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提交 c110f565 编写于 作者: D dongzhihong
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Merge remote-tracking branch 'origin/develop' into random_op

上级 5ad9474b 1294b3c5
隐藏空白更改
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Showing with 1966 addition and 399 deletion
CMakeLists.txt
浏览文件 @ c110f565
......@@ -36,6 +36,8 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." OFF)
option(WITH_MKLML "Compile PaddlePaddle with mklml package." OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......@@ -74,6 +76,10 @@ if(ANDROID)
"Disable PYTHON when cross-compiling for Android" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android" FORCE)
set(WITH_MKLDNN OFF CACHE STRING
"Disable MKLDNN when cross-compiling for Android" FORCE)
set(WITH_MKLML OFF CACHE STRING
"Disable MKLML package when cross-compiling for Android" FORCE)
endif(ANDROID)
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
......@@ -87,6 +93,7 @@ endif()
########################################################################################
include(external/mklml) # download mklml package
include(external/zlib) # download, build, install zlib
include(external/gflags) # download, build, install gflags
include(external/glog) # download, build, install glog
......@@ -94,6 +101,7 @@ include(external/gtest) # download, build, install gtest
include(external/protobuf) # download, build, install protobuf
include(external/python) # download, build, install python
include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/swig) # download, build, install swig
include(external/warpctc) # download, build, install warpctc
include(external/any) # download libn::any
......@@ -135,6 +143,10 @@ if(WITH_GPU)
endif(NOT WITH_DSO)
endif(WITH_GPU)
if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIBRARY} ${MKLDNN_IOMP_LIB})
endif()
if(USE_NNPACK)
include(external/nnpack)
list(APPEND EXTERNAL_LIBS ${NNPACK_LIBS})
......
cmake/cblas.cmake
浏览文件 @ c110f565
......@@ -15,23 +15,44 @@
set(CBLAS_FOUND OFF)
## Find MKL First.
set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
## Find MKLML First.
if(WITH_MKLML AND MKLML_INC_DIR AND MKLML_LIB)
set(CBLAS_FOUND ON)
set(CBLAS_PROVIDER MKLML)
set(CBLAS_INC_DIR ${MKLML_INC_DIR})
set(CBLAS_LIBRARIES ${MKLML_LIB})
add_definitions(-DPADDLE_USE_MKLML)
add_definitions(-DLAPACK_FOUND)
message(STATUS "Found cblas and lapack in MKLML "
"(include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
return()
endif()
## Then find MKL.
set(INTEL_MKL_ROOT "/opt/intel/mkl" CACHE PATH "Folder contains intel mkl libs")
set(MKL_ROOT $ENV{MKL_ROOT} CACHE PATH "Folder contains env MKL")
set(MKL_INCLUDE_SEARCH_PATHS
${MKL_ROOT}/include
${INTEL_MKL_ROOT}/include)
set(MKL_LIB_SEARCH_PATHS
${MKL_ROOT}/lib
${MKL_ROOT}/lib/intel64
${INTEL_MKL_ROOT}/lib
${INTEL_MKL_ROOT}/lib/intel64)
find_path(MKL_INC_DIR mkl.h PATHS
${MKL_ROOT}/include)
${MKL_INCLUDE_SEARCH_PATHS})
find_path(MKL_LAPACK_INC_DIR mkl_lapacke.h PATHS
${MKL_ROOT}/include)
${MKL_INCLUDE_SEARCH_PATHS})
find_library(MKL_CORE_LIB NAMES mkl_core PATHS
${MKL_ROOT}/lib
${MKL_ROOT}/lib/intel64)
${MKL_LIB_SEARCH_PATHS})
find_library(MKL_SEQUENTIAL_LIB NAMES mkl_sequential PATHS
${MKL_ROOT}/lib
${MKL_ROOT}/lib/intel64)
${MKL_LIB_SEARCH_PATHS})
find_library(MKL_INTEL_LP64 NAMES mkl_intel_lp64 PATHS
${MKL_ROOT}/lib
${MKL_ROOT}/lib/intel64)
${MKL_LIB_SEARCH_PATHS})
if(MKL_LAPACK_INC_DIR AND MKL_INC_DIR AND MKL_CORE_LIB AND MKL_SEQUENTIAL_LIB AND MKL_INTEL_LP64)
set(CBLAS_FOUND ON)
......
cmake/configure.cmake
浏览文件 @ c110f565
......@@ -67,6 +67,30 @@ else()
include_directories(${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 ${OPENMP_FLAGS})
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
set(CMAKE_C_FLAGS "${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} ${OpenMP_CXX_FLAGS}")
else()
message(WARNING "Can not find OpenMP."
"Some performance features in MKLDNN may not be available")
endif()
endif()
endif(WITH_MKLDNN)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
......
cmake/external/gtest.cmake
浏览文件 @ c110f565
......@@ -34,9 +34,15 @@ IF(WITH_TESTING)
"${GTEST_INSTALL_DIR}/lib/libgtest_main.a" CACHE FILEPATH "gtest main libraries." FORCE)
ENDIF(WIN32)
IF(WITH_MKLML)
# wait for mklml downloading completed
SET(GTEST_DEPENDS ${MKLML_PROJECT})
ENDIF()
ExternalProject_Add(
extern_gtest
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${GTEST_DEPENDS}
GIT_REPOSITORY "https://github.com/google/googletest.git"
GIT_TAG "release-1.8.0"
PREFIX ${GTEST_SOURCES_DIR}
......
cmake/external/mkldnn.cmake 0 → 100644
浏览文件 @ c110f565
# Copyright (c) 2017 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.
IF(NOT ${WITH_MKLDNN})
return()
ENDIF(NOT ${WITH_MKLDNN})
INCLUDE(ExternalProject)
SET(MKLDNN_PROJECT "extern_mkldnn")
SET(MKLDNN_SOURCES_DIR ${THIRD_PARTY_PATH}/mkldnn)
SET(MKLDNN_INSTALL_ROOT ${CMAKE_INSTALL_PREFIX})
IF(NOT "$ENV{HOME}" STREQUAL "/root")
SET(MKLDNN_INSTALL_ROOT "$ENV{HOME}")
ENDIF()
SET(MKLDNN_INSTALL_DIR "${MKLDNN_INSTALL_ROOT}/opt/paddle/third_party/mkldnn")
SET(MKLDNN_INCLUDE_DIR "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
IF(WIN32)
MESSAGE(WARNING "It is not supported compiling with mkldnn in windows Paddle yet."
"Force WITH_MKLDNN=OFF")
SET(WITH_MKLDNN OFF)
return()
ELSE(WIN32)
SET(MKLDNN_LIBRARY "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
#SET(CMAKE_MACOSX_RPATH 1) # hold for MacOS
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${MKLDNN_INCLUDE_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})
ENDIF()
ExternalProject_Add(
${MKLDNN_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "v0.9"
PREFIX ${MKLDNN_SOURCES_DIR}
CONFIGURE_COMMAND mkdir -p <SOURCE_DIR>/build
BUILD_COMMAND cd <SOURCE_DIR>/build
&& cmake .. -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR} -DMKLROOT=${MKLDNN_MKLROOT}
&& $(MAKE)
INSTALL_COMMAND cd <SOURCE_DIR>/build && $(MAKE) install
UPDATE_COMMAND ""
)
ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIBRARY})
ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIBRARY}")
LIST(APPEND external_project_dependencies mkldnn)
cmake/external/mklml.cmake 0 → 100644
浏览文件 @ c110f565
# Copyright (c) 2017 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.
IF(NOT ${WITH_MKLML})
return()
ENDIF(NOT ${WITH_MKLML})
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.20170425")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.9/${MKLML_VER}.tgz")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "opt/paddle/third_party/mklml")
SET(MKLML_INSTALL_ROOT "${CMAKE_INSTALL_PREFIX}")
IF(NOT "$ENV{HOME}" STREQUAL "/root")
SET(MKLML_INSTALL_ROOT "$ENV{HOME}")
ENDIF()
SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR}/${MKLML_VER})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
SET(mklml_cmakefile ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt)
FILE(WRITE ${mklml_cmakefile} "PROJECT(MKLML)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${MKLML_VER}\n"
" DESTINATION ${MKLML_DST_DIR})\n")
ExternalProject_Add(
${MKLML_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${MKLML_SOURCE_DIR}
DOWNLOAD_DIR ${MKLML_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate -O ${MKLML_DOWNLOAD_DIR}/${MKLML_VER}.tgz ${MKLML_URL}
&& tar -xzf ${MKLML_DOWNLOAD_DIR}/${MKLML_VER}.tgz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLML_INSTALL_ROOT}
)
ADD_LIBRARY(mklml SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mklml PROPERTY IMPORTED_LOCATION ${MKLML_LIB})
ADD_DEPENDENCIES(mklml ${MKLML_PROJECT})
LIST(APPEND external_project_dependencies mklml)
cmake/flags.cmake
浏览文件 @ c110f565
......@@ -124,6 +124,7 @@ set(GPU_COMMON_FLAGS
-Wno-error=literal-suffix
-Wno-error=unused-local-typedefs
-Wno-error=unused-function # Warnings in Numpy Header.
