diff --git a/CMakeLists.txt b/CMakeLists.txt index 08237cd850..5739c2a260 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -67,6 +67,9 @@ endif() if(ANDROID) if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "16") message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16") + elseif(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21") + # TODO: support glog for Android api 16 ~ 19 in the future + message(WARNING "Using the unofficial git repository instead") endif() set(WITH_GPU OFF CACHE STRING diff --git a/Dockerfile.android b/Dockerfile.android index 452aa15745..9d13a414f6 100644 --- a/Dockerfile.android +++ b/Dockerfile.android @@ -6,13 +6,14 @@ RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ub # ENV variables ARG ANDROID_ABI +ARG ANDROID_API ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"} +ENV ANDROID_API=${ANDROID_API:-21} ENV HOME=/root \ ANDROID_NDK_HOME=/opt/android-ndk-linux \ - ANDROID_ARM_STANDALONE_TOOLCHAIN=/opt/arm-toolchain \ - ANDROID_ARM64_STANDALONE_TOOLCHAIN=/opt/arm64-toolchain + ANDROID_TOOLCHAINS_DIR=/opt/toolchains RUN apt-get update && \ apt-get install -y \ @@ -42,14 +43,12 @@ RUN pip install --upgrade pip && \ pip install pre-commit # Android NDK -RUN mkdir /opt/android-ndk-tmp && \ +RUN mkdir -p ${ANDROID_TOOLCHAINS_DIR} && \ + mkdir -p /opt/android-ndk-tmp && \ cd /opt/android-ndk-tmp && \ wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \ unzip -q android-ndk-r14b-linux-x86_64.zip && \ mv android-ndk-r14b ${ANDROID_NDK_HOME} && \ - ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-23 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \ - ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-23 --install-dir=${ANDROID_ARM64_STANDALONE_TOOLCHAIN} && \ - rm -rf /opt/android-ndk-tmp && \ - rm -rf ${ANDROID_NDK_HOME} + rm -rf /opt/android-ndk-tmp CMD ["bash", "/paddle/paddle/scripts/docker/build_android.sh"] diff --git a/cmake/external/gflags.cmake b/cmake/external/gflags.cmake index 16e5bef4cd..01a2f4d5fa 100644 --- a/cmake/external/gflags.cmake +++ b/cmake/external/gflags.cmake @@ -18,9 +18,9 @@ SET(GFLAGS_SOURCES_DIR ${THIRD_PARTY_PATH}/gflags) SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags) SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE) IF(WIN32) - set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) + set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) ELSE(WIN32) - set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) + set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) ENDIF(WIN32) INCLUDE_DIRECTORIES(${GFLAGS_INCLUDE_DIR}) @@ -56,3 +56,12 @@ SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES}) ADD_DEPENDENCIES(gflags extern_gflags) LIST(APPEND external_project_dependencies gflags) + +IF(WITH_C_API) + INSTALL(DIRECTORY ${GFLAGS_INCLUDE_DIR} DESTINATION third_party/gflags) + IF(ANDROID) + INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib/${ANDROID_ABI}) + ELSE() + INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib) + ENDIF() +ENDIF() diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake index 8a594a825a..b450a30166 100644 --- a/cmake/external/glog.cmake +++ b/cmake/external/glog.cmake @@ -19,9 +19,9 @@ SET(GLOG_INSTALL_DIR ${THIRD_PARTY_PATH}/install/glog) SET(GLOG_INCLUDE_DIR "${GLOG_INSTALL_DIR}/include" CACHE PATH "glog include directory." FORCE) IF(WIN32) - SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE) + SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE) ELSE(WIN32) - SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE) + SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE) ENDIF(WIN32) INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR}) @@ -56,3 +56,12 @@ ADD_DEPENDENCIES(glog extern_glog gflags) LINK_LIBRARIES(glog gflags) LIST(APPEND external_project_dependencies glog) + +IF(WITH_C_API) + INSTALL(DIRECTORY ${GLOG_INCLUDE_DIR} DESTINATION third_party/glog) + IF(ANDROID) + INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib/${ANDROID_ABI}) + ELSE() + INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib) + ENDIF() +ENDIF() diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index f9e05af59f..4fc8d43fc1 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -73,6 +73,26 @@ IF(NOT ${CBLAS_FOUND}) UPDATE_COMMAND "" CONFIGURE_COMMAND "" ) + + IF(WITH_C_API) + INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas) + # Because libopenblas.a is a symbolic link of another library, thus need to + # install the whole directory. + IF(ANDROID) + SET(TMP_INSTALL_DIR third_party/openblas/lib/${ANDROID_ABI}) + ELSE() + SET(TMP_INSTALL_DIR third_party/openblas/lib) + ENDIF() + INSTALL(CODE "execute_process( + COMMAND ${CMAKE_COMMAND} -E copy_directory ${CBLAS_INSTALL_DIR}/lib + destination ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR} + )" + ) + INSTALL(CODE "MESSAGE(STATUS \"Installing: \" + \"${CBLAS_INSTALL_DIR}/lib -> ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}\" + )" + ) + ENDIF() ENDIF(NOT ${CBLAS_FOUND}) MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}") diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake index e629d61585..a887be2e2a 100644 --- a/cmake/external/protobuf.cmake +++ b/cmake/external/protobuf.cmake @@ -223,6 +223,15 @@ IF(NOT PROTOBUF_FOUND) SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY} CACHE FILEPATH "protoc library." FORCE) + IF(WITH_C_API) + INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf) + IF(ANDROID) + INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI}) + ELSE() + INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib) + ENDIF() + ENDIF() + IF(CMAKE_CROSSCOMPILING) PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf) ELSE() diff --git a/cmake/external/zlib.cmake b/cmake/external/zlib.cmake index 45ca5542b7..5aecab90ca 100644 --- a/cmake/external/zlib.cmake +++ b/cmake/external/zlib.cmake @@ -49,3 +49,12 @@ ExternalProject_Add( ) LIST(APPEND external_project_dependencies zlib) + +IF(WITH_C_API) + INSTALL(DIRECTORY ${ZLIB_INCLUDE_DIR} DESTINATION third_party/zlib) + IF(ANDROID) + INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib/${ANDROID_ABI}) + ELSE() + INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib) + ENDIF() +ENDIF() diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index 58665e9f2b..e3892849ab 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -262,7 +262,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs, - 生成库 - 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。 + `paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。 ## 实现单元测试 @@ -354,11 +354,7 @@ class TestMulGradOp(GradientChecker): ### 编译和执行单元测试 -单元测试编写完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)中添加以下内容，将单元测试加入工程： - -``` -py_test(test_mul_op SRCS test_mul_op.py) -``` +`python/paddle/v2/framework/tests` 目录下新增的 `test_*.py` 单元测试会被自动加入工程进行编译。 请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单元测试： diff --git a/doc/howto/dev/write_docs_cn.rst b/doc/howto/dev/write_docs_cn.rst index 36e5d420c9..731a63f945 100644 --- a/doc/howto/dev/write_docs_cn.rst +++ b/doc/howto/dev/write_docs_cn.rst @@ -5,15 +5,13 @@ PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。 -如何构建PaddlePaddle的文档 -========================== +如何构建文档 +============ -PaddlePaddle的文档构建有直接构建和基于Docker构建两种方式，我们提供了一个构建脚本build_docs.sh来进行构建。 -PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使用基于Docker来构建PaddlePaddle的文档。 +PaddlePaddle的文档构建有两种方式。 - -使用Docker构建PaddlePaddle的文档 --------------------------------- +使用Docker构建 +-------------- 使用Docker构建PaddlePaddle的文档，需要在系统里先安装好Docker工具包。Docker安装请参考 `Docker的官网 `_ 。安装好Docker之后可以使用源码目录下的脚本构建文档，即 @@ -21,58 +19,46 @@ PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使 cd TO_YOUR_PADDLE_CLONE_PATH cd paddle/scripts/tools/build_docs - bash build_docs.sh with_docker - -编译完成后，会在当前目录生成两个子目录\: - -* doc 英文文档目录 -* doc_cn 中文文档目录 + sh build_docs.sh +编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。 打开浏览器访问对应目录下的index.html即可访问本地文档。 - - -直接构建PaddlePaddle的文档 --------------------------- - -因为PaddlePaddle的v2 api文档生成过程依赖于py_paddle Python包，用户需要首先确认py_paddle包已经安装。 - -.. code-block:: bash - - python -c "import py_paddle" - -如果提示错误，那么用户需要在本地编译安装PaddlePaddle，请参考 `源码编译文档 `_ 。 -注意，用户在首次编译安装PaddlePaddle时，请将WITH_DOC选项关闭。在编译安装正确之后，请再次确认py_paddle包已经安装，即可进行下一步操作。 +直接构建 +-------- 如果提示正确，可以执行以下命令编译生成文档，即 .. code-block:: bash cd TO_YOUR_PADDLE_CLONE_PATH - cd paddle/scripts/tools/build_docs - bash build_docs.sh local - -编译完成之后，会在当前目录生成两个子目录\: - -* doc 英文文档目录 -* doc_cn 中文文档目录 + mkdir -p build + cd build + cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON + make gen_proto_py + make paddle_docs paddle_docs_cn +编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。 打开浏览器访问对应目录下的index.html即可访问本地文档。 -如何书写PaddlePaddle的文档 -========================== +如何书写文档 +============ PaddlePaddle文档使用 `sphinx`_ 自动生成，用户可以参考sphinx教程进行书写。 -如何更新www.paddlepaddle.org文档 -================================ +如何更新文档主题 +================ + +PaddlePaddle文档主题在 `TO_YOUR_PADDLE_CLONE_PATH/doc_theme` 文件夹下，包含所有和前端网页设计相关的文件。 -开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中，提交方式可参见 `贡献文档 `_ 。 +如何更新doc.paddlepaddle.org +============================ + +更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 `_ 。 目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 `_ 和 `英文文档 `_ 。 - .. _cmake: https://cmake.org/ .. _sphinx: http://www.sphinx-doc.org/en/1.4.8/ diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index dde99ab340..3af111eb57 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -64,9 +64,29 @@ link_paddle_exe(paddle_capi_shared) install(FILES ${CAPI_HEADERS} DESTINATION include/paddle) install(FILES ${CMAKE_CURRENT_BINARY_DIR}/config.h DESTINATION include/paddle) if(ANDROID) + execute_process( + COMMAND ${GIT_EXECUTABLE} log --pretty=oneline -1 + OUTPUT_VARIABLE GIT_COMMITS_LIST + RESULT_VARIABLE GIT_COMMITS_LIST_RESULT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + if(${GIT_COMMITS_LIST_RESULT}) + set(GIT_COMMITS_LIST "No commits.") + endif() install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library} DESTINATION lib/${ANDROID_ABI}) install(TARGETS paddle_capi_shared DESTINATION lib/${ANDROID_ABI}) + install(CODE "FILE(WRITE ${CMAKE_INSTALL_PREFIX}/lib/${ANDROID_ABI}/BUILD.txt + \"Compiler:\n\" + \"\\t${CMAKE_C_COMPILER}\\n\" + \"\\t${CMAKE_CXX_COMPILER}\\n\" + \"Compiler Flags:\\n\" + \"\\t${CMAKE_F_FLAGS}\\n\" + \"\\t${CMAKE_CXX_FLAGS}\\n\" + \"Android API: ${CMAKE_SYSTEM_VERSION}\\n\" + \"Lastest commit:\\n\" + \"\\t${GIT_COMMITS_LIST}\\n\" + )" + ) else(ANDROID) install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library} DESTINATION lib) install(TARGETS paddle_capi_shared DESTINATION lib) diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index c0838d9b75..3371962c63 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -9,6 +9,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS 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) +nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor) cc_test(variable_test SRCS variable_test.cc) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index c762811dfc..0a6d762bc8 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -2,11 +2,22 @@ ## Motivation -In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the gradient operators/expressions together with the chain rule. Every forward network needs a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass. +In Neural Network, many model is solved by the the backpropagation algorithm(known as BP) at present. Technically it caculates the gradient of the loss function, then distributed back through the networks. Follows the chain rule, so we need a module chains the gradient operators/expressions together with to construct the backward pass. Every forward network needs a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass. -## Backward Operator Registry +## Implementation -A backward network is built up with several backward operators. Backward operators take forward operators' inputs outputs, and output gradients and then calculate its input gradients. +In this design doc, we exported only one API for generating the backward pass. + +```c++ +std::unique_ptr Backward(const OperatorBase& forwardOp, + const std::unordered_set& no_grad_vars); +``` + +The implementation behind it can be divided into two parts, **Backward Operator Creating** and **Backward Operator Building**. + +### Backward Operator Registry + +A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs, and output gradients and then calculate its input gradients. | | forward operator | backward operator | ---------------------- | ---------------- |------------------------- | @@ -25,7 +36,7 @@ REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad); `mul_grad` is the type of backward operator, and `MulOpGrad` is its class name. -## Backward Opeartor Creating +### Backward Opeartor Creating Given a certain forward operator, we can get its corresponding backward operator by calling: @@ -43,40 +54,47 @@ The function `BuildGradOp` will sequentially execute following processes: 4. Building backward operator with `inputs`, `outputs` and forward operator's attributes. -## Backward Network Building - -A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and put them together. +### Backward Network Building -In our design, the network itself is also a kind of operator. So the operators contained by a big network may be some small network. - -given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`, `InputGradients`. +A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and append them together one by one. There is some corner case need to process specially. 1. Op - when the input forward network is an Op, return its gradient Operator Immediately. + When the input forward network is an Op, return its gradient Operator Immediately. If all of its outputs are in no gradient set, then return a special `NOP`. 2. NetOp - when the input forward network is a NetOp, it needs to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to the forward NetOp. + In our design, the network itself is also a kind of operator(**NetOp**). So the operators contained by a big network may be some small network. When the input forward network is a NetOp, it needs to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to the forward NetOp. + +3. RnnOp + + RnnOp is a nested stepnet operator. Backward module need to recusively call `Backward` for every stepnet. + +4. Sharing Variables + + **sharing variables**. As illustrated in the pictures, two operator's share the same variable name of W@GRAD, which will overwrite their sharing input variable. + +

+
- **shared variable**. As illustrated in the pictures, two operator's `Output` `Gradient` will overwrite their shared input variable. +​ pic 1. Sharing variables in operators. -

-
+

- 1. Shared variable in operators. +​ Sharing variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator to replace the overwrite links. -

+

+
- Share variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator replace the overwrite links. +​ pic 2. Replace sharing variable's gradient with `Add` operator. -

-
+

- 2. Replace shared variable's gradient with `Add` operator. +​ Because our framework finds variables accord to their names, we need to rename the output links. We add a suffix of number to represent its position in clockwise. -

