create global variable " << boot;
-      Variable* h_boot = scope_.NewVar(boot);
-      h_boot->GetMutable<Tensor>()->mutable_data<float>(
-          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
-          platform::CPUPlace());
-    }
-
-    LOG(INFO) << "create variable step_scopes";
-    scope_.NewVar("step_scopes");
-
-    LOG(INFO) << "create variable h";
-    scope_.NewVar("h");
-  }
-
-  void CreateRNNOp() {
-    framework::OpDesc op_desc;
-
-    op_desc.set_type("recurrent_op");
-    // inlinks 0
-    op_desc.add_inputs("x");
-    op_desc.add_inputs("x0");
-    op_desc.add_inputs("x1");
-    // boot_memories 3
-    op_desc.add_inputs("h_boot");
-    // step net 5
-    op_desc.add_inputs("step_net");
-    // outlinks 6
-    op_desc.add_outputs("h");
-    // step scopes 7
-    op_desc.add_outputs("step_scopes");
-
-    auto _input_format = std::vector<int>{
-        0,  // in_link
-        3,  // memories
-        4   // step_net
-    };
-    auto input_format = op_desc.add_attrs();
-    input_format->set_name("input_format");
-    input_format->set_type(paddle::framework::AttrType::INTS);
-    for (auto i : _input_format) {
-      input_format->add_ints(i);
-    }
-
-    auto output_format = op_desc.add_attrs();
-    output_format->set_name("output_format");
-    output_format->set_type(paddle::framework::AttrType::INTS);
-    for (auto i : std::vector<int>{0, 1, 2}) {
-      output_format->add_ints(i);
-    }
-
-    auto inlink_alias = op_desc.add_attrs();
-    inlink_alias->set_name("inlink_alias");
-    inlink_alias->set_type(paddle::framework::AttrType::STRINGS);
-
-    auto outlink_alias = op_desc.add_attrs();
-    outlink_alias->set_name("outlink_alias");
-    outlink_alias->set_type(paddle::framework::AttrType::STRINGS);
-
-    auto pre_memories = op_desc.add_attrs();
-    pre_memories->set_name("pre_memories");
-    pre_memories->set_type(paddle::framework::AttrType::STRINGS);
-
-    auto memories = op_desc.add_attrs();
-    memories->set_name("memories");
-    memories->set_type(paddle::framework::AttrType::STRINGS);
-
-    // create inlink_alias
-    for (const auto& item :
-         std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
-      inlink_alias->add_strings(item);
-    }
-    // pre memories
-    for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
-      pre_memories->add_strings(item);
-    }
-    // memories
-    for (const auto& item : std::vector<std::string>{"rnn/h"}) {
-      memories->add_strings(item);
-    }
-    // output alias
-    for (const auto& item : std::vector<std::string>{"h@alias"}) {
-      outlink_alias->add_strings(item);
-    }
-
-    rnn_op_ = OpRegistry::CreateOp(op_desc);
-
-    LOG(INFO) << "rnn_op finish init";
-  }
-
-  void CreateStepNet() {
-    LOG(INFO) << "create variable step_net";
-    Variable* var = scope_.NewVar("step_net");
-    auto net = var->GetMutable<NetOp>();
-    net->AddOp(
-        OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
-
-    net->AddOp(
-        OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
-    net->CompleteAddOp();
-  }
-
-  // father scope
-  Scope scope_;
-  std::shared_ptr<framework::OperatorBase> rnn_op_;
-};
-
-TEST_F(RecurrentOpTest, Run) {
-  platform::CPUDeviceContext ctx;
-  rnn_op_->InferShape(scope_);
-  rnn_op_->Run(scope_, ctx);
-}
-
-class RecurrentGradientAlgorithmTest : public ::testing::Test {
- protected:
-  virtual void SetUp() override {
-    CreateGlobalVariables();
-    CreateStepScopes();
-    CreateStepNet();
-    CreateRNNGradientAlgorithm();
-
-    // segment inputs
-    SegmentInputs();
-    // link forward memories
-    LinkeMemories();
-  }
-
-  virtual void TearDown() override {}
-
-  void CreateGlobalVariables() {
-    // inputs: x
-    LOG(INFO) << "create global variable x";
-    Variable* x = scope_.NewVar("x");
-    DDim dims =
-        make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
-    x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
-    // inputs: h_boot
-    LOG(INFO) << "create global variable h_boot";
-    Variable* h_boot = scope_.NewVar("h_boot");
-    h_boot->GetMutable<Tensor>()->mutable_data<float>(
-        make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
-    // inputs: w
-    LOG(INFO) << "create global variable w";
-    Variable* w = scope_.NewVar("rnn/w");
-    w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
-                                                 platform::CPUPlace());
-    // inputs: h_grad
-    LOG(INFO) << "create variable h_grad";
-    Variable* dh = scope_.NewVar("h_grad");
-    dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
-                                                  platform::CPUPlace());
-    // inputs: step_scopes
-    LOG(INFO) << "create variable step_scopes";
-    scope_.NewVar("step_scopes");
-    // inputs: step_net
-    LOG(INFO) << "create variable step_net";
-    scope_.NewVar("step_net");
-    // outputs: w_grad
-    LOG(INFO) << "create global variable w_grad";
-    scope_.NewVar("rnn/w_grad");
-    // outputs: x_grad
-    LOG(INFO) << "create global variable x_grad";
-    scope_.NewVar("x_grad");
-    // outputs: h_boot_grad
-    LOG(INFO) << "create global variable h_boot_grad";
-    scope_.NewVar("h_boot_grad");
-  }
-
-  void CreateStepScopes() {
-    auto step_scopes =
-        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-    for (int i = 0; i < 10; ++i) {
-      auto& scope = scope_.NewScope();
-      auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
-      pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
-      auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
-      tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
-
-      // for unit test of ConcatOutputs
-      auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
-      xg->mutable_data<float>({20, 30}, platform::CPUPlace());
-
-      step_scopes->emplace_back(&scope);
-    }
-
-    // last time step
-    auto g = (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
-    g->mutable_data<float>({20, 30}, platform::CPUPlace());
-  }
-
-  void CreateRNNGradientAlgorithm() {
-    std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
-    arg->step_net = "step_net";
-    arg->step_scopes = "step_scopes";
-    rnn::Link inlink;
-    inlink.external = "h_grad";
-    inlink.internal = "rnn/h_grad";
-    arg->inlinks = std::vector<rnn::Link>{inlink};
-
-    rnn::Link outlink;
-    outlink.external = "x_grad";
-    outlink.internal = "rnn/x_grad";
-    arg->outlinks = std::vector<rnn::Link>{outlink};
-
-    rnn::MemoryAttr mem_attr;
-    mem_attr.pre_var = "rnn/h_pre_grad";
-    mem_attr.var = "rnn/h_grad";
-    mem_attr.boot_var = "h_boot_grad";
-    arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
-
-    rnn_grad_algo_.Init(std::move(arg));
-  }
-
-  void CreateStepNet() {
-    LOG(INFO) << "create variable step_net";
-    Variable* var = scope_.NewVar("step_net");
-    auto net = var->GetMutable<NetOp>();
-    net->AddOp(OpRegistry::CreateOp("mul", {"rnn/h_pre", "rnn/w", "rnn/s_grad"},
-                                    {"rnn/h_pre_grad", "rnn/w_grad"}, {}));
-
-    net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
-                                    {"rnn/x_grad", "rnn/s_grad"}, {}));
-    net->CompleteAddOp();
-  }
-
-  void SegmentInputs() {
-    LOG(INFO) << "segment inputs";
-    std::vector<std::string> inlinks = {"x"};
-    std::vector<std::string> inlinks_alias = {"rnn/x"};
-
-    rnn::Link inlink;
-    inlink.external = "x";
-    inlink.internal = "rnn/x";
-    auto step_scopes =
-        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-    rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
-                       true /*infer_shape_mode*/);
-  }
-
-  void LinkeMemories() {
-    LOG(INFO) << "link memories";
-    rnn::MemoryAttr mem_attr;
-    mem_attr.pre_var = "rnn/h_pre";
-    mem_attr.var = "rnn/h";
-    mem_attr.boot_var = "boot_h";
-    std::vector<rnn::MemoryAttr> memories;
-    memories.push_back(mem_attr);
-    auto step_scopes =
-        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-    for (int i = 1; i < 10; ++i) {
-      rnn::LinkMemories(*step_scopes, memories, i, -1,
-                        true /*infer_shape_mode*/);
-    }
-  }
-
-  Scope scope_;
-  RecurrentGradientAlgorithm rnn_grad_algo_;
-};
-
-// TEST_F(RecurrentGradientAlgorithmTest, Run) {
-//   platform::CPUDeviceContext ctx;
-//   rnn_grad_algo_.Run(scope_, ctx);
-// }
-
-}  // namespace operators
-}  // namespace paddle
-
-TEST(RecurrentOp, LinkMemories) {
-  using namespace paddle::framework;
-  using namespace paddle::platform;
-  using namespace paddle::operators;
-
-  // create and init step scopes
-  size_t len = 10;
-  std::vector<Scope*> step_scopes;
-  for (size_t i = 0; i < len; ++i) {
-    auto scope = new Scope();
-    scope->NewVar("pre_h");
-    auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
-    float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
-    for (size_t j = 0; j < 15 * 20; ++j) {
-      data[j] = rand() * (1. / (double)RAND_MAX);
-    }
-    step_scopes.push_back(scope);
-  }
-
-  // create MemoryAttr
-  rnn::MemoryAttr mem_attr;
-  mem_attr.pre_var = "pre_h";
-  mem_attr.var = "h";
-  mem_attr.boot_var = "boot_h";
-  std::vector<rnn::MemoryAttr> memories;
-  memories.push_back(mem_attr);
-
-  for (size_t i = 1; i < len; ++i) {
-    rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
-  }
-  // check
-  for (size_t i = 0; i < len - 1; ++i) {
-    const float* a =
-        step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
-    const float* b = step_scopes[i + 1]
-                         ->FindVar("pre_h")
-                         ->GetMutable<Tensor>()
-                         ->data<float>();
-    for (size_t j = 0; j < 15 * 20; ++j) {
-      ASSERT_FLOAT_EQ(a[j], b[j]);
-    }
-  }
-
-  for (int i = len - 2; i >= 0; --i) {
-    rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
-  }
-  // check
-  for (int i = len - 2; i >= 0; --i) {
-    const float* a =
-        step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
-    const float* b =
-        step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
-    for (size_t j = 0; j < 15 * 20; ++j) {
-      ASSERT_FLOAT_EQ(a[j], b[j]);
-    }
-  }
-
-  for (auto s : step_scopes) {
-    delete s;
-  }
-}
-
-USE_OP(add_two);
-USE_OP(mul);
-USE_OP_WITHOUT_KERNEL(recurrent_op);

paddle/operators/rnn/recurrent_op_utils.cc

@@ -106,7 +106,6 @@ void LinkMemories(const std::vector<Scope*>& scopes,
 
 void InitArgument(const ArgumentName& name, Argument* arg,
                   const framework::OperatorBase& op) {
-  arg->step_net = op.Input(name.step_net);
   arg->step_scopes = op.Output(name.step_scopes);
 
   auto inlinks = op.Inputs(name.inlinks);

paddle/operators/rowwise_add_op.cc

@@ -17,25 +17,28 @@
 namespace paddle {
 namespace operators {
 
-class RowWiseAddOp : public framework::OperatorWithKernel {
+using framework::Tensor;
+
+class RowwiseAddOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 2UL,
-                   "Two inputs is needed by rowwise add");
-    auto dim0 = ctx.Input<Tensor>(0)->dims();
-    auto dim1 = ctx.Input<Tensor>(1)->dims();
+    auto dim0 = ctx.Input<Tensor>("X")->dims();
+    auto dim1 = ctx.Input<Tensor>("b")->dims();
 
     PADDLE_ENFORCE(dim0.size() == 2, "Input 0 must be matrix");
     PADDLE_ENFORCE(dim1.size() == 1, "The second input must be vector");
     PADDLE_ENFORCE(dim0[1] == dim1[0], "The width of two input must be same");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1, "The output size must be 1");
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    PADDLE_ENFORCE(ctx.OutputSize("Out") == 1, "The output size must be 1");
+    ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
 
-class RowWiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
+class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  RowWiseAddOpMaker(framework::OpProto *proto,
+  RowwiseAddOpMaker(framework::OpProto *proto,
                     framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The left input of row-wise add op, must be matrix");
@@ -48,11 +51,32 @@ for i in xrange(X.shape[0]):
 )DOC");
   }
 };
+class RowwiseAddGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto dims0 = ctx.Input<Tensor>("X")->dims();
+    auto dims1 = ctx.Input<Tensor>("b")->dims();
+    PADDLE_ENFORCE_EQ(1, dims1.size(), "b dims should be 1")
+    ctx.Output<Tensor>(framework::GradVarName("X"))->Resize(dims0);
+    ctx.Output<Tensor>(framework::GradVarName("b"))->Resize(dims1);
+  }
+};
 
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(rowwise_add, ops::RowWiseAddOp, ops::RowWiseAddOpMaker);
+REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
+            rowwise_add_grad, ops::RowwiseAddGradOp);
+REGISTER_OP_CPU_KERNEL(
+    rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(
-    rowwise_add, ops::RowWiseAddKernel<paddle::platform::CPUPlace, float>);
+    rowwise_add_grad,
+    ops::RowwiseAddGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rowwise_add_op.cu

@@ -17,4 +17,4 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
-    rowwise_add, ops::RowWiseAddKernel<paddle::platform::GPUPlace, float>);
+    rowwise_add, ops::RowwiseAddKernel<paddle::platform::GPUPlace, float>);

paddle/operators/rowwise_add_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
+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
+    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. */
+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/framework/eigen.h"
@@ -28,14 +28,14 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class RowWiseAddKernel : public framework::OpKernel {
+class RowwiseAddKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto out = context.Output<Tensor>(0);
+    auto out = context.Output<Tensor>("Out");
     out->mutable_data<T>(context.GetPlace());
 
-    auto input = EigenMatrix<T>::From(*context.Input<Tensor>(0));
-    auto bias = EigenVector<T>::From(*context.Input<Tensor>(1));
+    auto input = EigenMatrix<T>::From(*context.Input<Tensor>("X"));
+    auto bias = EigenVector<T>::From(*context.Input<Tensor>("b"));
     auto output = EigenMatrix<T>::From(*out);
 
     const int bias_size = bias.dimension(0);
@@ -47,5 +47,25 @@ class RowWiseAddKernel : public framework::OpKernel {
   }
 };
 