-Wno-error=array-bounds # Warnings in Eigen::array
)
if (APPLE)
......
go/pserver/client/etcd_client.go
浏览文件 @ c110f565
......@@ -80,10 +80,10 @@ func (p *EtcdClient) List() []Server {
for {
for i := 0; i < psDesired; i++ {
ctx, cancel := context.WithTimeout(context.Background(), p.timeout)
cancel()
psKey := pserver.PsPath + strconv.Itoa(i)
log.Debugf("checking %s", psKey)
resp, err := p.client.Get(ctx, psKey)
cancel()
if err != nil {
log.Infof("Get psKey= %s error, %v", psKey, err)
time.Sleep(p.timeout)
......
paddle/framework/eigen.h
浏览文件 @ c110f565
......@@ -61,25 +61,24 @@ struct EigenTensor {
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenMatrix : public EigenTensor<T, 2, MajorType, IndexType> {};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
// Flatten is to reshape a Tensor into a one dimension EigenVector
using Parent = EigenTensor<T, 1, MajorType, IndexType>;
static typename Parent::Type Flatten(Tensor& tensor) {
return Parent::From(tensor,
make_ddim({static_cast<int>(product(tensor.dims_))}));
// Flatten reshapes a Tensor into an EigenVector.
static typename EigenVector::Type Flatten(Tensor& tensor) {
return EigenVector::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
static typename Parent::ConstType Flatten(const Tensor& tensor) {
return Parent::From(tensor,
make_ddim({static_cast<int>(product(tensor.dims_))}));
static typename EigenVector::ConstType Flatten(const Tensor& tensor) {
return EigenVector::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = EigenTensor<T, 2, MajorType, IndexType>;
} // namespace framework
} // namespace paddle
paddle/framework/net.cc
浏览文件 @ c110f565
......@@ -39,19 +39,22 @@ void PlainNet::CompleteAddOp(bool calc) {
output_set.insert(opt);
}
}
inputs_.reserve(input_set.size());
std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs_));
std::sort(inputs_.begin(), inputs_.end());
outputs_.reserve(output_set.size());
std::copy(output_set.begin(), output_set.end(), std::back_inserter(outputs_));
std::sort(outputs_.begin(), outputs_.end());
std::vector<int> tmp_index;
tmp_index.reserve(temp_output.size());
int idx = 0;
for (auto& opt : output_set) {
if (Contains(temp_output, opt)) {
tmp_index.push_back(idx);
int output_len = static_cast<int>(outputs_.size());
for (int i = 0; i < output_len; ++i) {
if (Contains(temp_output, outputs_[i])) {
tmp_index.push_back(i);
}
outputs_.push_back(opt);
++idx;
}
attrs_["temporary_index"] = tmp_index;
......@@ -59,9 +62,12 @@ void PlainNet::CompleteAddOp(bool calc) {
std::string PlainNet::DebugString() const {
std::ostringstream os;
os << this->type_ << ":" << std::endl;
os << OperatorBase::DebugString() << std::endl;
for (auto& op : ops_) {
os << "\t" << op->DebugString() << std::endl;
std::istringstream is(op->DebugString());
for (std::string line; std::getline(is, line);) {
os << " " << line << std::endl;
}
}
return os.str();
}
......
paddle/framework/tensor.h
浏览文件 @ c110f565
......@@ -48,25 +48,27 @@ class Tensor {
template <typename T>
const T* data() const {
CheckDims<T>();
EnforceSufficientMemory<T>();
return reinterpret_cast<const T*>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
template <typename T>
T* data() {
CheckDims<T>();
EnforceSufficientMemory<T>();
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
template <typename T>
template <typename T, // must be POD types
typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
T* mutable_data(DDim dims, platform::Place place) {
set_dims(dims);
Resize(dims);
return mutable_data<T>(place);
}
template <typename T>
template <typename T, // must be POD types
typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
T* mutable_data(platform::Place place) {
PADDLE_ENFORCE(product(dims_) > 0,
"Tensor's numel must be larger than zero to call "
......@@ -95,11 +97,9 @@ class Tensor {
}
template <typename T>
void ShareDataFrom(const Tensor& src) {
src.CheckDims<T>();
holder_ = src.holder_;
set_dims(src.dims());
offset_ = src.offset_;
void ShareDataWith(const Tensor& src) {
src.EnforceSufficientMemory<T>();
*this = src;
}
template <typename T>
......@@ -107,9 +107,9 @@ class Tensor {
PADDLE_ENFORCE(platform::is_cpu_place(src.holder_->place()) &&
platform::is_cpu_place(dst_place),
"Tensor::CopyFrom only support CPU now.");
src.CheckDims<T>();
src.EnforceSufficientMemory<T>();
size_t size = product(src.dims_) * sizeof(T);
set_dims(src.dims());
Resize(src.dims());
const void* src_ptr = static_cast<const void*>(src.data<T>());
void* dst_ptr = static_cast<void*>(mutable_data<T>(dst_place));
memcpy(dst_ptr, src_ptr, size);
......@@ -117,34 +117,25 @@ class Tensor {
template <typename T>
Tensor Slice(const int& begin_idx, const int& end_idx) const {
CheckDims<T>();
PADDLE_ENFORCE(begin_idx >= 0 && end_idx <= dims_[0],
"Slice index is less than zero or out of bound.");
EnforceSufficientMemory<T>();
PADDLE_ENFORCE(begin_idx >= 0, "Slice begin index is less than zero.");
PADDLE_ENFORCE(end_idx <= dims_[0], "Slice end index is out of bound.");
PADDLE_ENFORCE(begin_idx < end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE(dims_[0] != 1, "Can not slice a tensor with dims_[0] = 1.");
std::vector<int> d = vectorize(dims_);
int base = 1;
for (size_t i = 1; i < d.size(); ++i) {
base *= d[i];
}
int base = product(dims_) / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.set_dims(dst_dims);
dst.Resize(dst_dims);
dst.offset_ = offset_ + begin_idx * base * sizeof(T);
return dst;
}
void set_dims(const DDim& dims) {
if (dims == dims_) {
return;
}
dims_ = dims;
}
void Resize(const DDim& dims) { dims_ = dims; }
DDim dims() const { return dims_; }
const DDim& dims() const { return dims_; }
private:
// Placeholder hides type T, so it doesn't appear as a template
......@@ -159,21 +150,9 @@ class Tensor {
template <typename T, typename PlaceType>
struct PlaceholderImpl : public Placeholder {
private:
template <typename PType>
class Deleter {
public:
Deleter(PType place) : place_(place) {}
void operator()(T* ptr) { memory::Free(place_, static_cast<void*>(ptr)); }
private:
PType place_;
};
public:
PlaceholderImpl(PlaceType place, size_t size)
: ptr_(static_cast<T*>(memory::Alloc(place, size)),
Deleter<PlaceType>(place)),
memory::PODDeleter<T, PlaceType>(place)),
place_(place),
size_(size) {}
......@@ -182,13 +161,13 @@ class Tensor {
virtual paddle::platform::Place place() const { return place_; }
virtual std::type_index type() const { return std::type_index(typeid(T)); }
std::unique_ptr<T, Deleter<PlaceType>> ptr_;
std::unique_ptr<T, memory::PODDeleter<T, PlaceType>> ptr_;
platform::Place place_; // record the place of ptr_.
size_t size_; // size of the memory block.
};
template <typename T>
inline void CheckDims() const {
inline void EnforceSufficientMemory() const {
PADDLE_ENFORCE(holder_ != nullptr,
"Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE(holder_->size() >= product(dims_) * sizeof(T) + offset_,
......@@ -198,7 +177,11 @@ class Tensor {
std::shared_ptr<Placeholder> holder_; // holds the memory block if allocated.
DDim dims_;
size_t offset_; // marks the begin of tensor data area.
// A PlaceHolder may be shared by more than one tensor. Some of them may be
// slices of the others. So the offset_ is introduced here to indicate the
// byte offset between PlaceHolder::ptr_ and where tensor's data really
// begins.
size_t offset_;
};
} // namespace framework
......
paddle/framework/tensor_test.cc
浏览文件 @ c110f565
......@@ -19,7 +19,7 @@ TEST(Tensor, Dims) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor tt;
tt.set_dims(make_ddim({2, 3, 4}));
tt.Resize(make_ddim({2, 3, 4}));
DDim dims = tt.dims();
ASSERT_EQ(arity(dims), 3);
for (int i = 0; i < 3; ++i) {
......@@ -97,7 +97,7 @@ TEST(Tensor, MutableData) {
#endif
}
TEST(Tensor, ShareDataFrom) {
TEST(Tensor, ShareDataWith) {
using namespace paddle::framework;
using namespace paddle::platform;
{
......@@ -106,7 +106,7 @@ TEST(Tensor, ShareDataFrom) {
// Try to share data form uninitialized tensor
bool caught = false;
try {
dst_tensor.ShareDataFrom<float>(src_tensor);
dst_tensor.ShareDataWith<float>(src_tensor);
} catch (std::runtime_error& err) {
caught = true;
std::string msg =
......@@ -119,7 +119,7 @@ TEST(Tensor, ShareDataFrom) {
ASSERT_TRUE(caught);
src_tensor.mutable_data<int>(make_ddim({2, 3, 4}), CPUPlace());
dst_tensor.ShareDataFrom<int>(src_tensor);
dst_tensor.ShareDataWith<int>(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
......@@ -128,7 +128,7 @@ TEST(Tensor, ShareDataFrom) {
Tensor src_tensor;
Tensor dst_tensor;
src_tensor.mutable_data<int>(make_ddim({2, 3, 4}), GPUPlace());
dst_tensor.ShareDataFrom<int>(src_tensor);
dst_tensor.ShareDataWith<int>(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
#endif
......
