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提交 94e86897 编写于 作者: D dangqingqing
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update code and fix conflicts.

上级 082bc7af 3375e3e2
隐藏空白更改
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Showing with 753 addition and 494 deletion
CMakeLists.txt
浏览文件 @ 94e86897
......@@ -36,8 +36,7 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......@@ -82,10 +81,8 @@ if(ANDROID OR IOS)
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLDNN OFF CACHE STRING
"Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLML OFF CACHE STRING
"Disable MKLML package when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
# Compile PaddlePaddle mobile inference library
if (NOT WITH_C_API)
......@@ -111,6 +108,17 @@ else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
if(WITH_MKL)
set(WITH_MKLML ON)
set(WITH_MKLDNN ${AVX2_FOUND})
if(NOT WITH_MKLDNN)
message(WARNING "Do not have AVX2 intrinsics and disabled MKL-DNN")
endif()
else()
set(WITH_MKLML OFF)
set(WITH_MKLDNN OFF)
endif()
########################################################################################
include(external/mklml) # download mklml package
......@@ -161,8 +169,12 @@ if(WITH_GPU)
include(cuda)
endif(WITH_GPU)
if(WITH_MKLML)
list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
endif()
if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
endif()
if(USE_NNPACK)
......
cmake/configure.cmake
浏览文件 @ 94e86897
......@@ -76,27 +76,14 @@ else()
include_directories(${CUDA_TOOLKIT_INCLUDE})
endif(NOT WITH_GPU)
if(WITH_MKLDNN)
add_definitions(-DPADDLE_USE_MKLDNN)
if (WITH_MKLML AND MKLDNN_IOMP_DIR)
message(STATUS "Enable Intel OpenMP at ${MKLDNN_IOMP_DIR}")
set(OPENMP_FLAGS "-fopenmp")
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
else()
find_package(OpenMP)
if(OPENMP_FOUND)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
else()
message(WARNING "Can not find OpenMP."
"Some performance features in MKLDNN may not be available")
endif()
endif()
endif(WITH_MKLDNN)
if (WITH_MKLML AND MKLML_IOMP_LIB)
message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
set(OPENMP_FLAGS "-fopenmp")
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
......
cmake/cross_compiling/ios.cmake
浏览文件 @ 94e86897
......@@ -76,11 +76,9 @@ set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform")
# Set the architecture for iOS
if(NOT DEFINED IOS_ARCH)
if(IOS_PLATFORM STREQUAL "OS")
# FIXME(liuyiqun): support "armv7;armv7s;arm64" future
set(IOS_ARCH "arm64")
set(IOS_ARCH "armv7;armv7s;arm64")
elseif(IOS_PLATFORM STREQUAL "SIMULATOR")
# FIXME(liuyiqun): support "i386;x86_64" future
set(IOS_ARCH "x86_64")
set(IOS_ARCH "i386;x86_64")
endif()
endif()
set(CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string "Build architecture for iOS")
......@@ -248,7 +246,7 @@ set(IOS_COMPILER_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} ${XCODE_IOS_BITCODE_
# Hidden visibilty is required for cxx on iOS
set(CMAKE_C_FLAGS "${IOS_COMPILER_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags")
set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
set(IOS_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first")
......
cmake/cuda.cmake
浏览文件 @ 94e86897
......@@ -63,7 +63,6 @@ function(select_nvcc_arch_flags out_variable)
set(archs_name_default "All")
if(NOT CMAKE_CROSSCOMPILING)
list(APPEND archs_names "Auto")
set(archs_name_default "Auto")
endif()
# set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
......
cmake/external/mkldnn.cmake
浏览文件 @ 94e86897
......@@ -40,10 +40,9 @@ INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
SET(MKLDNN_DEPENDS ${MKLML_PROJECT})
SET(MKLDNN_MKLROOT ${MKLML_ROOT})
SET(MKLDNN_IOMP_LIB ${MKLML_IOMP_LIB})
SET(MKLDNN_IOMP_DIR ${MKLML_LIB_DIR})
MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
MESSAGE(STATUS "Build MKLDNN with MKLML ${MKLML_ROOT}")
ELSE()
MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
ENDIF()
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
......@@ -57,15 +56,16 @@ ExternalProject_Add(
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DMKLROOT=${MKLDNN_MKLROOT}
CMAKE_ARGS -DMKLROOT=${MKLML_ROOT}
CMAKE_ARGS -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-DMKLROOT:PATH=${MKLDNN_MKLROOT}
-DMKLROOT:PATH=${MKLML_ROOT}
)
ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
add_definitions(-DPADDLE_USE_MKLDNN)
LIST(APPEND external_project_dependencies mkldnn)
cmake/external/openblas.cmake
浏览文件 @ 94e86897
......@@ -29,7 +29,7 @@ IF(NOT ${CBLAS_FOUND})
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER}")
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
IF(CMAKE_CROSSCOMPILING)
SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER})
......@@ -45,15 +45,14 @@ IF(NOT ${CBLAS_FOUND})
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0)
ENDIF()
ELSEIF(IOS)
# FIXME(liuyiqun): support multiple architectures
SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch armv7")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
IF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX})
ELSE()
MESSAGE(FATAL_ERROR "OpenBLAS only support arm64 architectures on iOS. "
"You can set IOS_USE_VECLIB_FOR_BLAS=ON or USE_EIGEN_FOR_BLAS=ON to use other blas library instead.")
ENDIF()
ELSEIF(RPI)
# use hardfp
......
cmake/external/warpctc.cmake
浏览文件 @ 94e86897
......@@ -12,6 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF(MOBILE_INFERENCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc)
......
cmake/util.cmake
浏览文件 @ 94e86897
......@@ -115,8 +115,8 @@ function(link_paddle_exe TARGET_NAME)
target_link_libraries(${TARGET_NAME} log)
endif(ANDROID)
if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
endif()
add_dependencies(${TARGET_NAME} ${external_project_dependencies})
......
doc/design/mkldnn/README.MD
浏览文件 @ 94e86897
......@@ -36,13 +36,13 @@ Figure 1. PaddlePaddle on IA.
我们把集成方案大致分为了如下几个方面。
### CMake
我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
### Layers
所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
......
doc/howto/dev/write_docs_cn.rst
浏览文件 @ 94e86897
......@@ -34,7 +34,7 @@ PaddlePaddle的文档构建有两种方式。
cd TO_YOUR_PADDLE_CLONE_PATH
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
make gen_proto_py
make paddle_docs paddle_docs_cn
......
doc/mobile/cross_compiling_for_android_cn.md
浏览文件 @ 94e86897
# 构建Android平台上的PaddlePaddle库
# Android平台编译指南
用户可通过如下两种方式，交叉编译Android平台上适用的PaddlePaddle库：
- 基于Docker容器的编译方式
......
doc/mobile/cross_compiling_for_ios_cn.md
浏览文件 @ 94e86897
# 构建iOS平台上的PaddlePaddle库
# iOS平台编译指南
交叉编译iOS平台上适用的PaddlePaddle库，需要在MacOS系统上进行。本文的将介绍在MacOS上，从源码交叉编译iOS平台上适用的PaddlePaddle库。
## 准备交叉编译环境
......@@ -25,7 +25,7 @@ iOS平台可选配置参数：
- `IOS_PLATFORM`，可设置为`OS/SIMULATOR`，默认值为`OS`。
- `OS`，构建目标为`arm`架构的iPhone或者iPad等物理设备。
- `SIMULATOR`，构建目标为`x86`架构的模拟器平台。
- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示：
- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示，默认编译所有架构：
<table class="docutils">
<colgroup>
......@@ -41,11 +41,11 @@ iOS平台可选配置参数：
<tbody valign="top">
<tr class="row-even">
<td>OS</td>
<td>armv7, armv7s, arm64 (默认)</td>
<td>armv7, armv7s, arm64 </td>
</tr>
<tr class="row-odd">
<td>SIMULATOR</td>
<td>i386, x86_64 (默认)</td>
<td>i386, x86_64 </td>
</tr>
</tbody>
</table>
......@@ -66,7 +66,7 @@ iOS平台可选配置参数：
```bash
cmake -DCMAKE_SYSTEM_NAME=iOS \
-DIOS_PLATFORM=OS \
-DIOS_ARCH="arm64" \
-DIOS_ARCH="armv7;arm64" \
-DIOS_ENABLE_BITCODE=ON \
-DIOS_USE_VECLIB_FOR_BLAS=ON \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
......@@ -112,6 +112,6 @@ $ make install
- `lib`目录，其中包含PaddlePaddle的C-API静态库
- `third_party`目录，其中包含所依赖的所有第三方库
注意，不同架构的PaddlePaddle库建议安装到不同的目录下，然后使用`lipo`工具将多个静态库合并成一个支持多个架构的fat库。
注意，如果PaddlePaddle库需要同时支持真机和模拟器，则需要分别编译真机和模拟器版本，然后使用`lipo`工具合并fat库。
自此，PaddlePaddle库已经安装完成，用户可将合成的fat库用于深度学习相关的iOS App中，调用方法见C-API文档。
doc/mobile/cross_compiling_for_raspberry_cn.md
浏览文件 @ 94e86897
# 构建Raspberry Pi平台上的PaddlePaddle库
# Raspberry Pi平台编译指南
通常有两个方法来构建基于 Rasspberry Pi 的版本：
......