+template <typename Place, typename T>
+class RowwiseAddGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* db = context.Output<Tensor>(framework::GradVarName("b"));
+    dX->mutable_data<T>(context.GetPlace());
+    db->mutable_data<T>(context.GetPlace());
+
+    auto OutGrad = EigenMatrix<T>::From(*dOut);
+    auto place = context.GetEigenDevice<Place>();
+    EigenMatrix<T>::From(*dX).device(place) = OutGrad;
+
+    // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
+    // colwise add
+    Eigen::array<int, 1> dims{{0}}; /* dimension to reduce */
+    EigenVector<T>::Flatten(*db).device(place) = OutGrad.sum(dims);
+  }
+};
 }  // namespace operators
 }  // namespace paddle

paddle/operators/scatter.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 <cstring>
+
+#include "paddle/framework/ddim.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/place.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
+// Implementation of CPU copy
+template <typename T>
+void CPUScatterUpdate(const paddle::framework::Tensor* src, const int* index,
+                      const size_t index_size,
+                      paddle::framework::Tensor* output) {
+  paddle::framework::DDim output_dims = output->dims();
+
+  for (size_t i = 0; i < index_size; ++i) {
+    int index_ = index[i];
+
+    paddle::framework::Tensor src_ = *src;
+    paddle::framework::Tensor output_ = *output;
+    if (index_size > 1) src_ = src->Slice<T>(i, i + 1);
+    if (output_dims[0] > 1) output_ = output->Slice<T>(index_, index_ + 1);
+
+    auto X = EigenVector<T>::Flatten(src_);
+    auto Y = EigenVector<T>::Flatten(output_);
+
+    Y = X + Y;
+  }
+}
+
+// Implementation of GPU scatter:
+template <typename T>
+void GPUScatterUpdate(const T* src, const int* index, const int slice_size,
+                      const int index_size, T* output);
+
+/**
+ * Return a updated tensor from source tensor, scattered according to index:
+ * dst[i] += src[index[i]]
+ * input[src]: type-T source Tensor
+ * input[index]: type-int index Tensor (1-D)
+ * return: output tensor
+ */
+template <typename T>
+void ScatterUpdate(const platform::Place& place,
+                   const paddle::framework::Tensor* src,
+                   const paddle::framework::Tensor* index,
+                   paddle::framework::Tensor* output) {
+  // check index of shape 1-D
+  PADDLE_ENFORCE(index->dims().size() == 1);
+  int index_size = index->dims()[0];
+
+  auto src_dims = src->dims();
+  auto dst_dims = output->dims();
+
+  // check src shape and dst shape should match
+  for (int i = 1; i < src_dims.size(); i++)
+    PADDLE_ENFORCE(src_dims[i] == dst_dims[i]);
+
+  // slice size
+  size_t slice_size = 1;
+  for (int i = 0; i < src_dims.size(); ++i) slice_size *= src_dims[i];
+
+  if (platform::is_cpu_place(place)) {
+    CPUScatterUpdate<T>(src, index->data<int>(), index_size, output);
+  } else {
+  }
+}
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/scatter_test.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/operators/scatter.h"
+#include "paddle/framework/ddim.h"
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/place.h"
+
+#include <gtest/gtest.h>
+#include <iostream>
+#include <string>
+
+TEST(scatter, ScatterUpdate) {
+  using namespace paddle::framework;
+  using namespace paddle::platform;
+  using namespace paddle::operators;
+
+  Tensor* src = new Tensor();
+  Tensor* index = new Tensor();
+  Tensor* output = new Tensor();
+
+  float* p_src = nullptr;
+  int* p_index = nullptr;
+  p_src = src->mutable_data<float>(make_ddim({1, 4}), CPUPlace());
+  p_index = index->mutable_data<int>(make_ddim({1}), CPUPlace());
+
+  for (size_t i = 0; i < 4; ++i) p_src[i] = float(i);
+  p_index[0] = 1;
+
+  float* p_output = output->mutable_data<float>(make_ddim({4, 4}), CPUPlace());
+
+  ScatterUpdate<float>(CPUPlace(), src, index, output);
+
+  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], float(0));
+  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(output->data<float>()[i], float(0));
+  for (size_t i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], float(i - 4));
+  for (size_t i = 4; i < 8; ++i)
+    EXPECT_EQ(output->data<float>()[i], float(i - 4));
+  for (size_t i = 8; i < 16; ++i) EXPECT_EQ(p_output[i], float(0));
+  for (size_t i = 8; i < 16; ++i) EXPECT_EQ(output->data<float>()[i], float(0));
+
+  delete src;
+  delete index;
+  delete output;
+}

paddle/operators/sgd_op.cc

@@ -18,16 +18,15 @@ namespace paddle {
 namespace operators {
 
 class SGDOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 2, "Input size of SGDOp must be two");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of SGDOp must be one");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "inputs[0] mast be set");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(1), "inputs[1] mast be set");
-    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0), "outputs[0] mast be set");
-    PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims() == ctx.Input<Tensor>(1)->dims(),
-                   "Two input of SGD Op's dimension must be same.");
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    PADDLE_ENFORCE(
+        ctx.Input<Tensor>("param")->dims() == ctx.Input<Tensor>("grad")->dims(),
+        "Two input of SGD Op's dimension must be same.");
+    ctx.Output<Tensor>("param_out")->Resize(ctx.Input<Tensor>("param")->dims());
   }
 };
 
@@ -52,6 +51,6 @@ param_out = param - learning_rate * grad;
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(sgd, ops::SGDOp, ops::SGDOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(sgd, ops::SGDOp, ops::SGDOpMaker);
 REGISTER_OP_CPU_KERNEL(sgd,
                        ops::SGDOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sgd_op.h

@@ -30,7 +30,7 @@ class SGDOpKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto param = ctx.Input<Tensor>("param");
     auto grad = ctx.Input<Tensor>("grad");
-    auto param_out = ctx.Output<Tensor>(0);
+    auto param_out = ctx.Output<Tensor>("param_out");
     float lr = ctx.op_.GetAttr<float>("learning_rate");
 
     param_out->mutable_data<T>(ctx.GetPlace());

paddle/operators/sigmoid_op.cc

@@ -18,11 +18,12 @@ namespace paddle {
 namespace operators {
 
 class SigmoidOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1, "Sigmoid Op only have one output");
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
 
@@ -38,9 +39,13 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
 };
 
 class SigmoidOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    ctx.Output<Tensor>(framework::GradVarName("X"))
+        ->Resize(ctx.Input<Tensor>("Y")->dims());
   }
 };
 
@@ -48,9 +53,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker);
-REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad);
-
+REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
+            ops::SigmoidOpGrad);
 REGISTER_OP_CPU_KERNEL(sigmoid,
                        ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/sigmoid_op.h

@@ -28,8 +28,8 @@ template <typename Place, typename T>
 class SigmoidKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto input = context.Input<Tensor>(0);
-    auto output = context.Output<Tensor>(0);
+    auto input = context.Input<Tensor>("X");
+    auto output = context.Output<Tensor>("Y");
     output->mutable_data<T>(context.GetPlace());
 
     // The clipping is used in Paddle's raw implenmention
@@ -37,7 +37,7 @@ class SigmoidKernel : public framework::OpKernel {
     auto Y = EigenVector<T>::Flatten(*output);
     auto place = context.GetEigenDevice<Place>();
 
-    Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp());
+    Y.device(place) = 1. / (1. + (-X).exp());
   }
 };
 

paddle/operators/softmax_op.cc

@@ -18,14 +18,13 @@ namespace paddle {
 namespace operators {
 
 class SoftmaxOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
-                      "Only one input is need for softmax");
-    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims().size(), 2UL,
-                      "The input of softmax op must be matrix");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
-                      "Only one output is need for softmax");
+    PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
+                   "The input of softmax op must be matrix");
     ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
@@ -42,13 +41,12 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
 };
 
 class SoftmaxOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL,
-                      "Input of SoftmaxOpGrad should be 3, X, Y, YG");
-    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
-                      "Output of SoftmaxOpGrad should be 1");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+    PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
                             "Input(Y@GRAD) should not be null");
     PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
@@ -64,9 +62,9 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
+REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
+            ops::SoftmaxOpGrad);
 REGISTER_OP_CPU_KERNEL(softmax,
                        ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
-REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
 REGISTER_OP_CPU_KERNEL(
     softmax_grad, ops::SoftmaxGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/uniform_random_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.
@@ -27,7 +24,7 @@ template <typename T>
 class CPUUniformRandomKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>(0);
+    auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
     unsigned int seed =
         static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
@@ -39,18 +36,22 @@ class CPUUniformRandomKernel : public framework::OpKernel {
     std::uniform_real_distribution<T> dist(
         static_cast<T>(context.op_.GetAttr<float>("min")),
         static_cast<T>(context.op_.GetAttr<float>("max")));
-    for (ssize_t i = 0; i < framework::product(tensor->dims()); ++i) {
+    ssize_t size = framework::product(tensor->dims());
+    for (ssize_t i = 0; i < size; ++i) {
       data[i] = dist(engine);
     }
   }
 };
 
 class UniformRandomOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext& ctx) const override {
     PADDLE_ENFORCE(GetAttr<float>("min") < GetAttr<float>("max"),
                    "uniform_random's min must less then max");
-    auto* tensor = ctx.Output<framework::Tensor>(0);
+    auto* tensor = ctx.Output<framework::Tensor>("Out");
     auto dims = GetAttr<std::vector<int>>("dims");
     tensor->Resize(framework::make_ddim(dims));
   }
@@ -63,7 +64,6 @@ class UniformRandomOpMaker : public framework::OpProtoAndCheckerMaker {
       : framework::OpProtoAndCheckerMaker(proto, op_checker) {
     AddOutput("Out", "The output tensor of uniform random op");
     AddComment(R"DOC(Uniform random operator.
-
 Used to initialize tensor with uniform random generator.
 )DOC");
     AddAttr<std::vector<int>>("dims", "the dimension of random tensor");
@@ -78,7 +78,7 @@ Used to initialize tensor with uniform random generator.
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(uniform_random, paddle::operators::UniformRandomOp,
-            paddle::operators::UniformRandomOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp,
+                             paddle::operators::UniformRandomOpMaker);
 REGISTER_OP_CPU_KERNEL(uniform_random,
                        paddle::operators::CPUUniformRandomKernel<float>);

paddle/operators/uniform_random_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.
@@ -46,7 +43,7 @@ template <typename T>
 class GPUUniformRandomKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>(0);
+    auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
     unsigned int seed =
         static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));

paddle/parameter/Parameter.cpp

@@ -48,7 +48,8 @@ Parameter::Parameter(const ParameterConfig& config, bool useGpu, bool doInit)
       deviceId_(-1),
       sharedCount_(0),
       updateCounter_(0),
-      updated_(false) {
+      updated_(false),
+      headerFormat_(PARAM_FORMAT_ORIGINAL) {
   setID(-1); /* capture uninitialized id */
   if (useGpu_ && FLAGS_parallel_nn) {
     /* gpu environment is specified by device property */
@@ -285,7 +286,7 @@ bool Parameter::save(const std::string& filename) const {
 bool Parameter::save(std::ostream& s) const {
   CpuVector vec(*bufs_[PARAMETER_VALUE].get());
   Header header;
-  header.version = kFormatVersion;
+  header.format = headerFormat_;
   header.valueSize = sizeof(real);
   header.size = getSize();
 
@@ -344,8 +345,9 @@ bool Parameter::load(std::istream& s) {
   Header header;
   CHECK(s.read(reinterpret_cast<char*>(&header), sizeof(header)))
       << "Fail to read parameter " << getName();
-  CHECK_EQ(header.version, kFormatVersion) << "Incorrect format version: "
-                                           << header.version;
+  CHECK(isHeaderFormatSupported(header.format)) << "Incorrect format version: "
+                                                << header.format;
+  headerFormat_ = header.format;
   CHECK_EQ(header.size, getSize())
       << "The size (" << header.size << ") in the file does not match the size "
       << "(" << getSize() << ") of the parameter: " << getName();

paddle/parameter/Parameter.h

@@ -34,6 +34,20 @@ limitations under the License. */
 
 namespace paddle {
 
+typedef enum {
+  /// The paddle original basic format
+  PARAM_FORMAT_ORIGINAL = 0,
+
+  /// See mkldnn_memory_format_t in
+  /// https://github.com/01org/mkl-dnn/blob/master/include/mkldnn_types.h
+  /// for a detailed description.
+  /// 2D weights tensor in the format (output channels, input channels).
+  PARAM_FORMAT_MKLDNN_OI,
+
+  /// The total format items numbers
+  PARAM_FORMAT_ITEMS,
+} PARAM_FORMAT;
+
 class SparsePrefetchRowCpuMatrix;
 
 class Parameter;
@@ -51,7 +65,10 @@ public:
   size_t getSize() const { return config_.size(); }
 
   bool isFullSize() const {
-    return this->getSize() == bufs_[PARAMETER_VALUE]->getSize();
+    if (bufs_[PARAMETER_VALUE]) {
+      return this->getSize() == bufs_[PARAMETER_VALUE]->getSize();
+    }
+    return false;
   }
 
   inline bool useGpu() const { return useGpu_; }
@@ -242,14 +259,30 @@ public:
   /// Initialize the value to 0
   void zeroMem();
 
-  static const int kFormatVersion = 0;
   /// file header structure
   struct Header {
-    int32_t version;     // = 0, file format version
+    int32_t format;      // = PARAM_FORMAT
     uint32_t valueSize;  // = sizeof(real)
     uint64_t size;       // = getSize()
   };
 
+  /**
+   * @brief Is the header format supported.
+   */
+  static bool isHeaderFormatSupported(int32_t fmt) {
+    return fmt < PARAM_FORMAT_ITEMS;
+  }
+
+  /**
+   * @brief Get the format in header.
+   */
+  int getHeaderFormat() { return headerFormat_; }
+
+  /**
+   * @brief Set the format in header.
+   */
+  void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; }
+
   /**
    * @brief  Parameter Update Hook.
    *
@@ -321,6 +354,9 @@ protected:
   bool updated_;
   SparseFormat format_;
 
+  /// The header format for saving or loading param
+  int32_t headerFormat_;
+
   std::vector<std::shared_ptr<IParameterUpdaterHook>> updaterHooks_;
 
 public:

paddle/platform/CMakeLists.txt

 cc_library(cpu_info SRCS cpu_info.cc DEPS gflags glog)
 cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
 
-nv_library(gpu_info SRCS gpu_info.cc DEPS gflags)
+nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog)
 
 cc_library(place SRCS place.cc)
 cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
@@ -9,6 +9,7 @@ cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
 add_subdirectory(dynload)
 
 cc_test(enforce_test SRCS enforce_test.cc DEPS stringpiece)
+cc_test(environment_test SRCS environment_test.cc DEPS stringpiece)
 
 IF(WITH_GPU)
     set(GPU_CTX_DEPS dynload_cuda dynamic_loader)
@@ -16,5 +17,8 @@ ELSE()
     set(GPU_CTX_DEPS)
 ENDIF()
 
-cc_library(device_context SRCS device_context.cc DEPS place eigen3 ${GPU_CTX_DEPS})
+# memcpy deoends on device_context, here add deps individually for
+# avoiding cycle dependencies
+cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
+    system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS})
 nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)

paddle/platform/device_context.cc

@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/platform/device_context.h"
+#include "paddle/memory/memory.h"
 
 namespace paddle {
 namespace platform {
@@ -36,6 +37,59 @@ Place CPUDeviceContext::GetPlace() const { return CPUPlace(); }
 