paddle/function/ConvOpTest.cpp
浏览文件 @ c110f565
......@@ -31,13 +31,22 @@ public:
ConvolutionTest(const std::string& conv1,
const std::string& conv2,
TestType type,
bool useGroups = true,
std::string algo = "auto") {
for (size_t batchSize : {1, 32}) {
for (size_t inputSize : {7, 14, 54}) {
for (size_t filterSize : {1, 3, 5}) {
for (size_t inputChannels : {3, 64}) {
for (size_t outputChannels : {3, 64, 128}) {
if (inputChannels < outputChannels) break;
for (size_t outputChannels : {3, 64}) {
if (inputChannels > outputChannels) break;
size_t groups;
if (!useGroups) {
groups = 1;
} else {
if (outputChannels % inputChannels != 0) continue;
groups = inputChannels;
}
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
......@@ -62,13 +71,24 @@ public:
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("groups", groups)
.set("algo", algo));
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape filter;
if (groups > 1)
filter = TensorShape({groups,
outputChannels / groups,
inputChannels / groups,
filterSize,
filterSize});
else
filter = TensorShape({outputChannels,
inputChannels,
filterSize,
filterSize});
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
......@@ -85,7 +105,8 @@ public:
} else if (type == kBackwardFilterTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
ADD_TO);
test.run();
}
}
......@@ -106,6 +127,7 @@ public:
ConvolutionTest2(const std::string& conv1,
const std::string& conv2,
TestType type,
bool useGroups = true,
std::string algo = "auto") {
for (size_t batchSize : {16}) {
for (size_t inputHeight : {7, 31}) {
......@@ -113,7 +135,15 @@ public:
for (size_t filterHeight : {1, 5}) {
for (size_t filterWidth : {3, 7}) {
for (size_t inputChannels : {7}) {
for (size_t outputChannels : {32}) {
for (size_t outputChannels : {7}) {
size_t groups;
if (!useGroups) {
groups = 1;
} else {
if (outputChannels % inputChannels != 0) continue;
groups = inputChannels;
}
size_t stride = 1;
size_t padding = 0;
size_t outputHeight =
......@@ -141,13 +171,24 @@ public:
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("groups", groups)
.set("algo", algo));
TensorShape input{
batchSize, inputChannels, inputHeight, inputWidth};
TensorShape filter{
outputChannels, inputChannels, filterHeight, filterWidth};
TensorShape filter;
if (groups > 1)
filter = TensorShape({groups,
outputChannels / groups,
inputChannels / groups,
filterHeight,
filterWidth});
else
filter = TensorShape({outputChannels,
inputChannels,
filterHeight,
filterWidth});
TensorShape output{
batchSize, outputChannels, outputHeight, outputWidth};
......@@ -164,7 +205,8 @@ public:
} else if (type == kBackwardFilterTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
ADD_TO);
test.run();
}
}
......@@ -177,34 +219,88 @@ public:
}
};
// ======Start Convolution TEST======
TEST(Forward, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest);
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest);
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
}
#ifndef PADDLE_ONLY_CPU
TEST(Forward, GEMM2) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConv-CPU", "GemmConv-GPU", kForwardTest);
"GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConv-CPU", "GemmConv-GPU", kForwardTest);
"GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
}
TEST(BackwardInput, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
"GemmConvGradInput-CPU",
"GemmConvGradInput-GPU",
kBackwardInputTest,
false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
"GemmConvGradInput-CPU",
"GemmConvGradInput-GPU",
kBackwardInputTest,
false);
}
TEST(BackwardFilter, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
"GemmConvGradFilter-CPU",
"GemmConvGradFilter-GPU",
kBackwardFilterTest,
false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
"GemmConvGradFilter-CPU",
"GemmConvGradFilter-GPU",
kBackwardFilterTest,
false);
}
#endif
// ======End Convolution TEST======
// ======Start DepthwiseConvolution TEST======
// TODO(zhaolong) The depthwise convolution cpu test will be added when the cpu
// version of depthwiseConv is implemented.
#ifndef PADDLE_ONLY_CPU
TEST(DepthwiseConvForward, GEMM2) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
}
TEST(DepthwiseConvBackwardInput, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradInput-CPU",
"DepthwiseConvGradInput-GPU",
kBackwardInputTest);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradInput-CPU",
"DepthwiseConvGradInput-GPU",
kBackwardInputTest);
}
TEST(DepthwiseConvBackwardFilter, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradFilter-CPU",
"DepthwiseConvGradFilter-GPU",
kBackwardFilterTest);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradFilter-CPU",
"DepthwiseConvGradFilter-GPU",
kBackwardFilterTest);
}
#endif
// ======End DepthwiseConvolution TEST======
} // namespace paddle
paddle/function/DepthwiseConvOp.cpp 0 → 100644
浏览文件 @ c110f565
/* 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 "DepthwiseConvOp.h"
#include "ConvOp.h"
#include "GemmFunctor.h"
namespace paddle {
template <class T>
class DepthwiseConvFunctor<DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* inputData,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* outputData) {
// TODO(zhaolong) : cpu implementation of depthwise convolution
}
};
template <class T>
class DepthwiseConvGradInputFunctor<DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* outputGrad,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* inputGrad) {}
// TODO(zhaolong) : cpu implementation of depthwise convolution
};
template <class T>
class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* outputGrad,
const T* inputData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* colData,
T* filterGrad) {}
// TODO(zhaolong) : cpu implementation of depthwise convolution
};
/*
* \brief Forward calculation of depthwise convolution.
*/
template <DeviceType Device>
class DepthwiseConvFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* outputData = outputs[0].data<real>();
DepthwiseConvFunctor<Device, real> depthwiseConv;
depthwiseConv(inputData,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH(),
strideW(),
paddingH(),
paddingW(),
outputData);
}
};
/*
* \brief Backward input calculation of depthwise convolution.
*/
template <DeviceType Device>
class DepthwiseConvGradInputFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& output = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& input = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
check(inputs, outputs);
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
const TensorShape& output = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& input = outputs[0].shape();
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
real* outputGrad = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* inputGrad = outputs[0].data<real>();
DepthwiseConvGradInputFunctor<Device, real> depthwiseConvGradInput;
depthwiseConvGradInput(outputGrad,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH(),
strideW(),
paddingH(),
paddingW(),
inputGrad);
}
};
/*
* \brief Backward filter calculation of depthwise convolution.
*/
template <DeviceType Device>
class DepthwiseConvGradFilterFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& output = inputs[0].shape();
const TensorShape& input = inputs[1].shape();
const TensorShape& filter = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
check(inputs, outputs);
const TensorShape& output = inputs[0].shape();
const TensorShape& input = inputs[1].shape();
const TensorShape& filter = outputs[0].shape();
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
real* outputGrad = inputs[0].data<real>();
real* inputData = inputs[1].data<real>();
real* filterGrad = outputs[0].data<real>();
int size = outputChannels * filterHeight * filterWidth * outputHeight *
outputWidth;
resizeBuffer<Device>(size);
real* colData = reinterpret_cast<real*>(memory_->getBuf());
DepthwiseConvGradFilterFunctor<Device, real> depthwiseConvGradFilter;
depthwiseConvGradFilter(outputGrad,
inputData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH(),
strideW(),
paddingH(),
paddingW(),
colData,
filterGrad);
}
};
REGISTER_TYPED_FUNC(DepthwiseConv, CPU, DepthwiseConvFunction);
REGISTER_TYPED_FUNC(DepthwiseConvGradInput,
CPU,
DepthwiseConvGradInputFunction);
REGISTER_TYPED_FUNC(DepthwiseConvGradFilter,
CPU,
DepthwiseConvGradFilterFunction);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC(DepthwiseConv, GPU, DepthwiseConvFunction);
REGISTER_TYPED_FUNC(DepthwiseConvGradInput,
GPU,
DepthwiseConvGradInputFunction);
REGISTER_TYPED_FUNC(DepthwiseConvGradFilter,
GPU,
DepthwiseConvGradFilterFunction);
#endif
} // namespace paddle
paddle/function/DepthwiseConvOp.h 0 → 100644
浏览文件 @ c110f565
/* 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 "TensorType.h"
namespace paddle {
/**
*\brief Depthwise convolution forward. The outputData
* of depthwise convolution is same with ExpandConvLayer
* when groups equals inputChannels in ExpandConvLayer.
*
* \param[in] inputData input data.
* \param[in] filterData the Paramters of the depthwise conv layer..
* \param[in] batchSize batch size of input data.
* \param[in] outputChannels channels of outputData.
* \param[in] outputHeight height of outputData.
* \param[in] outputWidth width of outputData.
* \param[in] inputChannels channels of inputData.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData..
* \param[in] filterMultiplier equals to outputChannels/groups_.
* \param[in] filterHeight height of filter.
* \param[in] filterWidth widht of filter.
* \param[in] strideH stride size in height direction.
* \param[in] strideW stride size in width direction.
* \param[in] paddingH padding size in height direction.
* \param[in] paddingW padding size in width direction.
* \param[out] outputData outputData.
*
*/
template <DeviceType Device, class T>
class DepthwiseConvFunctor {
public:
void operator()(const T* inputData,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* outputData);
};
/**
*\brief Functor tot compute the depthwise convolution backprop w.r.t input.
*
*
* \param[in] outputGradData the grad data of output.