paddle/cuda/include/hl_gpu.h
浏览文件 @ 94e86897
......@@ -25,7 +25,9 @@ limitations under the License. */
#include "hl_matrix.h"
#include "hl_sequence.h"
#include "hl_sparse.h"
#ifndef PADDLE_MOBILE_INFERENCE
#include "hl_warpctc_wrap.h"
#endif
#ifdef HPPL_STUB_FUNC
#include "stub/hl_aggregate_stub.h"
......
paddle/framework/backward.cc
浏览文件 @ 94e86897
......@@ -270,6 +270,19 @@ static bool AllGradInSet(const std::vector<std::string>& names,
return false;
}
}
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
sout << "All input {";
for (auto& name : names) {
sout << name << ",";
}
sout << "} is in {";
for (auto& name : set) {
sout << name << ",";
}
sout << "}";
VLOG(10) << sout.str();
}
return true;
}
......@@ -290,14 +303,12 @@ static void CreateGradVarInBlock(
auto ops = block_desc->AllOps();
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
bool need_infer_shape = false;
std::unordered_set<std::string> new_vars;
ForEachVarName(ops[op_index]->Outputs(),
[&](const std::string& grad_var_name) {
if (block_desc->HasVar(grad_var_name)) {
return false;
}
need_infer_shape = true;
auto var = block_desc->Var(grad_var_name);
new_vars.insert(var->Name());
auto it = param_name_map.find(grad_var_name);
......@@ -311,23 +322,21 @@ static void CreateGradVarInBlock(
grad_record.op_idx_ = static_cast<int>(op_index);
return false; /* not break */
});
if (need_infer_shape) {
ops[op_index]->InferVarType(block_desc);
for (auto& arg : ops[op_index]->OutputArgumentNames()) {
if (new_vars.find(arg) == new_vars.end()) {
continue;
}
auto pname = FwdName(arg);
auto* param = block_desc->FindVarRecursive(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
grad->SetDataType(DataType::FP32);
} else {
grad->SetDataType(param->GetDataType());
}
ops[op_index]->InferVarType(block_desc);
for (auto& arg : ops[op_index]->OutputArgumentNames()) {
if (new_vars.find(arg) == new_vars.end()) {
continue;
}
auto pname = FwdName(arg);
auto* param = block_desc->FindVarRecursive(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
grad->SetDataType(DataType::FP32);
} else {
grad->SetDataType(param->GetDataType());
}
ops[op_index]->InferShape(*block_desc);
}
ops[op_index]->InferShape(*block_desc);
}
}
......@@ -387,6 +396,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
VLOG(5) << "MakeBlockBackward";
BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDescBind*> op_descs = cur_block->AllOps();
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
......@@ -394,9 +404,10 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
std::vector<std::unique_ptr<OpDescBind>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
VLOG(5) << "Making backward " << (*it)->Type() << " op";
std::vector<std::unique_ptr<OpDescBind>> op_grads;
if ((*it)->Type() == "recurrent") {
if ((*it)->Type() == "recurrent" || (*it)->Type() == "while") {
int step_block_idx = (*it)->GetBlockAttr("step_block");
BlockDescBind* backward_block = CreateStepBlock(
program_desc, no_grad_vars, grad_to_var, step_block_idx);
......@@ -410,6 +421,15 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
}
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
sout << "Made ";
for (auto& op_grad : op_grads) {
sout << op_grad->Type() << " ";
}
VLOG(10) << sout.str();
}
for (const auto& desc : op_grads) {
for (const std::string& out_name : desc->OutputArgumentNames()) {
if (out_name.find("@GRAD") == std::string::npos) {
......@@ -425,6 +445,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs),
[](std::unique_ptr<OpDescBind>& ptr) { return std::move(ptr); });
}
VLOG(5) << "Appending Sums";
// Check whether some variables are written more than once
std::list<std::pair<size_t, std::unique_ptr<OpDescBind>>> pending_sum_ops;
for (const auto& dup : dup_out_ops) {
......@@ -432,16 +454,22 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
const std::vector<size_t> dup_op = dup.second;
if (out_name != kEmptyVarName && dup_op.size() > 1) {
std::vector<std::string> sum_op_inputs;
std::string next_g_name = out_name;
for (size_t i = 0; i < dup_op.size(); ++i) {
VLOG(10) << backward_descs[dup_op[i]]->Type() << " has " << out_name
<< " duplicated";
std::string new_name = out_name + "@RENAME@" + std::to_string(i);
backward_descs[dup_op[i]]->Rename(out_name, new_name);
backward_descs[dup_op[i]]->RenameOutput(out_name, new_name);
backward_descs[dup_op[i]]->RenameInput(out_name, next_g_name);
sum_op_inputs.emplace_back(new_name);
next_g_name = sum_op_inputs.back();
}
std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
"sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
}
}
pending_sum_ops.sort(
[](const std::pair<size_t, std::unique_ptr<OpDescBind>>& a,
const std::pair<size_t, std::unique_ptr<OpDescBind>>& b) {
......@@ -452,6 +480,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
std::move(p.second));
}
VLOG(5) << "MakeBlockBackward Finished";
return backward_descs;
}
......
paddle/framework/data_type.h
浏览文件 @ 94e86897
......@@ -29,6 +29,8 @@ inline DataType ToDataType(std::type_index type) {
return DataType::INT32;
} else if (typeid(int64_t).hash_code() == type.hash_code()) {
return DataType::INT64;
} else if (typeid(bool).hash_code() == type.hash_code()) {
return DataType::BOOL;
} else {
PADDLE_THROW("Not supported");
}
......
paddle/framework/ddim.cc
浏览文件 @ 94e86897
......@@ -60,8 +60,7 @@ void make_ddim(DDim& ddim, const int64_t* dims, int n) {
ddim = make_dim<9>(dims);
break;
default:
throw std::invalid_argument(
"Dynamic dimensions must have between [1, 9] dimensions.");
PADDLE_THROW("Dynamic dimensions must have between [1, 9] dimensions.");
}
}
......
paddle/framework/executor.cc
浏览文件 @ 94e86897
......@@ -120,6 +120,7 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
VLOG(10) << op->DebugString();
op->Run(*local_scope, *device);
}
if (create_local_scope) {
......
paddle/framework/op_desc.cc
浏览文件 @ 94e86897
......@@ -235,6 +235,23 @@ void OpDescBind::Rename(const std::string &old_name,
need_update_ = true;
}
void OpDescBind::RenameOutput(const std::string &old_name,
const std::string &new_name) {
for (auto &output : outputs_) {
std::replace(output.second.begin(), output.second.end(), old_name,
new_name);
}
need_update_ = true;
}
void OpDescBind::RenameInput(const std::string &old_name,
const std::string &new_name) {
for (auto &input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
need_update_ = true;
}
struct SetAttrDescVisitor : public boost::static_visitor<void> {
explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
mutable OpDesc::Attr *attr_;
......@@ -448,7 +465,12 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
auto var = block_.FindVarRecursive(name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
return framework::make_ddim(var->Shape());
try {
return framework::make_ddim(var->Shape());
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
}
}
void CompileTimeInferShapeContext::SetDim(const std::string &name,
......
paddle/framework/op_desc.h
浏览文件 @ 94e86897
......@@ -73,6 +73,10 @@ class OpDescBind {
void Rename(const std::string &old_name, const std::string &new_name);
void RenameOutput(const std::string &old_name, const std::string &new_name);
void RenameInput(const std::string &old_name, const std::string &new_name);
// Only be used in C++
const AttributeMap &GetAttrMap() const;
......
paddle/framework/operator.cc
浏览文件 @ 94e86897
......@@ -403,19 +403,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
void OperatorWithKernel::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
if (VLOG_IS_ON(1)) {
auto inputs = this->InputVars();
auto outputs = this->OutputVars(true);
std::ostringstream sout;
sout << "Run operator " << this->Type() << " From [";
std::ostream_iterator<std::string> out_it(sout, ",");
std::copy(inputs.begin(), inputs.end(), out_it);
sout << "] to [";
std::copy(outputs.begin(), outputs.end(), out_it);
sout << "]";
VLOG(1) << sout.str();
}
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
......