 #ifndef PADDLE_ONLY_CPU
 
+class EigenCudaStreamDevice : public Eigen::StreamInterface {
+ public:
+  EigenCudaStreamDevice() : scratch_(nullptr), semaphore_(nullptr) {
+    Eigen::initializeDeviceProp();
+  }
+  ~EigenCudaStreamDevice() override {}
+
+  void Reinitialize(const cudaStream_t* cuda_stream, GPUPlace place) {
+    stream_ = cuda_stream;
+    place_ = place;
+    device_prop_ = &Eigen::m_deviceProperties[place.device];
+  }
+
+  const cudaStream_t& stream() const override { return *stream_; }
+
+  const cudaDeviceProp& deviceProperties() const override {
+    return *device_prop_;
+  }
+
+  void* allocate(size_t num_bytes) const override {
+    return paddle::memory::Alloc(place_, num_bytes);
+  }
+
+  void deallocate(void* buffer) const override {
+    paddle::memory::Free(place_, buffer);
+  }
+
+  void* scratchpad() const override {
+    if (scratch_ == NULL) {
+      scratch_ = allocate(Eigen::kCudaScratchSize + sizeof(unsigned int));
+    }
+    return scratch_;
+  }
+
+  unsigned int* semaphore() const override {
+    if (semaphore_ == NULL) {
+      char* scratch =
+          static_cast<char*>(scratchpad()) + Eigen::kCudaScratchSize;
+      semaphore_ = reinterpret_cast<unsigned int*>(scratch);
+      PADDLE_ENFORCE(
+          cudaMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_));
+    }
+    return semaphore_;
+  }
+
+ private:
+  GPUPlace place_;
+  const cudaStream_t* stream_;         // not owned;
+  const cudaDeviceProp* device_prop_;  // not owned;
+  mutable void* scratch_;
+  mutable unsigned int* semaphore_;
+};
+
 template <>
 Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
   return reinterpret_cast<const CUDADeviceContext*>(this)->eigen_device();
@@ -43,19 +97,9 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
 
 CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) {
   SetDeviceId(place_.device);
-  // TODO(qijun) Pass a created cuda stream to Eigen::CudaStreamDevice directly
-  // here will cause segment fault. We must implement a class derived from
-  // Eigen::StreamInterface, and reinitialize it with a cuda stream and a gpu id
-  // later. Please refer to the implementation of class EigenCudaStreamDevice
-  // in TensorFlow.
-  //
-  // We find that CUDA 7 introduces a new option, the per-thread default stream,
-  // that has two effects. Please refer to https://devblogs.nvidia.com/
-  // parallelforall/gpu-pro-tip-cuda-7-streams-simplify-concurrency/
-  //
-  // So, we decide to use default stream and add –default-stream per-thread nvcc
-  // flag. Than, two threads with two CUDADeviceContexts will run parallelly.
-  eigen_stream_.reset(new Eigen::CudaStreamDevice());
+  PADDLE_ENFORCE(cudaStreamCreate(&stream_));
+  eigen_stream_.reset(new EigenCudaStreamDevice());
+  eigen_stream_->Reinitialize(&stream_, place);
   eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get()));
 }
 
@@ -70,17 +114,15 @@ CUDADeviceContext::~CUDADeviceContext() {
     PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
   }
 
-  if (curand_generator_) {
-    PADDLE_ENFORCE(dynload::curandDestroyGenerator(curand_generator_));
-  }
   eigen_stream_.reset();
   eigen_device_.reset();
+  PADDLE_ENFORCE(cudaStreamDestroy(stream_));
 }
 
 Place CUDADeviceContext::GetPlace() const { return place_; }
 
 void CUDADeviceContext::Wait() const {
-  PADDLE_ENFORCE(cudaStreamSynchronize(0));
+  PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
 }
 
 Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
@@ -91,6 +133,7 @@ cublasHandle_t CUDADeviceContext::cublas_handle() {
   if (!cublas_handle_) {
     SetDeviceId(place_.device);
     PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
+    PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
   }
   return cublas_handle_;
 }
@@ -99,20 +142,12 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() {
   if (!cudnn_handle_) {
     SetDeviceId(place_.device);
     PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
+    PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream_));
   }
   return cudnn_handle_;
 }
 
-curandGenerator_t CUDADeviceContext::curand_generator() {
-  if (!curand_generator_) {
-    SetDeviceId(place_.device);
-    PADDLE_ENFORCE(dynload::curandCreateGenerator(&curand_generator_,
-                                                  CURAND_RNG_PSEUDO_DEFAULT));
-    PADDLE_ENFORCE(
-        dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_));
-  }
-  return curand_generator_;
-}
+cudaStream_t CUDADeviceContext::stream() { return stream_; }
 
 #endif  // PADDLE_ONLY_CPU
 

paddle/platform/device_context.h

@@ -17,7 +17,6 @@ limitations under the License. */
 #ifndef PADDLE_ONLY_CPU
 #include "paddle/platform/dynload/cublas.h"
 #include "paddle/platform/dynload/cudnn.h"
-#include "paddle/platform/dynload/curand.h"
 #include "paddle/platform/gpu_info.h"
 #define EIGEN_USE_GPU
 #endif
@@ -40,7 +39,7 @@ class DeviceContext {
 class CPUDeviceContext : public DeviceContext {
  public:
   CPUDeviceContext();
-  explicit CPUDeviceContext(CPUPlace);
+  explicit CPUDeviceContext(CPUPlace place);
   virtual ~CPUDeviceContext() {}
 
   Eigen::DefaultDevice* eigen_device() const;
@@ -52,10 +51,11 @@ class CPUDeviceContext : public DeviceContext {
 };
 
 #ifndef PADDLE_ONLY_CPU
+class EigenCudaStreamDevice;
 
 class CUDADeviceContext : public DeviceContext {
  public:
-  explicit CUDADeviceContext(GPUPlace);
+  explicit CUDADeviceContext(GPUPlace place);
   virtual ~CUDADeviceContext();
 
   /*! \brief  Wait for all operations completion in the stream. */
@@ -74,24 +74,20 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return cudnn  handle in the device context. */
   cudnnHandle_t     cudnn_handle();
 
-  /*! \brief  Return curand handle in the device context. */
-  curandGenerator_t curand_generator();
+  /*! \brief  Return cuda stream in the device context. */
+  cudaStream_t      stream();
   // clang-format on
 
  private:
   GPUPlace place_;
 
- private:
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
-  std::unique_ptr<Eigen::CudaStreamDevice> eigen_stream_;
-
- private:
-  uint64_t seed_;
+  std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
 
   // clang-format off
-  cudnnHandle_t     cudnn_handle_     = nullptr;
-  cublasHandle_t    cublas_handle_    = nullptr;
-  curandGenerator_t curand_generator_ = nullptr;
+  cudaStream_t       stream_{nullptr};
+  cudnnHandle_t      cudnn_handle_{nullptr};
+  cublasHandle_t     cublas_handle_{nullptr};
   // clang-format on
 };
 

paddle/platform/device_context_test.cc

@@ -43,8 +43,7 @@ TEST(Device, CUDADeviceContext) {
     ASSERT_NE(nullptr, cudnn_handle);
     cublasHandle_t cublas_handle = device_context->cublas_handle();
     ASSERT_NE(nullptr, cublas_handle);
-    curandGenerator_t curand_handle = device_context->curand_generator();
-    ASSERT_NE(nullptr, curand_handle);
+    ASSERT_NE(nullptr, device_context->stream());
     delete device_context;
   }
 }

paddle/platform/dynload/cublas.h

@@ -62,12 +62,12 @@ extern void *cublas_dso_handle;
   DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name)
 
 #define CUBLAS_BLAS_ROUTINE_EACH(__macro) \
-  __macro(cublasSgemv);                   \
-  __macro(cublasDgemv);                   \
-  __macro(cublasSgemm);                   \
-  __macro(cublasDgemm);                   \
-  __macro(cublasSgeam);                   \
-  __macro(cublasDgeam);                   \
+  __macro(cublasSgemv_v2);                \
+  __macro(cublasDgemv_v2);                \
+  __macro(cublasSgemm_v2);                \
+  __macro(cublasDgemm_v2);                \
+  __macro(cublasSgeam_v2);                \
+  __macro(cublasDgeam_v2);                \
   __macro(cublasCreate_v2);               \
   __macro(cublasDestroy_v2);              \
   __macro(cublasSetStream_v2);            \

paddle/platform/enforce.h

@@ -14,14 +14,21 @@ limitations under the License. */
 
 #pragma once
 
-#include <execinfo.h>
+#include <dlfcn.h>     // for dladdr
+#include <execinfo.h>  // for backtrace
 #include <iomanip>
+#include <memory>
 #include <sstream>
 #include <stdexcept>
 #include <string>
+
 #include "paddle/string/printf.h"
 #include "paddle/string/to_string.h"
 
+#ifdef __GNUC__
+#include <cxxabi.h>  // for __cxa_demangle
+#endif
+
 #ifndef PADDLE_ONLY_CPU
 
 #include "paddle/platform/dynload/cublas.h"
@@ -39,6 +46,19 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 
+namespace {
+#ifdef __GNUC__
+inline std::string demangle(std::string name) {
+  int status = -4;  // some arbitrary value to eliminate the compiler warning
+  std::unique_ptr<char, void (*)(void*)> res{
+      abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
+  return (status == 0) ? res.get() : name;
+}
+#else
+inline std::string demangle(std::string name) { return name; }
+#endif
+}
+
 struct EnforceNotMet : public std::exception {
   std::exception_ptr exp_;
   std::string err_str_;
@@ -48,15 +68,29 @@ struct EnforceNotMet : public std::exception {
       std::rethrow_exception(exp_);
     } catch (const std::exception& exp) {
       std::ostringstream sout;
+
       sout << string::Sprintf("%s at [%s:%d]", exp.what(), f, l) << std::endl;
-      sout << "Call Stacks: " << std::endl;
+      sout << "PaddlePaddle Call Stacks: " << std::endl;
+
       void* call_stack[TRACE_STACK_LIMIT];
-      int sz = backtrace(call_stack, TRACE_STACK_LIMIT);
-      auto line = backtrace_symbols(call_stack, sz);
-      for (int i = 0; i < sz; ++i) {
-        sout << line[i] << std::endl;
+      auto size = backtrace(call_stack, TRACE_STACK_LIMIT);
+      auto symbols = backtrace_symbols(call_stack, size);
+
+      Dl_info info;
+      for (int i = 0; i < size; ++i) {
+        if (dladdr(call_stack[i], &info)) {
+          auto demangled = demangle(info.dli_sname);
+          auto addr_offset = static_cast<char*>(call_stack[i]) -
+                             static_cast<char*>(info.dli_saddr);
+          sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
+                                  2 + sizeof(void*) * 2, call_stack[i],
+                                  demangled, addr_offset);
+        } else {
+          sout << string::Sprintf("%-3d %*0p\n", i, 2 + sizeof(void*) * 2,
+                                  call_stack[i]);
+        }
       }
-      free(line);
+      free(symbols);
       err_str_ = sout.str();
     }
   }
@@ -170,7 +204,7 @@ inline void throw_on_error(T e) {
  *    PADDLE_ENFORCE_EQ(a, b);
  *
  *    will raise an expression described as follows:
- *    "enforce a == b failed, 1 != 2" with detailed stack infomation.
+ *    "enforce a == b failed, 1 != 2" with detailed stack information.
  *
  *    extra messages is also supported, for example:
  *    PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)

paddle/framework/op_desc.proto → paddle/platform/environment.h

@@ -12,45 +12,49 @@ 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. */
 
-syntax = "proto2";
-package paddle.framework;
-
-import "attribute.proto";
-
-// AttrDesc is used to describe Attributes of an Operator. It contain's
-// name, type, and value of Attribute.
-//
-// e.g, for scale=3.0: name=scala, type=AttrType.FLOAT, value=3.0
-message AttrDesc {
-  required string name = 1;
-  required AttrType type = 2;
-  optional int32 i = 3;
-  optional float f = 4;
-  optional string s = 5;
-  repeated int32 ints = 6;
-  repeated float floats = 7;
-  repeated string strings = 8;
-};
-
-// Protocol Message to describe an Operator.
-//
-// In PaddlePaddle, Operator is used to do a certain computation such
-// as "add", "sub", "cosine", etc.
-//  (1) Operator needs to know the input and output variable names.
-//  (2) Some ops may have special attributes such as "scale" in "CosineOp".
-//
-// 3rd-party language can build this proto message and call
-// AddOp(const OpDesc& op_desc) of Paddle core to create an Operator.
-message OpDesc {
-  // input names of this Operator.
-  repeated string inputs = 1;
-
-  // output names of this Operator.
-  repeated string outputs = 2;
-
-  // type of this Operator, such as "add", "sub", "fc".
-  required string type = 3;
-
-  // Attributes of this Operator. e.g., scale=3.0 in cosine op.
-  repeated AttrDesc attrs = 4;
-};
\ No newline at end of file
+#pragma once
+
+#include <stdlib.h>
+#include <unistd.h>
+#include <vector>
+
+#include "paddle/platform/enforce.h"
+#include "paddle/string/piece.h"
+
+extern char** environ;  // for environment variables
+
+namespace paddle {
+namespace platform {
+
+inline void SetEnvVariable(const std::string& name, const std::string& value) {
+  PADDLE_ENFORCE_NE(setenv(name.c_str(), value.c_str(), 1), -1,
+                    "Failed to set environment variable %s=%s", name, value);
+}
+
+inline void UnsetEnvVariable(const std::string& name) {
+  PADDLE_ENFORCE_NE(unsetenv(name.c_str()), -1,
+                    "Failed to unset environment variable %s", name);
+}
+
+inline bool IsEnvVarDefined(const std::string& name) {
+  return std::getenv(name.c_str()) != nullptr;
+}
+
+inline std::string GetEnvValue(const std::string& name) {
+  PADDLE_ENFORCE(IsEnvVarDefined(name),
+                 "Tried to access undefined environment variable %s", name);
+  return std::getenv(name.c_str());
+}
+
+inline std::vector<std::string> GetAllEnvVariables() {
+  std::vector<std::string> vars;
+  for (auto var = environ; *var != nullptr; ++var) {
+    auto tail = string::Index(*var, "=");
+    auto name = string::SubStr(*var, 0, tail).ToString();
+    vars.push_back(name);
+  }
+  return vars;
+}
+
+}  // namespace platform
+}  // namespace paddle

paddle/platform/environment_test.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/platform/environment.h"
+
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+TEST(ENVIRONMENT, ACCESS) {
+  namespace platform = paddle::platform;
+  namespace string = paddle::string;
+
+  platform::SetEnvVariable("PADDLE_USE_ENV", "TRUE");
+
+  EXPECT_TRUE(platform::IsEnvVarDefined("PADDLE_USE_ENV"));
+  EXPECT_EQ(platform::GetEnvValue("PADDLE_USE_ENV"), "TRUE");
+
+  platform::UnsetEnvVariable("PADDLE_USE_ENV");
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV"));
+
+  platform::SetEnvVariable("PADDLE_USE_ENV1", "Hello ");
+  platform::SetEnvVariable("PADDLE_USE_ENV2", "World, ");
+  platform::SetEnvVariable("PADDLE_USE_ENV3", "PaddlePaddle!");
+
+  std::string env_info;
+  auto vars = platform::GetAllEnvVariables();
+  for_each(vars.begin(), vars.end(), [&](const std::string& var) {
+    env_info += platform::GetEnvValue(var);
+  });
+
+  EXPECT_TRUE(string::Contains(env_info, "Hello World, PaddlePaddle!"));
+  platform::UnsetEnvVariable("PADDLE_USE_ENV1");
+  platform::UnsetEnvVariable("PADDLE_USE_ENV2");
+  platform::UnsetEnvVariable("PADDLE_USE_ENV3");
+
+  env_info.clear();
+  vars = platform::GetAllEnvVariables();
+  for_each(vars.begin(), vars.end(), [&](const std::string& var) {
+    env_info += platform::GetEnvValue(var);
+  });
+
+  EXPECT_FALSE(string::Contains(env_info, "Hello World, PaddlePaddle!"));
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV1"));
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV2"));
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV3"));
+}

paddle/platform/gpu_info.cc

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/platform/gpu_info.h"
+
 #include "gflags/gflags.h"
+
 #include "paddle/platform/enforce.h"
+#include "paddle/platform/environment.h"
 