* \param[in] filterData the Paramters of the depthwise conv layer..
* \param[in] batchSize batch size of input data.
* \param[in] outputChannels channels of outputData.
* \param[in] outputHeight height of outputData.
* \param[in] outputWidth width of outputData.
* \param[in] inputChannels channels of input data.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData.
* \param[in] filterMultiplier equals to outputChannels/groups_.
* \param[in] filterHeight height of filter.
* \param[in] filterWidth widht of filter.
* \param[in] strideH stride size in height direction.
* \param[in] strideW stride size in width direction.
* \param[in] paddingH padding size in height direction.
* \param[in] paddingW padding size in width direction.
* \param[out] inputGrad the grad data of input.
*
*/
template <DeviceType Device, class T>
class DepthwiseConvGradInputFunctor {
public:
void operator()(const T* outputGrad,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* inputGrad);
};
/**
*\brief Functor tot compute the depthwise convolution backprop w.r.t filter.
*
* \param[in] outputGradData the grad data of output.
* \param[in] inputData inputData.
* \param[in] batchSize batch size of input data.
* \param[in] outputChannels channels of outputData.
* \param[in] outputHeight height of outputData.
* \param[in] outputWidth width of outputData.
* \param[in] inputChannels channels of input data.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData.
* \param[in] filterMultiplier equals to outputChannels/groups_.
* \param[in] filterHeight height of filter.
* \param[in] filterWidth widht of filter.
* \param[in] strideH stride size in height direction.
* \param[in] strideW stride size in width direction.
* \param[in] paddingH padding size in height direction.
* \param[in] paddingW padding size in width direction.
* \param[in] colData Auxiliary data when calculating filterGrad.
* \param[in] multiplierData Auxiliary data when calculating filterGrad.
* \param[out] filterGrad the grad data of filter.
*
*/
template <DeviceType Device, class T>
class DepthwiseConvGradFilterFunctor {
public:
void operator()(const T* outputGrad,
const T* inputData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* colData,
T* filterGrad);
};
} // namespace paddle
paddle/function/DepthwiseConvOpGpu.cu 0 → 100644
浏览文件 @ c110f565
/* 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 "DepthwiseConvOp.h"
#include "GemmFunctor.h"
#include "paddle/math/BaseMatrix.h"
namespace paddle {
// CUDA kernel to compute the depthwise convolution forward pass
template <class T>
__global__
void ConvolutionDepthwiseForward(const int nthreads,
const T* const inputData, const T* const filterData,
const int batchSize, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight,
const int inputWidth, const int filterMultiplier, const int filterHeight,
const int filterWidth, const int strideH, const int strideW,
const int paddingH, const int paddingW, T* const outputData) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int batch = index / outputChannels / outputHeight / outputWidth;
const int c_out = (index / outputHeight / outputWidth) % outputChannels;
const int h_out = (index / outputWidth) % outputHeight;
const int w_out = index % outputWidth;
const int c_in = c_out / filterMultiplier;
const T* weight = filterData + c_out * filterHeight * filterWidth;
T value = 0;
const int h_in_start = -paddingH + h_out * strideH;
const int w_in_start = -paddingW + w_out * strideW;
const int h_in_end = -paddingH + h_out * strideH + filterHeight - 1;
const int w_in_end = -paddingW + w_out * strideW + filterWidth - 1;
if ((h_in_start >= 0) && (h_in_end < inputHeight)
&& (w_in_start >= 0) && (w_in_end < inputWidth)) {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
const int offset = ((batch * inputChannels + c_in)
* inputHeight + h_in) * inputWidth + w_in;
value += (*weight) * inputData[offset];
++weight;
}
}
} else {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int offset = ((batch * inputChannels + c_in)
* inputHeight + h_in) * inputWidth + w_in;
value += (*weight) * inputData[offset];
}
++weight;
}
}
}
outputData[index] = value;
}
}
// CUDA kernel to compute the depthwise convolution backprop w.r.t input.
template <class T>
__global__
void ConvolutionDepthwiseInputBackward(const int nthreads,
const T* const top_diff, const T* const weight_data,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight,
const int inputWidth, const int filterMultiplier, const int filterHeight,
const int filterWidth, const int strideH, const int strideW,
const int paddingH, const int paddingW, T* const bottom_diff) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int batch = index / inputChannels / inputHeight / inputWidth;
const int c_in = (index / inputHeight / inputWidth) % inputChannels;
const int h_in = (index / inputWidth) % inputHeight;
const int w_in = index % inputWidth;
const int c_out_start = c_in * filterMultiplier;
int h_out_start = (h_in - filterHeight + paddingH + strideH)/strideH;
h_out_start = 0 > h_out_start ? 0 : h_out_start;
int h_out_end = (h_in + paddingH)/strideH;
h_out_end = outputHeight - 1 < h_out_end? outputHeight - 1 : h_out_end;
int w_out_start = (w_in - filterWidth + paddingW + strideW)/strideW;
w_out_start = 0 > w_out_start ? 0 : w_out_start;
int w_out_end = (w_in + paddingW)/strideW;
w_out_end = outputWidth - 1 < w_out_end? outputWidth - 1 : w_out_end;
T value = 0;
for (int c_out = c_out_start;
c_out < c_out_start + filterMultiplier; c_out ++) {
for (int h_out = h_out_start; h_out <= h_out_end; ++h_out) {
const int filter_h = h_in + paddingH - h_out * strideH;
for (int w_out = w_out_start; w_out <= w_out_end; ++w_out) {
const int filter_w = w_in + paddingW - w_out * strideW;
const int filter_offset = c_out * filterHeight * filterWidth
+ filter_h * filterWidth + filter_w;
const int top_diff_offset = ((batch * outputChannels + c_out) *
outputHeight + h_out)* outputWidth + w_out;
value += top_diff[top_diff_offset] * weight_data[filter_offset];
}
}
}
bottom_diff[index] += value;
}
}
// CUDA kernel to compute the depthwise convolution backprop w.r.t filter.
template <class T>
__global__
void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads,
const T* const top_diff, const T* const inputData,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight,
const int inputWidth, const int filterMultiplier, const int filterHeight,
const int filterWidth, const int strideH, const int strideW,
const int paddingH, const int paddingW, T* const buffer_data) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int h_out = (index / outputWidth) % outputHeight;
const int w_out = index % outputWidth;
const int kh = (index / filterWidth / outputHeight / outputWidth)
% filterHeight;
const int kw = (index / outputHeight / outputWidth) % filterWidth;
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int c_out = index /
(filterHeight * filterWidth * outputHeight * outputWidth);
const int c_in = c_out / filterMultiplier;
const int batch = num_i;
const int top_offset = ((batch * outputChannels + c_out) *
outputHeight + h_out) * outputWidth + w_out;
const int bottom_offset = ((batch * inputChannels + c_in)
* inputHeight + h_in) * inputWidth + w_in;
buffer_data[index] = top_diff[top_offset] * inputData[bottom_offset];
} else {
buffer_data[index] = 0;
}
}
}
template <class T>
class DepthwiseConvFunctor<DEVICE_TYPE_GPU, T>{
public:
void operator()(const T* inputData,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* outputData){
int outputSize = batchSize * outputChannels * outputHeight * outputWidth;
size_t blocks = (outputSize + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
ConvolutionDepthwiseForward<T>
<<< grid, threads, 0, STREAM_DEFAULT >>>(
outputSize,
inputData,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
outputData);
}
};
template <class T>
class DepthwiseConvGradInputFunctor<DEVICE_TYPE_GPU, T>{
public:
void operator()(const T* outputGrad,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* inputGrad){
int inputSize = batchSize * inputChannels * inputHeight * inputWidth;
size_t blocks = (inputSize + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
ConvolutionDepthwiseInputBackward<T>
// NOLINT_NEXT_LINE(whitespace/operators)
<<< grid, threads, 0, STREAM_DEFAULT >>>(
inputSize,
outputGrad,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
inputGrad);
}
};
template <class T>
class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_GPU, T> {
public:
void operator()(const T* outputGrad,
const T* inputData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* colData,
T* filterGrad){
int colDataSize = outputChannels * filterHeight * filterWidth
* outputHeight * outputWidth;
size_t blocks = (colDataSize + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
BaseMatrix filterGradMatrix(outputChannels * filterHeight * filterWidth,
1, filterGrad, false, true);
for (int i = 0; i < batchSize; i++) {
ConvolutionDepthwiseFilterBackward<T>
<<< grid, threads, 0, STREAM_DEFAULT >>>(
i,
colDataSize,
outputGrad,
inputData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
colData);
int K = outputHeight * outputWidth;
int M = colDataSize / K;
BaseMatrix colMatrix(M, K, colData, false, true);
filterGradMatrix.sumRows(colMatrix, (T)1.0, (T)1.0);
}
}
};
#ifdef PADDLE_TYPE_DOUBLE
template class DepthwiseConvGradInputFunctor<DEVICE_TYPE_GPU, double>;
template class DepthwiseConvFunctor<DEVICE_TYPE_GPU, double>;
template class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_GPU, double>;
#else
template class DepthwiseConvGradInputFunctor<DEVICE_TYPE_GPU, float>;
template class DepthwiseConvFunctor<DEVICE_TYPE_GPU, float>;
template class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_GPU, float>;
#endif
} // namespace paddle
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ c110f565
......@@ -38,10 +38,25 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
inputShape_.resize(numInputs);
filterShape_.resize(numInputs);
outputShape_.resize(numInputs);
std::string convType;
std::string convGradInputType;
std::string convGradFilterType;
for (int i = 0; i < config_.inputs_size(); i++) {
std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
if (useGpu_ && (size_t)groups_[i] == (size_t)channels_[i] && !isDeconv_) {
convType = "DepthwiseConv";
convGradInputType = "DepthwiseConvGradInput";
convGradFilterType = "DepthwiseConvGradFilter";
} else {
convType = "GemmConv";
convGradInputType = "GemmConvGradInput";
convGradFilterType = "GemmConvGradFilter";
}
if (FLAGS_use_nnpack) {
CHECK_EQ(isDeconv_, false);
createFunction(forward_,
......@@ -53,21 +68,21 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
.set("algo", std::string("auto")));
} else {
createFunction(forward_,
!isDeconv_ ? "GemmConv" : "GemmConvGradInput",
!isDeconv_ ? convType : convGradInputType,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
!isDeconv_ ? "GemmConvGradInput" : "GemmConv",
!isDeconv_ ? convGradInputType : convType,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
"GemmConvGradFilter",
convGradFilterType,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ c110f565
......@@ -347,6 +347,55 @@ TEST(Layer, CosSimVecMatLayer) {
}
}
void testDepthwiseConvLayer(const string& type, bool useGpu) {
TestConfig config;
config.biasSize = 32;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(32);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_channels(16);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(16);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_img_size_y(8);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
config.layerConfig.num_filters());
testLayerGrad(config, "depthwise_conv", 100, false, useGpu);
// Use small batch_size and useWeight=true to test biasGrad
testLayerGrad(config, "depthwise_conv", 2, false, useGpu, true, 0.02);
}
TEST(Layer, depthwiseConvLayer) {
// 'depthwise_conv' is a sepecial case of 'exconv' whose
// groups size equals to the input channels size.