paddle/framework/scope.cc
浏览文件 @ 94e86897
......@@ -38,11 +38,12 @@ Scope& Scope::NewScope() const {
Variable* Scope::Var(const std::string& name) {
auto iter = vars_.find(name);
if (iter != vars_.end()) {
VLOG(3) << "Get existing variable " << name;
return iter->second;
}
Variable* v = new Variable();
vars_[name] = v;
VLOG(3) << "Create variable " << name << " on scope";
VLOG(3) << "Create variable " << name;
v->name_ = &(vars_.find(name)->first);
return v;
}
......
paddle/framework/shape_inference.h
浏览文件 @ 94e86897
......@@ -53,6 +53,10 @@ class InferShapeContext {
virtual bool IsRuntime() const = 0;
// Note: In while op, we need this to be public
void SetDims(const std::vector<std::string> &names,
const std::vector<framework::DDim> &dims);
protected:
virtual framework::DDim GetDim(const std::string &name) const = 0;
virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
......@@ -60,9 +64,6 @@ class InferShapeContext {
std::vector<framework::DDim> GetDims(
const std::vector<std::string> &names) const;
void SetDims(const std::vector<std::string> &names,
const std::vector<framework::DDim> &dims);
std::vector<VarDesc::VarType> GetVarTypes(
const std::vector<std::string> &names) const;
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ 94e86897
......@@ -22,7 +22,7 @@ namespace paddle {
bool MKLDNNLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "Please set WITH_MKL=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
......
paddle/math/Storage.cpp
浏览文件 @ 94e86897
......@@ -17,9 +17,13 @@ limitations under the License. */
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
#ifndef PADDLE_MOBILE_INFERENCE
DEFINE_int32(pool_limit_size,
536870912,
"maximum memory size managed by a memory pool, default is 512M");
#else
DEFINE_int32(pool_limit_size, 0, "default is 0");
#endif
namespace paddle {
......
paddle/operators/array_operator.h
浏览文件 @ 94e86897
......@@ -42,6 +42,7 @@ class ArrayOp : public framework::OperatorBase {
} else {
offset = static_cast<size_t>(*i_tensor.data<int64_t>());
}
VLOG(10) << " Offset = " << offset;
return offset;
}
};
......
paddle/operators/bilinear_tensor_product_op.h
浏览文件 @ 94e86897
......@@ -174,7 +174,7 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Caculate the gradient of Input(Bias).
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
auto d_bias_mat = EigenMatrix<T>::From(*d_bias);
auto d_bias_mat = framework::EigenVector<T>::Flatten(*d_bias);
d_bias_mat.device(place) = d_out_mat.sum(Eigen::DSizes<int, 1>(0));
}
}
......
paddle/operators/conv_transpose_op.cc
浏览文件 @ 94e86897
......@@ -30,11 +30,6 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
for (size_t i = 0; i < paddings.size(); ++i) {
PADDLE_ENFORCE_EQ(paddings[i], 0,
"No Padding allowed in conv transpose op.");
}
PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
"ConvTransposeOp intput should be 4-D or 5-D tensor.");
PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
......@@ -52,7 +47,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back((in_dims[i + 2] - 1) * strides[i] +
output_shape.push_back((in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] +
filter_dims[i + 2]);
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
......
paddle/operators/conv_transpose_op.h
浏览文件 @ 94e86897
......@@ -62,7 +62,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
Tensor* output = context.Output<Tensor>("Output");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
// Actually, no paddings and groups allowed in conv transpose.
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
......@@ -148,8 +147,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
} else if (filter_shape_vec.size() == 3) {
// col2vol: col_matrix -> dy
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
col2vol(context.device_context(), col, dilations, strides,
std::vector<int>{0, 0, 0}, &output_batch);
col2vol(context.device_context(), col, dilations, strides, paddings,
&output_batch);
}
}
}
......@@ -173,7 +172,6 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
if ((!input_grad) && (!filter_grad)) return;
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
// Actually, no paddings and groups allowed in conv transpose.
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
const int batch_size = static_cast<int>(input->dims()[0]);
......
paddle/operators/cos_sim_op.h
浏览文件 @ 94e86897
......@@ -132,7 +132,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
// compute dy
if (out_grad_y) {
out_grad_y->mutable_data<T>(context.GetPlace());
auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
auto dy = EigenVector<T>::Flatten(*out_grad_y);
auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({{0}}));
}
......
paddle/operators/detail/safe_ref.h 0 → 100644
浏览文件 @ 94e86897
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
namespace paddle {
namespace operators {
namespace detail {
/**
* Get Reference From Pointer with check. The error message is printf format,
* and passed by `args`
*/
template <typename T, typename... ARGS>
inline T &Ref(T *ptr, ARGS &&... args) {
PADDLE_ENFORCE(ptr != nullptr, args...);
return *ptr;
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ 94e86897
......@@ -101,4 +101,7 @@ REGISTER_OPERATOR(fill_constant_batch_size_like,
REGISTER_OP_CPU_KERNEL(
fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>);
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace,
int64_t>);
paddle/operators/fill_constant_batch_size_like_op.cu.cc
浏览文件 @ 94e86897
......@@ -19,4 +19,7 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>);
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace,
int64_t>);
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 94e86897
......@@ -54,5 +54,8 @@ namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
ops::FillZerosLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_zeros_like,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, double>,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, bool>);
paddle/operators/fill_zeros_like_op.cu.cc
浏览文件 @ 94e86897
......@@ -17,5 +17,8 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
fill_zeros_like,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, double>,
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, bool>);
paddle/operators/is_empty_op.cc 0 → 100644
浏览文件 @ 94e86897
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
constexpr char kInput[] = "X";
constexpr char kOutput[] = "Out";
class IsEmptyOp : public framework::OperatorBase {
public:
IsEmptyOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
// get input
auto *var = scope.FindVar(Input(kInput));
PADDLE_ENFORCE_NOT_NULL(var);
auto &tensor = var->Get<framework::LoDTensor>();
// get output
auto *out = scope.FindVar(Output(kOutput));
PADDLE_ENFORCE_NOT_NULL(out);
auto *out_tensor = out->GetMutable<framework::LoDTensor>();
out_tensor->Resize({1});
out_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
framework::product(tensor.dims()) == 0;
}
};
class IsEmptyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
IsEmptyOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(kInput, "(Tensor) Tensor which is to be checked.");
AddOutput(kOutput, "(Tensor) a boolean Tensor that indicate empty or not.");
AddComment(R"DOC(
IsEmpty Operator which checks whether a tensor is empty.
It will just return product(tensor.ddims()) > 0;
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(is_empty, paddle::operators::IsEmptyOp,
paddle::operators::IsEmptyOpProtoMaker);
paddle/operators/math/CMakeLists.txt
浏览文件 @ 94e86897
add_subdirectory(detail)
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context framework_proto)
nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function tensor)
nv_library(selected_rows_functor SRCS selected_rows_functor.cc selected_rows_functor.cu DEPS selected_rows math_function)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor)
......@@ -15,7 +15,7 @@ if(WITH_GPU)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
cc_library(softmax SRCS softmax.cc DEPS device_context)
cc_library(cross_entropy SRCS cross_entropy.cc DEPS device_context)
......
paddle/operators/math/im2col.cu
浏览文件 @ 94e86897
......@@ -119,8 +119,8 @@ __global__ void col2im(int n, const T* data_col, int im_height, int im_width,
if (index < n) {
T val = 0;
int w = index % im_width;
int h = (index / im_width) % im_height;
int w = index % im_width + padding_width;
int h = (index / im_width) % im_height + padding_height;
int c = index / (im_width * im_height);
// compute the start and end of the output
......
paddle/operators/math/math_function.cc
浏览文件 @ 94e86897
......@@ -250,6 +250,8 @@ void axpy<platform::CPUPlace, double>(const platform::DeviceContext& context,
template struct SetConstant<platform::CPUPlace, float>;
template struct SetConstant<platform::CPUPlace, double>;
template struct SetConstant<platform::CPUPlace, int>;
template struct SetConstant<platform::CPUPlace, int64_t>;
template struct SetConstant<platform::CPUPlace, bool>;
#define DEFINE_CPU_TRANS(RANK) \
template struct Transpose<platform::CPUPlace, float, RANK>; \
......