 DEFINE_double(fraction_of_gpu_memory_to_use, 0.95,
               "Default use 95% of GPU memory for PaddlePaddle,"
@@ -70,6 +73,13 @@ size_t GpuMaxChunkSize() {
 
   GpuMemoryUsage(available, total);
 
+  if (IsEnvVarDefined(kEnvFractionGpuMemoryToUse)) {
+    auto val = std::stod(GetEnvValue(kEnvFractionGpuMemoryToUse));
+    PADDLE_ENFORCE_GT(val, 0.0);
+    PADDLE_ENFORCE_LE(val, 1.0);
+    FLAGS_fraction_of_gpu_memory_to_use = val;
+  }
+
   // Reserving the rest memory for page tables, etc.
   size_t reserving = (1 - FLAGS_fraction_of_gpu_memory_to_use) * total;
 

paddle/platform/gpu_info.h

@@ -18,10 +18,15 @@ limitations under the License. */
 
 #include <cuda_runtime.h>
 #include <stddef.h>
+#include <string>
 
 namespace paddle {
 namespace platform {
 
+//! Environment variable: fraction of GPU memory to use on each device.
+const std::string kEnvFractionGpuMemoryToUse =
+    "PADDLE_FRACTION_GPU_MEMORY_TO_USE";
+
 //! Get the total number of GPU devices in system.
 int GetDeviceCount();
 

paddle/pserver/ParameterClient2.cpp

@@ -65,7 +65,6 @@ void ParameterClient2::initThreads() {
     LOG(INFO) << "parallel_thread_num dosent need to set";
   }
   syncThreadPool_.reset(new SyncThreadPool(threadNum_));
-
   startThreads();
 }
 
@@ -224,6 +223,14 @@ void ParameterClient2::prepareSendData(
     request.set_cost(cost);
     request.set_batch_status(batchStatus);
     CHECK_EQ(request.blocks_size(), 0);
+    VLOG(10) << "request: trainer_id: " << request.trainer_id()
+             << " update_mode" << request.update_mode()
+             << " send_back_parameter: " << request.send_back_parameter()
+             << " send_back_parameter_type: "
+             << request.send_back_parameter_type()
+             << " num_samples: " << request.num_samples()
+             << " cost: " << request.cost()
+             << " batch_status: " << request.batch_status();
   }
   for (const auto& segments : parameterSegments) {
     const auto it = parameterMap_.find(segments.id);
@@ -251,11 +258,17 @@ void ParameterClient2::prepareSendData(
       CHECK(sendMat != nullptr) << "sendMat is nullptr";
 
       syncThreadPool_->exec([&](int tid, size_t numThreads) {
+        std::lock_guard<std::mutex> guard(sparseAutoGrowthMutex_);
         const auto& localIndices = prefetchMat->getLocalIndices();
         /// num of sparse rows
         size_t nLocalBlocks = localIndices.size();
         uint64_t beginDim = 0;
         uint64_t endDim = 0;
+
+        // FIXME(typhoonzero): let it resize first
+        prefetchMat->getLocalRow(nLocalBlocks + 1);
+        sendMat->getLocalRow(nLocalBlocks + 1);
+
         for (size_t row = 0; row < nLocalBlocks; ++row) {
           int64_t blockId = localIndices[row];  // local row -> sparse row
           int serverId = std::abs((blockId + nameHash) % serviceNum_);
@@ -275,7 +288,6 @@ void ParameterClient2::prepareSendData(
           block->set_begin_pos(row * blockSize);
           /// block len
           block->set_block_size(endDim - beginDim);
-
           if (sendingPara) {
             sendJob->parallelInputIovs[serverId].push_back(
                 {sendMat->getLocalRow(row), sizeof(real) * (size_t)blockSize});

paddle/pserver/ParameterClient2.h

@@ -583,6 +583,7 @@ protected:
 #ifndef PADDLE_DISABLE_TIMER
   uint64_t forwardbackwordTime_;
 #endif
+  std::mutex sparseAutoGrowthMutex_;
 
   /// map id to parameter used for decoding protobuf data
   std::unordered_map<size_t, ParameterPtr> parameterMap_;

paddle/pserver/ParameterServer2.cpp

@@ -1032,8 +1032,8 @@ void ParameterServer2::loadValueVector(const LoadValueRequest& request,
   Parameter::Header header;
   CHECK(fs.read(reinterpret_cast<char*>(&header), sizeof(header)))
       << "Fail to read parameters in pserver";
-  CHECK_EQ(header.version, Parameter::kFormatVersion)
-      << "Incorrect format version: " << header.version;
+  CHECK(Parameter::isHeaderFormatSupported(header.format))
+      << "Incorrect format version: " << header.format;
   CHECK_EQ(header.size, (size_t)size_)
       << "The size (" << header.size << ") in the file does not match the size "
       << "(" << size_ << ") of the pserver: " << serverId_;
@@ -1063,7 +1063,8 @@ void ParameterServer2::saveValueVector(const SaveValueRequest& request,
   CpuVector& vec = vectors_[PARAMETER_APPLY] ? *vectors_[PARAMETER_APPLY]
                                              : *vectors_[PARAMETER_VALUE];
   Parameter::Header header;
-  header.version = Parameter::kFormatVersion;
+  // TODO(TJ): save param headerFormat_
+  header.format = PARAM_FORMAT_ORIGINAL;
   header.valueSize = sizeof(real);
   header.size = size_;
 

paddle/scripts/docker/build.sh

@@ -82,10 +82,6 @@ EOF
 fi
 
 
-# To build documentation, we need to run cmake again after installing
-# PaddlePaddle.  This awkwardness is due to
-# https://github.com/PaddlePaddle/Paddle/issues/1854.  It also
-# describes a solution.
 if [[ ${WITH_DOC:-OFF} == "ON" ]]; then
     cat <<EOF
 ========================================
@@ -93,11 +89,6 @@ Building documentation ...
    In /paddle/build_doc
 ========================================
 EOF
-    # build documentation need install Paddle before
-    make install -j `nproc`
-    pip install /usr/local/opt/paddle/share/wheels/*.whl
-    paddle version
-
     mkdir -p /paddle/build_doc
     pushd /paddle/build_doc
     cmake .. \
@@ -106,7 +97,8 @@ EOF
           -DWITH_AVX=${WITH_AVX:-ON} \
           -DWITH_SWIG_PY=ON \
           -DWITH_STYLE_CHECK=OFF
-    make paddle_docs paddle_docs_cn
+    make -j `nproc` gen_proto_py
+    make -j `nproc` paddle_docs paddle_docs_cn
     popd
 fi
 
@@ -128,25 +120,6 @@ EOF
     /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT
 fi
 
-# generate deb package for current build
-# FIXME(typhoonzero): should we remove paddle/scripts/deb ?
-if [[ ${WITH_DEB:-ON} == "ON" ]]; then
-    cat <<EOF
-========================================
-Generating .deb package ...
-========================================
-EOF
-    set +e
-    cpack -D CPACK_GENERATOR='DEB' -j `nproc` ..
-    err_code=$?
-    if [ ${err_code} -ne 0 ]; then
-        # cat error logs if cpack failed.
-        cat /paddle/build/_CPack_Packages/Linux/DEB/PreinstallOutput.log
-        exit ${err_code}
-    fi
-    set -e
-fi
-
 cat <<EOF
 ========================================
 Generate /paddle/build/Dockerfile ...
@@ -166,15 +139,15 @@ EOF
 fi
 
 cat >> /paddle/build/Dockerfile <<EOF
-# Use different deb file when building different type of images
-ADD *.deb /
+ADD python/dist/*.whl /
 # run paddle version to install python packages first
 RUN apt-get update &&\
     apt-get install -y wget python-pip && pip install -U pip && \
-    dpkg -i /*.deb ; apt-get install -f -y && \
+    pip install /*.whl; apt-get install -f -y && \
     apt-get clean -y && \
-    rm -f /*.deb && \
-    paddle version
+    rm -f /*.whl && \
+    paddle version && \
+    ldconfig
 ${DOCKERFILE_CUDNN_DSO}
 ${DOCKERFILE_GPU_ENV}
 ADD go/cmd/pserver/pserver /usr/bin/
@@ -182,3 +155,7 @@ ADD go/cmd/master/master /usr/bin/
 # default command shows the paddle version and exit
 CMD ["paddle", "version"]
 EOF
+
+set +xe
+printf "If you need to install PaddlePaddle in develop docker image,"
+printf "please make install or pip install build/python/dist/*.whl.\n"

paddle/scripts/submit_local.sh.in

@@ -18,6 +18,8 @@ function version(){
         echo "PaddlePaddle @PADDLE_VERSION@, compiled with"
         echo "    with_avx: @WITH_AVX@"
         echo "    with_gpu: @WITH_GPU@"
+        echo "    with_mkldnn: @WITH_MKLDNN"
+        echo "    with_mklml: @WITH_MKLML@"
         echo "    with_double: @WITH_DOUBLE@"
         echo "    with_python: @WITH_PYTHON@"
         echo "    with_rdma: @WITH_RDMA@"
@@ -54,8 +56,7 @@ if [ -z "${PADDLE_NO_STAT+x}" ]; then
     fi
 fi
 
-
-MYDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+PADDLE_BIN_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 
 if [ ! -z "${DEBUGGER}" ]; then
     echo "Using debug command ${DEBUGGER}"
@@ -91,34 +92,16 @@ else:
   sys.exit(0)
 EOF
 
-if [ $? -eq 1 ]; then  # Older version installed, or not installed at all
-    echo "First time run paddle, need to install some python dependencies."
-    # setuptools normalizes package version, so we need to use normalized
-    # package version for paddle python package
-    PYTHON_PADDLE_VERSION=$(python -c 'import packaging.version
-import setuptools
-print str(packaging.version.Version("@PADDLE_VERSION@"))
-' 2>/dev/null)
-    BASEDIR=$(dirname "$0")
-    pip install ${BASEDIR}/../opt/paddle/share/wheels/*-${PYTHON_PADDLE_VERSION}-*.whl
-    if [ $? -ne 0 ]; then
-	echo "pip install wheels failed. "
-	echo "Please use 'sudo paddle' at the first time you use PaddlePaddle"
-	echo "PaddlePaddle will install some python dependencies automatically."
-	exit 1
-    fi
-    echo "Python dependencies are installed."
-fi
 
 case "$1" in
     "train")
-        ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_trainer ${@:2}
+        ${DEBUGGER} $PADDLE_BIN_PATH/paddle_trainer ${@:2}
         ;;
     "merge_model")
-        ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_merge_model ${@:2}
+        ${DEBUGGER} $PADDLE_BIN_PATH/paddle_merge_model ${@:2}
         ;;
     "pserver")
-        ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_pserver_main ${@:2}
+        ${DEBUGGER} $PADDLE_BIN_PATH/paddle_pserver_main ${@:2}
         ;;
     "dump_config")
         python -m paddle.utils.dump_config ${@:2}
@@ -127,7 +110,7 @@ case "$1" in
         python -m paddle.utils.make_model_diagram ${@:2}
         ;;
     "usage")
-        $MYDIR/../opt/paddle/bin/paddle_usage ${@:2}
+        $PADDLE_BIN_PATH/paddle_usage ${@:2}
         ;;
     "version")
         version

paddle/trainer/NewRemoteParameterUpdater.cpp

@@ -68,7 +68,7 @@ void NewRemoteParameterUpdater::init(
     LOG(INFO) << "paddle_begin_init_params start";
     // NOTE: convert V1 OptimizatioinConfig proto to V2 OptimizerConfig.
     // This makes golang pserver compatible with handy V1 demos.
-    // TODO: Refine or remove these ugly converting lines
+    // TODO(wuyi): Refine or remove these ugly converting lines
     OptimizerConfig optimizerConfigV2;
     if (trainerConfig_.learning_method() == "momentum") {
       optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);

paddle/trainer/TrainerConfigHelper.cpp

@@ -29,7 +29,6 @@ DECLARE_bool(with_gpu);
 DECLARE_bool(parallel_nn);
 DECLARE_string(config_args);
 DECLARE_bool(use_mkldnn);
-DECLARE_bool(use_mkldnn_wgt);
 
 const char *kConfigParserModuleName = "paddle.trainer.config_parser";
 const char *kConfigParserFuncName = "parse_config_and_serialize";
@@ -47,7 +46,6 @@ TrainerConfigHelper::TrainerConfigHelper(const std::string &configFilePath)
              << ",with_cost=" << FLAGS_with_cost << ",use_gpu=" << FLAGS_use_gpu
              << ",parallel_nn=" << FLAGS_parallel_nn
              << ",use_mkldnn=" << FLAGS_use_mkldnn
-             << ",use_mkldnn_wgt=" << FLAGS_use_mkldnn_wgt
              << ",cudnn_version=" << hl_get_cudnn_lib_version();
   if (!FLAGS_config_args.empty()) {
     configArgs << "," << FLAGS_config_args;

paddle/utils/Flags.cpp

@@ -27,7 +27,6 @@ DEFINE_bool(use_mkldnn, false, "Default still keep use CPU training");
 DEFINE_bool(use_mkldnn, false, "Only support CPU training");
 #endif
 
-DEFINE_bool(use_mkldnn_wgt, false, "Init weight from CPU weight");
 DEFINE_bool(parallel_nn,
             false,
             "Whether to use multi-threads to calculate one neural network."

paddle/utils/Flags.h

@@ -41,4 +41,3 @@ DECLARE_string(predict_file);
 DECLARE_bool(prev_batch_state);
 DECLARE_string(init_model_path);
 DECLARE_bool(use_mkldnn);
-DECLARE_bool(use_mkldnn_wgt);

python/CMakeLists.txt

@@ -21,6 +21,18 @@ if(WITH_GOLANG)
   add_dependencies(copy_paddle_master paddle_master)
 endif(WITH_GOLANG)
 
+set(MKL_SHARED_LIBS "")
+set(MKL_DEPENDS "")
+if(WITH_MKLML)
+  list(APPEND MKL_SHARED_LIBS ${MKLML_LIB} ${MKLML_IOMP_LIB})
+  list(APPEND MKL_DEPENDS mklml)
+endif()
+
+if(WITH_MKLDNN)
+  list(APPEND MKL_SHARED_LIBS "${MKLDNN_LIB}" "${MKLDNN_LIB}.0")
+  list(APPEND MKL_DEPENDS mkldnn)
+endif()
+
 configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in
     ${CMAKE_CURRENT_BINARY_DIR}/setup.py)
 
@@ -38,8 +50,11 @@ add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
     COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_PYTHON_BUILD_DIR}/lib* ${PADDLE_PYTHON_BUILD_DIR}/lib-python
     DEPENDS gen_proto_py copy_paddle_pybind framework_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
 
-add_custom_target(paddle_python ALL DEPENDS
-    ${PADDLE_PYTHON_BUILD_DIR}/.timestamp paddle_pserver_main paddle_trainer paddle_merge_model python_api_wheel)
+set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp paddle_pserver_main paddle_trainer paddle_merge_model ${MKL_DEPENDS})
+if(WITH_SWIG_PY)
+    list(APPEND paddle_python_deps python_api_wheel)
+endif()
+add_custom_target(paddle_python ALL DEPENDS ${paddle_python_deps})
 
 set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/)
 

python/paddle/trainer/config_parser.py

@@ -2232,6 +2232,20 @@ class ClipLayer(LayerBase):
         self.config.inputs[0].clip_conf.max = max
 