+5. Part of Gradient is Zero. + In the whole graph, there is some case of that one operator's gradient is not needed, but its input's gradient is a dependency link of other operator, we need to fill a same shape gradient matrix in the position. In our implement, we insert a special `fillZeroLike` operator. -​ Then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it. +Follow these rules above, then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it. diff --git a/paddle/framework/images/duplicate_op2.graffle b/paddle/framework/images/duplicate_op2.graffle index ede3bca30a..5cec3bc64d 100644 Binary files a/paddle/framework/images/duplicate_op2.graffle and b/paddle/framework/images/duplicate_op2.graffle differ diff --git a/paddle/framework/images/duplicate_op2.png b/paddle/framework/images/duplicate_op2.png index 4e872dc2ca..21cdd5cabf 100644 Binary files a/paddle/framework/images/duplicate_op2.png and b/paddle/framework/images/duplicate_op2.png differ diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h index 154068fef6..568f4e8981 100644 --- a/paddle/framework/lod_tensor.h +++ b/paddle/framework/lod_tensor.h @@ -18,8 +18,10 @@ #ifndef PADDLE_ONLY_CPU #include #include +#include #endif +#include #include "paddle/framework/ddim.h" #include "paddle/framework/tensor.h" #include "paddle/platform/enforce.h" @@ -32,7 +34,8 @@ template using Vector = std::vector; #else template -using Vector = thrust::host_vector; +using Vector = thrust::host_vector< + T, thrust::system::cuda::experimental::pinned_allocator>; #endif using LoD = std::vector>; diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu new file mode 100644 index 0000000000..1079a36a2e --- /dev/null +++ b/paddle/framework/lod_tensor_test.cu @@ -0,0 +1,52 @@ +/* + 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 +#include +#include "paddle/framework/lod_tensor.h" +#include "paddle/platform/assert.h" + +#include + +__global__ void test(size_t* a, int size) { + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size; + i += blockDim.x * gridDim.x) { + a[i] *= 2; + } +} + +TEST(LoDTensor, LoDInGPU) { + paddle::framework::Tensor tensor; + paddle::framework::LoDTensor lod_tensor; + paddle::platform::GPUPlace place(0); + + paddle::framework::LoD src_lod; + src_lod.push_back(std::vector{0, 2, 4, 6, 8, 10, 12, 14}); + + tensor.Resize({14, 16}); + tensor.mutable_data(place); + + lod_tensor.set_lod(src_lod); + lod_tensor.set_tensor(&tensor); + CHECK_EQ(lod_tensor.lod_element(0, 2), 4); + CHECK_EQ(lod_tensor.lod_element(0, 4), 8); + + auto lod = lod_tensor.lod(); + + test<<<1, 8>>>(lod[0].data(), lod[0].size()); + cudaDeviceSynchronize(); + + for (size_t i = 0; i < src_lod[0].size(); ++i) { + CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2); + } +} diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h index ce938b2143..4b5a2ae523 100644 --- a/paddle/framework/tensor.h +++ b/paddle/framework/tensor.h @@ -81,6 +81,9 @@ class Tensor { /*! Return the dimensions of the memory block. */ inline const DDim& dims() const; + /*! Return the numel of the memory block. */ + inline int64_t numel() const; + /*! Resize the dimensions of the memory block. */ inline Tensor& Resize(const DDim& dims); @@ -162,6 +165,12 @@ class Tensor { /*! points to dimensions of memory block. */ DDim dims_; + /** + * A cache of the number of elements in a tensor. + * Would be 0 for an uninitialized tensor. + */ + int64_t numel_; + /** * @brief A PlaceHolder may be shared by more than one tensor. * diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h index 637f04ae00..642b53efc7 100644 --- a/paddle/framework/tensor_impl.h +++ b/paddle/framework/tensor_impl.h @@ -24,7 +24,7 @@ inline void Tensor::check_memory_size() const { PADDLE_ENFORCE_NOT_NULL( holder_, "Tenosr holds no memory. Call Tensor::mutable_data first."); PADDLE_ENFORCE_GE( - holder_->size(), product(dims_) * sizeof(T) + offset_, + holder_->size(), numel() * sizeof(T) + offset_, "Tensor's dims_ is out of bound. Call Tensor::mutable_data " "first to re-allocate memory.\n" "or maybe the required data-type mismatches the data already stored."); @@ -54,11 +54,11 @@ inline T* Tensor::mutable_data(DDim dims, platform::Place place) { template inline T* Tensor::mutable_data(platform::Place place) { static_assert(std::is_pod::value, "T must be POD"); - PADDLE_ENFORCE_GT(product(dims_), 0, + PADDLE_ENFORCE_GT(numel(), 0, "Tensor's numel must be larger than zero to call " "Tensor::mutable_data. Call Tensor::set_dim first."); /* some versions of boost::variant don't have operator!= */ - int64_t size = product(dims_) * sizeof(T); + int64_t size = numel() * sizeof(T); if (holder_ == nullptr || !(holder_->place() == place) || holder_->size() < size + offset_) { if (platform::is_cpu_place(place)) { @@ -97,7 +97,7 @@ inline void Tensor::CopyFrom(const Tensor& src, auto dst_ptr = static_cast(mutable_data(dst_place)); - auto size = product(src.dims_) * sizeof(T); + auto size = src.numel() * sizeof(T); if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) { memory::Copy(boost::get(dst_place), dst_ptr, @@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const { PADDLE_ENFORCE_LT(begin_idx, end_idx, "Begin index must be less than end index."); PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1."); - size_t base = product(dims_) / dims_[0]; + size_t base = numel() / dims_[0]; Tensor dst; dst.holder_ = holder_; DDim dst_dims = dims_; @@ -143,11 +143,14 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const { inline Tensor& Tensor::Resize(const DDim& dims) { dims_ = dims; + numel_ = product(dims_); return *this; } inline const DDim& Tensor::dims() const { return dims_; } +inline int64_t Tensor::numel() const { return numel_; } + template inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) { Tensor res; diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h index aefeea78ba..33722d3cac 100644 --- a/paddle/function/neon/NeonDepthwiseConv.h +++ b/paddle/function/neon/NeonDepthwiseConv.h @@ -594,7 +594,7 @@ struct StridePadding { float32x4_t s1 = vdupq_n_f32(0.f); for (int s = 0; s < step; s++) { float32x4_t s0 = vld1q_f32(input); - float32x4x2_t v = {s0, s1}; + float32x4x2_t v = {{s0, s1}}; vst2q_f32(inputPadding, v); input += 4; inputPadding += 8; diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp index 1b59ed60c5..3eea638649 100644 --- a/paddle/gserver/layers/DeConv3DLayer.cpp +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -53,27 +53,27 @@ bool DeConv3DLayer::init(const LayerMap &layerMap, size_t DeConv3DLayer::getSize() { CHECK_NE(inputLayers_.size(), 0UL); - outputH_.clear(); - outputW_.clear(); - outputD_.clear(); + imgSizeW_.clear(); + imgSizeH_.clear(); + imgSizeD_.clear(); N_.clear(); NOut_.clear(); size_t layerSize = 0; for (size_t i = 0; i < inputLayers_.size(); ++i) { - outputW_.push_back( - imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true)); - outputH_.push_back(imageSize( - imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true)); - outputD_.push_back(imageSize( - imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true)); - NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); - N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]); + imgSizeW_.push_back( + imageSize(outputW_[i], filterSize_[i], padding_[i], stride_[i], true)); + imgSizeH_.push_back(imageSize( + outputH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true)); + imgSizeD_.push_back(imageSize( + outputD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true)); + NOut_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]); + N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); layerSize += NOut_[i] * numFilters_; } - getOutput().setFrameHeight(outputH_[0]); - getOutput().setFrameWidth(outputW_[0]); - getOutput().setFrameDepth(outputD_[0]); + getOutput().setFrameHeight(imgSizeH_[0]); + getOutput().setFrameWidth(imgSizeW_[0]); + getOutput().setFrameDepth(imgSizeD_[0]); return layerSize; } @@ -103,9 +103,9 @@ void DeConv3DLayer::forward(PassType passType) { } colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(), numFilters_, - outputD_[i], - outputH_[i], - outputW_[i], + imgSizeD_[i], + imgSizeH_[i], + imgSizeW_[i], filterSizeZ_[i], filterSizeY_[i], filterSize_[i], @@ -144,9 +144,9 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) { colBuf_->vol2Col( getOutputGrad()->getData() + n * getOutputGrad()->getStride(), numFilters_, - outputD_[i], - outputH_[i], - outputW_[i], + imgSizeD_[i], + imgSizeH_[i], + imgSizeW_[i], filterSizeZ_[i], filterSizeY_[i], filterSize_[i], diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h index edef36194a..4002a3d074 100644 --- a/paddle/gserver/layers/Layer.h +++ b/paddle/gserver/layers/Layer.h @@ -49,6 +49,12 @@ struct LayerState { }; typedef std::shared_ptr LayerStatePtr; +/// Paddle device ID, MKLDNN is -2, CPU is -1 +enum PADDLE_DEVICE_ID { + MKLDNN_DEVICE = -2, + CPU_DEVICE = -1, +}; + /** * @brief Base class for layer. * Define necessary variables and functions for every layer. @@ -59,11 +65,6 @@ protected: LayerConfig config_; /// whether to use GPU bool useGpu_; - /// Paddle device ID, MKLDNN is -2, CPU is -1 - enum PADDLE_DEVICE_ID { - MKLDNN_DEVICE = -2, - CPU_DEVICE = -1, - }; /// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ... int deviceId_; /// Input layers diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index 8318c8c519..f70343251a 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -14,7 +14,6 @@ limitations under the License. */ #include "MKLDNNFcLayer.h" #include "paddle/utils/Logging.h" -#include "paddle/utils/Stat.h" using namespace mkldnn; // NOLINT typedef memory::format format; @@ -40,6 +39,8 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap, oc_ = getSize(); oh_ = 1; ow_ = 1; + ih_ = 1; + iw_ = 1; // input size can not change in FC iLayerSize_ = inputLayers_[0]->getSize(); @@ -77,111 +78,86 @@ void MKLDNNFcLayer::convertWeightsToPaddle() { wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim); } -void MKLDNNFcLayer::convertOutputToOtherDevice() { - copyOutputInfoToOtherDevice(); - // find other cpu device and reorder output to cpu device - int cnt = 0; - for (size_t i = 0; i < outputOtherDevice_.size(); i++) { - if (outputOtherDevice_[i].deviceId == CPU_DEVICE) { - // fc cpu output value do not need convert - // just share point - outputOtherDevice_[i].value = output_.value; - ++cnt; - } - } - - if (cnt > 1) { - LOG(WARNING) << "should not have more than one CPU devie"; - } -} +void MKLDNNFcLayer::reshape( + int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) { + reshapeInput(bs, ih, iw); -void MKLDNNFcLayer::reshape() { - const Argument& input = getInput(0, getPrev(0)->getDeviceId()); - int batchSize = input.getBatchSize(); - if (bs_ == batchSize) { - return; - } - bs_ = batchSize; - ih_ = input.getFrameHeight(); - iw_ = input.getFrameWidth(); - if (ih_ == 0) { - ih_ = 1; - } - if (iw_ == 0) { - iw_ = 1; - } CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize()); - ic_ = iLayerSize_ / (ih_ * iw_); - CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible"; - CHECK_EQ(size_t(oc_), getSize()); - printSizeInfo(); + ic = iLayerSize_ / (ih * iw); + CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible"; + CHECK_EQ(size_t(oc), getSize()); - // reset output - output_.setFrameHeight(oh_); - output_.setFrameWidth(ow_); - resetOutput(bs_, oc_); + reshapeOutput(oh, ow); + resizeOutput(bs, oc); - // reset mkldnn forward - resetFwd(); - needResetBwd_ = true; - - convertWeightsFromPaddle(); + printSizeInfo(); } -void MKLDNNFcLayer::resetFwd() { +void MKLDNNFcLayer::resetFwd(std::vector& pipeline, + MKLDNNMatrixPtr& in, + MKLDNNMatrixPtr& wgt, + MKLDNNMatrixPtr& bias, + MKLDNNMatrixPtr& out) { + pipeline.clear(); bool hasBias = biases_ && biases_->getW(); - const MatrixPtr& wgt = weight_->getW(); - const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr; - const MatrixPtr& out = output_.value; + const MatrixPtr& wgtVal = weight_->getW(); + const MatrixPtr& biasVal = hasBias ? biases_->getW() : nullptr; + const MatrixPtr& outVal = output_.value; if (inputIsOnlyMKLDNN()) { - const MatrixPtr& in = getInputValue(0); - inVal_ = std::dynamic_pointer_cast(in); - CHECK(inVal_) << "Input should be MKLDNNMatrix"; + const MatrixPtr& inVal = getInputValue(0); + in = std::dynamic_pointer_cast(inVal); + CHECK(in) << "Input should be MKLDNNMatrix"; } else { CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet"; - const MatrixPtr& in = getInputValue(0, CPU_DEVICE); - inVal_ = MKLDNNMatrix::create( - in, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_); - } - inVal_->downSpatial(); - wgtVal_ = MKLDNNMatrix::create( - wgt, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_); - wgtVal_->downSpatial(); - biasVal_ = - hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr; - outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_); + const MatrixPtr& inVal = getInputValue(0, CPU_DEVICE); + in = MKLDNNMatrix::create( + inVal, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_); + } + in->downSpatial(); + wgt = MKLDNNMatrix::create( + wgtVal, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_); + wgt->downSpatial(); + bias = hasBias ? MKLDNNMatrix::create(biasVal, {oc_}, format::x, engine_) + : nullptr; + out = MKLDNNMatrix::create(outVal, {bs_, oc_}, format::nc, engine_); // change original output value to mkldnn output value - output_.value = std::dynamic_pointer_cast(outVal_); + output_.value = std::dynamic_pointer_cast(out); if (!outputIsOnlyMKLDNN()) { - convertOutputToOtherDevice(); + // fc cpu output value do not need create convert + // just share point + getOutput(CPU_DEVICE).value->setData(output_.value->getData()); } // create forward handle prop_kind pk = prop_kind::forward; fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk, - inVal_->getMemoryDesc(), - wgtVal_->getMemoryDesc(), - biasVal_->getMemoryDesc(), - outVal_->getMemoryDesc()) + in->getMemoryDesc(), + wgt->getMemoryDesc(), + bias->getMemoryDesc(), + out->getMemoryDesc()) : fc_fwd::desc(pk, - inVal_->getMemoryDesc(), - wgtVal_->getMemoryDesc(), - outVal_->getMemoryDesc()); + in->getMemoryDesc(), + wgt->getMemoryDesc(), + out->getMemoryDesc()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); if (hasBias) { - fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_)); + fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *bias, *out)); } else { - fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_)); + fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *out)); } printValueFormatFlow(); - pipelineFwd_.clear(); - pipelineFwd_.push_back(*fwd_); + pipeline.push_back(*fwd_); } -void MKLDNNFcLayer::resetBwd() { +void MKLDNNFcLayer::resetBwd(std::vector& pipeline, + MKLDNNMatrixPtr& in, + MKLDNNMatrixPtr& wgt, + MKLDNNMatrixPtr& bias, + MKLDNNMatrixPtr& out) { + pipeline.clear(); if (!needResetBwd_) { return; } @@ -190,8 +166,8 @@ void MKLDNNFcLayer::resetBwd() { /// backward weight CHECK(inVal_) << "Should have input value"; - const MatrixPtr& wgt = weight_->getWGrad(); - const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr; + const MatrixPtr& wgtGrad = weight_->getWGrad(); + const MatrixPtr& biasGrad = hasBias ? biases_->getWGrad() : nullptr; // TODO(TJ): merge outgrad int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE; @@ -202,101 +178,66 @@ void MKLDNNFcLayer::resetBwd() { // for CPU device: // fc do not need to convert from cpu device since output is always nc format // only need create from cpu device - const MatrixPtr& out = getOutput(device).grad; - outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc()); - wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc()); - biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc()) - : nullptr; + const MatrixPtr& outGrad = getOutput(device).grad; + out = MKLDNNMatrix::create(outGrad, outVal_->getPrimitiveDesc()); + wgt = MKLDNNMatrix::create(wgtGrad, wgtVal_->getPrimitiveDesc()); + bias = hasBias ? MKLDNNMatrix::create(biasGrad, biasVal_->getPrimitiveDesc()) + : nullptr; // create memory primitive desc fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, inVal_->getMemoryDesc(), - wgtGrad_->getMemoryDesc(), - outGrad_->getMemoryDesc()); + wgt->getMemoryDesc(), + out->getMemoryDesc()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); fc_bwdWgt::desc bwdWgtDesc = hasBias ? fc_bwdWgt::desc(inVal_->getMemoryDesc(), - wgtGrad_->getMemoryDesc(), - biasGrad_->getMemoryDesc(), - outGrad_->getMemoryDesc()) + wgt->getMemoryDesc(), + bias->getMemoryDesc(), + out->getMemoryDesc()) : fc_bwdWgt::desc(inVal_->getMemoryDesc(), - wgtGrad_->getMemoryDesc(), - outGrad_->getMemoryDesc()); + wgt->getMemoryDesc(), + out->getMemoryDesc()); fc_bwdWgt::primitive_desc bwdWgtPD = fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD); if (hasBias) { - bwdWgt_.reset( - new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_)); + bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt, *bias)); } else { - bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_)); + bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt)); } - pipelineBwd_.clear(); - pipelineBwd_.push_back(*bwdWgt_); + pipeline.push_back(*bwdWgt_); /// backward data - device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE; - const MatrixPtr& in = getInputGrad(0, device); - if (in == nullptr) { + const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad; + if (inGrad == nullptr) { return; } - if (getInput(0, device).getAllCount() > 1) { - // TODO(TJ): use outputMaps_ ways when merge outgrad done + if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) { + // TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done } else { - inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc()); + in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc()); } - fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(), - wgtGrad_->getMemoryDesc(), - outGrad_->getMemoryDesc()); + fc_bwdData::desc bwdDataDesc = fc_bwdData::desc( + inVal_->getMemoryDesc(), wgt->getMemoryDesc(), out->getMemoryDesc()); fc_bwdData::primitive_desc bwdDataPD = fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD); CHECK(wgtVal_) << "Should have weight memory"; - bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_)); + bwdData_.reset(new fc_bwdData(bwdDataPD, *out, *wgtVal_, *in)); printGradFormatFlow(); - pipelineBwd_.push_back(*bwdData_); + pipeline.push_back(*bwdData_); } -void MKLDNNFcLayer::forward(PassType passType) { - Layer::forward(passType); - reshape(); - - { - REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str()); - syncInputValue(); - - // just submit forward pipeline - stream_->submit(pipelineFwd_); - } - - /* activation */ { - REGISTER_TIMER_INFO("FwActTimer", getName().c_str()); - forwardActivation(); - } +void MKLDNNFcLayer::updateInputData() { + inVal_->setData(getInputValue(0, CPU_DEVICE)->getData()); } -void MKLDNNFcLayer::backward(const UpdateCallback& callback) { - /* Do derivation */ { - REGISTER_TIMER_INFO("BpActTimer", getName().c_str()); - backwardActivation(); - } - - { - REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str()); - resetBwd(); - - syncOutputGrad(); - // just sumbmit backward pipeline - stream_->submit(pipelineBwd_); - } - - { - REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); - weight_->getParameterPtr()->incUpdate(callback); - if (biases_ && biases_->getWGrad()) { - biases_->getParameterPtr()->incUpdate(callback); - } +void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) { + weight_->getParameterPtr()->incUpdate(callback); + if (biases_ && biases_->getWGrad()) { + biases_->getParameterPtr()->incUpdate(callback); } } } // namespace paddle diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h index e138a6faf1..3119f86349 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.h +++ b/paddle/gserver/layers/MKLDNNFcLayer.h @@ -45,35 +45,28 @@ public: bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) override; - void convertWeightsFromPaddle() override; + void reshape( + int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override; - void convertWeightsToPaddle() override; + void resetFwd(std::vector& pipeline, + MKLDNNMatrixPtr& in, + MKLDNNMatrixPtr& wgt, + MKLDNNMatrixPtr& bias, + MKLDNNMatrixPtr& out) override; - void forward(PassType passType) override; + void resetBwd(std::vector& pipeline, + MKLDNNMatrixPtr& in, + MKLDNNMatrixPtr& wgt, + MKLDNNMatrixPtr& bias, + MKLDNNMatrixPtr& out) override; - void backward(const UpdateCallback& callback) override; + void updateInputData() override; -protected: - /** - * reshape the input image sizes - * and reset output buffer size - * and reset mkldnn forward - */ - void reshape(); - - /** - * reset the forward primitve and memory - * only would be called when input size changes - */ - void resetFwd(); - - /** - * reset the backward primitve and memory for mkldnn fc - * only would be called when needed - */ - void resetBwd(); - - void convertOutputToOtherDevice() override; + void updateWeights(const UpdateCallback& callback) override; + + void convertWeightsFromPaddle() override; + + void convertWeightsToPaddle() override; }; } // namespace paddle diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index b983b833d5..169679c829 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -19,6 +19,7 @@ limitations under the License. */ #include "MKLDNNBase.h" #include "mkldnn.hpp" #include "paddle/math/MKLDNNMatrix.h" +#include "paddle/utils/Stat.h" DECLARE_bool(use_mkldnn); @@ -33,6 +34,8 @@ typedef std::shared_ptr MKLDNNLayerPtr; */ class MKLDNNLayer : public Layer { protected: + // input value element count + size_t inputElemenCnt_; // batch size int bs_; // input image channel, height and width @@ -52,7 +55,7 @@ protected: std::vector pipelineFwd_; std::vector pipelineBwd_; - // MKLDNNMatrixPtr + // MKLDNNMatrixPtr with internal format MKLDNNMatrixPtr inVal_; MKLDNNMatrixPtr inGrad_; MKLDNNMatrixPtr outVal_; @@ -65,6 +68,7 @@ protected: public: explicit MKLDNNLayer(const LayerConfig& config) : Layer(config), + inputElemenCnt_(0), bs_(0), ic_(0), ih_(0), @@ -95,12 +99,104 @@ public: if (!Layer::init(layerMap, parameterMap)) { return false; } + checkCPUOutputsNumber(); stream_.reset(new MKLDNNStream()); engine_ = CPUEngine::Instance().getEngine(); return true; } + void forward(PassType passType) override { + passType_ = passType; + + { + REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str()); + CHECK(!inputLayers_.empty()); + copySeqInfoToOutputs(); + size_t elemenCnt = inputLayers_[0]->getOutput().value->getElementCnt(); + if (inputElemenCnt_ != elemenCnt) { + // reset when input total sizes changed, not only the batchsize + inputElemenCnt_ = elemenCnt; + reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_); + resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_); + convertWeightsFromPaddle(); + needResetBwd_ = true; + } + + if (inputLayers_[0]->getType() == "data") { + updateInputData(); + } + + stream_->submit(pipelineFwd_); + } + + /* activation */ { + REGISTER_TIMER_INFO("FwActTimer", getName().c_str()); + forwardActivation(); + } + } + + void backward(const UpdateCallback& callback) override { + /* Do derivation */ { + REGISTER_TIMER_INFO("BpActTimer", getName().c_str()); + backwardActivation(); + } + + { + REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str()); + if (needResetBwd_) { + resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_); + needResetBwd_ = false; + } + + stream_->submit(pipelineBwd_); + } + + { + REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); + updateWeights(callback); + } + } + + /** + * reshape the input image sizes + * and reset output image and buffer size + * output channel can not be changed + */ + virtual void reshape( + int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0; + + /** + * reset the mkldnn forward primitve and memory + * only would be called when input size changes + */ + virtual void resetFwd(std::vector& pipeline, + MKLDNNMatrixPtr& in, + MKLDNNMatrixPtr& wgt, + MKLDNNMatrixPtr& bias, + MKLDNNMatrixPtr& out) = 0; + + /** + * reset the mkldnn backward primitve and memory for mkldnn fc + * only would be called when needed + */ + virtual void resetBwd(std::vector& pipeline, + MKLDNNMatrixPtr& in, + MKLDNNMatrixPtr& wgt, + MKLDNNMatrixPtr& bias, + MKLDNNMatrixPtr& out) = 0; + + /** + * Update input value data when input layer is "data" type. + * Since the input value data address might be changed. + */ + virtual void updateInputData() {} + + /** + * Update weights and biases if necessary. + */ + virtual void updateWeights(const UpdateCallback& callback) {} + /** * convert weight from paddle format to mkldnn format * weight_ will be override @@ -114,10 +210,38 @@ public: virtual void convertWeightsToPaddle() {} /** - * convert MKLDNN output to other device. - * only support CPU device yet + * add this interface as public for unit test + */ + void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); } + +protected: + /** + * reshape the input image sizes and input batchsize */ - virtual void convertOutputToOtherDevice() {} + virtual void reshapeInput(int& batchsize, int& height, int& width) { + const Argument& input = inputLayers_[0]->getOutput(); + batchsize = input.getBatchSize(); + int h = input.getFrameHeight(); + int w = input.getFrameWidth(); + if (h != 0) { + height = h; + } + if (w != 0) { + width = w; + } + } + + /** + * reshape output image sizes + */ + virtual void reshapeOutput(size_t height, size_t width) { + output_.setFrameHeight(height); + output_.setFrameWidth(width); + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + outputOtherDevice_[i].setFrameHeight(height); + outputOtherDevice_[i].setFrameWidth(width); + } + } /** * print info about sizes @@ -133,8 +257,8 @@ public: */ virtual void printValueFormatFlow() { if (inVal_ && outVal_) { - VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat() - << " >>> " << outVal_->getFormat(); + VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>> " + << outVal_->getFormat(); } } @@ -143,29 +267,12 @@ public: */ virtual void printGradFormatFlow() { if (inGrad_ && outGrad_) { - VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat() - << " <<< " << outGrad_->getFormat(); + VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<< " + << outGrad_->getFormat(); } } protected: - /** - * copy image size and sequence info to other device - * @note: can not directly use Layer::copyOutputToOtherDevice since here only - * copy base info and do not copy data value - */ - void copyOutputInfoToOtherDevice() { - for (size_t i = 0; i < outputOtherDevice_.size(); i++) { - outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight()); - outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth()); - outputOtherDevice_[i].sequenceStartPositions = - output_.sequenceStartPositions; - outputOtherDevice_[i].subSequenceStartPositions = - output_.subSequenceStartPositions; - outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims; - } - } - /** * If input only has MKLDNN device. * Otherwise, only support the previous layer using CPU device. @@ -193,37 +300,12 @@ protected: return outputOtherDevice_.size() == 0; } - /** - * Sync input value data - */ - void syncInputValue() { - if (inputIsOnlyMKLDNN()) { - return; - } - real* iData = getInputValue(0, CPU_DEVICE)->getData(); - // update input data - // since it might be changed if this is after data layer - inVal_->updateData(iData); - } - - /** - * Sync output grad data - */ - void syncOutputGrad() { - if (outputIsOnlyMKLDNN()) { - return; - } - - // update diff - real* oDiff = getOutput(CPU_DEVICE).grad->getData(); - outGrad_->updateData(oDiff); - } - /** * Set deviceId of this layer. */ void setDevice(int id) { deviceId_ = id; } +private: /** * Set deviceId of the params used in this layer. */ @@ -247,6 +329,42 @@ protected: parameter->setDevice(id); } } + + /** + * Check the cpu device number of outputOtherDevice_. + * should have only one at most. + */ + void checkCPUOutputsNumber(int max = 1) { + int cnt = 0; + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + if (outputOtherDevice_[i].deviceId == CPU_DEVICE) { + ++cnt; + } + } + CHECK_LE(cnt, max) << "too much CPU devies"; + } + + /** + * copy SeqInfo from input layer to this output and other output devices. + * @note: do not use getInput(0) since it used this deviceId_, + * use "inputLayers_[0]->getOutput()" instead. + */ + void copySeqInfoToOutputs() { + if (inputLayers_.empty() || !needSequenceInfo_) { + return; + } + const Argument& input = inputLayers_[0]->getOutput(); + output_.sequenceStartPositions = input.sequenceStartPositions; + output_.subSequenceStartPositions = input.subSequenceStartPositions; + output_.cpuSequenceDims = input.cpuSequenceDims; + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + outputOtherDevice_[i].sequenceStartPositions = + output_.sequenceStartPositions; + outputOtherDevice_[i].subSequenceStartPositions = + output_.subSequenceStartPositions; + outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims; + } + } }; } // namespace paddle diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp index d7eee6eaf0..e97809141a 100644 --- a/paddle/gserver/layers/SwitchOrderLayer.cpp +++ b/paddle/gserver/layers/SwitchOrderLayer.cpp @@ -83,8 +83,7 @@ void SwitchOrderLayer::forward(PassType passType) { setOutDims(); resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]); if (heightAxis_.size() > 0) { - getOutputValue()->reshape(reshapeHeight_, reshapeWidth_); - getOutputGrad()->reshape(reshapeHeight_, reshapeWidth_); + resetOutput(reshapeHeight_, reshapeWidth_); } // switch NCHW to NHWC diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp index de1635be2a..2f48e5b2d3 100644 --- a/paddle/gserver/tests/MKLDNNTester.cpp +++ b/paddle/gserver/tests/MKLDNNTester.cpp @@ -63,8 +63,12 @@ void MKLDNNTester::reset(const TestConfig& dnn, initTestLayer( configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i])); } - dnnLayer_ = testLayers_[DNN]; refLayer_ = testLayers_[REF]; + dnnLayer_ = std::dynamic_pointer_cast(testLayers_[DNN]); + CHECK(dnnLayer_); + // for comparison with Paddle reference results, + // need manually add cpu device output for test + dnnLayer_->addOutputArgument(CPU_DEVICE); EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size()); EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size()); @@ -109,20 +113,22 @@ void MKLDNNTester::randomBotDatas() { void MKLDNNTester::randomTopDiffs() { refLayer_->getOutputGrad()->randomizeUniform(); - dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad())); - VLOG(lvl_) << "Random dom Backward Input, TopDiff: "; + dnnLayer_->getOutput(CPU_DEVICE) + .grad->copyFrom(*(refLayer_->getOutputGrad())); + VLOG(lvl_) << "Random Backward Input, TopDiff: "; printMatrix(refLayer_->getOutputGrad()); } void MKLDNNTester::checkForward() { - printTopDatas(); - double delta = compareMatrix(testLayers_[DNN]->getOutputValue(), - testLayers_[REF]->getOutputValue()); VLOG(MKLDNN_ALL) << "Check Forward"; + printTopDatas(); + double delta = compareMatrix(dnnLayer_->getOutput(-1).value, + refLayer_->getOutputValue()); EXPECT_LE(fabs(delta), eps_); } void MKLDNNTester::checkBackwardData() { + VLOG(MKLDNN_ALL) << "Check Backward Data"; // TODO(TJ): uncomment me when batch norm ready // const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm"; for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) { @@ -144,14 +150,12 @@ void MKLDNNTester::checkBackwardData() { } void MKLDNNTester::checkBackwardWgts() { + VLOG(MKLDNN_ALL) << "Check Backward Weight"; CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size()); vector dnnWgts; // used to temply save mkldnn weights saveWgt(parameters_[DNN], dnnWgts); - const MKLDNNLayerPtr dnnlayer = - std::dynamic_pointer_cast(dnnLayer_); - CHECK(dnnlayer); - dnnlayer->convertWeightsToPaddle(); + dnnLayer_->convertWeightsToPaddle(); for (size_t i = 0; i < parameters_[DNN].size(); ++i) { const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE); const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE); @@ -189,38 +193,38 @@ void MKLDNNTester::restoreWgt(const vector& from, } // clear parameters grad -void MKLDNNTester::clearWgtDiffs() { +void MKLDNNTester::clearWgtDiffs(size_t id) { + CHECK_LE(id, parameters_.size()); for (size_t n = 0; n < parameters_.size(); ++n) { - for (size_t i = 0; i < parameters_[n].size(); ++i) { - const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT); - if (grad) { - grad->zeroMem(); + if (id == n || id == parameters_.size()) { + for (size_t i = 0; i < parameters_[n].size(); ++i) { + const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT); + if (grad) { + grad->zeroMem(); + } } } } } -void MKLDNNTester::clearBotDiffs() { - // dnn and ref +void MKLDNNTester::clearBotDiffs(size_t id) { + CHECK_LE(id, dataLayers_.size()); for (size_t n = 0; n < dataLayers_.size(); ++n) { - // all inputs layers - for (size_t i = 0; i < dataLayers_[n].size(); ++i) { - dataLayers_[n][i]->getOutputGrad()->zeroMem(); + if (id == n || id == dataLayers_.size()) { + // clear inputs layers of this specific layer + for (size_t i = 0; i < dataLayers_[n].size(); ++i) { + dataLayers_[n][i]->getOutputGrad()->zeroMem(); + } } } } -void MKLDNNTester::clearBotDiffs(int n) { - CHECK_LT(n, NUM); - // all inputs layers - for (size_t i = 0; i < dataLayers_[n].size(); ++i) { - dataLayers_[n][i]->getOutputGrad()->zeroMem(); - } -} - -void MKLDNNTester::clearTopDatas() { +void MKLDNNTester::clearTopDatas(size_t id) { + CHECK_LE(id, testLayers_.size()); for (size_t i = 0; i < testLayers_.size(); ++i) { - testLayers_[i]->getOutputValue()->zeroMem(); + if (id == i || id == testLayers_.size()) { + testLayers_[i]->getOutputValue()->zeroMem(); + } } } @@ -300,16 +304,24 @@ void MKLDNNTester::runOnce() { checkForward(); // test backward + // simple updater + UpdateCallback updateCallback = [](Parameter* para) { + auto& grad = para->getBuf(PARAMETER_GRADIENT); + auto& value = para->getBuf(PARAMETER_VALUE); + real lr = 1e-3; + value->add(*grad, lr); + }; randomTopDiffs(); - dnnLayer_->backward(nullptr); - refLayer_->backward(nullptr); + dnnLayer_->backward(updateCallback); + refLayer_->backward(updateCallback); checkBackwardData(); checkBackwardWgts(); // clear buffers // ref code will addto the diff, dnn code will writeto it - // and clearTopDatas() and clearWgtDiffs() should be coverd by test layers + // and clearTopDatas(REF) should be coverd by ref layers clearBotDiffs(REF); + clearWgtDiffs(REF); } void MKLDNNTester::run(const TestConfig& dnn, diff --git a/paddle/gserver/tests/MKLDNNTester.h b/paddle/gserver/tests/MKLDNNTester.h index e55e4493ff..5ac885638c 100644 --- a/paddle/gserver/tests/MKLDNNTester.h +++ b/paddle/gserver/tests/MKLDNNTester.h @@ -18,6 +18,7 @@ limitations under the License. */ #include #include "LayerGradUtil.h" #include "paddle/gserver/layers/MKLDNNBase.h" +#include "paddle/gserver/layers/MKLDNNLayer.h" namespace paddle { @@ -40,7 +41,8 @@ protected: vector layerMaps_; vector> parameters_; vector testLayers_; - LayerPtr dnnLayer_, refLayer_; + LayerPtr refLayer_; + MKLDNNLayerPtr dnnLayer_; /// run some iterations, all the result should pass size_t iter_; @@ -88,10 +90,10 @@ private: void checkBackwardData(); void checkBackwardWgts(); - void clearWgtDiffs(); - void clearBotDiffs(); - void clearBotDiffs(int n); // clear specific layer - void clearTopDatas(); + // clear specific layer, clear all when id equals NUM + void clearWgtDiffs(size_t id = NUM); + void clearBotDiffs(size_t id = NUM); + void clearTopDatas(size_t id = NUM); void printTopDatas(); void printMatrix(const MatrixPtr& m); diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0e6be2df9e..090bde7b20 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -2302,26 +2302,27 @@ void test3DDeConvLayer(const string& type, bool trans, bool useGpu) { conv->set_stride(2); conv->set_stride_y(2); conv->set_stride_z(2); - conv->set_img_size(IMAGE_SIZE); - conv->set_img_size_y(IMAGE_SIZE_Y); - conv->set_img_size_z(IMAGE_SIZE_Z); - conv->set_output_x(imageSize(conv->img_size(), + conv->set_output_x(IMAGE_SIZE); + conv->set_output_y(IMAGE_SIZE_Y); + conv->set_output_z(IMAGE_SIZE_Z); + + conv->set_img_size(imageSize(conv->output_x(), conv->filter_size(), conv->padding(), conv->stride(), true)); - conv->set_output_y(imageSize(conv->img_size_y(), - conv->filter_size_y(), - conv->padding_y(), - conv->stride_y(), - true)); - conv->set_output_z(imageSize(conv->img_size_z(), - conv->filter_size_z(), - conv->padding_z(), - conv->stride_z(), - true)); - config.layerConfig.set_size(conv->output_x() * conv->output_y() * - conv->output_z() * NUM_FILTERS); + conv->set_img_size_y(imageSize(conv->output_y(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y(), + true)); + conv->set_img_size_z(imageSize(conv->output_z(), + conv->filter_size_z(), + conv->padding_z(), + conv->stride_z(), + true)); + config.layerConfig.set_size(conv->img_size() * conv->img_size_y() * + conv->img_size_z() * NUM_FILTERS); conv->set_groups(1); conv->set_filter_channels(conv->channels() / conv->groups()); config.inputDefs.push_back( diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp index 0a355e2644..c4063e5069 100644 --- a/paddle/math/MKLDNNMatrix.cpp +++ b/paddle/math/MKLDNNMatrix.cpp @@ -33,14 +33,12 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) { size_t width = cnts / dims[0]; m = Matrix::create(height, width, false, false); } - CHECK(m) << " Matrix should not be empty"; + CpuMatrixPtr