paddle/operators/math/math_function.cu
浏览文件 @ 94e86897
......@@ -256,6 +256,8 @@ void axpy<platform::GPUPlace, double>(const platform::DeviceContext& context,
template struct SetConstant<platform::GPUPlace, float>;
template struct SetConstant<platform::GPUPlace, double>;
template struct SetConstant<platform::GPUPlace, int>;
template struct SetConstant<platform::GPUPlace, int64_t>;
template struct SetConstant<platform::GPUPlace, bool>;
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::GPUPlace, float, RANK>; \
......
paddle/operators/sum_op.cc
浏览文件 @ 94e86897
......@@ -12,6 +12,7 @@ limitations under the License. */
#include "paddle/operators/sum_op.h"
#include <vector>
#include "paddle/framework/var_type_inference.h"
#include "paddle/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
......@@ -59,13 +60,16 @@ class SumOp : public framework::OperatorWithKernel {
x_vars[0]->Get<framework::SelectedRows>().value().type()),
ctx.device_context());
} else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
auto& array = x_vars[0]->Get<framework::LoDTensorArray>();
for (auto& each : array) {
if (each.numel() != 0) {
return framework::OpKernelType(framework::ToDataType(each.type()),
ctx.device_context());
for (auto& x_var : x_vars) {
auto& array = x_var->Get<framework::LoDTensorArray>();
for (auto& each : array) {
if (each.numel() != 0) {
return framework::OpKernelType(framework::ToDataType(each.type()),
ctx.device_context());
}
}
}
PADDLE_THROW("Cannot find the input data type by all input data");
}
PADDLE_THROW("Unexpected branch. Input type is %s",
x_vars[0]->Type().name());
......@@ -96,6 +100,11 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
auto& inputs = op_desc.Input("X");
auto var_type = framework::VarDesc::SELECTED_ROWS;
for (auto& name : op_desc.Input("X")) {
VLOG(10) << name << " "
<< block->FindRecursiveOrCreateVar(name)->GetType();
}
bool any_input_is_lod_tensor = std::any_of(
inputs.begin(), inputs.end(), [block](const std::string& name) {
return block->FindRecursiveOrCreateVar(name)->GetType() ==
......@@ -103,7 +112,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
});
auto is_tensor_array = [block](const std::string& name) {
return block->FindRecursiveOrCreateVar(name)->GetType() ==
return detail::Ref(block->FindRecursiveOrCreateVar(name)).GetType() ==
framework::VarDesc::LOD_TENSOR_ARRAY;
};
......@@ -113,14 +122,26 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
std::all_of(inputs.begin(), inputs.end(), is_tensor_array);
if (any_input_is_tensor_array) {
PADDLE_ENFORCE(all_inputs_are_tensor_array);
if (!all_inputs_are_tensor_array) {
std::ostringstream os;
for (auto& each : inputs) {
os << " " << each << " type is "
<< detail::Ref(block->FindRecursiveOrCreateVar(each)).GetType()
<< "\n";
}
PADDLE_ENFORCE(all_inputs_are_tensor_array,
"Not all inputs are tensor array:\n%s", os.str());
}
var_type = framework::VarDesc::LOD_TENSOR_ARRAY;
} else if (any_input_is_lod_tensor) {
var_type = framework::VarDesc::LOD_TENSOR;
}
auto out_var_name = op_desc.Output("Out").front();
block->FindRecursiveOrCreateVar(out_var_name)->SetType(var_type);
auto& out_var = detail::Ref(block->FindRecursiveOrCreateVar(out_var_name));
out_var.SetType(var_type);
auto& in_var = detail::Ref(block->FindVarRecursive(inputs.front()));
out_var.SetDataType(in_var.GetDataType());
}
};
......
paddle/operators/tensor_array_read_write_op.cc
浏览文件 @ 94e86897
......@@ -12,7 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/array_operator.h"
#include "paddle/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
......@@ -33,6 +33,8 @@ class WriteToArrayOp : public ArrayOp {
auto *out =
scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensorArray>();
if (offset >= out->size()) {
VLOG(10) << "Resize " << Output("Out") << " from " << out->size()
<< " to " << offset + 1;
out->resize(offset + 1);
}
auto *out_tensor = &out->at(offset);
......@@ -85,11 +87,15 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
for (auto &out_var : op_desc.OutputArgumentNames()) {
VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
block->FindRecursiveOrCreateVar(out_var)->SetType(
framework::VarDesc::LOD_TENSOR_ARRAY);
}
auto x_name = op_desc.Input("X")[0];
auto out_name = op_desc.Output("Out")[0];
VLOG(10) << "Set Variable " << out_name << " as LOD_TENSOR_ARRAY";
auto &out = detail::Ref(block->FindRecursiveOrCreateVar(out_name),
"Cannot found %s", out_name);
out.SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
auto &x =
detail::Ref(block->FindVarRecursive(x_name), "Cannot found %s", x_name);
out.SetDataType(x.GetDataType());
}
};
......@@ -107,11 +113,11 @@ class ReadFromArrayOp : public ArrayOp {
auto &x_array = x->Get<framework::LoDTensorArray>();
auto *out = scope.FindVar(Output("Out"));
PADDLE_ENFORCE(out != nullptr, "Out must be set");
auto *out_tesnor = out->GetMutable<framework::LoDTensor>();
auto *out_tensor = out->GetMutable<framework::LoDTensor>();
size_t offset = GetOffset(scope, dev_ctx);
PADDLE_ENFORCE_LT(offset, x_array.size());
out_tesnor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
out_tesnor->set_lod(x_array[offset].lod());
out_tensor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
out_tensor->set_lod(x_array[offset].lod());
}
};
......
paddle/operators/while_op.cc
浏览文件 @ 94e86897
......@@ -14,8 +14,10 @@
#include <vector>
#include "paddle/framework/executor.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
......@@ -26,8 +28,9 @@ using LoDTensor = framework::LoDTensor;
constexpr char kStepBlock[] = "step_block";
constexpr char kCondition[] = "Condition";
constexpr char kStepScopes[] = "StepScopes";
constexpr char kParamGrads[] = "X@Grad";
constexpr char kParameters[] = "X";
constexpr char kParamGrads[] = "X@GRAD";
constexpr char kOutputs[] = "Out";
class WhileOp : public framework::OperatorBase {
public:
......@@ -71,9 +74,9 @@ class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
kCondition,
"(Bool) An scalar. When it's False, the While Op will be terminated.")
.AsDuplicable();
AddOutput("Out",
AddOutput(kOutputs,
"A set of variables, which will be assigned with values "
"generated by perators inside the block of While Op.")
"generated by the operators inside the block of While Op.")