 
+@config_layer('scale_shift')
+class ScaleShiftLayer(LayerBase):
+    def __init__(self, name, inputs, bias=True, **xargs):
+        super(ScaleShiftLayer, self).__init__(
+            name, 'scale_shift', 0, inputs=inputs, **xargs)
+        config_assert(
+            len(self.inputs) == 1,
+            'ScaleShiftLayer must have one and only one input.')
+        input_layer = self.get_input_layer(0)
+        self.set_layer_size(input_layer.size)
+        self.create_input_parameter(0, 1, [1, 1])
+        self.create_bias_parameter(bias, 1)
+
+
 # key: cost type
 # value: cost class
 g_cost_map = {}

python/paddle/trainer_config_helpers/evaluators.py

@@ -298,8 +298,8 @@ def pnpair_evaluator(
         input,
         label,
         info,
-        name=None,
-        weight=None, ):
+        weight=None,
+        name=None, ):
     """
     Positive-negative pair rate Evaluator which adapts to rank task like
     learning to rank. This evaluator must contain at least three layers.
@@ -308,27 +308,31 @@ def pnpair_evaluator(
 
     .. code-block:: python
 
-       eval = pnpair_evaluator(input, info, label)
+       eval = pnpair_evaluator(input, label, info)
 
-    :param name: Evaluator name.
-    :type name: None|basestring
     :param input: Input Layer name. The output prediction of network.
     :type input: LayerOutput
     :param label: Label layer name.
     :type label: LayerOutput
-    :param info: Label layer name. (TODO, explaination)
+    :param info: Info layer name. (TODO, explaination)
     :type info: LayerOutput
     :param weight: Weight Layer name. It should be a matrix with size
                   [sample_num, 1]. (TODO, explaination)
     :type weight: LayerOutput
+    :param name: Evaluator name.
+    :type name: None|basestring
     """
+    if not isinstance(input, list):
+        input = [input]
+    if label:
+        input.append(label)
+    if info:
+        input.append(info)
     evaluator_base(
-        name=name,
-        type="pnpair",
         input=input,
-        label=label,
-        info=info,
-        weight=weight)
+        type="pnpair",
+        weight=weight,
+        name=name, )
 
 
 @evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@@ -429,12 +433,12 @@ def chunk_evaluator(
 
     .. code-block:: text
 
-        Scheme    Description                                                                                  
+        Scheme    Description
         plain    Use the same label for the whole chunk.
-        IOB      Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside. 
+        IOB      Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
         IOE      Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
-        IOBES    Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk. 
-   
+        IOBES    Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
+
     To make it clear, let's illustrate by an NER example.
     Assuming that there are three named entity types including ORG, PER and LOC which are called 'chunk type' here,
     if 'IOB' scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
@@ -451,7 +455,7 @@ def chunk_evaluator(
         tagType = label % numTagType
         chunkType = label / numTagType
         otherChunkType = numChunkTypes
-    
+
     The following table shows the mapping rule between tagType and tag type in each scheme.
 
     .. code-block:: text
@@ -475,7 +479,7 @@ def chunk_evaluator(
         O      6
 
     In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
-    "IOB" so tagType has two values: 0 for B and 1 for I. 
+    "IOB" so tagType has two values: 0 for B and 1 for I.
     Here we will use I-LOC to explain the above mapping rules in detail.
     For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
     and the tag is I.
@@ -486,7 +490,7 @@ def chunk_evaluator(
 
        eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
 
-    
+
     :param input: The input layers.
     :type input: LayerOutput
     :param label: An input layer containing the ground truth label.

python/paddle/trainer_config_helpers/layers.py

@@ -134,6 +134,7 @@ __all__ = [
     'slice_projection',
     'seq_slice_layer',
     'kmax_sequence_score_layer',
+    'scale_shift_layer',
 ]
 
 
@@ -232,6 +233,7 @@ class LayerType(object):
     SEQ_SLICE = 'seq_slice'
 
     KMAX_SEQ_SCORE = 'kmax_seq_score'
+    SCALE_SHIFT_LAYER = 'scale_shift'
 
     @staticmethod
     def is_layer_type(type_name):
@@ -6278,3 +6280,43 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1):
 
     return LayerOutput(
         name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size)
+
+
+@wrap_name_default("scale_shift")
+@wrap_param_attr_default()
+@wrap_bias_attr_default()
+def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None):
+    """
+    A layer applies a linear transformation to each element in each row of 
+    the input matrix. For each element, the layer first re-scale it and then 
+    adds a bias to it.
+
+    This layer is very like the SlopeInterceptLayer, except the scale and 
+    bias are trainable.
+
+    .. math::
+
+        y = w * x + b
+
+    .. code-block:: python
+
+        scale_shift = scale_shift_layer(input=input_layer, bias_attr=False)
+
+    :param name: The Layer Name.
+    :type name: basestring
+    :param input: The input layer.
+    :type input: LayerOutput.
+    :param param_attr: The parameter attribute of scaling.
+    :type param_attr: ParameterAttribute
+    :param bias_attr: The parameter attribute of shifting.
+    :type bias_attr: ParameterAttribute
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    Layer(
+        name=name,
+        type=LayerType.SCALE_SHIFT_LAYER,
+        inputs=Input(input.name, **param_attr.attr),
+        bias=ParamAttr.to_bias(bias_attr))
+    return LayerOutput(
+        name, LayerType.SCALE_SHIFT_LAYER, parents=[input], size=input.size)

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -8,6 +8,7 @@ test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
 test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
 test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
 test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
-test_seq_slice_layer test_kmax_seq_socre_layer test_seq_select_layers)
+test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer
+test_seq_slice_layer)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr

+type: "nn"
+layers {
+  name: "data"
+  type: "data"
+  size: 100
+  active_type: ""
+}
+layers {
+  name: "__scale_shift_0__"
+  type: "scale_shift"
+  size: 100
+  active_type: ""
+  inputs {
+    input_layer_name: "data"
+    input_parameter_name: "___scale_shift_0__.w0"
+  }
+}
+layers {
+  name: "__scale_shift_1__"
+  type: "scale_shift"
+  size: 100
+  active_type: ""
+  inputs {
+    input_layer_name: "data"
+    input_parameter_name: "___scale_shift_1__.w0"
+  }
+  bias_parameter_name: "___scale_shift_1__.wbias"
+}
+parameters {
+  name: "___scale_shift_0__.w0"
+  size: 1
+  initial_mean: 0.0
+  initial_std: 1.0
+  dims: 1
+  dims: 1
+  initial_strategy: 0
+  initial_smart: true
+}
+parameters {
+  name: "___scale_shift_1__.w0"
+  size: 1
+  initial_mean: 0.0
+  initial_std: 1.0
+  dims: 1
+  dims: 1
+  initial_strategy: 0
+  initial_smart: true
+}
+parameters {
+  name: "___scale_shift_1__.wbias"
+  size: 1
+  initial_mean: 0.0
+  initial_std: 0.0
+  dims: 1
+  dims: 1
+  initial_strategy: 0
+  initial_smart: false
+}
+input_layer_names: "data"
+output_layer_names: "__scale_shift_0__"
+output_layer_names: "__scale_shift_1__"
+sub_models {
+  name: "root"
+  layer_names: "data"
+  layer_names: "__scale_shift_0__"
+  layer_names: "__scale_shift_1__"
+  input_layer_names: "data"
+  output_layer_names: "__scale_shift_0__"
+  output_layer_names: "__scale_shift_1__"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py

+from paddle.trainer_config_helpers import *
+
+data = data_layer(name='data', size=100)
+
+scale = scale_shift_layer(input=data, bias_attr=False)
+
+scale_shift = scale_shift_layer(input=data)
+
+outputs(scale, scale_shift)

python/paddle/v2/framework/op.py

 import paddle.v2.framework.core as core
-import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
-import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
-import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2
+import paddle.v2.framework.proto.framework_pb2 as framework_pb2
 
 
 def get_all_op_protos():
@@ -12,22 +10,26 @@ def get_all_op_protos():
     protostrs = core.get_all_op_protos()
     ret_values = []
     for pbstr in protostrs:
-        op_proto = op_proto_pb2.OpProto.FromString(str(pbstr))
+        op_proto = framework_pb2.OpProto.FromString(str(pbstr))
         ret_values.append(op_proto)
     return ret_values
 
 
+def is_str(s):
+    return isinstance(s, str) or isinstance(s, unicode)
+
+
 class OpDescCreationMethod(object):
     """
     A Functor object to convert user input(use key word args) to OpDesc based on
     OpProto.
-    
+
     :param op_proto: The OpProto object.
     :type op_proto: op_proto_pb2.OpProto
     """
 
     def __init__(self, op_proto):
-        if not isinstance(op_proto, op_proto_pb2.OpProto):
+        if not isinstance(op_proto, framework_pb2.OpProto):
             raise TypeError("Argument should be OpProto")
         self.__op_proto__ = op_proto
 
@@ -39,26 +41,34 @@ class OpDescCreationMethod(object):
         """
         if len(args) != 0:
             raise ValueError("Only keyword arguments is supported by Paddle")
-        op_desc = op_desc_pb2.OpDesc()
-
-        # Inputs
-        ipts, ipt_format, _ = OpDescCreationMethod.extract_input_or_output(
-            "input", kwargs, self.__op_proto__.inputs)
-        op_desc.inputs.extend(ipts)
-        if ipt_format is not None:
-            op_desc.attrs.extend([ipt_format])
-
-        # Outputs
-        outs, out_format, tmp_index = OpDescCreationMethod.extract_input_or_output(
-            "output", kwargs, self.__op_proto__.outputs)
-        op_desc.outputs.extend(outs)
-        if out_format is not None:
-            op_desc.attrs.extend([out_format])
-        if len(tmp_index) != 0:
-            tmp_index_attr = op_desc.attrs.add()
-            tmp_index_attr.type = attribute_pb2.INTS
-            tmp_index_attr.name = "temporary_index"
-            tmp_index_attr.ints.extend(tmp_index)
+        op_desc = framework_pb2.OpDesc()
+
+        for input_parameter in self.__op_proto__.inputs:
+            input_arguments = kwargs.get(input_parameter.name, [])
+            if is_str(input_arguments):
+                input_arguments = [input_arguments]
+
+            if not input_parameter.duplicable and len(input_arguments) > 1:
+                raise ValueError("Input %s only accepts one input, but give %d"
+                                 % (input_parameter.name, len(input_arguments)))
+
+            ipt = op_desc.inputs.add()
+            ipt.parameter = input_parameter.name
+            ipt.arguments.extend(input_arguments)
+
+        for output_parameter in self.__op_proto__.outputs:
+            output_arguments = kwargs.get(output_parameter.name, [])
+            if is_str(output_arguments):
+                output_arguments = [output_arguments]
+
+            if not output_parameter.duplicable and len(output_arguments) > 1:
+                raise ValueError(
+                    "Output %s only accepts one output, but give %d" %
+                    (output_parameter.name, len(output_arguments)))
+
+            out = op_desc.outputs.add()
+            out.parameter = output_parameter.name
+            out.arguments.extend(output_arguments)
 
         # Types
         op_desc.type = self.__op_proto__.type
@@ -72,17 +82,17 @@ class OpDescCreationMethod(object):
                 new_attr = op_desc.attrs.add()
                 new_attr.name = attr.name
                 new_attr.type = attr.type
-                if attr.type == attribute_pb2.INT:
+                if attr.type == framework_pb2.INT:
                     new_attr.i = user_defined_attr
-                elif attr.type == attribute_pb2.FLOAT:
+                elif attr.type == framework_pb2.FLOAT:
                     new_attr.f = user_defined_attr
-                elif attr.type == attribute_pb2.STRING:
+                elif attr.type == framework_pb2.STRING:
                     new_attr.s = user_defined_attr
-                elif attr.type == attribute_pb2.INTS:
+                elif attr.type == framework_pb2.INTS:
                     new_attr.ints.extend(user_defined_attr)
-                elif attr.type == attribute_pb2.FLOATS:
+                elif attr.type == framework_pb2.FLOATS:
                     new_attr.floats.extend(user_defined_attr)
-                elif attr.type == attribute_pb2.STRINGS:
+                elif attr.type == framework_pb2.STRINGS:
                     new_attr.strings.extend(user_defined_attr)
                 else:
                     raise NotImplementedError("Not support attribute type " +
@@ -90,50 +100,6 @@ class OpDescCreationMethod(object):
 
         return op_desc
 
-    @staticmethod
-    def extract_input_or_output(in_out, kwargs, meta):
-        """
-        Extract input variable names or output variable names from key-word 
-        arguments, which base on VarProtos.
-        
-        :param in_out: "input" or "output"
-        :param kwargs: key-word arguments that user inputted.
-        :param meta: a list of VarProto
-        :return: The three object will be return. The variable names. The 
-        input_format or output_format attribute(None if the input or output is 
-        not multiple). The temporary variable index list.
-        """
-        multiple = OpDescCreationMethod.any_is_true((m.multiple for m in meta))
-        tmp_index = []
-        retv = []
-        if multiple:
-            var_format = op_desc_pb2.AttrDesc()
-            var_format.type = attribute_pb2.INTS
-            var_format.name = "%s_format" % in_out
-            var_format.ints.append(0)
-
-            for var in meta:
-                var_name = var.name
-
-                if var.temporary:
-                    var_name = [core.var_names.temp()]
-                    tmp_index.append(len(retv))
-                else:
-                    var_name = kwargs.get(var_name, [])
-                if not isinstance(var_name, list):
-                    var_name = [var_name]
-                retv.extend(var_name)
-                var_format.ints.append(len(var_name) + var_format.ints[-1])
-            return retv, var_format, tmp_index
-        else:
-            for var in meta:
-                if var.temporary:
-                    retv.append(kwargs.get(var.name, core.var_names.temp()))
-                    tmp_index.append(len(retv))
-                else:
-                    retv.append(kwargs.get(var.name, core.var_names.empty()))
-            return retv, None, tmp_index
-
     @staticmethod
     def any_is_true(generator):
         """
@@ -146,13 +112,12 @@ class OpDescCreationMethod(object):
 
 
 class OpInfo(object):
-    def __init__(self, name, method, inputs, outputs, attrs, no_temp_outputs):
+    def __init__(self, name, method, inputs, outputs, attrs):
         self.name = name
         self.method = method
         self.inputs = inputs
         self.outputs = outputs
         self.attrs = attrs
-        self.no_temp_outputs = no_temp_outputs
 
 
 def create_op_creation_method(op_proto):
@@ -170,10 +135,7 @@ def create_op_creation_method(op_proto):
         name=op_proto.type,
         inputs=[var.name for var in op_proto.inputs],
         outputs=[var.name for var in op_proto.outputs],
-        attrs=[attr.name for attr in op_proto.attrs],
-        no_temp_outputs=[
-            var.name for var in op_proto.outputs if not var.temporary
-        ])
+        attrs=[attr.name for attr in op_proto.attrs])
 
 
 class OperatorFactory(object):
@@ -214,8 +176,27 @@ class OperatorFactory(object):
     def get_op_attr_names(self, type):
         return self.get_op_info(type).attrs
 