testDepthwiseConvLayer("exconv", /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
testDepthwiseConvLayer("exconv", /* useGpu= */ true);
#endif
}
void testConvLayer(const string& type, bool trans, bool useGpu) {
TestConfig config;
config.biasSize = 16;
......
paddle/math/MathFunctions.cpp
浏览文件 @ c110f565
......@@ -202,7 +202,7 @@ double dotProduct<double>(const int n, const double* x, const double* y) {
return cblas_ddot(n, x, 1, y, 1);
}
#ifdef PADDLE_USE_MKL
#if defined(PADDLE_USE_MKL) || defined(PADDLE_USE_MKLML)
template <>
void vExp<float>(const int n, const float* a, float* r) {
......@@ -243,7 +243,55 @@ template <>
void vAdd<double>(const int n, const double* a, const double* b, double* r) {
vdAdd(n, a, b, r);
}
#else
DEFINE_MATRIX_BINARY_OP(vExp, b = std::exp(a));
template <class T>
void vExp(const int n, const T* a, T* r) {
hl_cpu_apply_binary_op<T, binary::vExp<T>, 0, 0>(
binary::vExp<T>(), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_BINARY_OP(vLog, b = std::log(a));
template <class T>
void vLog(const int n, const T* a, T* r) {
hl_cpu_apply_binary_op<T, binary::vLog<T>, 0, 0>(
binary::vLog<T>(), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(vPow, ONE_PARAMETER, b = std::pow(a, p));
template <class T>
void vPow(const int n, const T* a, const T b, T* r) {
hl_cpu_apply_binary_op<T, binary::vPow<T>, 0, 0>(
binary::vPow<T>(b), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_TERNARY_OP(vAdd, c = a + b);
template <class T>
void vAdd(const int n, const T* a, const T* b, T* r) {
hl_cpu_apply_ternary_op<T, ternary::vAdd<T>, 0, 0>(ternary::vAdd<T>(),
const_cast<T*>(a),
const_cast<T*>(b),
r,
1,
n,
n,
n,
n);
}
template void vExp(const int n, const float* a, float* r);
template void vExp(const int n, const double* a, double* r);
template void vLog(const int n, const float* a, float* r);
template void vLog(const int n, const double* a, double* r);
template void vPow(const int n, const float* a, const float b, float* r);
template void vPow(const int n, const double* a, const double b, double* r);
template void vAdd(const int n, const float* a, const float* b, float* r);
template void vAdd(const int n, const double* a, const double* b, double* r);
#endif
#ifdef PADDLE_USE_MKL
template <>
void vInvSqrt<float>(const int n, const float* a, float* r) {
vsInvSqrt(n, a, r);
......@@ -275,20 +323,6 @@ void vTanh<double>(const int n, const double* a, double* r) {
}
#else
DEFINE_MATRIX_BINARY_OP(vExp, b = std::exp(a));
template <class T>
void vExp(const int n, const T* a, T* r) {
hl_cpu_apply_binary_op<T, binary::vExp<T>, 0, 0>(
binary::vExp<T>(), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_BINARY_OP(vLog, b = std::log(a));
template <class T>
void vLog(const int n, const T* a, T* r) {
hl_cpu_apply_binary_op<T, binary::vLog<T>, 0, 0>(
binary::vLog<T>(), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_BINARY_OP(vInvSqrt, b = 1.0f / std::sqrt(a));
template <class T>
void vInvSqrt(const int n, const T* a, T* r) {
......@@ -312,41 +346,12 @@ void vTanh(const int n, const T* a, T* r) {
binary::vTanh<T>(), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(vPow, ONE_PARAMETER, b = std::pow(a, p));
template <class T>
void vPow(const int n, const T* a, const T b, T* r) {
hl_cpu_apply_binary_op<T, binary::vPow<T>, 0, 0>(
binary::vPow<T>(b), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_TERNARY_OP(vAdd, c = a + b);
template <class T>
void vAdd(const int n, const T* a, const T* b, T* r) {
hl_cpu_apply_ternary_op<T, ternary::vAdd<T>, 0, 0>(ternary::vAdd<T>(),
const_cast<T*>(a),
const_cast<T*>(b),
r,
1,
n,
n,
n,
n);
}
template void vExp(const int n, const float* a, float* r);
template void vExp(const int n, const double* a, double* r);
template void vLog(const int n, const float* a, float* r);
template void vLog(const int n, const double* a, double* r);
template void vInvSqrt(const int n, const double* a, double* r);
template void vInvSqrt(const int n, const float* a, float* r);
template void vLog1p(const int n, const float* a, float* r);
template void vLog1p(const int n, const double* a, double* r);
template void vTanh(const int n, const float* a, float* r);
template void vTanh(const int n, const double* a, double* r);
template void vPow(const int n, const float* a, const float b, float* r);
template void vPow(const int n, const double* a, const double b, double* r);
template void vAdd(const int n, const float* a, const float* b, float* r);
template void vAdd(const int n, const double* a, const double* b, double* r);
#endif
......
paddle/math/MathFunctions.h
浏览文件 @ c110f565
......@@ -15,6 +15,12 @@ limitations under the License. */
#ifndef MATHFUNCTIONS_H_
#define MATHFUNCTIONS_H_
#ifdef PADDLE_USE_MKLML
#include <mkl_cblas.h>
#include <mkl_lapacke.h>
#include <mkl_vml_functions.h>
#endif
#ifdef PADDLE_USE_MKL
#include <mkl.h>
#include <mkl_lapacke.h>
......
paddle/memory/CMakeLists.txt
浏览文件 @ c110f565
add_subdirectory(detail)
cc_library(memory SRCS memory.cc)
cc_library(memcpy SRCS memcpy.cc DEPS device_context)
cc_library(paddle_memory
DEPS
memory meta_data
meta_cache memory_block
buddy_allocator system_allocator)
memory
memcpy
meta_data
meta_cache
memory_block
buddy_allocator
system_allocator)
cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory)
paddle/memory/README.md
浏览文件 @ c110f565
## Design
# Region-based Heterogeneous Memory Management
### Usage
To allocate 4KB CPU memory:
```cpp
p = memory::Alloc(platform::CPUPlace(), 4*1024);
```
To allocate 4KB memory on the 3rd GPU:
```cpp
p = memory::Alloc(platform::GPUPlace(2), 4*1024);
```
To free memory and check the so-far used amount of memory on a place:
```cpp
auto pl = platform::GPUPlace(0);
p = memory::Alloc(pl, 4*1024);
cout << memory::Used(pl);
memory::Free(pl, p);
```
### API
In `paddle/memory/memory.h` we have:
```cpp
namespace memory {
template <typename Place> void* Alloc(Place, size_t);
template <typename Place> void Free(Place, void*);
template <typename Place> size_t Used(Place);
} // namespace memory
```
These function templates have specializations on either `platform::CPUPlace` or `platform::GPUPlace`:
```cpp
template<>
void* Alloc<CPUPlace>(CPUPlace p, size_t size) {
return GetCPUBuddyAllocator()->Alloc(size);
}
```
and
```cpp
template<>
void Alloc<GPUPlace>(GPUPlace p, size_t size) {
return GetGPUBuddyAllocator(p.id)->Alloc(size);
}
```
Similar specializations exist for `Free` and `Used`.
### Implementation
`GetCPUBuddyAllocator` and `GetGPUBuddyAllocator` are singletions.