.AsDuplicable();
AddOutput(kStepScopes,
"(StepScopeVar) A vector of local scope, which size equals the "
......@@ -104,17 +107,64 @@ class WhileGradOp : public framework::OperatorBase {
auto *step_scopes =
scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();
auto outside_og_names = Inputs(framework::GradVarName(kOutputs));
auto inside_og_names =
Attr<std::vector<std::string>>("original_output_grad");
PADDLE_ENFORCE_EQ(outside_og_names.size(), inside_og_names.size());
for (auto cur_scope_iter = step_scopes->rbegin();
cur_scope_iter != step_scopes->rend(); ++cur_scope_iter) {
VLOG(3) << "Start backward at time_step "
<< cur_scope_iter - step_scopes->rbegin();
framework::Scope &cur_scope = **cur_scope_iter;
// Link OG from outside to inside
for (size_t i = 0; i < outside_og_names.size(); ++i) {
auto outside_og_name = outside_og_names[i];
auto inside_og_name = inside_og_names[i];
VLOG(10) << "Linking outside " << outside_og_name << " --> inside "
<< inside_og_name;
auto &og_outside = detail::Ref(scope.FindVar(outside_og_name));
auto &og_inside = detail::Ref(cur_scope.Var(inside_og_name));
if (og_outside.Type().hash_code() ==
typeid(framework::LoDTensor).hash_code()) {
auto &outside_tensor = og_outside.Get<framework::LoDTensor>();
auto &inside_tensor =
detail::Ref(og_inside.GetMutable<framework::LoDTensor>());
inside_tensor.set_lod(outside_tensor.lod());
inside_tensor.ShareDataWith(outside_tensor);
} else if (og_outside.Type().hash_code() ==
typeid(framework::LoDTensorArray).hash_code()) {
auto &outside_array = og_outside.Get<framework::LoDTensorArray>();
auto &inside_array =
detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());
VLOG(10) << outside_og_name << " size = " << outside_array.size();
inside_array.resize(outside_array.size());
for (size_t j = 0; j < inside_array.size(); ++j) {
VLOG(10) << j << " " << outside_array[j].numel();
if (outside_array[j].numel() != 0) {
inside_array[j].set_lod(outside_array[j].lod());
inside_array[j].ShareDataWith(outside_array[j]);
} else {
PADDLE_ENFORCE_EQ(inside_array[j].numel(), 0);
}
}
}
}
executor.Run(*program, *cur_scope_iter, block->ID(), false);
auto &pg_names = Outputs(kParamGrads);
auto &p_names = Inputs(kParameters);
PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
for (size_t prog_id = 0; prog_id < pg_names.size(); ++prog_id) {
auto inside_grad_name = framework::GradVarName(p_names[prog_id]);
for (size_t param_id = 0; param_id < pg_names.size(); ++param_id) {
if (pg_names[param_id] == framework::kEmptyVarName) {
continue; // iterator doesn't have gradient
}
auto inside_grad_name = framework::GradVarName(p_names[param_id]);
// // TODO(tonyyang-savil: Not sure we need the following
// // TODO(tonyyang-svail): Not sure we need the following
// // If does not compute gradient of that variable inside rnn,
// just
// // continue
......@@ -126,7 +176,7 @@ class WhileGradOp : public framework::OperatorBase {
// zero gradient variable in step 0
if (cur_scope_iter == step_scopes->rbegin()) {
auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
PADDLE_ENFORCE_NOT_NULL(var);
PADDLE_ENFORCE_NOT_NULL(var, "Can not find var %s", inside_grad_name);
if (var->IsType<LoDTensor>()) {
auto &inside_tensor = var->Get<framework::LoDTensor>();
framework::AttributeMap attrs;
......@@ -135,27 +185,18 @@ class WhileGradOp : public framework::OperatorBase {
attrs["value"] = 0.0f;
auto zero_op = framework::OpRegistry::CreateOp(
"fill_constant", {}, {{"Out", {pg_names[prog_id]}}}, attrs);
"fill_constant", {}, {{"Out", {pg_names[param_id]}}}, attrs);
zero_op->Run(scope, dev_ctx);
}
}
// sum gradient
auto *outside_var = scope.FindVar(pg_names[prog_id]);
PADDLE_ENFORCE_NOT_NULL(outside_var);
auto &outside_tensor = *outside_var->GetMutable<framework::LoDTensor>();
std::string result_var_name;
auto *local_result_var = (*cur_scope_iter)->Var(&result_var_name);
auto &local_result_tensor =
*local_result_var->GetMutable<framework::LoDTensor>();
local_result_tensor.ShareDataWith(outside_tensor);
auto new_inside_name = cur_scope.Rename(inside_grad_name);
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {result_var_name, inside_grad_name}}},
{{"Out", {result_var_name}}}, {});
sum_op->Run(**cur_scope_iter, dev_ctx);
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}}, {});
sum_op->Run(cur_scope, dev_ctx);
cur_scope.Rename(new_inside_name, inside_grad_name);
}
}
}
......@@ -169,29 +210,110 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
virtual std::unique_ptr<framework::OpDescBind> Apply() const {
auto *grad = new framework::OpDescBind();
grad->SetType("while_grad");
for (auto &input_param : this->InputNames()) {
grad->SetInput(input_param, this->Input(input_param));
grad->SetOutput(framework::GradVarName(input_param),
this->InputGrad(input_param));
grad->SetInput(kParameters, Input(kParameters));
grad->SetOutput(
framework::GradVarName(kParameters),
InputGrad(kParameters, /*do not drop empty gradient*/ false));
grad->SetInput(kOutputs, Output(kOutputs));
// OG should be re-calculated by step blocks, since many outputs of while op
// do not need to calculate gradients.
std::unordered_set<std::string> block_ins;
{
for (auto &p : Input(kParameters)) {
block_ins.insert(p);
}
for (auto &o : Output(kOutputs)) {
block_ins.insert(o);
}
}
std::unordered_set<std::string> extra_inputs;
for (size_t i = 0; i < grad_block_[0]->OpSize(); ++i) {
for (auto &input_name : grad_block_[0]->Op(i)->InputArgumentNames()) {
if (block_ins.find(input_name) != block_ins.end()) {
continue;
}
extra_inputs.insert(input_name);
}
for (auto &output_param : this->OutputNames()) {
grad->SetInput(output_param, this->Output(output_param));
if (output_param != kStepScopes) {
grad->SetInput(framework::GradVarName(output_param),
this->OutputGrad(output_param));
for (auto &output_name : grad_block_[0]->Op(i)->OutputArgumentNames()) {
block_ins.insert(output_name);
}
}
std::vector<std::string> extra_inputs_list;
extra_inputs_list.resize(extra_inputs.size());
std::copy(extra_inputs.begin(), extra_inputs.end(),
extra_inputs_list.begin());
grad->SetInput(framework::GradVarName(kOutputs), extra_inputs_list);
grad->SetInput(kStepScopes, Output(kStepScopes));
grad->SetAttrMap(this->Attrs());
grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
// record the original output gradient names, since the gradient name of
// while operator could be renamed.
grad->SetAttr("original_output_grad", extra_inputs_list);
return std::unique_ptr<framework::OpDescBind>(grad);
}
};
class WhileGradOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
auto p_names = op_desc.Input(kParameters);
auto pg_names = op_desc.Output(framework::GradVarName(kParameters));
for (size_t i = 0; i < p_names.size(); ++i) {
auto &p_var = detail::Ref(block->FindVarRecursive(p_names[i]));
auto *g_var = block->FindVarRecursive(pg_names[i]);
if (g_var != nullptr) { // Gradient could be @EMPTY@
VLOG(5) << "Setting " << pg_names[i] << " following " << p_names[i]
<< " type: " << p_var.GetType();
g_var->SetType(p_var.GetType());
g_var->SetDataType(p_var.GetDataType());
}
}
}
};
class WhileGradOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
ctx->HasInputs(kParameters);
ctx->HasOutputs(framework::GradVarName(kParameters));
ctx->HasInputs(kOutputs);
ctx->HasInputs(framework::GradVarName(kOutputs));
auto p_names = ctx->Inputs(kParameters);
auto pg_names = ctx->Outputs(kParamGrads);
auto dims = ctx->GetInputsDim(kParameters);
auto var_types = ctx->GetInputsVarType(kParameters);
std::vector<std::string> names_to_set;
std::vector<framework::DDim> dims_to_set;
for (size_t i = 0; i < p_names.size(); ++i) {
if (pg_names[i] == framework::kEmptyVarName) {
continue;
}
if (var_types[i] == framework::VarDesc::LOD_TENSOR) {
names_to_set.push_back(pg_names[i]);
dims_to_set.push_back(dims[i]);
} else if (var_types[i] == framework::VarDesc::LOD_TENSOR_ARRAY) {
// not sure how to set the dim of LOD_TENSOR_ARRAY
names_to_set.push_back(pg_names[i]);
dims_to_set.push_back(dims[i]);
}
}
ctx->SetDims(names_to_set, dims_to_set);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(while, paddle::operators::WhileOp,
paddle::operators::WhileOpMaker,
paddle::operators::WhileGradOpDescMaker);
REGISTER_OPERATOR(while_grad, paddle::operators::WhileGradOp,
paddle::operators::WhileGradOpShapeInference,
paddle::operators::WhileGradOpVarTypeInference);
paddle/scripts/docker/README.md
浏览文件 @ 94e86897
......@@ -57,8 +57,7 @@ Users can specify the following Docker build arguments with either "ON" or "OFF"
| `WITH_GPU` | OFF | Generates NVIDIA CUDA GPU code and relies on CUDA libraries. |
| `WITH_AVX` | OFF | Set to "ON" to enable AVX support. |
| `WITH_TESTING` | ON | Build unit tests binaries. |
| `WITH_MKLDNN` | ON | Build with [Intel® MKL DNN](https://github.com/01org/mkl-dnn) support. |
| `WITH_MKLML` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) support. |
| `WITH_MKL` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) and [Intel® MKL-DNN](https://github.com/01org/mkl-dnn) support. |
| `WITH_GOLANG` | ON | Build fault-tolerant parameter server written in go. |
| `WITH_SWIG_PY` | ON | Build with SWIG python API support. |
| `WITH_C_API` | OFF | Build capi libraries for inference. |
......