-    def get_op_no_temp_output_names(self, type):
-        return self.get_op_info(type).no_temp_outputs
+
+class __RecurrentOp__(object):
+    __proto__ = None
+    type = 'recurrent_op'
+
+    def __init__(self):
+        # cache recurrent_op's proto
+        if self.__proto__ is None:
+            for op_proto in get_all_op_protos():
+                if op_proto.type == self.type:
+                    self.__proto__ = op_proto
+
+    def __call__(self, *args, **kwargs):
+        if self.type not in args and 'type' not in kwargs:
+            kwargs['type'] = self.type
+        # create proto
+        create_method = OpDescCreationMethod(self.__proto__)
+        proto = create_method(*args, **kwargs)
+        # create rnnop
+        return core.RecurrentOp.create(proto.SerializeToString())
 
 
 Operator = OperatorFactory()  # Default global factory
+RecurrentOp = __RecurrentOp__()

python/paddle/v2/framework/tests/CMakeLists.txt

 py_test(test_net SRCS test_net.py)
 
-py_test(test_fc_op SRCS test_fc_op.py)
 py_test(test_scope SRCS test_scope.py)
 
 py_test(test_tensor SRCS test_tensor.py)
@@ -23,5 +22,8 @@ py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py)
 py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
 
 py_test(test_operator SRCS test_operator.py)
-# py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
+py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
 py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
+py_test(test_recurrent_op SRCS test_recurrent_op.py)
+py_test(test_sgd_op SRCS test_sgd_op.py)
+py_test(test_gradient_checker SRCS test_gradient_checker.py)

python/paddle/v2/framework/tests/gradient_checker.py

 import unittest
 
 import numpy
+import itertools
 import paddle.v2.framework.core as core
 from paddle.v2.framework.op import Operator
 
@@ -8,6 +9,7 @@ __all__ = ['get_numeric_gradient']
 
 
 def create_op(op_type):
+    # TODO need to set attrs
     kwargs = dict()
     for in_name in Operator.get_op_input_names(op_type):
         kwargs[in_name] = in_name
@@ -53,17 +55,19 @@ def get_numeric_gradient(op,
         tensor.set(input_values[var_name], core.CPUPlace())
 
     # Create all output variable in local_scope
-    for output in op.outputs():
-        if local_scope.find_var(output) is None:
-            local_scope.new_var(output).get_tensor()
-
+    opts = op.outputs()
+    for key in opts:
+        for output in opts[key]:
+            if local_scope.find_var(output) is None:
+                local_scope.new_var(output).get_tensor()
     op.infer_shape(local_scope)
 
     # allocate output memory
-    for output in op.outputs():
-        local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace())
+    for key in opts:
+        for output in opts[key]:
+            local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace(
+            ))
 
-    # TODO(yuyang18): Only CPU is support now.
     cpu_ctx = core.DeviceContext.create(core.CPUPlace())
 
     def get_output():
@@ -73,31 +77,143 @@ def get_numeric_gradient(op,
     def product(dim):
         return reduce(lambda a, b: a * b, dim, 1)
 
+    # get the input tensor that we want to get it's numeric gradient.
     tensor_to_check = local_scope.find_var(input_to_check).get_tensor()
     tensor_size = product(tensor_to_check.get_dims())
+    # prepare a numpy array to store the gradient.
     gradient_flat = numpy.zeros(shape=(tensor_size, ), dtype='float32')
+
+    # we only compute gradient of one element each time.
+    # we use a for loop to compute the gradient of every element.
     for i in xrange(tensor_size):
+        # get one input element throw it's index i.
         origin = tensor_to_check.get_float_element(i)
+
+        # add delta to it, run op and then get the sum of the result tensor.
         x_pos = origin + delta
         tensor_to_check.set_float_element(i, x_pos)
         y_pos = get_output()
 
+        # plus delta to this element, run op and get the sum of the result tensor.
         x_neg = origin - delta
         tensor_to_check.set_float_element(i, x_neg)
         y_neg = get_output()
 
-        tensor_to_check.set_float_element(i, origin)  # restore old value
+        # restore old value
+        tensor_to_check.set_float_element(i, origin)
+
+        # compute the gradient of this element and store it into a numpy array.
         gradient_flat[i] = (y_pos - y_neg) / delta / 2
+
+    # reshape the gradient result to the shape of the source tensor.
     return gradient_flat.reshape(tensor_to_check.get_dims())
 
 
 class GradientChecker(unittest.TestCase):
-    def assert_is_close(self, numeric_grads, scope, max_relative_error,
-                        msg_prefix):
-        for name in numeric_grads:
-            b = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
-            a = numeric_grads[name]
-
+    def __get_gradient(self, forward_op, backward_op, input_value, grad_names,
+                       place):
+        """Get the input gradients after running forward and backward operators
+        on the given places.
+
+        :param forward_op: forward operator
+        :type forward_op: Operator
+        :param backward_op: backward operator
+        :type backward_op: Operator
+        :param input_value: input values.
+        :type input_value: dict{string:numpy.array}
+        :param grad_names: the names of returned input gradients.
+        :type input_value: a list of string
+        :param place: the device type.
+        :type place: CPUPlace or GPUPlace
+        :return: the input grdients of given grad_names.
+        :rtype: a list of numpy.array
+        """
+        scope = core.Scope()
+        ctx = core.DeviceContext.create(place)
+
+        inputs = forward_op.inputs()
+        in_names = [item for k in inputs for item in inputs[k]]
+        outputs = forward_op.outputs()
+        out_names = [item for k in outputs for item in outputs[k]]
+
+        # create input var and set value
+        for name, value in input_value.iteritems():
+            if name not in in_names:
+                raise ValueError(name + "does not exist in Op's inputs.")
+            var = scope.new_var(name).get_tensor()
+            var.set_dims(value.shape)
+            var.set(value, place)
+
+        # run forward op
+        for out_name in out_names:
+            scope.new_var(out_name)
+        forward_op.infer_shape(scope)
+        forward_op.run(scope, ctx)
+
+        # set output var's shape
+        # set output grad to ones
+        for name in out_names:
+            out_tensor = scope.find_var(name).get_tensor()
+            grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
+            grad_tensor.set_dims(out_tensor.shape())
+            data = numpy.ones(out_tensor.shape(), dtype=numpy.float32)
+            grad_tensor.set(data, place)
+
+        # run backward op
+        for name in backward_op.outputs():
+            scope.new_var(name)
+        backward_op.infer_shape(scope)
+        backward_op.run(scope, ctx)
+
+        outs = [
+            numpy.array(scope.find_var(name).get_tensor())
+            for name in grad_names
+        ]
+        return outs
+
+    def compare_grad(self, forward_op, input_value):
+        """ Compare the input gradients between CPU and GPU for the given forward
+        operator.
+
+        :param forward_op: forward operator
+        :type forward_op: Operator
+        :param input_value: input values.
+        :type input_value: dict{string:numpy.array}
+        :raises: AssertionError, there is different gradient value.
+        """
+        backward_op = core.Operator.backward(forward_op, set())
+        # return if not compile with GPU or not implementing GPU kernel
+        if not (core.is_compile_gpu() and backward_op.support_gpu()):
+            return
+
+        outputs = backward_op.outputs()
+        out_names = [item for k in outputs for item in outputs[k]]
+        cpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
+                                        out_names, core.CPUPlace())
+        gpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
+                                        out_names, core.GPUPlace(0))
+
+        for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads,
+                                                   out_names):
+            self.assertTrue(
+                numpy.allclose(
+                    c_grad, g_grad, atol=1e-4),
+                "output name: " + name + " has diff")
+
+    def __assert_is_close(self, numeric_grads, analytic_grads, names,
+                          max_relative_error, msg_prefix):
+        """Use relative error for the comparison.
+
+        :param numeric_grads: the numerical graidents.
+        :type numeric_grads: a list of numpy.array 
+        :param analytic_grads: the analytical graidents.
+        :type analytic_grads: a list of numpy.array 
+        :param name: the names of gradients, used to print for debug.
+        :type names: a list of string
+        :param msg_prefix: string info, used to print for debug.
+        :type msf_prefix: string
+        """
+        for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
             abs_a = numpy.abs(a)
             # if abs_a is nearly zero, then use abs error for a, not relative
             # error.
@@ -136,107 +252,32 @@ class GradientChecker(unittest.TestCase):
         if no_grad_set is None:
             no_grad_set = set()
 
-        tmp_outs = forward_op.temp_outputs()
-        no_tmp_out = filter(lambda name: name not in tmp_outs,
-                            forward_op.outputs())
+        no_tmp_out = forward_op.no_intermediate_outputs()
         if len(no_tmp_out) != 1:
             raise ValueError("non temp out_names should be 1")
 
-        in_names = forward_op.inputs()
+        inputs = forward_op.inputs()
+        in_names = [item for k in inputs for item in inputs[k]]
         for no_grad in no_grad_set:
             if no_grad not in in_names:
                 raise ValueError("no_grad should be in in_names")
-
         backward_op = core.Operator.backward(forward_op, no_grad_set)
 
         places = [core.CPUPlace()]
         if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu():
             places.append(core.GPUPlace(0))
 
-        numeric_grad = dict()
-        # get numeric gradient
-        for check_name in inputs_to_check:
-            numeric_grad[check_name] = \
-                get_numeric_gradient(forward_op, input_vars, output_name,
-                                     check_name)
+        # get numerical gradients
+        numeric_grads = [
+            get_numeric_gradient(forward_op, input_vars, output_name, name)
+            for name in inputs_to_check
+        ]
 
-        # get operator gradient according to different device
+        check_names = [grad_var_name(name) for name in inputs_to_check]
         for place in places:
-            scope = core.Scope()
-            ctx = core.DeviceContext.create(place)
-
-            # create input var and set value
-            for name, value in input_vars.iteritems():
-                if name not in in_names:
-                    raise ValueError(name + " not in op.inputs_")
-                var = scope.new_var(name).get_tensor()
-                var.set_dims(value.shape)
-                var.set(value, place)
-
-            # create output var
-            for out_name in forward_op.outputs():
-                scope.new_var(out_name).get_tensor()
-
-            # infer the shape of output var and compute/set value of output var
-            forward_op.infer_shape(scope)
-            forward_op.run(scope, ctx)
-
-            # create output grad var
-            # set shape as the output var
-            # set value of this grad to ones
-            for name in forward_op.outputs():
-                out_tensor = scope.find_var(name).get_tensor()
-                grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
-                grad_tensor.set_dims(out_tensor.shape())
-                data = 1.0 * numpy.ones(out_tensor.shape())
-                grad_tensor.set(data, place)
-
-            # create input grad var
-            for name in backward_op.outputs():
-                scope.new_var(name).get_tensor()
-
-            # infer the shape of input gradient var and compute/set it's value
-            # with backward op
-            backward_op.infer_shape(scope)
-            backward_op.run(scope, ctx)
-
-            self.assert_is_close(numeric_grad, scope, max_relative_error,
-                                 "Gradient Check On %s" % str(place))
-
-
-if __name__ == '__main__':
-
-    class GetNumericGradientTest(unittest.TestCase):
-        def test_add_op(self):
-            add_op = Operator('add_two', X="X", Y="Y", Out="Z")
-            x = numpy.random.random((10, 1)).astype("float32")
-            y = numpy.random.random((10, 1)).astype("float32")
-
-            arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
-            self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-2)
-
-        def test_softmax_op(self):
-            def stable_softmax(x):
-                """Compute the softmax of vector x in a numerically stable way."""
-                shiftx = x - numpy.max(x)
-                exps = numpy.exp(shiftx)
-                return exps / numpy.sum(exps)
-
-            def label_softmax_grad(Y, dY):
-                dX = Y * 0.0
-                for i in range(Y.shape[0]):
-                    d = numpy.dot(Y[i, :], dY[i, :])
-                    dX[i, :] = Y[i, :] * (dY[i, :] - d)
-                return dX
-
-            softmax_op = Operator("softmax", X="X", Y="Y")
-
-            X = numpy.random.random((2, 2)).astype("float32")
-            Y = numpy.apply_along_axis(stable_softmax, 1, X)
-            dY = numpy.ones(Y.shape)
-            dX = label_softmax_grad(Y, dY)
-
-            arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
-            numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
-
-    unittest.main()
+            # get analytical gradients according to different device
+            analytic_grads = self.__get_gradient(forward_op, backward_op,
+                                                 input_vars, check_names, place)
+            self.__assert_is_close(numeric_grads, analytic_grads, check_names,
+                                   max_relative_error,
+                                   "Gradient Check On %s" % str(place))

python/paddle/v2/framework/tests/op_test_util.py

@@ -64,7 +64,8 @@ class OpTestMeta(type):
                     actual = numpy.array(scope.find_var(out_name).get_tensor())
                     expect = self.outputs[out_name]
                     self.assertTrue(
-                        numpy.allclose(actual, expect),
+                        numpy.allclose(
+                            actual, expect, atol=1e-05),
                         "output name: " + out_name + "has diff")
 
         obj.test_all = test_all

python/paddle/v2/framework/tests/test_add_two_op.py

@@ -19,14 +19,5 @@ class TestAddOp(unittest.TestCase):
         self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
 
 
-class TestAddGradOp(unittest.TestCase):
-    def test_add_grad(self):
-        op = Operator('add_two', X="X", Y="Y", Out="Out")
-        backward_op = core.Operator.backward(op, set())
-        self.assertEqual(backward_op.type(), "add_two_grad")
-        expected = '''Op(add_two_grad), inputs:(X, Y, Out, Out@GRAD), outputs:(X@GRAD, Y@GRAD).'''
-        self.assertEqual(expected, str(backward_op))
-
-
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_cross_entropy_op.py