```cpp
BuddyAllocator* GetCPUBuddyAllocator() {
static BuddyAllocator* a = NULL;
if (a == NULL) {
a = new BuddyAllocator(new CPUAllocator /*backup allocator*/, ...);
}
return a;
}
BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
static BuddyAllocator* as = NULL;
if (as == NULL) {
as = new BuddyAllocator*[platform::NumGPUs()];
for (int gpu = 0; gpu < platform::NumGPUs(); gpu++) {
as[gpu] = new BuddyAllocator(new GPUAllocator(gpu) /* backup allocator */, ...);
}
}
return as[gpu_id);
```
#### `BuddyAllocator`
`BuddyAllocator` implements the buddy allocation algorithm. Its constructor takes parameters only related with the algorithm:
```cpp
BuddyAllocator::BuddyAllocator(initial_pool_size, max_pool_size) {
...
}
```
Please be aware that **`BuddyAllocator` always allocate aligned memory**, aligned on 32-bytes, which can hold a `BuddyAllocator::Block` object:
```cpp
class BuddyAllocator {
private:
struct Block {
size_t size;
Block* left, right;
size_t index; // allocator id
};
...
};
```
Because BuddyAllocator has the meta-data of each block, it can trace the used memory -- record the amount returned by `Alloc` freed in `Free`. Instead, `CPUAllocator` and `GPUAllocator` doesn't know the size of freed memory block and cannot do the trace.
#### System Allocators
The `GPUAllocator` and `CPUAllocator` are calls *system allocators*. They work as the fallback allocators of `BuddyAllocator`.
## Justification
I got inspiration from Majel and Caffe2, though above design look different from both.
### Caffe2
In Caffe2, `Tensor<Context>::mutable_data()` allocates the memroy. In particular, [`Tensor<Context>::mutable_data`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L523) calls [`Tensor<Context>::raw_mutable_data`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L459), which in turn calls [`Context::New`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L479).
There are two implementations of `Context`:
1. [`CPUContext`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.h#L105), whose [`New` method](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.h#L131) calls [`g_cpu_allocator.get()->New(size_t)`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.cc#L15) to allocate the memory.
1. [`CUDAContext`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.h#L99), which has a data member [`int gpu_id_`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.h#L202). This looks very similar to class `majel::GPUPlace`, who also has an `int id_` data member. `CUDAContext::New(size_t)` calls [`g_cub_allocator->DeviceAllocate(&ptr, nbytes)`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.cu#L355) to allocate the memory.
### Majel
In Majel, there are basically two allocator types:
1. `cpu::SystemAllocator`, which has similar functionality to `caffe2::CPUContext::New/Delete`.
1. `gpu::SystemAllocator`, which has similar functionality to `caffe2::CUDAContext::New/Delete`.
However, memory allocation is not via these two allocators. Instead, these two allocators are defined in hidden namespaces.
In Majel there are hidden global variables like:
1. `cpu::SystemAllocator g_cpu_allocator`, and
1. `vector<gpu::SystemAllocator*> g_gpu_allocators(NUM_GPUS)`.
Programs allocate memory via a BuddyAllocator, which can take the `g_cpu_allocator` or a `g_gpu_allocators[gpu_id]` as its *fallback allocator*, so that if BuddyAllocator cannot find a block in its memory pool, it extends its memory pool by calling the fallback allocator's `New(size_t)`.
Please check out the [design documentation](http://gangliao.me) to find out more details about
buddy memory allocator for both CPU and GPU.
paddle/memory/memcpy.cc 0 → 100644
浏览文件 @ c110f565
/* 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/memory/memcpy.h"
#include <cstring> // for memcpy
#include "paddle/platform/device_context.h"
namespace paddle {
namespace memory {
template <>
void Copy<platform::CPUPlace, platform::CPUPlace>(platform::CPUPlace, void* dst,
platform::CPUPlace,
const void* src, size_t num) {
std::memcpy(dst, src, num);
}
#ifndef PADDLE_ONLY_CPU
template <>
void Copy<platform::CPUPlace, platform::GPUPlace>(platform::CPUPlace dst_place,
void* dst,
platform::GPUPlace src_place,
const void* src, size_t num,
cudaStream_t stream) {
platform::GPUPlaceGuard g(src_place.device);
platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
}
template <>
void Copy<platform::GPUPlace, platform::CPUPlace>(platform::GPUPlace dst_place,
void* dst,
platform::CPUPlace src_place,
const void* src, size_t num,
cudaStream_t stream) {
platform::GPUPlaceGuard g(dst_place.device);
platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
}
template <>
void Copy<platform::GPUPlace, platform::GPUPlace>(platform::GPUPlace dst_place,
void* dst,
platform::GPUPlace src_place,
const void* src, size_t num,
cudaStream_t stream) {
if (dst_place == src_place) {
platform::GPUPlaceGuard g(src_place.device);
platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToDevice, stream);
} else {
platform::GpuMemcpyPeer(dst, dst_place.device, src, src_place.device, num,
stream);
}
}
#endif // PADDLE_ONLY_CPU
} // namespace memory
} // namespace paddle
paddle/memory/memcpy.h 0 → 100644
浏览文件 @ c110f565
/* 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/platform/gpu_info.h"
#include "paddle/platform/place.h"
namespace paddle {
namespace memory {
template <typename DstPlace, typename SrcPlace>
void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num);
#ifndef PADDLE_ONLY_CPU
template <typename DstPlace, typename SrcPlace>
void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num,
cudaStream_t stream);
#endif // PADDLE_ONLY_CPU
} // namespace memory
} // namespace paddle
paddle/memory/memory.cc
浏览文件 @ c110f565
......@@ -15,7 +15,8 @@ limitations under the License. */
#include "paddle/memory/memory.h"
#include "paddle/memory/detail/buddy_allocator.h"
#include "paddle/memory/detail/system_allocator.h"
#include "paddle/platform/assert.h"
#include <cstring> // for memcpy
namespace paddle {
namespace memory {
......
paddle/memory/memory.h
浏览文件 @ c110f565
......@@ -14,19 +14,32 @@ limitations under the License. */
#pragma once
#include "paddle/platform/gpu_info.h"
#include "paddle/platform/place.h"
namespace paddle {
namespace memory {
template <class Place>
template <typename Place>
void* Alloc(Place, size_t);
template <class Place>
template <typename Place>
void Free(Place, void*);
template <class Place>
template <typename Place>
size_t Used(Place);
template <typename T, /* must be POD types */
typename Place /* platform::GPUPlace or platform::CPUPlace */,
typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
class PODDeleter {
public:
PODDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr)); }
private:
Place place_;
};
} // namespace memory
} // namespace paddle
paddle/operators/CMakeLists.txt
浏览文件 @ c110f565
......@@ -49,6 +49,7 @@ op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
op_library(sigmoid_op SRCS sigmoid_op.cu sigmoid_op.cc)
op_library(softmax_op SRCS softmax_op.cc softmax_op.cu)
op_library(random_op SRCS random_op.cc random_op.cu)
op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
op_library(fc_op SRCS fc_op.cc DEPS mul_op rowwise_add_op sigmoid_op
softmax_op net)
......
paddle/operators/add_op.cc
浏览文件 @ c110f565
......@@ -31,7 +31,7 @@ protected:
"Inputs/Outputs of AddOp must all be set");
PADDLE_ENFORCE(inputs[0]->dims() == inputs[1]->dims(),
"Two input of Add Op's dimension must be same.");
outputs[0]->set_dims(inputs[0]->dims());
outputs[0]->Resize(inputs[0]->dims());
}
};
......@@ -53,6 +53,5 @@ The equation is: Out = X + Y
} // namespace paddle
REGISTER_OP(add_two, paddle::operators::AddOp, paddle::operators::AddOpMaker);
typedef paddle::operators::AddKernel<::paddle::platform::CPUPlace, float>
AddKernel_CPU_float;
REGISTER_OP_CPU_KERNEL(add_two, AddKernel_CPU_float);
REGISTER_OP_CPU_KERNEL(
add_two, paddle::operators::AddKernel<paddle::platform::CPUPlace, float>);
paddle/operators/add_op.cu
浏览文件 @ c110f565
#include "paddle/operators/add_op.h"
#include "paddle/framework/op_registry.h"
typedef paddle::operators::AddKernel<::paddle::platform::GPUPlace, float> AddKernel_GPU_float;
REGISTER_OP_GPU_KERNEL(add_two,
AddKernel_GPU_float);
\ No newline at end of file
paddle::operators::AddKernel<paddle::platform::GPUPlace, float>);
\ No newline at end of file
paddle/operators/cross_entropy_op.cc 0 → 100644
浏览文件 @ c110f565
/* 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/cross_entropy_op.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
protected:
void InferShape(
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2,
"Input size of OnehotCrossEntropyOp must be two");
PADDLE_ENFORCE(outputs.size() == 1,
"Output size of OnehotCrossEntropyOp must be one");
PADDLE_ENFORCE(inputs[0] != nullptr && inputs[1] != nullptr,
"Inputs of OnehotCrossEntropyOp must all be set");
PADDLE_ENFORCE(outputs[0] != nullptr,
"Outputs of OnehotCrossEntropyOp must all be set");
PADDLE_ENFORCE(inputs[0]->dims().size() == 2, "X's dimension must be 2.");
PADDLE_ENFORCE(outputs[0]->dims().size() == 1,
"label's dimension must be 1.");
outputs[0]->Resize(framework::make_ddim({inputs[0]->dims()[0]}));
}
};
class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
OnehotCrossEntropyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of OnehotCrossEntropyOp");
AddInput("label", "The second input of OnehotCrossEntropyOp");
AddOutput("Y", "The output of OnehotCrossEntropyOp");
AddComment(R"DOC(
OnehotCrossEntropy Operator.