paddle/scripts/docker/build.sh
浏览文件 @ 94e86897
......@@ -34,9 +34,7 @@ function cmake_gen() {
${PYTHON_FLAGS}
-DWITH_DOC=OFF
-DWITH_GPU=${WITH_GPU:-OFF}
-DCUDA_ARCH_NAME=All
-DWITH_MKLDNN=${WITH_MKLDNN:-ON}
-DWITH_MKLML=${WITH_MKLML:-ON}
-DWITH_MKL=${WITH_MKL:-ON}
-DWITH_AVX=${WITH_AVX:-OFF}
-DWITH_GOLANG=${WITH_GOLANG:-ON}
-DWITH_SWIG_PY=ON
......@@ -57,9 +55,7 @@ EOF
${PYTHON_FLAGS} \
-DWITH_DOC=OFF \
-DWITH_GPU=${WITH_GPU:-OFF} \
-DCUDA_ARCH_NAME=All \
-DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
-DWITH_MKLML=${WITH_MKLML:-ON} \
-DWITH_MKL=${WITH_MKL:-ON} \
-DWITH_AVX=${WITH_AVX:-OFF} \
-DWITH_GOLANG=${WITH_GOLANG:-ON} \
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
......
paddle/scripts/submit_local.sh.in
浏览文件 @ 94e86897
......@@ -18,8 +18,8 @@ function version(){
echo "PaddlePaddle @PADDLE_VERSION@, compiled with"
echo " with_avx: @WITH_AVX@"
echo " with_gpu: @WITH_GPU@"
echo " with_mkl: @WITH_MKL@"
echo " with_mkldnn: @WITH_MKLDNN@"
echo " with_mklml: @WITH_MKLML@"
echo " with_double: @WITH_DOUBLE@"
echo " with_python: @WITH_PYTHON@"
echo " with_rdma: @WITH_RDMA@"
......@@ -45,8 +45,8 @@ function ver2num() {
function cpu_config() {
# auto set KMP_AFFINITY and OMP_DYNAMIC from Hyper Threading Status
# only when MKLDNN or MKLML enabled
if [ "@WITH_MKLDNN@" == "OFF" ] && [ "@WITH_MKLML@" == "OFF"]; then
# only when MKL enabled
if [ "@WITH_MKL@" == "OFF" ]; then
return 0
fi
ht=`lscpu |grep "per core"|awk -F':' '{print $2}'|xargs`
......@@ -70,8 +70,8 @@ function cpu_config() {
function threads_config() {
# auto set OMP_NUM_THREADS and MKL_NUM_THREADS
# according to trainer_count and total processors
# only when MKLDNN or MKLML enabled
if [ "@WITH_MKLDNN@" == "OFF" ] && [ "@WITH_MKLML@" == "OFF"]; then
# only when MKL enabled
if [ "@WITH_MKL@" == "OFF" ]; then
return 0
fi
processors=`grep "processor" /proc/cpuinfo|sort -u|wc -l`
......
paddle/scripts/travis/build_doc.sh
浏览文件 @ 94e86897
......@@ -6,7 +6,7 @@ mkdir -p $TRAVIS_BUILD_DIR/build
cd $TRAVIS_BUILD_DIR/build
# Compile Documentation only.
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
make -j `nproc` gen_proto_py
make -j `nproc` paddle_docs paddle_docs_cn
......
paddle/trainer/Trainer.cpp
浏览文件 @ 94e86897
......@@ -137,6 +137,10 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
}
}
if (FLAGS_use_mkldnn) {
CHECK_EQ(FLAGS_trainer_count, 1UL) << "MKLDNN only need 1 trainer";
}
if (testing) {
LOG(INFO) << "trainer: in testing mode";
if (config_->getOptConfig().use_sparse_remote_updater() ||
......
python/paddle/v2/fluid/framework.py
浏览文件 @ 94e86897
......@@ -12,9 +12,9 @@ def unique_name(prefix):
return "_".join([prefix, str(uid)])
def _debug_string_(proto):
def _debug_string_(proto, throw_on_error=True):
error_fields = list()
if not proto.IsInitialized(error_fields):
if not proto.IsInitialized(error_fields) and throw_on_error:
raise ValueError("{0} are not initialized\nThe message is {1}".format(
error_fields, proto))
return proto.__str__()
......@@ -101,9 +101,12 @@ class Variable(object):
self.stop_gradient = stop_gradient
def __str__(self):
return self.to_string(True)
def to_string(self, throw_on_error):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.VarDesc.FromString(str(protostr))
return _debug_string_(proto)
return _debug_string_(proto, throw_on_error)
__repr__ = __str__
......@@ -291,10 +294,13 @@ class Operator(object):
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
def __str__(self):
def to_string(self, throw_on_error):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.OpDesc.FromString(str(protostr))
return _debug_string_(proto)
return _debug_string_(proto, throw_on_error)
def __str__(self):
return self.to_string(True)
__repr__ = __str__
......@@ -349,9 +355,12 @@ class Block(object):
self.program = program
def __str__(self):
return self.to_string(True)
def to_string(self, throw_on_error):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.BlockDesc.FromString(str(protostr))
return _debug_string_(proto)
return _debug_string_(proto, throw_on_error)
__repr__ = __str__
......@@ -454,9 +463,12 @@ class Program(object):
self.current_block_idx = 0
def __str__(self):
return self.to_string(True)
def to_string(self, throw_on_error):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.ProgramDesc.FromString(str(protostr))
return _debug_string_(proto)
return _debug_string_(proto, throw_on_error)
def clone(self):
p = Program()
......@@ -512,7 +524,14 @@ class Program(object):
assert isinstance(target, Variable)
if no_grad_set is None:
no_grad_set = set()
param_to_grad_info = self.desc.append_backward(target.desc, no_grad_set)
try:
param_to_grad_info = self.desc.append_backward(target.desc,
no_grad_set)
except Exception as e:
raise core.EnforceNotMet(
str(e) + "\nCurrent protobuf is\n{0}".format(
self.to_string(False)))
self.sync_with_cpp()
return param_to_grad_info
......
python/paddle/v2/fluid/net_drawer.py
浏览文件 @ 94e86897
......@@ -66,10 +66,13 @@ def parse_graph(program, graph, var_dict, **kwargs):
if not var_dict.has_key(var):
var_dict[var] = "Feed"
temp_id = 0
proto = framework_pb2.ProgramDesc.FromString(
program.desc.serialize_to_string())
for block in proto.blocks:
for op in block.ops:
op.type = op.type + "_" + str(temp_id)
temp_id += 1
graph.node(**draw_node(op))
for o in op.outputs:
for arg in o.arguments:
......@@ -78,6 +81,7 @@ def parse_graph(program, graph, var_dict, **kwargs):
for arg in e.arguments:
if var_dict.has_key(arg):
graph.edge(**draw_edge(var_dict, op, e, arg))
break # only plot the first block
def draw_graph(startup_program, main_program, **kwargs):
......
python/paddle/v2/fluid/tests/book/test_fit_a_line.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
from paddle.v2.fluid.io import save_persistables, load_persistables
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.io import save_persistables, load_persistables
from paddle.v2.fluid.optimizer import SGDOptimizer
import numpy as np
x = layers.data(
name='x',
shape=[13],
data_type='float32')
x = layers.data(name='x', shape=[13], data_type='float32')
y_predict = layers.fc(input=x,
size=1,
act=None)
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(
name='y',
shape=[1],
data_type='float32')
y = layers.data(name='y', shape=[1], data_type='float32')
cost = layers.square_error_cost(
input=y_predict,
label=y)
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer = SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
......