cpuMatrix = std::dynamic_pointer_cast(m); CHECK(cpuMatrix) << "Only support create from CPU matrix yet"; - - CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match"; - return std::make_shared( - m->getData(), m->getHeight(), m->getWidth(), pd); + CHECK_EQ(cpuMatrix->getElementCnt(), cnts) << "Count size does not match"; + return std::make_shared(cpuMatrix, pd); } MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, @@ -138,7 +136,7 @@ void MKLDNNMatrix::downSpatial() { mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr), "could not create a memory primitive"); reset(result); - set_data_handle(getData()); + set_data_handle(data_); } } // namespace paddle diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h index e50f698b49..eef3b429e6 100644 --- a/paddle/math/MKLDNNMatrix.h +++ b/paddle/math/MKLDNNMatrix.h @@ -30,11 +30,10 @@ typedef std::shared_ptr MKLDNNMatrixPtr; */ class MKLDNNMatrix : public CpuMatrix, public mkldnn::memory { public: - MKLDNNMatrix(real* data, - size_t height, - size_t width, - mkldnn::memory::primitive_desc pd) - : CpuMatrix(data, height, width, false), mkldnn::memory(pd, data) {} + MKLDNNMatrix(CpuMatrixPtr m, mkldnn::memory::primitive_desc pd) + : CpuMatrix(m->getData(), m->getHeight(), m->getWidth(), false), + mkldnn::memory(pd, m->getData()), + m_(m) {} ~MKLDNNMatrix() {} @@ -81,11 +80,29 @@ public: void downSpatial(); /** - * Update the memory data handle. + * set the memory data handle. * Caution: This will not check the buffer size of the data, * it should be coverd by user. */ - void updateData(void* data) { set_data_handle(data); } + void setData(real* data) { + set_data_handle(data); + CpuMatrix::setData(data); + m_.reset(); + } + + /** + * override Matrix::getData + * check data before return + */ + real* getData() override { + CHECK_EQ((void*)data_, get_data_handle()); + return data_; + } + + const real* getData() const override { + CHECK_EQ((void*)data_, get_data_handle()); + return data_; + } /** * Get primitive descriptor. @@ -143,6 +160,10 @@ protected: memory::format srcFmt, memory::format dstFmt, memory::dims dm); + +private: + // save the CpuMatrixPtr in case the buffer released outside + CpuMatrixPtr m_; }; } // namespace paddle diff --git a/paddle/operators/concat_op.cc b/paddle/operators/concat_op.cc new file mode 100644 index 0000000000..0ebefbab26 --- /dev/null +++ b/paddle/operators/concat_op.cc @@ -0,0 +1,79 @@ +/* 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/concat_op.h" +#include + +namespace paddle { +namespace operators { +using framework::Tensor; + +class ConcatOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto ins = ctx.MultiInput("X"); + auto *out = ctx.Output("Out"); + size_t axis = static_cast(ctx.Attr("axis")); + size_t n = ins.size(); + + PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1."); + + auto out_dims = ins[0]->dims(); + size_t in_zero_dims_size = out_dims.size(); + for (size_t i = 1; i < n; i++) { + for (size_t j = 0; j < in_zero_dims_size; j++) { + if (j == axis) { + out_dims[axis] += ins[i]->dims()[j]; + continue; + } + PADDLE_ENFORCE_EQ(out_dims[j], ins[i]->dims()[j], + "Input tensors should have the same " + "elements except the specify axis.") + } + } + out->Resize(out_dims); + } +}; + +class ConcatOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ConcatOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "the input tensors of concat operator.").AsDuplicable(); + AddOutput("Out", "the output tensor of concat operator."); + AddComment(R"DOC( + Join the input tensors along with the axis. + Examples: + Input[0] = [[1,2],[3,4]] + Input[1] = [[5,6]] + axis = 0 + Output = [[1,2], + [3,4], + [5,6]] + )DOC"); + AddAttr("axis", "The axis which the inputs will be joined with.") + .SetDefault(0); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(concat, ops::ConcatOp, ops::ConcatOpMaker) +REGISTER_OP_CPU_KERNEL(concat, + ops::ConcatKernel) diff --git a/paddle/operators/concat_op.cu b/paddle/operators/concat_op.cu new file mode 100644 index 0000000000..38fee7473d --- /dev/null +++ b/paddle/operators/concat_op.cu @@ -0,0 +1,19 @@ +/* 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/concat_op.h" + +namespace ops = paddle::operators; +// TODO(Yancey1989) Add GPU kernel diff --git a/paddle/operators/concat_op.h b/paddle/operators/concat_op.h new file mode 100644 index 0000000000..f977054fdf --- /dev/null +++ b/paddle/operators/concat_op.h @@ -0,0 +1,64 @@ +/* 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 +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class ConcatKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto ins = ctx.MultiInput("X"); + auto* out = ctx.Output("Out"); + int64_t axis = static_cast(ctx.Attr("axis")); + size_t n = ins.size(); + size_t output_axis_dim = 0; + size_t before = 1, after = 1; + for (size_t i = 0; i < n; i++) { + output_axis_dim += ins[i]->dims()[axis]; + } + auto& input_zero = ins[0]; + for (int64_t i = 0; i < input_zero->dims().size(); i++) { + if (i == axis) { + continue; + } + if (i < axis) { + before *= input_zero->dims()[i]; + } else { + after *= input_zero->dims()[i]; + } + } + size_t output_offset = 0; + for (size_t i = 0; i < n; i++) { + auto& in = ins[i]; + auto axis_dim = in->dims()[axis]; + for (size_t j = 0; j < before; j++) { + size_t len = axis_dim * after * sizeof(T); + const T* src = in->data() + axis_dim * after * j; + T* out_data = out->mutable_data(platform::CPUPlace()); + T* dest = out_data + output_offset + output_axis_dim * after * j; + memcpy(dest, src, len); + } + output_offset += axis_dim * after; + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h index 9e2bcebe3b..0dc5099525 100644 --- a/paddle/operators/cos_sim_op.h +++ b/paddle/operators/cos_sim_op.h @@ -42,7 +42,7 @@ class CosSimKernel : public framework::OpKernel { output_y_norm->mutable_data(context.GetPlace()); auto dims = input_x->dims(); - int size = static_cast(framework::product(dims)); + int64_t size = input_x->numel(); auto new_dims = framework::make_ddim({dims[0], size / dims[0]}); auto x = EigenMatrix::From(*input_x, new_dims); auto y = EigenMatrix::From(*input_y, new_dims); @@ -72,7 +72,7 @@ class CosSimGradKernel : public framework::OpKernel { auto* input_grad_z = context.Input(framework::GradVarName("Out")); auto dims = input_x->dims(); - int size = static_cast(framework::product(dims)); + int64_t size = input_x->numel(); auto new_dims = framework::make_ddim({dims[0], size / dims[0]}); auto x = EigenMatrix::From(*input_x, new_dims); auto y = EigenMatrix::From(*input_y, new_dims); diff --git a/paddle/operators/crop_op.cc b/paddle/operators/crop_op.cc index 6033a45dc5..1c048d3a70 100644 --- a/paddle/operators/crop_op.cc +++ b/paddle/operators/crop_op.cc @@ -118,6 +118,23 @@ class CropOpGrad : public framework::OperatorWithKernel { } }; +int64_t transIndex(std::vector out_shape, std::vector x_shape, + std::vector> crop_rules, size_t index) { + int64_t dim_size = out_shape.size(); + std::vector pos(dim_size); + + for (int64_t i = out_shape.size() - 1; i >= 0; --i) { + pos[i] = (index % out_shape[i]) + crop_rules[i].first; + index = index / out_shape[i]; + } + + size_t result = pos[0]; + for (size_t i = 1; i < x_shape.size(); ++i) { + result = result * x_shape[i] + pos[i]; + } + return result; +} + template class CropCPUKernel : public framework::OpKernel { public: diff --git a/paddle/operators/crop_op.h b/paddle/operators/crop_op.h index 54e7b6abd1..ff1d7694dc 100644 --- a/paddle/operators/crop_op.h +++ b/paddle/operators/crop_op.h @@ -26,23 +26,6 @@ using EigenTensor = framework::EigenTensor; using Tensor = framework::Tensor; -int64_t transIndex(std::vector out_shape, std::vector x_shape, - std::vector> crop_rules, size_t index) { - int64_t dim_size = out_shape.size(); - int64_t pos[dim_size]; - - for (int64_t i = out_shape.size() - 1; i >= 0; --i) { - pos[i] = (index % out_shape[i]) + crop_rules[i].first; - index = index / out_shape[i]; - } - - size_t result = pos[0]; - for (size_t i = 1; i < x_shape.size(); ++i) { - result = result * x_shape[i] + pos[i]; - } - return result; -} - template void CropGradFunction(const framework::ExecutionContext& context) { auto* d_out = context.Input(framework::GradVarName("Out")); diff --git a/paddle/operators/elementwise_mul_op.cc b/paddle/operators/elementwise_mul_op.cc new file mode 100644 index 0000000000..1742925545 --- /dev/null +++ b/paddle/operators/elementwise_mul_op.cc @@ -0,0 +1,109 @@ +/* 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/elementwise_mul_op.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +class ElementWiseMulOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + 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"); + auto x_dim = ctx.Input("X")->dims(); + auto y_dim = ctx.Input("Y")->dims(); + PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(), + "Rank of first input must >= rank of second input.") + ctx.Output("Out")->Resize(x_dim); + } +}; + +class ElementWiseMulOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ElementWiseMulOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The first input of elementwise mul op"); + AddInput("Y", "The second input of elementwise mul op"); + AddAttr("axis", + R"DOC( +When shape(Y) does not equal shape(X),Y will be broadcasted +to match the shape of X and axis should be dimension index Y in X + )DOC") + .SetDefault(-1) + .EqualGreaterThan(-1); + + AddOutput("Out", "The output of elementwise mul op"); + AddComment(R"DOC( +Limited elementwise multiple operator.The equation is: Out = X ⊙ Y. +1. The shape of Y should be same with X or +2. Y's shape is a subset of X. + Y will be broadcasted to match the shape of X and axis should be dimension index Y in X. + example: + shape(X) = (2, 3, 4, 5), shape(Y) = (,) + shape(X) = (2, 3, 4, 5), shape(Y) = (5,) + shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) + shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 + shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 +)DOC"); + } +}; + +class ElementWiseMulOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + 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("X")->dims(); + auto y_dims = ctx.Input("Y")->dims(); + auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); + auto *x_grad = ctx.Output(framework::GradVarName("X")); + auto *y_grad = ctx.Output(framework::GradVarName("Y")); + + PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(), + "Rank of first input must >= rank of second input.") + + if (x_grad) { + x_grad->Resize(x_dims); + } + + if (y_grad) { + y_grad->Resize(y_dims); + } + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(elementwise_mul, ops::ElementWiseMulOp, ops::ElementWiseMulOpMaker, + elementwise_mul_grad, ops::ElementWiseMulOpGrad); +REGISTER_OP_CPU_KERNEL( + elementwise_mul, + ops::ElementWiseMulKernel); +REGISTER_OP_CPU_KERNEL( + elementwise_mul_grad, + ops::ElementWiseMulGradKernel); diff --git a/paddle/operators/elementwise_mul_op.cu b/paddle/operators/elementwise_mul_op.cu new file mode 100644 index 0000000000..56f2087c22 --- /dev/null +++ b/paddle/operators/elementwise_mul_op.cu @@ -0,0 +1,25 @@ +/* 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/elementwise_mul_op.h" + +namespace ops = paddle::operators; + +REGISTER_OP_GPU_KERNEL( + elementwise_mul, + ops::ElementWiseMulKernel); +REGISTER_OP_GPU_KERNEL( + elementwise_mul_grad, + ops::ElementWiseMulGradKernel); diff --git a/paddle/operators/elementwise_mul_op.h b/paddle/operators/elementwise_mul_op.h new file mode 100644 index 0000000000..e9ed679179 --- /dev/null +++ b/paddle/operators/elementwise_mul_op.h @@ -0,0 +1,185 @@ +/* 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 +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" +#include "paddle/operators/math/math_function.h" + +namespace paddle { +namespace operators { +/* + * Out = X ⊙ Y + * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 + * pre=2, n=3*4, post=5 + * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5) + * pre=2*3, n=4*5, post=1 + */ + +inline void get_mid_dims(const framework::DDim& x_dims, + const framework::DDim& y_dims, const int axis, + int& pre, int& n, int& post) { + pre = 1; + n = 1; + post = 1; + for (int i = 0; i < axis; ++i) { + pre *= x_dims[i]; + } + + for (int i = 0; i < y_dims.size(); ++i) { + PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i], + "Broadcast dimension mismatch."); + n *= y_dims[i]; + } + + for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) { + post *= x_dims[i]; + } +} + +template +class ElementWiseMulKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + using Tensor = framework::Tensor; + + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto* z = ctx.Output("Out"); + z->mutable_data(ctx.GetPlace()); + + auto x_e = framework::EigenVector::Flatten(*x); + auto y_e = framework::EigenVector::Flatten(*y); + auto z_e = framework::EigenVector::Flatten(*z); + + auto x_dims = x->dims(); + auto y_dims = y->dims(); + PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(), + "Rank of first input must >= rank of second input.") + + if (x_dims == y_dims || product(y_dims) == 1) { + z_e.device(ctx.GetEigenDevice()) = x_e * y_e; + return; + } + + int axis = ctx.Attr("axis"); + axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis); + PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(), + "Axis should be in range [0, x_dims)"); + + int pre, n, post; + get_mid_dims(x_dims, y_dims, axis, pre, n, post); + if (post == 1) { + auto y_bcast = y_e.reshape(Eigen::DSizes(1, n)) + .broadcast(Eigen::DSizes(pre, 1)) + .reshape(Eigen::DSizes(x_e.size())); + z_e.device(ctx.GetEigenDevice()) = x_e * y_bcast; + return; + } else { + auto y_bcast = y_e.reshape(Eigen::DSizes(1, n, 1)) + .broadcast(Eigen::DSizes(pre, 1, post)) + .reshape(Eigen::DSizes(x_e.size())); + z_e.device(ctx.GetEigenDevice()) = x_e * y_bcast; + return; + } + } +}; + +template +class ElementWiseMulGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + using Tensor = framework::Tensor; + + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto* dout = ctx.Input(framework::GradVarName("Out")); + + auto x_e = framework::EigenVector::Flatten(*x); + auto y_e = framework::EigenVector::Flatten(*y); + auto dout_e = framework::EigenVector::Flatten(*dout); + + auto x_dims = x->dims(); + auto y_dims = y->dims(); + + auto* dx = ctx.Output(framework::GradVarName("X")); + auto* dy = ctx.Output(framework::GradVarName("Y")); + if (dx) { + dx->mutable_data(ctx.GetPlace()); + } + if (dy) { + dy->mutable_data(ctx.GetPlace()); + } + + if (x_dims == y_dims || product(y_dims) == 1) { + if (dx) { + auto dx_e = framework::EigenVector::Flatten(*dx); + dx_e.device(ctx.GetEigenDevice()) = dout_e * y_e; + } + + if (dy) { + auto dy_e = framework::EigenVector::Flatten(*dy); + dy_e.device(ctx.GetEigenDevice()) = x_e * dout_e; + } + return; + } + + int axis = ctx.Attr("axis"); + axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis); + + int pre, n, post; + get_mid_dims(x_dims, y_dims, axis, pre, n, post); + + // TODO(gongweibao): wrap reshape to a function. + if (post == 1) { + auto y_e_bcast = y_e.reshape(Eigen::DSizes(1, n)) + .broadcast(Eigen::DSizes(pre, 1)) + .reshape(Eigen::DSizes(x_e.size())); + if (dx) { + auto dx_e = framework::EigenVector::Flatten(*dx); + dx_e.device(ctx.GetEigenDevice()) = dout_e * y_e_bcast; + } + + if (dy) { + auto dy_e = framework::EigenVector::Flatten(*dy); + dy_e.device(ctx.GetEigenDevice()) = + (x_e * dout_e) + .reshape(Eigen::DSizes(pre, n)) + .sum(Eigen::array{{0}}); + } + return; + } else { + auto y_e_bcast = y_e.reshape(Eigen::DSizes(1, n, 1)) + .broadcast(Eigen::DSizes(pre, 1, post)) + .reshape(Eigen::DSizes(x_e.size())); + if (dx) { + auto dx_e = framework::EigenVector::Flatten(*dx); + dx_e.device(ctx.GetEigenDevice()) = dout_e * y_e_bcast; + } + + if (dy) { + auto dy_e = framework::EigenVector::Flatten(*dy); + dy_e.device(ctx.GetEigenDevice()) = + (x_e * dout_e) + .reshape(Eigen::DSizes(pre, n, post)) + .sum(Eigen::array{{0, 2}}); + } + return; + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index 6574880c0e..3d76516405 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -31,7 +31,7 @@ class CPUGaussianRandomKernel : public framework::OpKernel { } engine.seed(seed); std::normal_distribution dist(mean, std); - int64_t size = framework::product(tensor->dims()); + int64_t size = tensor->numel(); for (int64_t i = 0; i < size; ++i) { data[i] = dist(engine); } diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index d9dbc1dcfe..2d63b30499 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -50,8 +50,8 @@ class GPUGaussianRandomKernel : public framework::OpKernel { T mean = static_cast(context.Attr("mean")); T std = static_cast(context.Attr("std")); thrust::counting_iterator index_sequence_begin(0); - ssize_t N = framework::product(tensor->dims()); - thrust::transform(index_sequence_begin, index_sequence_begin + N, + int64_t size = tensor->numel(); + thrust::transform(index_sequence_begin, index_sequence_begin + size, thrust::device_ptr(data), GaussianGenerator(mean, std, seed)); } diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu index 27eee3436a..7083440467 100644 --- a/paddle/operators/lookup_table_op.cu +++ b/paddle/operators/lookup_table_op.cu @@ -70,7 +70,7 @@ class LookupTableCUDAKernel : public framework::OpKernel { size_t N = table_t->dims()[0]; size_t D = table_t->dims()[1]; - size_t K = product(ids_t->dims()); + size_t K = ids_t->numel(); auto ids = ids_t->data(); auto table = table_t->data(); auto output = output_t->mutable_data(context.GetPlace()); @@ -91,7 +91,7 @@ class LookupTableGradCUDAKernel : public framework::OpKernel { int N = d_table_t->dims()[0]; int D = d_table_t->dims()[1]; - int K = product(ids_t->dims()); + int K = ids_t->numel(); const int32_t* ids = ids_t->data(); const T* d_output = d_output_t->data(); T* d_table = d_table_t->mutable_data(context.GetPlace()); diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h index 877b36cef4..a1298906dd 100644 --- a/paddle/operators/lookup_table_op.h +++ b/paddle/operators/lookup_table_op.h @@ -35,7 +35,7 @@ class LookupTableKernel : public framework::OpKernel { auto ids = ids_t->data(); auto table = table_t->data(); auto output = output_t->mutable_data(context.GetPlace()); - for (ssize_t i = 0; i < product(ids_t->dims()); ++i) { + for (int64_t i = 0; i < ids_t->numel(); ++i) { PADDLE_ENFORCE_LT(ids[i], N); PADDLE_ENFORCE_GE(ids[i], 0); memcpy(output + i * D, table + ids[i] * D, D * sizeof(T)); @@ -61,7 +61,7 @@ class LookupTableGradKernel : public framework::OpKernel { t.device(context.GetEigenDevice()) = t.constant(static_cast(0)); - for (ssize_t i = 0; i < product(ids_t->dims()); ++i) { + for (int64_t i = 0; i < ids_t->numel(); ++i) { PADDLE_ENFORCE_LT(ids[i], N); PADDLE_ENFORCE_GE(ids[i], 0); for (int j = 0; j < D; ++j) { diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc index 186a33edce..4f380388b1 100644 --- a/paddle/operators/math/im2col_test.cc +++ b/paddle/operators/math/im2col_test.cc @@ -119,4 +119,4 @@ TEST(math, im2col) { #ifndef PADDLE_ONLY_CPU testIm2col(); #endif -} \ No newline at end of file +} diff --git a/paddle/operators/mean_op.h b/paddle/operators/mean_op.h index 9848af280b..ce31e178d8 100644 --- a/paddle/operators/mean_op.h +++ b/paddle/operators/mean_op.h @@ -49,12 +49,11 @@ class MeanGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto OG = context.Input(framework::GradVarName("Out")); - PADDLE_ENFORCE(framework::product(OG->dims()) == 1, - "Mean Gradient should be scalar"); + PADDLE_ENFORCE(OG->numel() == 1, "Mean Gradient should be scalar"); auto IG = context.Output(framework::GradVarName("X")); IG->mutable_data(context.GetPlace()); - T ig_size = (T)framework::product(IG->dims()); + T ig_size = static_cast(IG->numel()); Eigen::DSizes bcast(ig_size); EigenVector::Flatten(*IG).device(context.GetEigenDevice()) = diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc index 069fb5e1ab..a4876feb2e 100644 --- a/paddle/operators/minus_op.cc +++ b/paddle/operators/minus_op.cc @@ -31,8 +31,7 @@ class MinusOp : public framework::OperatorWithKernel { auto *right_tensor = ctx.Input("Y"); PADDLE_ENFORCE_EQ( - framework::product(left_tensor->dims()), - framework::product(right_tensor->dims()), + left_tensor->numel(), right_tensor->numel(), "Minus operator must take two tensor with same num of elements"); ctx.Output("Out")->Resize(left_tensor->dims()); } diff --git a/paddle/operators/name_convention.md b/paddle/operators/name_convention.md new file mode 100644 index 0000000000..a090e0b545 --- /dev/null +++ b/paddle/operators/name_convention.md @@ -0,0 +1,59 @@ +## Operator's Parameter Name Convention + +To make the operator document itself more clear, we recommend operator names obey the listing conventions. + +### OpProtoMaker names + +When defining an operator in Paddle, a corresponding [OpProtoMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L170) (TODO: OpProtoMaker Doc)need to be defined. All the Input/Output and Attributes will write into the [OpProto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L61) , and will be used in client language to create operator. + +- Input/Output. + - Input/Output names follow the **CamelCase**. e.g. `X`, `Y`, `Matrix`, `LastAxisInMatrix`. Input/Output much more like Variables, we prefer to meaningful English words. + - If an operator's Input/Output are tensors in math, not match to any meaningful words, input name should starts from `X`. e.g. `X`, `Y`, and output name should starts from `Out`. e.g. `Out`. This rule intends making operators which have few inputs/outputs unified. + +- Attribute. + - Attribute name follows the **camelCase**. e.g. `x`, `y`, `axis`, `rowwiseMatrix`. Also, attribute name prefers to meaningful English words. + +- Comments. + - Input/Output/Attr comment follow the format of **(type,default value) usage**, corresponding to which type it can be and how it will be used in the operator. e.g. Attribute in Accumulator`"gamma" `,`(float, default 1.0) Accumulation multiplier`. + - Operator comment format of` R"DOC(your comment here)DOC"`. You should explain the input/output of the operator first. If there is math calculation in this operator, you should write the equation in the comment. e.g. `Out = X + Y`. + +- Order. + - Follow the order of Input/Output, then Attribute, then Comments. See the example in best practice. + +### Best Practice + +Here we give some examples to show how these rules will be used. + +- The operator has one input, one output. e.g.`relu`, inputs: `X`, outputs: `Out`. + +- The operator has two input, one output. e.g. `rowwise_add`, inputs : `X`, `Y`, outputs : `Out`. + +- The operator contains attribute. e.g. `cosine`, inputs : `X`, `axis`, outputs : `Out`. + + We give a full example of Accumulator Operator. + +```c++ +class AccumulateOpMaker : public framework::OpProtoAndCheckerMaker { +public: + AccumulateOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done."); + AddOutput("Out", "(Tensor) Accumulated output tensor"); + AddAttr("gamma", "(float, default 1.0) Accumulation multiplier"); + AddComment(R"DOC( +Accumulate operator accumulates the input tensor to the output tensor. If the +output tensor already has the right size, we add to it; otherwise, we first +initialize the output tensor to all zeros, and then do accumulation. Any +further calls to the operator, given that no one else fiddles with the output +in the interim, will do simple accumulations. +Accumulation is done as shown: + +Out = 1*X + gamma*Out + +where X is the input tensor, Y is the output tensor and gamma is the multiplier +argument. +)DOC"); + } +}; +``` diff --git a/paddle/operators/pad_op.cc b/paddle/operators/pad_op.cc new file mode 100644 index 0000000000..7e78b6ec13 --- /dev/null +++ b/paddle/operators/pad_op.cc @@ -0,0 +1,112 @@ +/* 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/pad_op.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; + +class PadOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto x_dim = ctx.Input("X")->dims(); + auto paddings = Attr>("paddings"); + PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()), + "Size of paddings should be equal to 2 * dimension size " + "of input tensor."); + std::vector out_dims(x_dim.size()); + for (int i = 0; i < x_dim.size(); ++i) { + out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1]; + } + ctx.Output("Out")->Resize(framework::make_ddim(out_dims)); + } +}; + +class PadOpMaker : public framework::OpProtoAndCheckerMaker { + public: + PadOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "The input of pad op. " + "The input should be a k-D tensor(k > 0 and k < 7)"); + AddOutput("Out", + "The output of pad op." + "A tensor with the same shape as X.") + .NotInGradient(); + AddComment(R"DOC( +Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example: + +Given: + +X = [[1, 2], + [3, 4]] + +and + +paddings = [0, 1, 1, 2] + +and + +pad_value = 0 + +then we get + +Out = [[0, 1, 2, 0, 0] + [0, 3, 4, 0, 0] + [0, 0, 0, 0, 0]] +)DOC"); + AddAttr>( + "paddings", + "A list to describes padding rules for each dimension." + " For 2-D image tensor, paddings=[0, 1, 2, 3] means" + " padding 0 row to top, 1 row to bottom, 2 columns to left" + " and 3 columns to right.Size of paddings should be equal to" + " 2 * dimension size of input tensor."); + AddAttr("pad_value", + "(float) default to 0; " + "The value to fill padded areas.") + .SetDefault(0.0f); + } +}; + +class PadOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + 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(framework::GradVarName("Out")), + "Input(Out@GRAD) should not be null"); + auto x_dims = ctx.Input("X")->dims(); + auto *x_grad = ctx.Output(framework::GradVarName("X")); + if (x_grad != nullptr) { + x_grad->Resize(x_dims); + } + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad); +REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel); +REGISTER_OP_CPU_KERNEL(pad_grad, + ops::PadGradKernel); diff --git a/paddle/operators/pad_op.cu b/paddle/operators/pad_op.cu new file mode 100644 index 0000000000..555a7dba23 --- /dev/null +++ b/paddle/operators/pad_op.cu @@ -0,0 +1,21 @@ +/* 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/pad_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(pad, ops::PadKernel); +REGISTER_OP_GPU_KERNEL(pad_grad, + ops::PadGradKernel); diff --git a/paddle/operators/pad_op.h b/paddle/operators/pad_op.h new file mode 100644 index 0000000000..2cc3b945ae --- /dev/null +++ b/paddle/operators/pad_op.h @@ -0,0 +1,132 @@ +/* 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/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +using EigenTensor = framework::EigenTensor; + +template +void PadFunction(const framework::ExecutionContext& context) { + auto pads = context.Attr>("paddings"); + Eigen::array, D> paddings; + for (size_t i = 0; i < paddings.size(); ++i) { + paddings[i].first = pads[i * 2]; + paddings[i].second = pads[i * 2 + 1]; + } + T pad_value = context.Attr("pad_value"); + + auto* x = context.Input("X"); + auto* out = context.Output("Out"); + out->mutable_data(context.GetPlace()); + + auto x_tensor = EigenTensor::From(*x); + auto out_tensor = EigenTensor::From(*out); + auto place = context.GetEigenDevice(); + out_tensor.device(place) = x_tensor.pad(paddings, pad_value); +} + +template +class PadKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + int rank = context.Input("X")->dims().size(); + switch (rank) { + case 1: + PadFunction(context); + break; + case 2: + PadFunction(context); + break; + case 3: + PadFunction(context); + break; + case 4: + PadFunction(context); + break; + case 5: + PadFunction(context); + break; + case 6: + PadFunction(context); + break; + default: + PADDLE_THROW( + "PadOp only support tensors with no more than 6 dimensions."); + } + } +}; + +template +void PadGradFunction(const framework::ExecutionContext& context) { + auto pads = context.Attr>("paddings"); + Eigen::array, D> paddings; + for (size_t i = 0; i < paddings.size(); ++i) { + paddings[i].first = -pads[i * 2]; + paddings[i].second = -pads[i * 2 + 1]; + } + auto* d_out = context.Input(framework::GradVarName("Out")); + auto* d_x = context.Output(framework::GradVarName("X")); + if (d_x != nullptr) { + d_x->mutable_data(context.GetPlace()); + auto d_x_tensor = EigenTensor::From(*d_x); + auto d_out_tensor = EigenTensor::From(*d_out); + auto place = context.GetEigenDevice(); + d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0); + } +} + +template +class PadGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + size_t rank = + context.Input(framework::GradVarName("Out"))->dims().size(); + switch (rank) { + case 1: + PadGradFunction(context); + break; + case 2: + PadGradFunction(context); + break; + case 3: + PadGradFunction(context); + break; + case 4: + PadGradFunction(context); + break; + case 5: + PadGradFunction(context); + break; + case 6: + PadGradFunction(context); + break; + default: + PADDLE_THROW( + "PadOp only support tensors with no more than 6 dimensions."); + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc new file mode 100644 index 0000000000..b7061153d2 --- /dev/null +++ b/paddle/operators/reshape_op.cc @@ -0,0 +1,107 @@ + +/* 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/reshape_op.h" + +namespace paddle { +namespace operators { + +class ReshapeOp : public framework::OperatorWithKernel { + public: + ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + // input check + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null"); + auto shape = ctx.Attr>("shape"); + PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty."); + for (auto dim : shape) { + PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive."); + } + // capacity check + int64_t capacity = + std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); + auto *in = ctx.Input("X"); + int64_t in_size = framework::product(in->dims()); + PADDLE_ENFORCE_EQ(capacity, in_size, + "The size of Input(X) mismatches with Attr(shape)."); + // resize output + std::vector shape_int64(shape.size(), 0); + std::transform(shape.begin(), shape.end(), shape_int64.begin(), + [](int a) { return static_cast(a); }); + auto out_dims = framework::make_ddim(shape_int64); + ctx.Output("Out")->Resize(out_dims); + } +}; + +class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ReshapeOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input tensor of reshape operator."); + AddOutput("Out", "The output tensor of reshape operator."); + AddAttr>("shape", "Target shape of reshape operator."); + AddComment(R"DOC(Reshape operator + +Reshape Input(X) into the shape specified by Attr(shape). + +An example: +Given a 2-D tensor X with 2 rows and 2 columns + + [[1, 2], [3, 4]] + +with target shape = [1, 4], the reshape operator will transform +the tensor X into a 1-D tensor: + + [1, 2, 3, 4] + +)DOC"); + } +}; + +class ReshapeGradOp : public framework::OperatorWithKernel { + public: + ReshapeGradOp(const std::string &type, + const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null."); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), + "Input(Out@GRAD) shouldn't be null."); + auto dims = ctx.Input("X")->dims(); + auto *d_in = ctx.Output(framework::GradVarName("X")); + d_in->Resize(dims); + } +}; + +} // namespace operators +} // namespace paddle +namespace ops = paddle::operators; + +REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad, + ops::ReshapeGradOp); +REGISTER_OP_CPU_KERNEL(reshape, + ops::ReshapeKernel); +REGISTER_OP_CPU_KERNEL( + reshape_grad, ops::ReshapeGradKernel); diff --git a/paddle/operators/reshape_op.cu b/paddle/operators/reshape_op.cu new file mode 100644 index 0000000000..23dbe089d3 --- /dev/null +++ b/paddle/operators/reshape_op.cu @@ -0,0 +1,22 @@ +/* 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/reshape_op.h" + +REGISTER_OP_GPU_KERNEL( + reshape, + paddle::operators::ReshapeKernel); +REGISTER_OP_GPU_KERNEL( + reshape_grad, + paddle::operators::ReshapeGradKernel); diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h new file mode 100644 index 0000000000..873acf3078 --- /dev/null +++ b/paddle/operators/reshape_op.h @@ -0,0 +1,55 @@ +/* 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/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class ReshapeKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const { + auto* out = ctx.Output("Out"); + auto* in = ctx.Input("X"); + out->mutable_data(ctx.GetPlace()); + + auto shape = ctx.Attr>("shape"); + std::vector shape_int64(shape.size(), 0); + std::transform(shape.begin(), shape.end(), shape_int64.begin(), + [](int a) { return static_cast(a); }); + auto out_dims = framework::make_ddim(shape_int64); + out->CopyFrom(*in, ctx.GetPlace()); + out->Resize(out_dims); + } +}; + +template +class ReshapeGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const { + auto* d_out = ctx.Input(framework::GradVarName("Out")); + auto* d_x = ctx.Output(framework::GradVarName("X")); + d_x->mutable_data(ctx.GetPlace()); + + auto in_dims = d_x->dims(); + d_x->CopyFrom(*d_out, ctx.GetPlace()); + d_x->Resize(in_dims); + } +}; +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc index dc30644a5e..9f51d3efa8 100644 --- a/paddle/operators/squared_l2_distance_op.cc +++ b/paddle/operators/squared_l2_distance_op.cc @@ -41,8 +41,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel { int rank = framework::arity(x_dims); PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2."); - PADDLE_ENFORCE_EQ(framework::product(x_dims) / x_dims[0], - framework::product(y_dims) / y_dims[0], + PADDLE_ENFORCE_EQ(x->numel() / x_dims[0], y->numel() / y_dims[0], "Product of dimensions expcet the first dimension of " "input and target must be equal."); PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0], @@ -50,8 +49,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel { "or to 1."); ctx.Output("sub_result") - ->Resize({static_cast(x_dims[0]), - static_cast(framework::product(x_dims) / x_dims[0])}); + ->Resize({x_dims[0], x->numel() / x_dims[0]}); ctx.Output("Out")->Resize({x_dims[0], 1}); } }; diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h index ad3347a0b3..097ac04fc0 100644 --- a/paddle/operators/squared_l2_distance_op.h +++ b/paddle/operators/squared_l2_distance_op.h @@ -39,7 +39,7 @@ class SquaredL2DistanceKernel : public framework::OpKernel { auto in0_dims = in0->dims(); auto in1_dims = in1->dims(); - int cols = framework::product(in0_dims) / in0_dims[0]; + int cols = in0->numel() / in0_dims[0]; // reduce dimensions except the first auto x = EigenMatrix::From(*in0, framework::make_ddim({in0_dims[0], cols})); @@ -82,7 +82,7 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel { auto x_dims = x_g->dims(); auto y_dims = y_g->dims(); - int cols = framework::product(x_dims) / x_dims[0]; + int cols = x_g->numel() / x_dims[0]; // calculate gradient auto grad_mat = 2 * (out_grad.broadcast(Eigen::array({{1, cols}}))) * diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index f2aeef6c31..b8fbc9b52a 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -35,7 +35,7 @@ class CPUUniformRandomKernel : public framework::OpKernel { std::uniform_real_distribution dist( static_cast(context.Attr("min")), static_cast(context.Attr("max"))); - int64_t size = framework::product(tensor->dims()); + int64_t size = tensor->numel(); for (int64_t i = 0; i < size; ++i) { data[i] = dist(engine); } diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index c2c041b144..6614b53b3f 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -53,8 +53,8 @@ class GPUUniformRandomKernel : public framework::OpKernel { T min = static_cast(context.Attr("min")); T max = static_cast(context.Attr("max")); thrust::counting_iterator index_sequence_begin(0); - ssize_t N = framework::product(tensor->dims()); - thrust::transform(index_sequence_begin, index_sequence_begin + N, + int64_t size = tensor->numel(); + thrust::transform(index_sequence_begin, index_sequence_begin + size, thrust::device_ptr(data), UniformGenerator(min, max, seed)); } diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 5aeae4dff3..851399a91c 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -17,6 +17,7 @@ limitations under the License. */ #include #include "paddle/framework/backward.h" +#include "paddle/framework/lod_tensor.