Y[i] = -log(X[i][j])
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(onehot_cross_entropy,
paddle::operators::OnehotCrossEntropyOp,
paddle::operators::OnehotCrossEntropyOpMaker);
REGISTER_OP_CPU_KERNEL(
onehot_cross_entropy,
paddle::operators::OnehotCrossEntropyOpKernel<::paddle::platform::CPUPlace,
float>);
paddle/operators/cross_entropy_op.cu 0 → 100644
浏览文件 @ c110f565
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/framework/op_registry.h"
REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
paddle::operators::OnehotCrossEntropyOpKernel<
::paddle::platform::GPUPlace, float>);
\ No newline at end of file
paddle/operators/cross_entropy_op.h 0 → 100644
浏览文件 @ c110f565
/* 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 "glog/logging.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class OnehotCrossEntropyOpKernel : public framework::OpKernel {
public:
constexpr T LOG_THRESHOLD() const { return static_cast<T>(1e-20); }
void Compute(const framework::KernelContext& context) const override {
auto X = context.Input(0)->Get<framework::Tensor>();
const T* X_data = X.data<T>();
const int* label_data =
context.Input(1)->Get<framework::Tensor>().data<int>();
auto* Y = context.Output(0)->GetMutable<framework::Tensor>();
Y->mutable_data<T>(context.GetPlace());
T* Y_data = Y->data<T>();
int batch_size = X.dims()[0];
int class_num = X.dims()[1];
// Y[i] = -log(X[i][j])
for (int i = 0; i < batch_size; ++i) {
Y_data[i] = -std::log(
std::max(X_data[i * class_num + label_data[i]], LOG_THRESHOLD()));
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/mul_op.cc
浏览文件 @ c110f565
......@@ -12,9 +12,9 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/framework/tensor.h>
#include <paddle/operators/mul_op.h>
#include "paddle/operators/mul_op.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
......@@ -33,7 +33,7 @@ protected:
dim0[1] == dim1[0],
"First matrix's width must be equal with second matrix's height.");
PADDLE_ENFORCE(outputs.size() == 1, "The mul op must take one output");
outputs[0]->set_dims({dim0[0], dim1[1]});
outputs[0]->Resize({dim0[0], dim1[1]});
}
};
......@@ -57,4 +57,4 @@ The equation is: Out = X * Y
REGISTER_OP(mul, paddle::operators::MulOp, paddle::operators::MulOpMaker);
REGISTER_OP_CPU_KERNEL(
mul, paddle::operators::MulKernel<paddle::platform::CPUPlace>);
mul, paddle::operators::MulKernel<paddle::platform::CPUPlace, float>);
paddle/operators/mul_op.cu
浏览文件 @ c110f565
......@@ -12,9 +12,9 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/operators/mul_op.h>
#include <paddle/framework/op_registry.h>
#include "paddle/operators/mul_op.h"
#include "paddle/framework/op_registry.h"
REGISTER_OP_GPU_KERNEL(mul,
paddle::operators::MulKernel<paddle::platform
::GPUPlace>);
\ No newline at end of file
::GPUPlace, float>);
\ No newline at end of file
paddle/operators/mul_op.h
浏览文件 @ c110f565
......@@ -14,17 +14,30 @@
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place>
template <typename Place, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Mul kernel in " << typeid(Place).name();
void Compute(const framework::KernelContext& context) const override {
Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
{Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
auto input0 = context.Input(0)->Get<framework::Tensor>();
auto input1 = context.Input(1)->Get<framework::Tensor>();
auto* output = context.Output(0)->GetMutable<framework::Tensor>();
output->mutable_data<T>(context.GetPlace());
framework::EigenMatrix<T>::From(*output).device(
*(context.GetEigenDevice<Place>())) =
framework::EigenMatrix<T>::From(input0).contract(
framework::EigenMatrix<T>::From(input1), dim_pair);
}
};
} // namespace operators
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ c110f565
......@@ -12,8 +12,8 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/operators/rowwise_add_op.h>
#include "paddle/operators/rowwise_add_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
......@@ -30,7 +30,7 @@ protected:
PADDLE_ENFORCE(dim1.size() == 1, "The second input must be vector");
PADDLE_ENFORCE(dim0[1] == dim1[0], "The width of two input must be same");
PADDLE_ENFORCE(outputs.size() == 1, "The output size must be 1");
outputs[0]->set_dims(inputs[0]->dims());
outputs[0]->Resize(inputs[0]->dims());
}
};
......@@ -58,4 +58,4 @@ REGISTER_OP(rowwise_add,
paddle::operators::RowWiseAddOpMaker);
REGISTER_OP_CPU_KERNEL(
rowwise_add,
paddle::operators::RowWiseAddKernel<paddle::platform::CPUPlace>);
paddle::operators::RowWiseAddKernel<paddle::platform::CPUPlace, float>);
paddle/operators/rowwise_add_op.cu
浏览文件 @ c110f565
#include <paddle/framework/op_registry.h>
#include <paddle/operators/rowwise_add_op.h>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/rowwise_add_op.h"
REGISTER_OP_GPU_KERNEL(
rowwise_add,
paddle::operators::RowWiseAddKernel<paddle::platform ::GPUPlace>);
paddle::operators::RowWiseAddKernel<paddle::platform ::GPUPlace, float>);
paddle/operators/rowwise_add_op.h
浏览文件 @ c110f565
......@@ -13,17 +13,32 @@
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place>
template <typename Place, typename T>
class RowWiseAddKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "RowWiseAdd kernel in " << typeid(Place).name();
void Compute(const framework::KernelContext& context) const override {
auto in0 = context.Input(0)->Get<framework::Tensor>();
auto in1 = context.Input(1)->Get<framework::Tensor>();
auto* out = context.Output(0)->GetMutable<framework::Tensor>();
out->mutable_data<T>(context.GetPlace());
auto input = framework::EigenMatrix<T>::From(in0);
auto bias = framework::EigenVector<T>::From(in1);
auto output = framework::EigenMatrix<T>::From(*out);
const int bias_size = bias.dimension(0);
const int rest_size = input.size() / bias_size;
Eigen::DSizes<int, 1> one_d(input.size());
Eigen::DSizes<int, 1> bcast(rest_size);
output.reshape(one_d).device(*(context.GetEigenDevice<Place>())) =
input.reshape(one_d) + bias.broadcast(bcast).reshape(one_d);
}
};
......
paddle/operators/sgd_op.cc
浏览文件 @ c110f565
......@@ -31,7 +31,7 @@ protected:
PADDLE_ENFORCE(outputs[0] != nullptr, "outputs[0] mast be set");
PADDLE_ENFORCE(inputs[0]->dims() == inputs[1]->dims(),
"Two input of SGD Op's dimension must be same.");
outputs[0]->set_dims(inputs[0]->dims());
outputs[0]->Resize(inputs[0]->dims());
}
};
......
paddle/operators/sigmoid_op.cc
浏览文件 @ c110f565
......@@ -12,8 +12,8 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/operators/sigmoid_op.h>
#include "paddle/operators/sigmoid_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
......@@ -24,7 +24,7 @@ protected:
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(outputs.size() == 1, "Sigmoid Op only have one output");
outputs[0]->set_dims(inputs[0]->dims());
outputs[0]->Resize(inputs[0]->dims());
}
};
......@@ -34,7 +34,7 @@ public:
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "sigmoid input");
AddInput("Y", "sigmoid output");
AddOutput("Y", "sigmoid output");
AddComment("Sigmoid function");
}
};
......@@ -46,4 +46,5 @@ REGISTER_OP(sigmoid,
paddle::operators::SigmoidOp,
paddle::operators::SigmoidOpMaker);
REGISTER_OP_CPU_KERNEL(
sigmoid, paddle::operators::SigmoidKernel<paddle::platform::CPUPlace>);
sigmoid,
paddle::operators::SigmoidKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sigmoid_op.cu
浏览文件 @ c110f565
#include <paddle/operators/sigmoid_op.h>
#include <paddle/framework/op_registry.h>
#include "paddle/operators/sigmoid_op.h"
#include "paddle/framework/op_registry.h"
REGISTER_OP_GPU_KERNEL(
sigmoid, paddle::operators::SigmoidKernel<paddle::platform::GPUPlace>);
sigmoid, paddle::operators::SigmoidKernel<paddle::platform::GPUPlace, float>);
paddle/operators/sigmoid_op.h
浏览文件 @ c110f565
......@@ -14,17 +14,25 @@
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place>
template <typename Place, typename T>
class SigmoidKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Sigmoid kernel in " << typeid(Place).name();
void Compute(const framework::KernelContext& context) const override {
auto input = context.Input(0)->Get<framework::Tensor>();
auto* output = context.Output(0)->GetMutable<framework::Tensor>();
output->mutable_data<T>(context.GetPlace());
framework::EigenVector<T>::Flatten(*output).device(
*(context.GetEigenDevice<Place>())) =
1.0 / (1.0 + (-1.0 * framework::EigenVector<T>::Flatten(input)).exp());
}
};
} // namespace operators
......