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.optimizer as optimizer
from paddle.v2.fluid.executor import Executor
import paddle.v2.fluid.framework as framework
from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu'):
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
tmp = layers.conv2d(
input=input,
filter_size=filter_size,
......@@ -24,9 +19,7 @@ def resnet_cifar10(input, depth=32):
padding=padding,
act=None,
bias_attr=False)
return layers.batch_norm(
input=tmp,
act=act)
return layers.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride, program, init_program):
if ch_in != ch_out:
......@@ -35,28 +28,11 @@ def resnet_cifar10(input, depth=32):
else:
return input
def basicblock(input,
ch_in,
ch_out,
stride):
tmp = conv_bn_layer(
input,
ch_out,
3,
stride,
1)
tmp = conv_bn_layer(
tmp,
ch_out,
3,
1,
1,
act=None)
def basicblock(input, ch_in, ch_out, stride):
tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
short = shortcut(input, ch_in, ch_out, stride)
return layers.elementwise_add(
x=tmp,
y=short,
act='relu')
return layers.elementwise_add(x=tmp, y=short, act='relu')
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
tmp = block_func(input, ch_in, ch_out, stride)
......@@ -67,45 +43,17 @@ def resnet_cifar10(input, depth=32):
assert (depth - 2) % 6 == 0
n = (depth - 2) / 6
conv1 = conv_bn_layer(
input=input,
ch_out=16,
filter_size=3,
stride=1,
padding=1)
res1 = layer_warp(
basicblock,
conv1,
16,
16,
n,
1)
res2 = layer_warp(
basicblock,
res1,
16,
32,
n,
2)
res3 = layer_warp(
basicblock,
res2,
32,
64,
n,
2)
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
pool = layers.pool2d(
input=res3,
pool_size=8,
pool_type='avg',
pool_stride=1)
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
return pool
def vgg16_bn_drop(input):
def conv_block(input,
num_filter,
groups,
dropouts):
def conv_block(input, num_filter, groups, dropouts):
return nets.img_conv_group(
input=input,
pool_size=2,
......@@ -123,22 +71,14 @@ def vgg16_bn_drop(input):
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = layers.dropout(
x=conv5,
dropout_prob=0.5)
drop = layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = layers.fc(input=drop,
size=512,
act=None,
param_attr={"initializer": XavierInitializer()})
reshape1 = layers.reshape(
x=fc1,
shape=list(fc1.shape + (1, 1)))
bn = layers.batch_norm(
input=reshape1,
act='relu')
drop2 = layers.dropout(
x=bn,
dropout_prob=0.5)
reshape1 = layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
bn = layers.batch_norm(input=reshape1, act='relu')
drop2 = layers.dropout(x=bn, dropout_prob=0.5)
fc2 = layers.fc(input=drop2,
size=512,
act=None,
......@@ -165,8 +105,8 @@ cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
accuracy = layers.accuracy(input=predict, label=label)
# optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
optimizer = optimizer.AdamOptimizer(learning_rate=0.001)
# optimizer = SGDOptimizer(learning_rate=0.001)
optimizer = AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)
BATCH_SIZE = 128
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import numpy as np
images = layers.data(
name='pixel',
shape=[1, 28, 28],
data_type='float32')
label = layers.data(
name='label',
shape=[1],
data_type='int64')
images = layers.data(name='pixel', shape=[1, 28, 28], data_type='float32')
label = layers.data(name='label', shape=[1], data_type='int64')
conv_pool_1 = nets.simple_img_conv_pool(
input=images,
filter_size=5,
......@@ -32,17 +25,13 @@ conv_pool_2 = nets.simple_img_conv_pool(
pool_stride=2,
act="relu")
predict = layers.fc(input=conv_pool_2,
size=10,
act="softmax")
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
optimizer = optimizer.AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999)
optimizer = AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999)
opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(
input=predict,
label=label)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
BATCH_SIZE = 50
PASS_NUM = 3
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.regularizer import L2DecayRegularizer
from paddle.v2.fluid.initializer import UniformInitializer
import numpy as np
from paddle.v2.fluid.optimizer import MomentumOptimizer
from paddle.v2.fluid.regularizer import L2DecayRegularizer
BATCH_SIZE = 128
image = layers.data(
name='x',
shape=[784],
data_type='float32')
image = layers.data(name='x', shape=[784], data_type='float32')
param_attr = {
'name': None,
......@@ -22,32 +18,21 @@ param_attr = {
'regularization': L2DecayRegularizer(0.0005 * BATCH_SIZE)
}
hidden1 = layers.fc(input=image,
size=128,
act='relu',
param_attr=param_attr)
hidden2 = layers.fc(input=hidden1,
size=64,
act='relu',
param_attr=param_attr)
hidden1 = layers.fc(input=image, size=128, act='relu', param_attr=param_attr)
hidden2 = layers.fc(input=hidden1, size=64, act='relu', param_attr=param_attr)
predict = layers.fc(input=hidden2,
size=10,
act='softmax',
param_attr=param_attr)
label = layers.data(
name='y',
shape=[1],
data_type='int64')
label = layers.data(name='y', shape=[1], data_type='int64')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
accuracy = layers.accuracy(
input=predict,
label=label)
accuracy = layers.accuracy(input=predict, label=label)
optimizer = optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
opts = optimizer.minimize(avg_cost)
train_reader = paddle.batch(
......
python/paddle/v2/fluid/tests/book/test_recommender_system.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
import numpy as np
from paddle.v2.fluid.optimizer import SGDOptimizer
IS_SPARSE = True
USE_GPU = False
......@@ -19,10 +18,7 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(
name='user_id',
shape=[1],
data_type='int64')
uid = layers.data(name='user_id', shape=[1], data_type='int64')
usr_emb = layers.embedding(
input=uid,
......@@ -31,15 +27,11 @@ def get_usr_combined_features():
param_attr={'name': 'user_table'},
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb,
size=32)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(
name='gender_id',
shape=[1],
data_type='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], data_type='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
......@@ -47,14 +39,10 @@ def get_usr_combined_features():
param_attr={'name': 'gender_table'},
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb,
size=16)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(
name='age_id',
shape=[1],
data_type="int64")
usr_age_id = layers.data(name='age_id', shape=[1], data_type="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
......@@ -62,14 +50,10 @@ def get_usr_combined_features():
is_sparse=IS_SPARSE,
param_attr={'name': 'age_table'})
usr_age_fc = layers.fc(input=usr_age_emb,
size=16)
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(
name='job_id',
shape=[1],
data_type="int64")
usr_job_id = layers.data(name='job_id', shape=[1], data_type="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
......@@ -77,16 +61,12 @@ def get_usr_combined_features():
param_attr={'name': 'job_table'},
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb,
size=16)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc],
axis=1)
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed,
size=200,
act="tanh")
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return usr_combined_features
......@@ -95,10 +75,7 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(
name='movie_id',
shape=[1],
data_type='int64')
mov_id = layers.data(name='movie_id', shape=[1], data_type='int64')
mov_emb = layers.embedding(
input=mov_id,
......@@ -107,36 +84,24 @@ def get_mov_combined_features():
param_attr={'name': 'movie_table'},
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb,
size=32)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(
name='category_id',
shape=[1],
data_type='int64')
category_id = layers.data(name='category_id', shape=[1], data_type='int64')
mov_categories_emb = layers.embedding(
input=category_id,
size=[CATEGORY_DICT_SIZE, 32],
is_sparse=IS_SPARSE)
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb,
pool_type="sum")
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(
name='movie_title',
shape=[1],
data_type='int64')
mov_title_id = layers.data(name='movie_title', shape=[1], data_type='int64')
mov_title_emb = layers.embedding(
input=mov_title_id,
size=[MOV_TITLE_DICT_SIZE, 32],
is_sparse=IS_SPARSE)
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
......@@ -146,13 +111,10 @@ def get_mov_combined_features():
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv],
axis=1)
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
# FIXME(dzh) : need tanh operator
mov_combined_features = layers.fc(input=concat_embed,
size=200,
act="tanh")
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
......@@ -162,18 +124,11 @@ def model():
mov_combined_features = get_mov_combined_features()
# need cos sim
inference = layers.cos_sim(
X=usr_combined_features,
Y=mov_combined_features)
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
label = layers.data(
name='score',
shape=[1],
data_type='float32')
label = layers.data(name='score', shape=[1], data_type='float32')
square_cost = layers.square_error_cost(
input=inference,
label=label)
square_cost = layers.square_error_cost(input=inference, label=label)
avg_cost = layers.mean(x=square_cost)
......@@ -182,7 +137,7 @@ def model():
def main():
cost = model()
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.2)
sgd_optimizer = SGDOptimizer(learning_rate=0.2)
opts = sgd_optimizer.minimize(cost)
if USE_GPU:
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
import numpy as np
from paddle.v2.fluid.optimizer import AdamOptimizer
def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
......@@ -31,7 +30,7 @@ def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
act="softmax")
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
return avg_cost, acc
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
import numpy as np
from paddle.v2.fluid.optimizer import AdamOptimizer
def stacked_lstm_net(input_dim,
......@@ -41,7 +39,7 @@ def stacked_lstm_net(input_dim,
act='softmax')
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
return avg_cost, acc
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
import numpy as np
from paddle.v2.fluid.optimizer import AdamOptimizer
def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
......@@ -33,7 +32,7 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
......