@@ -8,9 +8,8 @@ class TestCrossEntropy(unittest.TestCase):
     __metaclass__ = OpTestMeta
 
     def setUp(self):
-        # TODO this unit test is not passed
         self.type = "onehot_cross_entropy"
-        batch_size = 100
+        batch_size = 30
         class_num = 10
         X = numpy.random.random((batch_size, class_num)).astype("float32")
         label = 5 * numpy.ones(batch_size).astype("int32")
@@ -22,9 +21,9 @@ class TestCrossEntropy(unittest.TestCase):
 
 
 class CrossEntropyGradOpTest(GradientChecker):
-    def test_softmax_grad(self):
+    def test_check_grad(self):
         op = create_op("onehot_cross_entropy")
-        batch_size = 100
+        batch_size = 30
         class_num = 10
         inputs = {
             "X": numpy.random.uniform(

python/paddle/v2/framework/tests/test_fc_op.py

-import paddle.v2.framework.core as core
-import unittest
-import numpy
-from paddle.v2.framework.op import Operator
-
-
-class TestFc(unittest.TestCase):
-    def test_fc(self):
-        scope = core.Scope()
-        place = core.CPUPlace()
-        x = scope.new_var("X")
-
-        x_tensor = x.get_tensor()
-        x_tensor.set_dims([1000, 784])
-        x_tensor.alloc_float(place)
-
-        w = scope.new_var("W")
-        w_tensor = w.get_tensor()
-        w_tensor.set_dims([784, 100])
-        w_tensor.alloc_float(place)
-
-        w_tensor.set(numpy.random.random((784, 100)).astype("float32"), place)
-
-        # Set a real numpy array here.
-        # x_tensor.set(numpy.array([]))
-
-        op = Operator("fc", X="X", Y="Y", W="W")
-
-        for out in op.outputs():
-            if scope.find_var(out) is None:
-                scope.new_var(out).get_tensor()
-
-        tensor = scope.find_var("Y").get_tensor()
-        op.infer_shape(scope)
-        self.assertEqual([1000, 100], tensor.shape())
-
-        ctx = core.DeviceContext.create(place)
-
-        op.run(scope, ctx)
-
-        # After complete all ops, check Y is expect or not.
-
-
-if __name__ == '__main__':
-    unittest.main()

python/paddle/v2/framework/tests/test_gradient_checker.py

+import unittest
+import numpy
+from paddle.v2.framework.op import Operator
+from gradient_checker import GradientChecker
+from gradient_checker import get_numeric_gradient
+
+
+class GetNumericGradientTest(unittest.TestCase):
+    def test_add_op(self):
+        add_op = Operator('add_two', X="X", Y="Y", Out="Z")
+        x = numpy.random.random((10, 1)).astype("float32")
+        y = numpy.random.random((10, 1)).astype("float32")
+
+        arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
+        self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
+
+    def test_softmax_op(self):
+        def stable_softmax(x):
+            """Compute the softmax of vector x in a numerically stable way."""
+            shiftx = x - numpy.max(x)
+            exps = numpy.exp(shiftx)
+            return exps / numpy.sum(exps)
+
+        def label_softmax_grad(Y, dY):
+            dX = Y * 0.0
+            for i in range(Y.shape[0]):
+                d = numpy.dot(Y[i, :], dY[i, :])
+                dX[i, :] = Y[i, :] * (dY[i, :] - d)
+            return dX
+
+        softmax_op = Operator("softmax", X="X", Y="Y")
+
+        X = numpy.random.random((2, 2)).astype("float32")
+        Y = numpy.apply_along_axis(stable_softmax, 1, X)
+        dY = numpy.ones(Y.shape)
+        dX = label_softmax_grad(Y, dY)
+
+        arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
+        numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_mean_op.py

 import unittest
 from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
 import numpy as np
 
 
@@ -12,5 +13,12 @@ class TestMeanOp(unittest.TestCase):
         self.outputs = {'Out': np.mean(self.inputs['X'])}
 
 
+class MeanGradOpTest(GradientChecker):
+    def test_normal(self):
+        op = create_op("mean")
+        inputs = {"X": np.random.random((10, 10)).astype("float32")}
+        self.check_grad(op, inputs, set("X"), "Out")
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_mul_op.py

 import unittest
-from op_test_util import OpTestMeta
 import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
 
 
 class TestMulOp(unittest.TestCase):
@@ -15,5 +16,19 @@ class TestMulOp(unittest.TestCase):
         self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
 
 
+class MulGradOpTest(GradientChecker):
+    def test_mul(self):
+        op = create_op("mul")
+        inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((84, 100)).astype("float32")
+        }
+        # mul op will enlarge the relative error
+        self.check_grad(
+            op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+
+
+# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_net.py

@@ -3,25 +3,34 @@ from paddle.v2.framework.op import Operator
 import unittest
 
 
+def fc(X, W, Y):
+    ret_v = core.Net.create()
+
+    ret_v.append_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
+    ret_v.append_op(Operator("sigmoid", X="pre_activation", Y=Y))
+    ret_v.complete_add_op(True)
+    return ret_v
+
+
 class TestNet(unittest.TestCase):
     def test_net_all(self):
         net = core.Net.create()
         op1 = Operator("add_two", X="X", Y="Y", Out="Out")
-        net.add_op(op1)
+        net.append_op(op1)
 
         net2 = core.Net.create()
-        net2.add_op(Operator("fc", X="X", W="w", Y="fc.out"))
+        net2.append_op(fc(X="X", W="w", Y="fc.out"))
         net2.complete_add_op(True)
-        net.add_op(net2)
+        net.append_op(net2)
         net.complete_add_op(True)
 
         expected = '''
-Op(plain_net), inputs:(@EMPTY@, X, Y, w), outputs:(@TEMP@fc@0, Out, fc.out).
-    Op(add_two), inputs:(X, Y), outputs:(Out).
-    Op(plain_net), inputs:(@EMPTY@, X, w), outputs:(@TEMP@fc@0, fc.out).
-        Op(fc), inputs:(X, w, @EMPTY@), outputs:(fc.out, @TEMP@fc@0).
-            Op(mul), inputs:(X, w), outputs:(@TEMP@fc@0).
-            Op(sigmoid), inputs:(@TEMP@fc@0), outputs:(fc.out).
+Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
+    Op(add_two), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
+    Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
+        Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
+            Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
+            Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Y[fc.out]}.
 '''
         self.assertEqual(expected, "\n" + str(net))
 

python/paddle/v2/framework/tests/test_operator.py

 import unittest
 import paddle.v2.framework.op as op
 import paddle.v2.framework.core as core
-import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
-import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
-import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2
+import paddle.v2.framework.proto.framework_pb2 as framework_pb2
 
 
 class TestGetAllProtos(unittest.TestCase):
@@ -17,7 +15,7 @@ class TestGetAllProtos(unittest.TestCase):
 
 class TestOpDescCreationMethod(unittest.TestCase):
     def test_plain_input_output(self):
-        op_proto = op_proto_pb2.OpProto()
+        op_proto = framework_pb2.OpProto()
         op_proto.type = "test"
         ipt = op_proto.inputs.add()
         ipt.name = "X"
@@ -37,25 +35,32 @@ class TestOpDescCreationMethod(unittest.TestCase):
 
         method = op.OpDescCreationMethod(op_proto)
         output = method(X="a", Y="b", Z="c")
-
-        expected = op_desc_pb2.OpDesc()
+        expected = framework_pb2.OpDesc()
         expected.type = "test"
-        expected.inputs.extend(["a", "b"])
-        expected.outputs.append("c")
+        ipt_0 = expected.inputs.add()
+        ipt_0.parameter = "X"
+        ipt_0.arguments.extend(["a"])
+        ipt_1 = expected.inputs.add()
+        ipt_1.parameter = 'Y'
+        ipt_1.arguments.extend(['b'])
+        opt = expected.outputs.add()
+        opt.parameter = "Z"
+        opt.arguments.extend(["c"])
+
         self.assertEqual(expected, output)
 
     def test_multiple_input_plain_output(self):
-        op_proto = op_proto_pb2.OpProto()
+        op_proto = framework_pb2.OpProto()
         op_proto.type = "fc"
         ipt = op_proto.inputs.add()
         ipt.name = "X"
         ipt.comment = ""
-        ipt.multiple = True
+        ipt.duplicable = True
 
         ipt = op_proto.inputs.add()
         ipt.name = "W"
         ipt.comment = ""
-        ipt.multiple = True
+        ipt.duplicable = True
 
         ipt = op_proto.inputs.add()
         ipt.name = "b"
@@ -70,30 +75,50 @@ class TestOpDescCreationMethod(unittest.TestCase):
         method = op.OpDescCreationMethod(op_proto)
 
         generated1 = method(X="x", W="w", b="b", Y="y")
-        expected1 = op_desc_pb2.OpDesc()
-        expected1.inputs.extend(['x', 'w', 'b'])
-        expected1.outputs.extend(['y'])
+        expected1 = framework_pb2.OpDesc()
+        tmp = expected1.inputs.add()
+        tmp.parameter = "X"
+        tmp.arguments.extend(['x'])
+
+        tmp = expected1.inputs.add()
+        tmp.parameter = 'W'
+        tmp.arguments.extend(['w'])
+
+        tmp = expected1.inputs.add()
+        tmp.parameter = 'b'
+        tmp.arguments.extend(['b'])
+
+        tmp = expected1.outputs.add()
+        tmp.parameter = 'Y'
+        tmp.arguments.extend(['y'])
         expected1.type = 'fc'
-        attr = expected1.attrs.add()
-        attr.name = 'input_format'
-        attr.type = attribute_pb2.INTS
-        attr.ints.extend([0, 1, 2, 3])
         self.assertEqual(expected1, generated1)
 
         generated2 = method(
             X=['x1', 'x2', 'x3'], b='b', W=['w1', 'w2', 'w3'], Y='y')
-        expected2 = op_desc_pb2.OpDesc()
-        expected2.inputs.extend(['x1', 'x2', 'x3', 'w1', 'w2', 'w3', 'b'])
-        expected2.outputs.extend(['y'])
+        expected2 = framework_pb2.OpDesc()
+
+        tmp = expected2.inputs.add()
+        tmp.parameter = "X"
+        tmp.arguments.extend(['x1', 'x2', 'x3'])
+
+        tmp = expected2.inputs.add()
+        tmp.parameter = 'W'
+        tmp.arguments.extend(['w1', 'w2', 'w3'])
+
+        tmp = expected2.inputs.add()
+        tmp.parameter = 'b'
+        tmp.arguments.extend(['b'])
+
+        tmp = expected2.outputs.add()
+        tmp.parameter = 'Y'
+        tmp.arguments.extend(['y'])
+
         expected2.type = 'fc'
-        attr = expected2.attrs.add()
-        attr.name = 'input_format'
-        attr.type = attribute_pb2.INTS
-        attr.ints.extend([0, 3, 6, 7])
         self.assertEqual(expected2, generated2)
 
     def test_attrs(self):
-        op_proto = op_proto_pb2.OpProto()
+        op_proto = framework_pb2.OpProto()
         op_proto.type = "test"
         ipt = op_proto.inputs.add()
         ipt.name = 'X'
@@ -105,12 +130,12 @@ class TestOpDescCreationMethod(unittest.TestCase):
             attr.comment = ""
             attr.type = type
 
-        __add_attr__("int_attr", attribute_pb2.INT)
-        __add_attr__("float_attr", attribute_pb2.FLOAT)
-        __add_attr__("string_attr", attribute_pb2.STRING)
-        __add_attr__("ints_attr", attribute_pb2.INTS)
-        __add_attr__("floats_attr", attribute_pb2.FLOATS)
-        __add_attr__("strings_attr", attribute_pb2.STRINGS)
+        __add_attr__("int_attr", framework_pb2.INT)
+        __add_attr__("float_attr", framework_pb2.FLOAT)
+        __add_attr__("string_attr", framework_pb2.STRING)
+        __add_attr__("ints_attr", framework_pb2.INTS)
+        __add_attr__("floats_attr", framework_pb2.FLOATS)
+        __add_attr__("strings_attr", framework_pb2.STRINGS)
 
         op_proto.comment = ""
         self.assertTrue(op_proto.IsInitialized())
@@ -126,76 +151,52 @@ class TestOpDescCreationMethod(unittest.TestCase):
             floats_attr=[0.2, 3.2, 4.5],
             strings_attr=["a", "b", "c"])
 
-        expected = op_desc_pb2.OpDesc()
+        expected = framework_pb2.OpDesc()
         expected.type = "test"
-        expected.inputs.extend(['a'])
+
+        ipt = expected.inputs.add()
+        ipt.parameter = "X"
+        ipt.arguments.extend(['a'])
+
         attr = expected.attrs.add()
         attr.name = "int_attr"
-        attr.type = attribute_pb2.INT
+        attr.type = framework_pb2.INT
         attr.i = 10
 
         attr = expected.attrs.add()
         attr.name = "float_attr"
-        attr.type = attribute_pb2.FLOAT
+        attr.type = framework_pb2.FLOAT
         attr.f = 3.2
 
         attr = expected.attrs.add()
         attr.name = "string_attr"
-        attr.type = attribute_pb2.STRING
+        attr.type = framework_pb2.STRING
         attr.s = "test_str"
 
         attr = expected.attrs.add()
         attr.name = "ints_attr"
-        attr.type = attribute_pb2.INTS
+        attr.type = framework_pb2.INTS
         attr.ints.extend([0, 1, 2, 3, 4])
 
         attr = expected.attrs.add()
         attr.name = 'floats_attr'
-        attr.type = attribute_pb2.FLOATS
+        attr.type = framework_pb2.FLOATS
         attr.floats.extend([0.2, 3.2, 4.5])
 
         attr = expected.attrs.add()
         attr.name = 'strings_attr'
-        attr.type = attribute_pb2.STRINGS
+        attr.type = framework_pb2.STRINGS
         attr.strings.extend(['a', 'b', 'c'])
 
         self.assertEqual(expected, generated)
 
-    def test_input_temporary_output(self):
-        op_proto = op_proto_pb2.OpProto()
-        op_proto.type = "test"
-        out = op_proto.outputs.add()
-        out.name = "OUT"
-        out.comment = ""
-
-        out = op_proto.outputs.add()
-        out.name = "TMP"
-        out.comment = ""
-        out.temporary = True
-
-        out = op_proto.outputs.add()
-        out.name = "OUT2"
-        out.comment = ""
-        op_proto.comment = ""
-
-        method = op.OpDescCreationMethod(op_proto)
-        generated = method(OUT="a", OUT2="b")
-        desc = op_desc_pb2.OpDesc()
-        desc.outputs.extend(["a", core.var_names.temp(), "b"])
-        desc.type = "test"
-        attr = desc.attrs.add()
-        attr.name = "temporary_index"
-        attr.type = attribute_pb2.INTS
-        attr.ints.append(2)
-        self.assertEqual(generated, desc)
-
 
 class TestOpCreations(unittest.TestCase):
     def test_all(self):
         add_op = op.Operator("add_two", X="a", Y="b", Out="z")
         self.assertIsNotNone(add_op)
         # Invoke C++ DebugString()
-        self.assertEqual('Op(add_two), inputs:(a, b), outputs:(z).',
+        self.assertEqual('Op(add_two), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
                          str(add_op))
 
 

python/paddle/v2/framework/tests/test_protobuf.py

-import paddle.v2.framework.proto.op_proto_pb2 as op_proto_lib
-import paddle.v2.framework.proto.attribute_pb2 as attr_type_lib
+import paddle.v2.framework.proto.framework_pb2 as framework_pb2
 import unittest
 
 
 class TestFrameworkProto(unittest.TestCase):
     def test_all(self):
-        op_proto = op_proto_lib.OpProto()
+        op_proto = framework_pb2.OpProto()
         ipt0 = op_proto.inputs.add()
         ipt0.name = "a"
         ipt0.comment = "the input of cosine op"
@@ -19,7 +18,7 @@ class TestFrameworkProto(unittest.TestCase):
         attr = op_proto.attrs.add()
         attr.name = "scale"
         attr.comment = "scale of cosine op"
-        attr.type = attr_type_lib.FLOAT
+        attr.type = framework_pb2.FLOAT
         op_proto.type = "cos"
         self.assertTrue(op_proto.IsInitialized())
 

python/paddle/v2/framework/tests/test_recurrent_op.py

@@ -2,19 +2,74 @@ import logging
 import paddle.v2.framework.core as core
 import unittest
 import numpy as np
-import paddle.v2.framework.create_op_creation_methods as creation
+from paddle.v2.framework.op import Operator, RecurrentOp
 
-ops = creation.op_creations
 
+def py_sigmoid(x):
+    return 1. / (1. + np.exp(-x))
 