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" @@ -34,6 +35,7 @@ USE_OP(add); USE_OP(onehot_cross_entropy); USE_OP(sgd); USE_OP(mul); +USE_OP(elementwise_mul); USE_OP(mean); USE_OP(sigmoid); USE_OP(softmax); @@ -48,16 +50,21 @@ USE_NO_KERNEL_OP(identity); USE_OP(minus); USE_OP(cos_sim); USE_CPU_ONLY_OP(gather); +USE_OP(pad); USE_CPU_ONLY_OP(scatter); USE_OP(crop); +USE_CPU_ONLY_OP(concat); USE_OP(top_k); USE_OP(squared_l2_distance); USE_OP(sum); +USE_OP(reshape); namespace paddle { namespace framework { using Tensor = framework::Tensor; +using LoDTensor = framework::LoDTensor; +using LoD = framework::LoD; static size_t UniqueIntegerGenerator() { static std::atomic generator; @@ -117,6 +124,60 @@ PYBIND11_PLUGIN(core) { return self.data()[offset]; }); + py::class_(m, "LoDTensor", R"DOC(LoD(Leval of Ddetails) Tensor. + +The tensor and LoD info should be created before creating the LoDTensor, then +call the set_tensor and set_lod functions to set them. + +)DOC") + .def("__init__", + [](LoDTensor &instance, + const std::vector> &lod, + Tensor *t) { +#ifdef PADDLE_ONLY_CPU + new (&instance) LoDTensor(lod, t); +#else + paddle::framework::LoD new_lod; + new_lod.reserve(lod.size()); + std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); + new (&instance) LoDTensor(new_lod, t); +#endif + }) + .def("set_tensor", + [](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); }) + .def("set_lod", + [](LoDTensor &self, const std::vector> &lod) { +#ifdef PADDLE_ONLY_CPU + self.set_lod(lod); +#else + paddle::framework::LoD new_lod; + new_lod.reserve(lod.size()); + std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); + self.set_lod(new_lod); +#endif + }) + .def("tensor", + [](LoDTensor &self) -> Tensor & { return self.tensor(); }, + py::return_value_policy::reference) + .def("lod", [](LoDTensor &self) -> std::vector> { +#ifdef PADDLE_ONLY_CPU + return self.lod(); +#else + auto lod = self.lod(); + std::vector> new_lod; + new_lod.reserve(lod.size()); + std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod), + [](paddle::framework::Vector item) -> + std::vector { + std::vector v; + v.reserve(item.size()); + std::copy(item.begin(), item.end(), std::back_inserter(v)); + return v; + }); + return new_lod; +#endif + }); + py::class_(m, "Variable", R"DOC(Variable Class. All parameter, weight, gradient are variables in Paddle. @@ -128,6 +189,11 @@ All parameter, weight, gradient are variables in Paddle. .def("get_tensor", [](Variable &self) -> Tensor * { return self.GetMutable(); }, py::return_value_policy::reference) + .def("get_lod_tensor", + [](Variable &self) -> LoDTensor * { + return self.GetMutable(); + }, + py::return_value_policy::reference) .def("get_net", [](Variable &self) -> operators::NetOp * { return self.GetMutable(); diff --git a/paddle/scripts/docker/build_android.sh b/paddle/scripts/docker/build_android.sh index aabd2da5e4..11612ad4be 100644 --- a/paddle/scripts/docker/build_android.sh +++ b/paddle/scripts/docker/build_android.sh @@ -2,8 +2,30 @@ set -xe +if [ $ANDROID_ABI == "arm64-v8a" ]; then + ANDROID_ARCH=arm64 +else # armeabi, armeabi-v7a + ANDROID_ARCH=arm +fi + +ANDROID_STANDALONE_TOOLCHAIN=$ANDROID_TOOLCHAINS_DIR/$ANDROID_ARCH-android-$ANDROID_API + +cat </dev/null || true mkdir -p $BUILD_ROOT @@ -11,7 +33,7 @@ cd $BUILD_ROOT if [ $ANDROID_ABI == "armeabi-v7a" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ - -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \ -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_NEON=ON \ -DANDROID_ARM_MODE=ON \ @@ -26,7 +48,7 @@ if [ $ANDROID_ABI == "armeabi-v7a" ]; then .. elif [ $ANDROID_ABI == "arm64-v8a" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ - -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \ -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_MODE=ON \ -DHOST_C_COMPILER=/usr/bin/gcc \ @@ -40,12 +62,12 @@ elif [ $ANDROID_ABI == "arm64-v8a" ]; then .. elif [ $ANDROID_ABI == "armeabi" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ - -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \ -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_MODE=ON \ -DHOST_C_COMPILER=/usr/bin/gcc \ -DHOST_CXX_COMPILER=/usr/bin/g++ \ - -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DCMAKE_INSTALL_PREFIX=$DEST_ROOT \ -DCMAKE_BUILD_TYPE=Release \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ @@ -55,5 +77,10 @@ else echo "Invalid ANDROID_ABI: $ANDROID_ABI" fi +cat < max_relative_error) - return "%s Variable %s max gradient diff %f over limit %f, the first " \ - "error element is %d" % ( - msg_prefix, name, max_diff, max_relative_error, offset) - - self.assertLessEqual(max_diff, max_relative_error, err_msg()) - - def check_grad(self, - forward_op, - input_vars, - inputs_to_check, - output_name, - no_grad_set=None, - only_cpu=False, - in_place=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: the output variable name of forward network. - :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: - """ - if no_grad_set is None: - no_grad_set = set() - - no_tmp_out = forward_op.no_intermediate_outputs() - if len(no_tmp_out) != 1: - raise ValueError("non temp out_names should be 1") - - 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") - if no_grad in inputs_to_check: - raise ValueError("no_grad should not be in inputs_to_check") - - 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)) - - # get numerical gradients - numeric_grads = [ - get_numeric_gradient( - forward_op, input_vars, output_name, name, in_place=in_place) - for name in inputs_to_check - ] - - check_names = [grad_var_name(name) for name in inputs_to_check] - for place in places: - 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)) diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py index 3a6a5dca4c..9936fd76ba 100644 --- a/python/paddle/v2/framework/tests/op_test.py +++ b/python/paddle/v2/framework/tests/op_test.py @@ -9,7 +9,7 @@ def grad_var_name(var_name): return var_name + "@GRAD" -def create_op(scope, op_type, inputs, outputs, attrs=None): +def create_op(scope, op_type, inputs, outputs, attrs): kwargs = dict() for in_name, in_dup in Operator.get_op_inputs(op_type): @@ -17,7 +17,7 @@ def create_op(scope, op_type, inputs, outputs, attrs=None): kwargs[in_name] = [] if in_dup: sub_in = inputs[in_name] - for sub_in_name in sub_in: + for sub_in_name, _ in sub_in: var = scope.new_var(sub_in_name) kwargs[in_name].append(sub_in_name) else: @@ -29,15 +29,16 @@ def create_op(scope, op_type, inputs, outputs, attrs=None): kwargs[out_name] = [] if out_dup: sub_in = outputs[out_name] - for sun_in_name in sub_in: - var = scope.new_var(sun_in_name) - kwargs[out_name].append(sun_in_name) + for sub_in_name, _ in sub_in: + var = scope.new_var(sub_in_name) + kwargs[out_name].append(sub_in_name) else: var = scope.new_var(out_name) kwargs[out_name].append(out_name) for attr_name in Operator.get_op_attr_names(op_type): - kwargs[attr_name] = attrs[attr_name] + if attr_name in attrs: + kwargs[attr_name] = attrs[attr_name] return Operator(op_type, **kwargs) @@ -46,12 +47,11 @@ def set_input(scope, op, inputs, place): if in_name in inputs: if in_dup: sub_in = inputs[in_name] - for sub_in_name in sub_in: + for sub_in_name, sub_in_array in sub_in: var = scope.find_var(sub_in_name) tensor = var.get_tensor() - arr = sub_in[sub_in_name] - tensor.set_dims(arr.shape) - tensor.set(arr, place) + tensor.set_dims(sub_in_array.shape) + tensor.set(sub_in_array, place) else: var = scope.find_var(in_name) tensor = var.get_tensor() @@ -65,7 +65,7 @@ def set_output_grad(scope, op, outputs, place): if out_name in outputs: if out_dup: sub_out = outputs[out_name] - for sub_out_name in sub_out: + for sub_out_name, _ in sub_out: out_tensor = scope.find_var(sub_out_name).get_tensor() grad_tensor = scope.new_var(grad_var_name( sub_out_name)).get_tensor() @@ -85,7 +85,7 @@ def get_numeric_gradient(scope, op, inputs, input_to_check, - output_name, + output_names, delta=0.005, in_place=False): @@ -100,8 +100,11 @@ def get_numeric_gradient(scope, ctx = core.DeviceContext.create(core.CPUPlace()) def get_output(): - op.run(scope, ctx) - return np.array(scope.find_var(output_name).get_tensor()).sum() + sum = 0.0 + for output_name in output_names: + op.run(scope, ctx) + sum += np.array(scope.find_var(output_name).get_tensor()).sum() + return sum tensor_to_check = scope.find_var(input_to_check).get_tensor() tensor_size = product(tensor_to_check.get_dims()) @@ -110,7 +113,7 @@ def get_numeric_gradient(scope, # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): if in_place: - set_input(op, inputs, core.CPUPlace()) + set_input(scope, op, inputs, core.CPUPlace()) # get one input element throw it's index i. origin = tensor_to_check.get_float_element(i) @@ -120,7 +123,7 @@ def get_numeric_gradient(scope, y_pos = get_output() if in_place: - set_input(op, inputs, core.CPUPlace()) + set_input(scope, op, inputs, core.CPUPlace()) x_neg = origin - delta tensor_to_check.set_float_element(i, x_neg) @@ -168,7 +171,10 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place, class OpTest(unittest.TestCase): def check_output_with_place(self, place): self.scope = core.Scope() - self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs) + op_inputs = self.inputs if hasattr(self, "inputs") else dict() + op_attrs = self.attrs if hasattr(self, "attrs") else dict() + self.op = create_op(self.scope, self.op_type, op_inputs, self.outputs, + op_attrs) if isinstance(place, core.GPUPlace) and not self.op.support_gpu(): return set_input(self.scope, self.op, self.inputs, place) @@ -222,22 +228,28 @@ class OpTest(unittest.TestCase): def check_grad(self, inputs_to_check, - output_name, + output_names, no_grad_set=None, in_place=False, max_relative_error=0.005): self.scope = core.Scope() - self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs) + op_inputs = self.inputs if hasattr(self, "inputs") else dict() + op_attrs = self.attrs if hasattr(self, "attrs") else dict() + self.op = create_op(self.scope, self.op_type, op_inputs, self.outputs, + op_attrs) if no_grad_set is None: no_grad_set = set() + if not type(output_names) is list: + output_names = [output_names] + numeric_grads = [ get_numeric_gradient( self.scope, self.op, self.inputs, input_to_check, - output_name, + output_names, in_place=in_place) for input_to_check in inputs_to_check ] grad_names = [ diff --git a/python/paddle/v2/framework/tests/test_add_two_op.py b/python/paddle/v2/framework/tests/test_add_two_op.py index a578e74eca..3ca34d9b9f 100644 --- a/python/paddle/v2/framework/tests/test_add_two_op.py +++ b/python/paddle/v2/framework/tests/test_add_two_op.py @@ -1,23 +1,20 @@ import unittest +import numpy as np +from op_test import OpTest -import numpy -import paddle.v2.framework.core as core -from paddle.v2.framework.op import Operator - -from op_test_util import OpTestMeta - - -class TestAddOp(unittest.TestCase): - __metaclass__ = OpTestMeta +class TestAddOp(OpTest): def setUp(self): - self.type = "add" + self.op_type = "add" self.inputs = { - 'X': numpy.random.random((102, 105)).astype("float32"), - 'Y': numpy.random.random((102, 105)).astype("float32") + 'X': np.random.random((102, 105)).astype("float32"), + 'Y': np.random.random((102, 105)).astype("float32") } self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']} + def test_check_output(self): + self.check_output() + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_concat_op.py b/python/paddle/v2/framework/tests/test_concat_op.py new file mode 100644 index 0000000000..656563f96e --- /dev/null +++ b/python/paddle/v2/framework/tests/test_concat_op.py @@ -0,0 +1,22 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestConcatOp(OpTest): + def setUp(self): + self.op_type = "concat" + x0 = np.random.random((2, 3, 2, 5)).astype('float32') + x1 = np.random.random((2, 3, 3, 5)).astype('float32') + x2 = np.random.random((2, 3, 4, 5)).astype('float32') + axis = 2 + self.inputs = {'X': [('x0', x0), ('x1', x1), ('x2', x2)]} + self.attrs = {'axis': axis} + self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)} + + def test_check_output(self): + self.check_output() + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_cos_sim_op.py b/python/paddle/v2/framework/tests/test_cos_sim_op.py index 32013a7999..797cbd8cc5 100644 --- a/python/paddle/v2/framework/tests/test_cos_sim_op.py +++ b/python/paddle/v2/framework/tests/test_cos_sim_op.py @@ -1,17 +1,14 @@ import unittest import numpy as np -from gradient_checker import GradientChecker, create_op -from op_test_util import OpTestMeta +from op_test import OpTest -class TestCosSimOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestCosSimOp(OpTest): def setUp(self): - self.type = "cos_sim" + self.op_type = "cos_sim" self.inputs = { - 'X': np.random.random((32, 64)).astype("float32"), - 'Y': np.random.random((32, 64)).astype("float32") + 'X': np.random.random((10, 5)).astype("float32"), + 'Y': np.random.random((10, 5)).astype("float32") } expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1) expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1) @@ -23,38 +20,20 @@ class TestCosSimOp(unittest.TestCase): 'Out': np.expand_dims(expect_out, 1) } + def test_check_output(self): + self.check_output() -class TestCosSimGradOp(GradientChecker): - def setUp(self): - self.op = create_op("cos_sim") - self.inputs = { - 'X': np.random.random((10, 5)).astype("float32"), - 'Y': np.random.random((10, 5)).astype("float32") - } - - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.05) + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.05, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.05, no_grad_set=set('X')) - def test_ignore_y(self): + def test_check_grad_ignore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.05, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y')) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_crop_op.py b/python/paddle/v2/framework/tests/test_crop_op.py index 28595b2858..8aed80e472 100644 --- a/python/paddle/v2/framework/tests/test_crop_op.py +++ b/python/paddle/v2/framework/tests/test_crop_op.py @@ -26,9 +26,7 @@ def crop(data, offsets, crop_shape): return np.array(result).reshape(crop_shape) -class TCropOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TCropOp(OpTest): def setUp(self): self.initTestCase() self.type = "crop" diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py index fb6a440e23..c2fc102a8b 100644 --- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py +++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py @@ -21,7 +21,7 @@ class TestCrossEntropy(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(["X"], "Y") + self.check_grad(['X'], 'Y') if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_elementwise_mul_op.py b/python/paddle/v2/framework/tests/test_elementwise_mul_op.py new file mode 100644 index 0000000000..e268cfddb2 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_elementwise_mul_op.py @@ -0,0 +1,157 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestElementwiseMulOp_Matrix(OpTest): + def setUp(self): + self.op_type = "elementwise_mul" + """ Warning + CPU gradient check error! + 'X': np.random.random((32,84)).astype("float32"), + 'Y': np.random.random((32,84)).astype("float32") + """ + self.inputs = { + 'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"), + 'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32") + } + self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1) + + def test_check_grad_ingore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X")) + + def test_check_grad_ingore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y')) + + +class TestElementwiseMulOp_Vector(OpTest): + def setUp(self): + self.op_type = "elementwise_mul" + self.inputs = { + 'X': np.random.random((32, )).astype("float32"), + 'Y': np.random.random((32, )).astype("float32") + } + self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1) + + def test_check_grad_ingore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X")) + + def test_check_grad_ingore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y')) + + +class TestElementwiseMulOp_broadcast_0(OpTest): + def setUp(self): + self.op_type = "elementwise_mul" + self.inputs = { + 'X': np.random.rand(2, 3, 4).astype(np.float32), + 'Y': np.random.rand(2).astype(np.float32) + } + + self.attrs = {'axis': 0} + self.outputs = { + 'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1) + + def test_check_grad_ingore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X")) + + def test_check_grad_ingore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y')) + + +class