python/paddle/v2/fluid/tests/book/test_word2vec.py
浏览文件 @ 94e86897
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
import numpy as np
from paddle.v2.fluid.optimizer import SGDOptimizer
PASS_NUM = 100
EMBED_SIZE = 32
......@@ -17,26 +16,11 @@ IS_SPARSE = True
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
first_word = layers.data(
name='firstw',
shape=[1],
data_type='int64')
second_word = layers.data(
name='secondw',
shape=[1],
data_type='int64')
third_word = layers.data(
name='thirdw',
shape=[1],
data_type='int64')
forth_word = layers.data(
name='forthw',
shape=[1],
data_type='int64')
next_word = layers.data(
name='nextw',
shape=[1],
data_type='int64')
first_word = layers.data(name='firstw', shape=[1], data_type='int64')
second_word = layers.data(name='secondw', shape=[1], data_type='int64')
third_word = layers.data(name='thirdw', shape=[1], data_type='int64')
forth_word = layers.data(name='forthw', shape=[1], data_type='int64')
next_word = layers.data(name='nextw', shape=[1], data_type='int64')
embed_first = layers.embedding(
input=first_word,
......@@ -64,19 +48,12 @@ embed_forth = layers.embedding(
param_attr={'name': 'shared_w'})
concat_embed = layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth],
axis=1)
hidden1 = layers.fc(input=concat_embed,
size=HIDDEN_SIZE,
act='sigmoid')
predict_word = layers.fc(input=hidden1,
size=dict_size,
act='softmax')
cost = layers.cross_entropy(
input=predict_word,
label=next_word)
input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
hidden1 = layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax')
cost = layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = layers.mean(x=cost)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer = SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
......
python/paddle/v2/fluid/tests/test_conv2d_op.py
浏览文件 @ 94e86897
......@@ -110,13 +110,30 @@ class TestConv2dOp(OpTest):
self.op_type = "conv2d"
class TestWithPad(TestConv2dOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3]
class TestWithStride(TestConv2dOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [2, 2]
self.input_size = [2, 3, 6, 6] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3]
class TestWithGroup(TestConv2dOp):
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv2d"
class TestWith1x1(TestConv2dOp):
def init_test_case(self):
......@@ -127,15 +144,9 @@ class TestWith1x1(TestConv2dOp):
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 1, 1]
def init_dilation(self):
self.dilations = [1, 1]
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv2d"
class TestWithDilation(TestConv2dOp):
def init_test_case(self):
......@@ -152,14 +163,19 @@ class TestWithDilation(TestConv2dOp):
def init_group(self):
self.groups = 3
#----------------Conv2dCudnn----------------
class TestCudnn(TestConv2dOp):
def init_op_type(self):
self.op_type = "conv2d"
self.op_type = "conv_cudnn"
#----------------Conv2dCudnn----------------
class TestCudnnWithPad(TestWithPad):
def init_op_type(self):
self.op_type = "conv_cudnn"
class TestCudnn(TestConv2dOp):
class TestCudnnWithStride(TestWithStride):
def init_op_type(self):
self.op_type = "conv_cudnn"
......
python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
浏览文件 @ 94e86897
......@@ -4,9 +4,7 @@ from op_test import OpTest
def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
# [2, 3, 5, 5]
in_n, in_c, in_h, in_w = input_.shape
# [3, 6, 3, 3]
f_c, out_c, f_h, f_w = filter_.shape
assert in_c == f_c
......@@ -29,6 +27,7 @@ def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
j1, j2 = j * stride[0], j * stride[0] + f_w
out[n, k, i1:i2, j1:j2] += tmp_out
out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]]
return out
......@@ -36,8 +35,6 @@ class TestConv2dTransposeOp(OpTest):
def setUp(self):
# init as conv transpose
self.init_op_type()
# [2, 3, 5, 5] -> kernel [3, 6, 3, 3] -> output [2, 6, 7, 7]
self.init_test_case()
conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad}
......@@ -55,7 +52,6 @@ class TestConv2dTransposeOp(OpTest):
self.outputs = {'Output': output}
def test_check_output(self):
print 'check output here for', self.op_type
self.check_output()
def test_check_grad_no_input(self):
......@@ -88,6 +84,26 @@ class TestConv2dTransposeOp(OpTest):
self.op_type = "conv2d_transpose"
class TestWithPad(TestConv2dTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
class TestWithStride(TestConv2dTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [2, 2]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
# ------------ test_cudnn ------------
class TestCudnn(TestConv2dTransposeOp):
def init_op_type(self):
......
python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
浏览文件 @ 94e86897
......@@ -4,9 +4,7 @@ from op_test import OpTest
def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
# [2, 3, 5, 5, 5]
in_n, in_c, in_d, in_h, in_w = input_.shape
# [3, 6, 3, 3, 3]
f_c, out_c, f_d, f_h, f_w = filter_.shape
assert in_c == f_c
......@@ -14,7 +12,6 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
out_d = (in_d - 1) * stride[0] + f_d
out_h = (in_h - 1) * stride[1] + f_h
out_w = (in_w - 1) * stride[2] + f_w
out = np.zeros((in_n, out_c, out_d, out_h, out_w))
for n in range(in_n):
......@@ -33,6 +30,8 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
j1, j2 = j * stride[2], j * stride[2] + f_w
out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out
out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
pad[2]]
return out
......@@ -40,8 +39,6 @@ class TestConv3dTransposeOp(OpTest):
def setUp(self):
# init as conv transpose
self.init_op_type()
# [2, 3, 5, 5, 5] -> kernel [3, 6, 3, 3, 3] -> output [2, 6, 7, 7, 7]
self.init_test_case()
conv3dtranspose_param = {'stride': self.stride, 'pad': self.pad}
......@@ -49,7 +46,6 @@ class TestConv3dTransposeOp(OpTest):
filter_ = np.random.random(self.filter_size).astype("float32")
output = conv3dtranspose_forward_naive(
input_, filter_, conv3dtranspose_param).astype("float32")
# print 'deconv output py', output, output.shape
self.inputs = {'Input': input_, 'Filter': filter_}
self.attrs = {
......@@ -60,7 +56,6 @@ class TestConv3dTransposeOp(OpTest):
self.outputs = {'Output': output}
def test_check_output(self):
print 'check output here'
self.check_output()
def test_check_grad(self):
......@@ -85,7 +80,7 @@ class TestConv3dTransposeOp(OpTest):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCHW
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
......@@ -93,5 +88,25 @@ class TestConv3dTransposeOp(OpTest):
self.op_type = "conv3d_transpose"
class TestWithPad(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
class TestWithStride(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_is_empty_op.py 0 → 100644
浏览文件 @ 94e86897
import unittest
import numpy as np
from paddle.v2.fluid.op import Operator
import paddle.v2.fluid.core as core
def create_tensor(scope, name, np_data):
tensor = scope.var(name).get_tensor()
tensor.set_dims(np_data.shape)
tensor.set(np_data, core.CPUPlace())
return tensor
class TestIsEmptyOp(unittest.TestCase):
def setUp(self):
self.scope = core.Scope()
# create input variables
np_data0 = np.array([0, 1, 2])
create_tensor(self.scope, "X0", np_data0)
np_data1 = np.array([1])
t = create_tensor(self.scope, "X1", np_data1)
t.set_dims([0])
# create output variables
self.scope.var("out")
def test_no_empty(self):
self.one_case("X0", False)
def test_empty(self):
self.one_case("X1", True)
def one_case(self, input, target):
op = Operator(type="is_empty", X=input, Out="out")
ctx = core.DeviceContext.create(core.CPUPlace())
op.run(self.scope, ctx)
out = self.scope.var("out").get_tensor()
self.assertEqual(np.array(out)[0], target)
if __name__ == "__main__":
unittest.main()
python/paddle/v2/fluid/tests/test_while_op.py
浏览文件 @ 94e86897
......@@ -2,6 +2,7 @@ import unittest
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
import paddle.v2.fluid.core as core
from paddle.v2.fluid.backward import append_backward_ops
import numpy
......@@ -16,7 +17,7 @@ class TestWhileOp(unittest.TestCase):
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
init = layers.zeros(shape=[10], dtype='float32')
mem_array = layers.array_write(init, i=i)
mem_array = layers.array_write(x=init, i=i)
data_array = layers.array_write(x=d0, i=i)
i = layers.increment(i)
......@@ -29,17 +30,23 @@ class TestWhileOp(unittest.TestCase):
i.stop_gradient = True
array_len = layers.fill_constant(shape=[1], dtype='int64', value=3)
array_len.stop_gradient = True
cond = layers.less_than(x=i, y=array_len)
while_op = layers.While(cond=cond)
with while_op.block():
d = layers.array_read(array=data_array, i=i)
prev = layers.array_read(array=mem_array, i=i)
i = layers.increment(x=i, in_place=True)
result = layers.sums(input=[d, prev])
i = layers.increment(x=i, in_place=True)
layers.array_write(result, i=i, array=mem_array)
layers.less_than(x=i, y=array_len, cond=cond)
sum_result = layers.array_read(mem_array, i=array_len)
sum_result = layers.array_read(array=mem_array, i=i)
loss = layers.mean(x=sum_result)
append_backward_ops(loss)
cpu = core.CPUPlace()
exe = Executor(cpu)
......
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