-def create_tensor(scope, name, shape):
+
+class PySimpleRNN(object):
+    '''
+    A simple implementation of RNN based on numpy, to futhur test RecurrentOp's alogorithm
+    '''
+
+    def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11):
+        self.x = np.random.normal(size=(sent_len, batch_size, input_dim))
+        self.W = np.random.normal(size=(input_dim, input_dim))
+        self.U = np.random.normal(size=(input_dim, input_dim))
+        self.h_boot = np.random.normal(size=(batch_size, input_dim))
+
+        # memories
+        self.mems = [
+            np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len)
+        ]
+
+    def forward(self):
+        xs = self.segment_inputs()
+        for step_id in range(self.x.shape[0]):
+            self.step(step_id, xs[step_id])
+        return self.concat_outputs()
+
+    def segment_inputs(self):
+        return [self.x[i] for i in range(self.x.shape[0])]
+
+    def concat_outputs(self):
+        return np.array(self.mems)
+
+    def step(self, step_id, x):
+        '''
+        run a step
+        '''
+        mem = self.mems[step_id]
+        if step_id > 0:
+            pre_mem = self.mems[step_id - 1]
+        else:
+            pre_mem = self.h_boot
+        xW = np.matmul(x, self.W)
+        hU = np.matmul(mem, self.U)
+
+        sum = xW + hU
+        self.mems[step_id] = py_sigmoid(sum)
+
+
+class PySimpleRNNTest(unittest.TestCase):
+    def setUp(self):
+        self.rnn = PySimpleRNN()
+
+    def test_forward(self):
+        output = self.rnn.forward()
+        print 'output', output
+
+
+def create_tensor(scope, name, shape, np_data):
     tensor = scope.new_var(name).get_tensor()
     tensor.set_dims(shape)
-    tensor.set(np.random.random(shape), core.CPUPlace())
+    tensor.set(np_data, core.CPUPlace())
     return tensor
 
 
-class TestRNN(unittest.TestCase):
+class TestRecurrentOp(unittest.TestCase):
     '''
     Test RNNOp
 
@@ -28,7 +83,7 @@ class TestRNN(unittest.TestCase):
     memories:
         - h
     outputs:
-        - h
+       - h
     '''
 
     input_dim = 30
@@ -36,33 +91,44 @@ class TestRNN(unittest.TestCase):
     weight_dim = 15
     sent_len = 11
 
-    def init(self):
+    def setUp(self):
+        self.py_rnn = PySimpleRNN(self.input_dim, self.batch_size,
+                                  self.weight_dim, self.sent_len)
 
+    def forward(self):
         self.scope = core.Scope()
-
         self.create_global_variables()
+        self.create_rnn_op()
         self.create_step_net()
-        rnn_op = self.create_rnn_op()
         ctx = core.DeviceContext.create(core.CPUPlace())
-        print 'infer_shape'
-        rnn_op.infer_shape(self.scope)
-
-        rnn_op.run(self.scope, ctx)
+        self.rnnop.infer_shape(self.scope)
+        self.rnnop.run(self.scope, ctx)
+        return np.array(self.scope.find_var("h").get_tensor())
 
     def create_global_variables(self):
         # create inlink
+        x_np_data = self.py_rnn.x
         create_tensor(self.scope, "x",
-                      [self.sent_len, self.batch_size, self.input_dim])
-        create_tensor(self.scope, "W", [self.input_dim, self.input_dim])
-        create_tensor(self.scope, "U", [self.input_dim, self.input_dim])
-        create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim])
+                      [self.sent_len, self.batch_size, self.input_dim],
+                      x_np_data)
+        W_np_data = self.py_rnn.W
+        create_tensor(self.scope, "W", [self.input_dim, self.input_dim],
+                      W_np_data)
+
+        U_np_data = self.py_rnn.U
+        create_tensor(self.scope, "U", [self.input_dim, self.input_dim],
+                      U_np_data)
+
+        h_boot_np_data = self.py_rnn.h_boot
+        create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim],
+                      h_boot_np_data)
         self.scope.new_var("step_scopes")
         self.scope.new_var("h@alias")
         self.scope.new_var("h")
 
     def create_rnn_op(self):
         # create RNNOp
-        rnnop = ops.recurrent_op(
+        self.rnnop = RecurrentOp(
             # inputs
             inlinks=["x"],
             boot_memories=["h_boot"],
@@ -75,23 +141,27 @@ class TestRNN(unittest.TestCase):
             outlink_alias=["h@alias"],
             pre_memories=["h@pre"],
             memories=["h@alias"])
-        return rnnop
 
     def create_step_net(self):
-        var = self.scope.new_var("stepnet")
-        stepnet = var.get_net()
-
-        x_fc_op = ops.fc(X="x@alias", W="W", Y="Wx")
-        h_fc_op = ops.fc(X="h@pre", W="U", Y="Uh")
-        sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
-        sig_op = ops.sigmoid(X="sum", Y="h@alias")
+        stepnet = core.Net.create()
+        x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
+        h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
+        sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
+        sig_op = Operator("sigmoid", X="sum", Y="h@alias")
 
         for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
-            stepnet.add_op(op)
+            stepnet.append_op(op)
         stepnet.complete_add_op(True)
-
-    def test_recurrent(self):
-        self.init()
+        self.rnnop.set_stepnet(stepnet)
+
+    def test_forward(self):
+        print 'test recurrent op forward'
+        pd_output = self.forward()
+        py_output = self.py_rnn.forward()
+        print 'pd_output', pd_output
+        print
+        print 'py_output', py_output
+        self.assertEqual(pd_output.shape, py_output.shape)
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_rowwise_add_op.py

 import unittest
-from op_test_util import OpTestMeta
 import numpy as np
+from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
 
 
 class TestRowwiseAddOp(unittest.TestCase):
@@ -15,5 +16,15 @@ class TestRowwiseAddOp(unittest.TestCase):
         self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
 
 
+class RowwiseAddGradOpTest(GradientChecker):
+    def test_rowwise_add(self):
+        op = create_op("rowwise_add")
+        inputs = {
+            "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
+            "b": np.random.uniform(0.1, 1, [10]).astype("float32")
+        }
+        self.check_grad(op, inputs, set(["X", "b"]), "Out")
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_sigmoid_op.py

 import unittest
-from op_test_util import OpTestMeta
 import numpy as np
+from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
 
 
 class TestSigmoidOp(unittest.TestCase):
@@ -8,12 +9,20 @@ class TestSigmoidOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "sigmoid"
-        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.inputs = {'X': np.random.random((15, 31)).astype("float32")}
         self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
 
 
-#class TestSigmoidGradOp(unittest.TestCase):
-#TODO(qingqing) add unit test
+class TestSigmoidGradOp(GradientChecker):
+    def test_grad(self):
+        op = create_op("sigmoid")
+        inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")}
+        # compare gpu and cpu results for backward op.
+        # this test will be skiped if only compiling CPU version.
+        self.compare_grad(op, inputs)
+        # check gradients 
+        self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/parameters.py

@@ -127,16 +127,7 @@ class Parameters(object):
         """
         return iter(self.__param_conf__)
 
-    def __getitem__(self, key):
-        """
-        Get parameter by parameter name. It uses Python dict syntax.
-
-        :note: It will always copy the parameter from C++ side.
-        :param key: Parameter name
-        :type key: basestring
-        :return: parameter value
-        :rtype: np.ndarray
-        """
+    def __getter_inner(self, key, param_type):
         import py_paddle.swig_paddle as api
         shape = self.get_shape(key)
 
@@ -152,7 +143,7 @@ class Parameters(object):
                     each_gradient_machine, key)
                 # for simplify implementation now, we always copy from C++
                 assert isinstance(param, api.Parameter)
-                val = param.getBuf(api.PARAMETER_VALUE)
+                val = param.getBuf(param_type)
                 assert isinstance(val, api.Vector)
                 val = val.copyToNumpyArray()
                 return val
@@ -160,6 +151,19 @@ class Parameters(object):
 
             raise RuntimeError("Unexpected branch")
 
+    def __getitem__(self, key):
+        """
+        Get parameter by parameter name. It uses Python dict syntax.
+
+        :note: It will always copy the parameter from C++ side.
+        :param key: Parameter name
+        :type key: basestring
+        :return: parameter value
+        :rtype: np.ndarray
+        """
+        import py_paddle.swig_paddle as api
+        return self.__getter_inner(key, api.PARAMETER_VALUE)
+
     def get_shape(self, key):
         """
         get shape of the parameter.
@@ -216,6 +220,19 @@ class Parameters(object):
         """
         return self.__getitem__(key=parameter_name)
 
+    def get_grad(self, key):
+        """
+        Get grandient by parameter name.
+
+        :note: It will always copy the parameter from C++ side.
+        :param key: parameter name
+        :type key: basestring
+        :return: The grandient matrix.
+        :rtype: np.ndarray
+        """
+        import py_paddle.swig_paddle as api
+        return self.__getter_inner(key, api.PARAMETER_GRADIENT)
+
     def set(self, parameter_name, value):
         """
         Set parameter by parameter name & matrix.
@@ -264,7 +281,13 @@ class Parameters(object):
         size = reduce(lambda a, b: a * b, param.shape)
         f.write(struct.pack("IIQ", 0, 4, size))
         param = param.astype(np.float32)
-        f.write(param.tostring())
+        s = param.tostring()
+        wrote_size = 0
+        buf = buffer(s, wrote_size, 65535)
+        while buf:  # f.write crashes with big data blog.
+            f.write(buf)
+            wrote_size += 65535
+            buf = buffer(s, wrote_size, 65535)
 
     def deserialize(self, name, f):
         """

python/paddle/v2/reader/creator.py

@@ -57,7 +57,7 @@ def text_file(path):
     return reader
 
 
-def recordio_local(paths, buf_size=100):
+def recordio(paths, buf_size=100):
     """
     Creates a data reader from given RecordIO file paths separated by ",",
         glob pattern is supported.
@@ -67,15 +67,19 @@ def recordio_local(paths, buf_size=100):
 
     import recordio as rec
     import paddle.v2.reader.decorator as dec
+    import cPickle as pickle
 
     def reader():
-        a = ','.join(paths)
-        f = rec.reader(a)
+        if isinstance(paths, basestring):
+            path = paths
+        else:
+            path = ",".join(paths)
+        f = rec.reader(path)
         while True:
             r = f.read()
             if r is None:
                 break
-            yield r
+            yield pickle.loads(r)
         f.close()
 
     return dec.buffered(reader, buf_size)

python/paddle/v2/reader/tests/creator_test.py

@@ -34,5 +34,27 @@ class TestTextFile(unittest.TestCase):
             self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
 
 
+class TestRecordIO(unittest.TestCase):
+    def do_test(self, path):
+        reader = paddle.v2.reader.creator.recordio(path)
+        idx = 0
+        for e in reader():
+            if idx == 0:
+                self.assertEqual(e, (1, 2, 3))
+            elif idx == 1:
+                self.assertEqual(e, (4, 5, 6))
+            idx += 1
+        self.assertEqual(idx, 2)
+
+    def test_recordIO(self):
+        self.do_test(
+            os.path.join(
+                os.path.dirname(__file__), "test_reader_recordio.dat"))
+        self.do_test([
+            os.path.join(
+                os.path.dirname(__file__), "test_reader_recordio.dat")
+        ])
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/trainer.py

@@ -27,16 +27,24 @@ class SGD(object):
     SGD Trainer combines data reader, network topolopy and update_equation together
     to train/test a neural network.
 
-    :param update_equation: The optimizer object.
-    :type update_equation: paddle.v2.optimizer.Optimizer
     :param cost: Target cost that neural network should be optimized.
     :type cost: paddle.v2.config_base.Layer
     :param parameters: The parameters dictionary.
     :type parameters: paddle.v2.parameters.Parameters
+    :param update_equation: The optimizer object.
+    :type update_equation: paddle.v2.optimizer.Optimizer
     :param extra_layers: Some layers in the neural network graph are not
                          in the path of cost layer.
-    :param pserver_spec: pserver location, eg: localhost:3000
     :type extra_layers: paddle.v2.config_base.Layer
+    :param is_local: Whether trainning locally
+    :type is_local: bool
+    :param pserver_spec: comma string for pserver location,
+                         eg:127.10.0.10:3000,127.10.0.11:3000,
+                         and this parameter is only used for fault
+                         tolerant mode cluster training.
+    :type pserver_spec: string
+    :param use_etcd: Whether using etcd pserver.
+    :param use_etcd: bool
     """
 
     def __init__(self,
@@ -161,14 +169,14 @@ class SGD(object):
                     self.__parameter_updater__.update(each_param)
                 cost_sum = out_args.sum()
                 cost = cost_sum / len(data_batch)
-                self.__parameter_updater__.finishBatch(cost)
-                batch_evaluator.finish()
                 event_handler(
                     v2_event.EndIteration(
                         pass_id=pass_id,
                         batch_id=batch_id,
                         cost=cost,
                         evaluator=batch_evaluator))
+                self.__parameter_updater__.finishBatch(cost)
+                batch_evaluator.finish()
 
             self.__parameter_updater__.finishPass()
             pass_evaluator.finish()

python/requirements.txt

+requests==2.9.2
+numpy>=1.12
+protobuf==3.1
+recordio>=0.1.0
+matplotlib
+rarfile
+scipy>=0.19.0
+Pillow
+nltk>=3.2.2

python/setup.py.in

 from setuptools import setup, Distribution
-
 class BinaryDistribution(Distribution):
     def has_ext_modules(foo):
         return True
@@ -18,19 +17,25 @@ packages=['paddle',
           'paddle.v2.framework.proto',
           'py_paddle']
 
-setup_requires=["requests",
-                "numpy>=1.12",
-                "protobuf==3.1",
-                "recordio",
-                "matplotlib",
-                "rarfile",
-                "scipy>=0.19.0",
-                "Pillow",
-                "nltk>=3.2.2"]
+with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f:
+    setup_requires = f.read().splitlines()
 
 if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']:
     setup_requires+=["opencv-python"]
 
+# the prefix is sys.prefix which should always be usr
+paddle_bin_dir = 'opt/paddle/bin'
+paddle_bins = ['${PADDLE_BINARY_DIR}/paddle/scripts/paddle_usage',
+               '${PADDLE_BINARY_DIR}/paddle/trainer/paddle_trainer',
+               '${PADDLE_BINARY_DIR}/paddle/trainer/paddle_merge_model',
+               '${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main',
+               '${PADDLE_BINARY_DIR}/paddle/scripts/paddle']
+
+paddle_rt_lib_dir = 'lib'
+paddle_rt_libs = ['${WARPCTC_LIBRARIES}']
+if '${MKL_SHARED_LIBS}'!= '':
+  paddle_rt_libs += '${MKL_SHARED_LIBS}'.split(';')
+
 setup(name='paddlepaddle',
       version='${PADDLE_VERSION}',
       description='Parallel Distributed Deep Learning',
@@ -48,11 +53,7 @@ setup(name='paddlepaddle',
           'paddle.v2.framework.proto': '${PADDLE_BINARY_DIR}/paddle/framework',
           'py_paddle': '${PADDLE_SOURCE_DIR}/paddle/py_paddle'
       },
-      scripts=['${PADDLE_BINARY_DIR}/paddle/scripts/paddle'],
+      scripts=paddle_bins,
       distclass=BinaryDistribution,
-      data_files=[('/usr/local/opt/paddle/bin',
-                       ['${PADDLE_BINARY_DIR}/paddle/scripts/paddle_usage',
-                        '${PADDLE_BINARY_DIR}/paddle/trainer/paddle_trainer',
-                        '${PADDLE_BINARY_DIR}/paddle/trainer/paddle_merge_model',
-                        '${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main'])]
+      data_files=[(paddle_rt_lib_dir, paddle_rt_libs)]
 )