TestElementwiseMulOp_broadcast_1(OpTest): + def setUp(self): + self.op_type = "elementwise_mul" + self.inputs = { + 'X': np.random.rand(2, 3, 4).astype(np.float32), + 'Y': np.random.rand(3).astype(np.float32) + } + + self.attrs = {'axis': 1} + self.outputs = { + 'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 1) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1) + + def test_check_grad_ingore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X")) + + def test_check_grad_ingore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y')) + + +class TestElementwiseMulOp_broadcast_2(OpTest): + def setUp(self): + self.op_type = "elementwise_mul" + self.inputs = { + 'X': np.random.rand(2, 3, 4).astype(np.float32), + 'Y': np.random.rand(4).astype(np.float32) + } + + self.outputs = { + 'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 4) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1) + + def test_check_grad_ingore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X")) + + def test_check_grad_ingore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y')) + + +class TestElementwiseMulOp_broadcast_3(OpTest): + def setUp(self): + self.op_type = "elementwise_mul" + self.inputs = { + 'X': np.random.rand(2, 3, 4, 5).astype(np.float32), + 'Y': np.random.rand(3, 4).astype(np.float32) + } + + self.attrs = {'axis': 1} + self.outputs = { + 'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 4, 1) + } + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py b/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py index e5c862605f..2473daaba2 100644 --- a/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py +++ b/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py @@ -1,16 +1,17 @@ import unittest -from op_test_util import OpTestMeta -import numpy +import numpy as np +from op_test import OpTest -class TestFillZerosLikeOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestFillZerosLikeOp(OpTest): def setUp(self): - self.type = "fill_zeros_like" - self.inputs = {'Src': numpy.random.random((219, 232)).astype("float32")} - self.outputs = {'Dst': numpy.zeros_like(self.inputs['Src'])} + self.op_type = "fill_zeros_like" + self.inputs = {'Src': np.random.random((219, 232)).astype("float32")} + self.outputs = {'Dst': np.zeros_like(self.inputs["Src"])} + + def test_check_output(self): + self.check_output() -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py index e3de3fd0a1..b0ab429ef1 100644 --- a/python/paddle/v2/framework/tests/test_gather_op.py +++ b/python/paddle/v2/framework/tests/test_gather_op.py @@ -1,30 +1,20 @@ import unittest -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op -import numpy -import paddle.v2.framework.core as core -from paddle.v2.framework.op import Operator +import numpy as np +from op_test import OpTest -class TestGatherOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestGatherOp(OpTest): def setUp(self): - self.type = "gather" - xnp = numpy.random.random((10, 20)).astype("float32") - self.inputs = { - 'X': xnp, - 'Index': numpy.array([1, 3, 5]).astype("int32") - } - self.outputs = {'Out': self.inputs['X'][self.inputs['Index']]} + self.op_type = "gather" + xnp = np.random.random((10, 20)).astype("float32") + self.inputs = {'X': xnp, 'Index': np.array([1, 3, 5]).astype("int32")} + self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]} + def test_check_output(self): + self.check_output() -class TestGatherGradOp(GradientChecker): - def test_gather_grad(self): - op = create_op("gather") - xnp = numpy.random.random((10, 20)).astype("float32") - inputs = {'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32")} - self.check_grad(op, inputs, set("X"), "Out") + def test_check_grad(self): + self.check_grad(['X'], 'Out') if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_gaussian_random_op.py b/python/paddle/v2/framework/tests/test_gaussian_random_op.py index f95ed70b58..1f9e4db783 100644 --- a/python/paddle/v2/framework/tests/test_gaussian_random_op.py +++ b/python/paddle/v2/framework/tests/test_gaussian_random_op.py @@ -14,11 +14,11 @@ class GaussianRandomTest(unittest.TestCase): def gaussian_random_test(self, place): scope = core.Scope() - scope.new_var("Out").get_tensor() + scope.new_var('Out').get_tensor() op = Operator( "gaussian_random", - Out="Out", + Out='Out', dims=[1000, 784], mean=.0, std=1., @@ -27,10 +27,10 @@ class GaussianRandomTest(unittest.TestCase): op.infer_shape(scope) context = core.DeviceContext.create(place) op.run(scope, context) - tensor = numpy.array(scope.find_var("Out").get_tensor()) + tensor = numpy.array(scope.find_var('Out').get_tensor()) self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1) self.assertAlmostEqual(numpy.std(tensor), 1., delta=0.1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py index e8a7f848df..85117bf960 100644 --- a/python/paddle/v2/framework/tests/test_gradient_checker.py +++ b/python/paddle/v2/framework/tests/test_gradient_checker.py @@ -1,42 +1,45 @@ import unittest -import numpy -from paddle.v2.framework.op import Operator -from gradient_checker import GradientChecker -from gradient_checker import get_numeric_gradient +import numpy as np +import paddle.v2.framework.core as core +from op_test import get_numeric_gradient +from op_test import create_op class GetNumericGradientTest(unittest.TestCase): def test_add_op(self): - add_op = Operator("add", 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") + x = np.random.random((10, 1)).astype("float32") + y = np.random.random((10, 1)).astype("float32") + z = x + y + scope = core.Scope() + add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict()) + arr = get_numeric_gradient(scope, add_op, {'X': x, + 'Y': y}, 'X', ['Out']) 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) + shiftx = x - np.max(x) + exps = np.exp(shiftx) + return exps / np.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, :]) + d = np.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) + X = np.random.random((2, 2)).astype("float32") + Y = np.apply_along_axis(stable_softmax, 1, X) + dY = np.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) + scope = core.Scope() + softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict()) + + arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y") + np.testing.assert_almost_equal(arr, dX, decimal=1e-2) if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py index 4b7ce92c0f..b259bb67e8 100644 --- a/python/paddle/v2/framework/tests/test_lookup_table.py +++ b/python/paddle/v2/framework/tests/test_lookup_table.py @@ -1,31 +1,22 @@ import unittest import numpy as np -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op +from op_test import OpTest -class TestLookupTableOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestLookupTableOp(OpTest): def setUp(self): - self.type = 'lookup_table' - table = np.random.random((17, 31)).astype('float32') - ids = np.random.randint(0, 17, 4).astype('int32') + self.op_type = "lookup_table" + table = np.random.random((17, 31)).astype("float32") + ids = np.random.randint(0, 17, 4).astype("int32") self.inputs = {'W': table, 'Ids': ids} self.outputs = {'Out': table[ids]} + def test_check_output(self): + self.check_output() -class TestLookupTableGradOp(GradientChecker): - def test_grad(self): - op = create_op('lookup_table') - table = np.random.random((17, 31)).astype('float32') - ids = np.random.randint(0, 17, 4).astype('int32') - inputs = {'W': table, 'Ids': ids} - # comapre gradients - self.compare_grad(op, inputs, set(['Ids'])) - # check gradients - self.check_grad(op, inputs, set('W'), 'Out') + def test_check_grad(self): + self.check_grad(['W'], 'Out', no_grad_set=set('Ids')) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_mean_op.py b/python/paddle/v2/framework/tests/test_mean_op.py index f32b3160d6..7823abd8f8 100644 --- a/python/paddle/v2/framework/tests/test_mean_op.py +++ b/python/paddle/v2/framework/tests/test_mean_op.py @@ -1,24 +1,20 @@ import unittest -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op import numpy as np +from op_test import OpTest -class TestMeanOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestMeanOp(OpTest): def setUp(self): - self.type = "mean" - self.inputs = {'X': np.random.random((32, 784)).astype("float32")} - self.outputs = {'Out': np.mean(self.inputs['X'])} + self.op_type = "mean" + self.inputs = {'X': np.random.random((10, 10)).astype("float32")} + self.outputs = {'Out': np.mean(self.inputs["X"])} + def test_check_output(self): + self.check_output() -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") + def test_checkout_grad(self): + self.check_grad(['X'], 'Out') -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_minus_op.py b/python/paddle/v2/framework/tests/test_minus_op.py index 5abdd4a69b..dea797a1fe 100644 --- a/python/paddle/v2/framework/tests/test_minus_op.py +++ b/python/paddle/v2/framework/tests/test_minus_op.py @@ -1,30 +1,23 @@ import unittest import numpy as np -from gradient_checker import GradientChecker, create_op -from op_test_util import OpTestMeta +from op_test import OpTest -class MinusOpTest(unittest.TestCase): - __metaclass__ = OpTestMeta - +class MinusOpTest(OpTest): def setUp(self): - self.type = "minus" + self.op_type = "minus" self.inputs = { 'X': np.random.random((32, 84)).astype("float32"), 'Y': np.random.random((32, 84)).astype("float32") } self.outputs = {'Out': (self.inputs['X'] - self.inputs['Y'])} + def test_check_output(self): + self.check_output() -class MinusGradTest(GradientChecker): - def test_left(self): - op = create_op("minus") - inputs = { - "X": np.random.random((10, 10)).astype("float32"), - "Y": np.random.random((10, 10)).astype("float32") - } - self.check_grad(op, inputs, ["X", 'Y'], "Out") + def test_check_grad(self): + self.check_grad(['X', 'Y'], 'Out') -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/test_mnist.py similarity index 100% rename from python/paddle/v2/framework/tests/mnist.py rename to python/paddle/v2/framework/tests/test_mnist.py diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py index 8c827e242e..b3d95a56b8 100644 --- a/python/paddle/v2/framework/tests/test_mul_op.py +++ b/python/paddle/v2/framework/tests/test_mul_op.py @@ -1,27 +1,35 @@ import unittest import numpy as np -from gradient_checker import GradientChecker, create_op -from op_test_util import OpTestMeta -from paddle.v2.framework.op import Operator +from op_test import OpTest -class TestMulOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestMulOp(OpTest): def setUp(self): - self.type = "mul" + self.op_type = "mul" self.inputs = { 'X': np.random.random((32, 84)).astype("float32"), 'Y': np.random.random((84, 100)).astype("float32") } self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) -class TestMulOp2(unittest.TestCase): - __metaclass__ = OpTestMeta + def test_check_grad_ingore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) + def test_check_grad_ingore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) + + +class TestMulOp2(OpTest): def setUp(self): - self.type = "mul" + self.op_type = "mul" self.inputs = { 'X': np.random.random((15, 4, 12, 10)).astype("float32"), 'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32") @@ -32,72 +40,20 @@ class TestMulOp2(unittest.TestCase): self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9)) } + def test_check_output(self): + self.check_output() -class TestMulGradOp(GradientChecker): - def setUp(self): - self.op = create_op("mul") - self.inputs = { - 'X': np.random.random((32, 84)).astype("float32"), - 'Y': np.random.random((84, 100)).astype("float32") - } - - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): - # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) - - def test_ignore_x(self): - self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) - - def test_ignore_y(self): - self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) - - -class TestMulGradTest2(GradientChecker): - def setUp(self): - self.op = Operator( - "mul", X="X", Y="Y", Out="Out", x_num_col_dims=2, y_num_col_dims=2) - self.inputs = { - "X": np.random.random((15, 4, 12, 10)).astype("float32"), - "Y": np.random.random((4, 30, 8, 2, 9)).astype("float32") - } - - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set('X')) - def test_ignore_y(self): + def test_check_grad_ignore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py index e4b7cd480c..50cfb855f2 100644 --- a/python/paddle/v2/framework/tests/test_net.py +++ b/python/paddle/v2/framework/tests/test_net.py @@ -35,5 +35,5 @@ Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]} self.assertEqual(expected, "\n" + str(net)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_pad_op.py b/python/paddle/v2/framework/tests/test_pad_op.py new file mode 100644 index 0000000000..456b765e33 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_pad_op.py @@ -0,0 +1,55 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestPadOp(OpTest): + def setUp(self): + self.initTestCase() + self.op_type = "pad" + self.inputs = {'X': np.random.random(self.shape).astype("float32"), } + self.attrs = {} + self.attrs['paddings'] = np.array(self.paddings).flatten() + self.attrs['pad_value'] = self.pad_value + self.outputs = { + 'Out': np.pad(self.inputs['X'], + self.paddings, + mode='constant', + constant_values=self.pad_value) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X'], 'Out') + + def initTestCase(self): + self.shape = (16, 16) + self.paddings = [(0, 1), (2, 3)] + self.pad_value = 0 + + +class TestCase1(TestPadOp): + def initTestCase(self): + self.shape = (2, 3, 4, 4) + self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)] + self.pad_value = 0.5 + + +class TestCase2(TestPadOp): + def initTestCase(self): + self.shape = (2, 2, 2) + self.paddings = [(0, 0), (0, 0), (1, 2)] + self.pad_value = 1 + + +class TestCase3(TestPadOp): + def initTestCase(self): + self.shape = (8) + self.paddings = [(0, 1)] + self.pad_value = 0.9 + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py new file mode 100644 index 0000000000..16bb6bb2af --- /dev/null +++ b/python/paddle/v2/framework/tests/test_reshape_op.py @@ -0,0 +1,21 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestReshapeOp(OpTest): + def setUp(self): + self.op_type = "reshape" + self.inputs = {'X': np.random.random((10, 20)).astype("float32")} + self.attrs = {'shape': [10 * 20]} + self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["X"], "Out") + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index 8378c1cd21..336645bd99 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -1,68 +1,51 @@ import unittest import numpy as np -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op +from op_test import OpTest -class TestRowwiseAddOp(unittest.TestCase): - __metaclass__ = OpTestMeta - - def setUp(self): - self.type = "rowwise_add" - self.inputs = { - 'X': np.random.random((32, 84)).astype("float32"), - 'b': np.random.random(84).astype("float32") - } - self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} - - -class TestRowwiseAddOp2(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestRowwiseAddOp(OpTest): def setUp(self): - self.type = "rowwise_add" + self.op_type = "rowwise_add" self.inputs = { - 'X': np.random.random((13, 6, 7, 8)).astype("float32"), - 'b': np.random.random((7, 8)).astype("float32") + 'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"), + 'b': np.random.uniform(0.1, 1, [10]).astype("float32") } self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} + def test_check_output(self): + self.check_output() -class TestRowwiseAddGradOp(GradientChecker): - def setUp(self): - self.op = create_op("rowwise_add") - self.inputs = { - "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"), - "b": np.random.uniform(0.1, 1, [10]).astype("float32") - } + def test_check_grad_normal(self): + self.check_grad(['X', 'b'], 'Out') - def test_normal(self): - self.check_grad(self.op, self.inputs, ["X", "b"], "Out") + def test_check_grad_ingore_b(self): + self.check_grad(['X'], 'Out', no_grad_set=set('b')) - def test_ignore_b(self): - self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"}) + def test_check_grad_ingore_x(self): + self.check_grad(['b'], 'Out', no_grad_set=set('X')) - def test_ignore_x(self): - self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"}) - -class TestRowwiseAddGradOp2(GradientChecker): +class TestRowwiseAddOp2(OpTest): def setUp(self): - self.op = create_op("rowwise_add") + self.op_type = "rowwise_add" self.inputs = { - "X": np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"), - "b": np.random.uniform(0.1, 1, [2, 5]).astype("float32") + 'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"), + 'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32") } + self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} + + def test_check_output(self): + self.check_output() - def test_normal(self): - self.check_grad(self.op, self.inputs, ["X", "b"], "Out") + def test_check_grad_normal(self): + self.check_grad(['X', 'b'], 'Out') - def test_ignore_b(self): - self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"}) + def test_check_grad_ignore_b(self): + self.check_grad(['X'], 'Out', no_grad_set=set('b')) - def test_ignore_x(self): - self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"}) + def test_check_grad_ignore_x(self): + self.check_grad(['b'], 'Out', no_grad_set=set('X')) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_scale_and_identity_op.py b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py index 69b301c376..05d76d4282 100644 --- a/python/paddle/v2/framework/tests/test_scale_and_identity_op.py +++ b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py @@ -1,43 +1,34 @@ import unittest -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op import numpy as np -from paddle.v2.framework.op import Operator +from op_test import OpTest -class IdentityTest(unittest.TestCase): - __metaclass__ = OpTestMeta - +class IdentityTest(OpTest): def setUp(self): - self.type = "identity" - self.inputs = {'X': np.random.random((32, 784)).astype("float32")} + self.op_type = "identity" + self.inputs = {'X': np.random.random((10, 10)).astype("float32")} self.outputs = {'Out': self.inputs['X']} + def test_check_output(self): + self.check_output() -class IdentityGradOpTest(GradientChecker): - def test_normal(self): - op = create_op("identity") - inputs = {"X": np.random.random((10, 10)).astype("float32")} - self.check_grad(op, inputs, set("X"), "Out") - + def test_check_grad(self): + self.check_grad(['X'], 'Out') -class ScaleTest(unittest.TestCase): - __metaclass__ = OpTestMeta +class ScaleTest(OpTest): def setUp(self): - self.type = "scale" - self.inputs = {'X': np.random.random((32, 784)).astype("float32")} + self.op_type = "scale" + self.inputs = {'X': np.random.random((10, 10)).astype("float32")} self.attrs = {'scale': -2.3} self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']} + def test_check_output(self): + self.check_output() -class ScaleGradTest(GradientChecker): - def test_normal(self): - op = Operator("scale", X="X", Out="Out", scale=3.2) - self.check_grad(op, - {"X": np.random.random((10, 10)).astype("float32")}, - set("X"), "Out") + def test_check_grad(self): + self.check_grad(['X'], 'Out') -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py index c1f9444889..33c73c5263 100644 --- a/python/paddle/v2/framework/tests/test_scatter_op.py +++ b/python/paddle/v2/framework/tests/test_scatter_op.py @@ -1,37 +1,24 @@ import unittest -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op -import numpy -import paddle.v2.framework.core as core -from paddle.v2.framework.op import Operator +import numpy as np +from op_test import OpTest -class TestScatterOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestScatterOp(OpTest): def setUp(self): - self.type = "scatter" - ref_np = numpy.ones((3, 3)).astype("float32") - index_np = numpy.array([1, 2]).astype("int32") - updates_np = numpy.random.random((2, 3)).astype("float32") - output_np = numpy.copy(ref_np) + self.op_type = "scatter" + ref_np = np.ones((3, 3)).astype("float32") + index_np = np.array([1, 2]).astype("int32") + updates_np = np.random.random((2, 3)).astype("float32") + output_np = np.copy(ref_np) output_np[index_np] += updates_np self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} + def test_check_output(self): + self.check_output() -class TestScatterGradOp(GradientChecker): - def test_scatter_grad(self): - op = create_op("scatter") - # test data setup - ref_np = numpy.ones((3, 10)).astype("float32") - index_np = numpy.array([1, 2]).astype("int32") - updates_np = numpy.random.random((2, 10)).astype("float32") - output_np = numpy.copy(ref_np) - output_np[index_np] += updates_np - inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} - self.check_grad( - op, inputs, set(["Updates", "Ref"]), "Out", in_place=True) + def test_check_grad(self): + self.check_grad(['Updates', 'Ref'], 'Out', in_place=True) if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_sgd_op.py b/python/paddle/v2/framework/tests/test_sgd_op.py index e5f9ef865e..557cf15ace 100644 --- a/python/paddle/v2/framework/tests/test_sgd_op.py +++ b/python/paddle/v2/framework/tests/test_sgd_op.py @@ -1,21 +1,22 @@ import unittest -import numpy -from op_test_util import OpTestMeta +import numpy as np +from op_test import OpTest -class TestSGD(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestSGD(OpTest): def setUp(self): - self.type = "sgd" - w = numpy.random.random((102, 105)).astype("float32") - g = numpy.random.random((102, 105)).astype("float32") + self.op_type = "sgd" + w = np.random.random((102, 105)).astype("float32") + g = np.random.random((102, 105)).astype("float32") lr = 0.1 self.inputs = {'param': w, 'grad': g} self.attrs = {'learning_rate': lr} self.outputs = {'param_out': w - lr * g} + def test_check_output(self): + self.check_output() + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_softmax_op.py b/python/paddle/v2/framework/tests/test_softmax_op.py index 0d590fa706..1b948f252f 100644 --- a/python/paddle/v2/framework/tests/test_softmax_op.py +++ b/python/paddle/v2/framework/tests/test_softmax_op.py @@ -1,9 +1,6 @@ import unittest - import numpy as np - -from gradient_checker import GradientChecker, create_op -from op_test_util import OpTestMeta +from op_test import OpTest def stable_softmax(x): @@ -13,26 +10,21 @@ def stable_softmax(x): return exps / np.sum(exps) -class TestSoftmaxOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestSoftmaxOp(OpTest): def setUp(self): - self.type = "softmax" - self.inputs = {"X": np.random.random((10, 10)).astype("float32")} + self.op_type = "softmax" + self.inputs = { + 'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32") + } self.outputs = { - "Y": np.apply_along_axis(stable_softmax, 1, self.inputs["X"]) + 'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X']) } + def test_check_output(self): + self.check_output() -class TestSoftmaxGradOp(GradientChecker): - def setUp(self): - self.op = create_op("softmax") - self.inputs = { - "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32") - } - - def test_softmax_grad(self): - self.check_grad(self.op, self.inputs, ["X"], "Y") + def test_check_grad(self): + self.check_grad(['X'], 'Y') if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py index 2bcdf37df4..dc6ebf5d30 100644 --- a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py +++ b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py @@ -1,17 +1,14 @@ import unittest -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op import numpy as np +from op_test import OpTest -class TestSquaredL2DistanceOp_f0(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestSquaredL2DistanceOp_f0(OpTest): def setUp(self): - self.type = 'squared_l2_distance' + self.op_type = "squared_l2_distance" self.inputs = { - 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'), - 'Y': np.random.uniform(0.1, 1., (32, 64)).astype('float32') + 'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"), + 'Y': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32") } sub_res = self.inputs['X'] - self.inputs['Y'] output = sub_res * sub_res @@ -20,15 +17,19 @@ class TestSquaredL2DistanceOp_f0(unittest.TestCase): 'Out': np.expand_dims(output.sum(1), 1) } + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X', 'Y'], 'Out') -class TestSquaredL2DistanceOp_f1(unittest.TestCase): - __metaclass__ = OpTestMeta +class TestSquaredL2DistanceOp_f1(OpTest): def setUp(self): - self.type = 'squared_l2_distance' + self.op_type = "squared_l2_distance" self.inputs = { - 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'), - 'Y': np.random.uniform(0.1, 1., (1, 64)).astype('float32') + 'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"), + 'Y': np.random.uniform(0.1, 0.6, (1, 3)).astype("float32") } sub_res = self.inputs['X'] - self.inputs['Y'] output = sub_res * sub_res @@ -37,53 +38,34 @@ class TestSquaredL2DistanceOp_f1(unittest.TestCase): 'Out': np.expand_dims(output.sum(1), 1) } + def test_check_output(self): + self.check_output() -class TestSquaredL2DistanceOp_f2(unittest.TestCase): - __metaclass__ = OpTestMeta + def test_check_grad(self): + self.check_grad(['X', 'Y'], 'Out') + +class TestSquaredL2DistanceOp_f2(OpTest): def setUp(self): - self.type = 'squared_l2_distance' + self.op_type = "squared_l2_distance" self.inputs = { - 'X': np.random.uniform(0.1, 1., (32, 64, 128)).astype('float32'), - 'Y': np.random.uniform(0.1, 1., (1, 64, 128)).astype('float32') + 'X': np.random.uniform(0.1, 0.6, (2, 3, 4)).astype("float32"), + 'Y': np.random.uniform(0.1, 0.6, (1, 3, 4)).astype("float32") } sub_res = self.inputs['X'] - self.inputs['Y'] - sub_res = sub_res.reshape((32, 64 * 128)) + sub_res = sub_res.reshape((2, 3 * 4)) output = sub_res * sub_res self.outputs = { 'sub_result': sub_res, 'Out': np.expand_dims(output.sum(1), 1) } + def test_check_output(self): + self.check_output() -class TestSquaredL2DistanceGradOp(GradientChecker): - def test_squared_l2_distance_b0(self): - op = create_op("squared_l2_distance") - inputs = { - 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'), - 'Y': np.random.uniform(0.1, .6, (2, 3)).astype('float32') - } - self.compare_grad(op, inputs) - self.check_grad(op, inputs, set(["X", "Y"]), "Out") - - def test_squared_l2_distance_b1(self): - op = create_op("squared_l2_distance") - inputs = { - 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'), - 'Y': np.random.uniform(0.1, .6, (1, 3)).astype('float32') - } - self.compare_grad(op, inputs) - self.check_grad(op, inputs, set(["X", "Y"]), "Out") - - def test_squared_l2_distance_b2(self): - op = create_op("squared_l2_distance") - inputs = { - 'X': np.random.uniform(0.1, .6, (2, 3, 4)).astype('float32'), - 'Y': np.random.uniform(0.1, .6, (1, 3, 4)).astype('float32') - } - self.compare_grad(op, inputs) - self.check_grad(op, inputs, set(["X", "Y"]), "Out") + def test_check_grad(self): + self.check_grad(['X', 'Y'], 'Out') -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py index 66417d70e8..60254291e2 100644 --- a/python/paddle/v2/framework/tests/test_sum_op.py +++ b/python/paddle/v2/framework/tests/test_sum_op.py @@ -9,7 +9,7 @@ class TestSumOp(OpTest): x0 = np.random.random((3, 4)).astype('float32') x1 = np.random.random((3, 4)).astype('float32') x2 = np.random.random((3, 4)).astype('float32') - self.inputs = {"X": {"x0": x0, "x1": x1, "x2": x2}} + self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]} y = x0 + x1 + x2 self.outputs = {'Out': y} @@ -17,8 +17,8 @@ class TestSumOp(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(["x0"], "Out") + self.check_grad(['x0'], 'Out') -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_tensor.py b/python/paddle/v2/framework/tests/test_tensor.py index 1af39818a3..f26ed4964c 100644 --- a/python/paddle/v2/framework/tests/test_tensor.py +++ b/python/paddle/v2/framework/tests/test_tensor.py @@ -3,7 +3,7 @@ import unittest import numpy -class TestScope(unittest.TestCase): +class TestTensor(unittest.TestCase): def test_int_tensor(self): scope = core.Scope() var = scope.new_var("test_tensor") @@ -20,8 +20,8 @@ class TestScope(unittest.TestCase): tensor.set(tensor_array, place) tensor_array_2 = numpy.array(tensor) - self.assertEqual(1.0, tensor_array_2[3, 9]) - self.assertEqual(2.0, tensor_array_2[19, 11]) + self.assertEqual(1, tensor_array_2[3, 9]) + self.assertEqual(2, tensor_array_2[19, 11]) def test_float_tensor(self): scope = core.Scope() @@ -43,6 +43,84 @@ class TestScope(unittest.TestCase): self.assertAlmostEqual(1.0, tensor_array_2[3, 9]) self.assertAlmostEqual(2.0, tensor_array_2[19, 11]) + def test_int_lod_tensor(self): + places = [core.CPUPlace(), core.GPUPlace(0)] + for place in places: + scope = core.Scope() + var = scope.new_var("test_tensor") + var_lod = scope.new_var("test_lod_tensor") + + tensor = var.get_tensor() + lod_tensor = var_lod.get_lod_tensor() + + tensor.set_dims([4, 4, 6]) + tensor.alloc_int(place) + array = numpy.array(tensor) + array[0, 0, 0] = 3 + array[3, 3, 5] = 10 + tensor.set(array, place) + + lod_tensor.set_tensor(tensor) + lod_tensor.set_lod([[0, 2, 4]]) + + lod_v = numpy.array(lod_tensor.tensor()) + self.assertTrue(numpy.alltrue(array == lod_v)) + + lod = lod_tensor.lod() + self.assertEqual(0, lod[0][0]) + self.assertEqual(2, lod[0][1]) + self.assertEqual(4, lod[0][2]) + + def test_float_lod_tensor(self): + places = [core.CPUPlace(), core.GPUPlace(0)] + for place in places: + scope = core.Scope() + var = scope.new_var("test_tensor") + var_lod = scope.new_var("test_lod_tensor") + + tensor = var.get_tensor() + lod_tensor = var_lod.get_lod_tensor() + + tensor.set_dims([5, 2, 3, 4]) + tensor.alloc_float(place) + + tensor_array = numpy.array(tensor) + self.assertEqual((5, 2, 3, 4), tensor_array.shape) + tensor_array[0, 0, 0, 0] = 1.0 + tensor_array[0, 0, 0, 1] = 2.0 + tensor.set(tensor_array, place) + + lod_tensor.set_tensor(tensor) + + lod_v = numpy.array(lod_tensor.tensor()) + self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0]) + self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1]) + self.assertEqual(len(lod_tensor.lod()), 0) + + lod_py = [[0, 2, 5], [0, 2, 4, 5]] + lod_tensor.set_lod(lod_py) + lod = lod_tensor.lod() + self.assertListEqual(lod_py, lod) + + def test_lod_tensor_init(self): + scope = core.Scope() + var = scope.new_var("test_tensor") + place = core.CPUPlace() + tensor = var.get_tensor() + tensor.set_dims([5, 2, 3, 4]) + tensor.alloc_float(place) + tensor_array = numpy.array(tensor) + tensor_array[0, 0, 0, 0] = 1.0 + tensor_array[0, 0, 0, 1] = 2.0 + tensor.set(tensor_array, place) + lod_py = [[0, 2, 5], [0, 2, 4, 5]] + + lod_tensor = core.LoDTensor(lod_py, tensor) + lod_v = numpy.array(lod_tensor.tensor()) + self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0]) + self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1]) + self.assertListEqual(lod_py, lod_tensor.lod()) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/v2/framework/tests/test_top_k_op.py b/python/paddle/v2/framework/tests/test_top_k_op.py index e841d96d26..cab799256d 100644 --- a/python/paddle/v2/framework/tests/test_top_k_op.py +++ b/python/paddle/v2/framework/tests/test_top_k_op.py @@ -1,14 +1,11 @@ import unittest import numpy as np -from gradient_checker import GradientChecker, create_op -from op_test_util import OpTestMeta +from op_test import OpTest -class TestTopkOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestTopkOp(OpTest): def setUp(self): - self.type = "top_k" + self.op_type = "top_k" k = 1 input = np.random.random((32, 84)).astype("float32") output = np.ndarray((32, k)) @@ -25,11 +22,9 @@ class TestTopkOp(unittest.TestCase): self.outputs = {'Out': output, 'Indices': indices} -class TestTopkOp3d(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestTopkOp3d(OpTest): def setUp(self): - self.type = "top_k" + self.op_type = "top_k" k = 1 input = np.random.random((32, 2, 84)).astype("float32") input_flat_2d = input.reshape(64, 84) @@ -48,5 +43,5 @@ class TestTopkOp3d(unittest.TestCase): self.outputs = {'Out': output, 'Indices': indices} -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_uniform_random_op.py b/python/paddle/v2/framework/tests/test_uniform_random_op.py index c3d2bb44da..76a5e36e56 100644 --- a/python/paddle/v2/framework/tests/test_uniform_random_op.py +++ b/python/paddle/v2/framework/tests/test_uniform_random_op.py @@ -14,11 +14,11 @@ class UniformRandomTest(unittest.TestCase): def uniform_random_test(self, place): scope = core.Scope() - scope.new_var("X").get_tensor() + scope.new_var('X').get_tensor() op = Operator( "uniform_random", - Out="X", + Out='X', dims=[1000, 784], min=-5.0, max=10.0, @@ -27,9 +27,9 @@ class UniformRandomTest(unittest.TestCase): op.infer_shape(scope) ctx = core.DeviceContext.create(place) op.run(scope, ctx) - tensor = numpy.array(scope.find_var("X").get_tensor()) + tensor = numpy.array(scope.find_var('X').get_tensor()) self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py index 8acea6155c..e80456d9bb 100644 --- a/python/paddle/v2/inference.py +++ b/python/paddle/v2/inference.py @@ -2,6 +2,7 @@ import numpy import collections import topology import minibatch +import cPickle __all__ = ['infer', 'Inference'] @@ -25,11 +26,23 @@ class Inference(object): :type parameters: paddle.v2.parameters.Parameters """ - def __init__(self, output_layer, parameters): + def __init__(self, parameters, output_layer=None, fileobj=None): import py_paddle.swig_paddle as api - topo = topology.Topology(output_layer) - gm = api.GradientMachine.createFromConfigProto( - topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE]) + + if output_layer is not None: + topo = topology.Topology(output_layer) + gm = api.GradientMachine.createFromConfigProto( + topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE]) + self.__data_types__ = topo.data_type() + elif fileobj is not None: + tmp = cPickle.load(fileobj) + gm = api.GradientMachine.createByConfigProtoStr( + tmp['protobin'], api.CREATE_MODE_TESTING, + [api.PARAMETER_VALUE]) + self.__data_types__ = tmp['data_type'] + else: + raise ValueError("Either output_layer or fileobj must be set") + for param in gm.getParameters(): val = param.getBuf(api.PARAMETER_VALUE) name = param.getName() @@ -43,7 +56,6 @@ class Inference(object): # called here, but it's better to call this function in one place. param.setValueUpdated() self.__gradient_machine__ = gm - self.__data_types__ = topo.data_type() def iter_infer(self, input, feeding=None): from data_feeder import DataFeeder diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index a20e878d08..2db66be250 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -18,6 +18,7 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig import paddle.trainer_config_helpers as conf_helps import layer as v2_layer import config_base +import cPickle __all__ = ['Topology'] @@ -100,6 +101,14 @@ class Topology(object): return layer return None + def serialize_for_inference(self, stream): + protobin = self.proto().SerializeToString() + data_type = self.data_type() + cPickle.dump({ + 'protobin': protobin, + 'data_type': data_type + }, stream, cPickle.HIGHEST_PROTOCOL) + def __check_layer_type__(layer): if not isinstance(layer, config_base.Layer):