paddle/operators/softmax_op.cc
浏览文件 @ c110f565
......@@ -11,8 +11,8 @@
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/operators/softmax_op.h>
#include "paddle/operators/softmax_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
......@@ -23,9 +23,11 @@ protected:
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 1, "Only one input is need for softmax");
PADDLE_ENFORCE(inputs[0]->dims().size() == 2,
"The input of softmax op must be matrix");
PADDLE_ENFORCE(outputs.size() == 1, "Only one output is need for softmax");
outputs[0]->set_dims(inputs[0]->dims());
outputs[0]->Resize(inputs[0]->dims());
}
};
......@@ -46,4 +48,5 @@ public:
namespace ops = paddle::operators;
REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<paddle::platform::CPUPlace>);
REGISTER_OP_CPU_KERNEL(softmax,
ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
paddle/operators/softmax_op.cu
浏览文件 @ c110f565
#include <paddle/framework/op_registry.h>
#include <paddle/operators/softmax_op.h>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/softmax_op.h"
REGISTER_OP_GPU_KERNEL(
softmax, paddle::operators::SoftmaxKernel<paddle::platform::GPUPlace>);
softmax, paddle::operators::SoftmaxKernel<paddle::platform::GPUPlace, float>);
paddle/operators/softmax_op.h
浏览文件 @ c110f565
......@@ -14,17 +14,49 @@
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place>
template <typename Place, typename T>
class SoftmaxKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Softmax kernel in " << typeid(Place).name();
void Compute(const framework::KernelContext& context) const override {
auto input = context.Input(0)->Get<framework::Tensor>();
auto* output = context.Output(0)->GetMutable<framework::Tensor>();
output->mutable_data<T>(context.GetPlace());
auto logits = framework::EigenMatrix<T>::From(input);
auto softmax = framework::EigenMatrix<T>::From(*output);
const int kBatchDim = 0;
const int kClassDim = 1;
const int batch_size = logits.dimension(kBatchDim);
const int num_classes = logits.dimension(kClassDim);
Eigen::DSizes<int, 1> along_class(kClassDim);
Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
Eigen::DSizes<int, 2> one_by_class(1, num_classes);
auto shifted_logits = (logits -
logits.maximum(along_class)
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class));
softmax.device(*(context.GetEigenDevice<Place>())) = shifted_logits.exp();
softmax.device(*(context.GetEigenDevice<Place>())) =
(softmax *
softmax.sum(along_class)
.inverse()
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class));
}
};
} // namespace operators
......
paddle/platform/enforce.h
浏览文件 @ c110f565
......@@ -43,10 +43,26 @@ namespace platform {
// For more details, please check https://stackoverflow.com/a/43870188/724872.
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
template <typename T>
inline void throw_on_error(T e) {
throw_on_error(e, "");
}
template <typename... Args>
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
int stat, const Args&... args) {
if (UNLIKELY(!(stat))) {
throw std::runtime_error(
string::Sprintf(args...) +
string::Sprintf(" at [%s:%s];", __FILE__, __LINE__));
}
}
#ifndef PADDLE_ONLY_CPU
template <typename... Args>
inline void throw_on_error(cudaError_t e, const Args&... args) {
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
cudaError_t e, const Args&... args) {
if (UNLIKELY(e)) {
// clang-format off
throw thrust::system_error(
......@@ -58,7 +74,8 @@ inline void throw_on_error(cudaError_t e, const Args&... args) {
}
template <typename... Args>
inline void throw_on_error(curandStatus_t stat, const Args&... args) {
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
curandStatus_t stat, const Args&... args) {
if (stat != CURAND_STATUS_SUCCESS) {
// clang-format off
throw thrust::system_error(
......@@ -70,7 +87,8 @@ inline void throw_on_error(curandStatus_t stat, const Args&... args) {
}
template <typename... Args>
inline void throw_on_error(cudnnStatus_t stat, const Args&... args) {
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
cudnnStatus_t stat, const Args&... args) {
if (stat == CUDNN_STATUS_SUCCESS) {
return;
} else {
......@@ -84,7 +102,8 @@ inline void throw_on_error(cudnnStatus_t stat, const Args&... args) {
}
template <typename... Args>
inline void throw_on_error(cublasStatus_t stat, const Args&... args) {
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
cublasStatus_t stat, const Args&... args) {
std::string err;
if (stat == CUBLAS_STATUS_SUCCESS) {
return;
......@@ -113,15 +132,6 @@ inline void throw_on_error(cublasStatus_t stat, const Args&... args) {
#endif // PADDLE_ONLY_CPU
template <typename... Args>
inline void throw_on_error(int stat, const Args&... args) {
if (UNLIKELY(!(stat))) {
throw std::runtime_error(
string::Sprintf(args...) +
string::Sprintf(" at [%s:%s];", __FILE__, __LINE__));
}
}
#define PADDLE_THROW(...) \
do { \
throw std::runtime_error( \
......@@ -129,12 +139,9 @@ inline void throw_on_error(int stat, const Args&... args) {
string::Sprintf(" at [%s:%s];", __FILE__, __LINE__)); \
} while (0)
/**
* @brief Enforce a condition, otherwise throw an EnforceNotMet
*/
#define PADDLE_ENFORCE(condition, ...) \
do { \
::paddle::platform::throw_on_error(condition, __VA_ARGS__); \
#define PADDLE_ENFORCE(...) \
do { \
::paddle::platform::throw_on_error(__VA_ARGS__); \
} while (0)
} // namespace platform
......
paddle/platform/gpu_info.cc
浏览文件 @ c110f565
......@@ -44,7 +44,7 @@ void SetDeviceId(int id) {
"cudaSetDevice failed in paddle::platform::SetDeviceId");
}
void GpuMemoryUsage(size_t& available, size_t& total) {
void GpuMemoryUsage(size_t &available, size_t &total) {
PADDLE_ENFORCE(cudaMemGetInfo(&available, &total),
"cudaMemGetInfo failed in paddle::platform::GetMemoryUsage");
}
......@@ -82,5 +82,28 @@ size_t GpuMaxChunkSize() {
return usable;
}
void GpuMemcpyAsync(void *dst, const void *src, size_t count,
enum cudaMemcpyKind kind, cudaStream_t stream) {
PADDLE_ENFORCE(cudaMemcpyAsync(dst, src, count, kind, stream),
"cudaMemcpyAsync failed in paddle::platform::GpuMemcpyAsync");
}
void GpuMemcpySync(void *dst, const void *src, size_t count,
enum cudaMemcpyKind kind) {
PADDLE_ENFORCE(cudaMemcpy(dst, src, count, kind),
"cudaMemcpy failed in paddle::platform::GpuMemcpySync");
// note: cudaMemcpy may actually be asynchronous with respect to the caller,
// block on stream 0 to make sure the copy has completed
PADDLE_ENFORCE(
cudaStreamSynchronize(0),
"cudaStreamSynchronize failed in paddle::platform::GpuMemcpySync");
}
void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device,
size_t count, cudaStream_t stream) {
PADDLE_ENFORCE(
cudaMemcpyPeerAsync(dst, dst_device, src, src_device, count, stream),
"cudaMemcpyPeerAsync failed in paddle::platform::GpuMemcpyPeer");
}
} // namespace platform
} // namespace paddle
paddle/platform/gpu_info.h
浏览文件 @ c110f565
......@@ -16,6 +16,7 @@ limitations under the License. */
#ifndef PADDLE_ONLY_CPU
#include <cuda_runtime.h>
#include <stddef.h>
namespace paddle {
......@@ -31,7 +32,7 @@ int GetCurrentDeviceId();
void SetDeviceId(int device_id);
//！Get the memory usage of current GPU device.
void GpuMemoryUsage(size_t& available, size_t& total);
void GpuMemoryUsage(size_t &available, size_t &total);
//! Get the maximum allocation size of current GPU device.
size_t GpuMaxAllocSize();
......@@ -42,6 +43,18 @@ size_t GpuMinChunkSize();
//! Get the maximum chunk size for GPU buddy allocator.
size_t GpuMaxChunkSize();
//! Copy memory from address src to dst asynchronously.
void GpuMemcpyAsync(void *dst, const void *src, size_t count,
enum cudaMemcpyKind kind, cudaStream_t stream);
//! Copy memory from address src to dst synchronously.
void GpuMemcpySync(void *dst, const void *src, size_t count,
enum cudaMemcpyKind kind);
//! Copy memory from one device to another device.
void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device,
size_t count, cudaStream_t stream);
} // namespace platform
} // namespace paddle
......
paddle/pybind/CMakeLists.txt
浏览文件 @ c110f565
cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python
add_op fc_op sgd_op)
add_op fc_op sgd_op cross_entropy_op)
paddle/pybind/pybind.cc
浏览文件 @ c110f565
......@@ -13,25 +13,43 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <Python.h>
#include <paddle/framework/op_registry.h>
#include <paddle/framework/operator.h>
#include <paddle/framework/scope.h>
#include <paddle/pybind/tensor_bind.h>
#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <fstream>
#include <vector>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/pybind/tensor_bind.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
namespace py = pybind11;
namespace pd = paddle::framework;
USE_OP(add_two);
USE_OP(onehot_cross_entropy);
USE_OP_WITHOUT_KERNEL(fc);
USE_OP(sgd);
USE_OP(mul);
USE_OP(sigmoid);
USE_OP(softmax);
USE_OP(rowwise_add);
template <typename ClassType>
void ExposeOperator(ClassType& m) {
m.def("infer_shape", &ClassType::type::InferShape)
.def("run", &ClassType::type::Run)
.def("outputs",
[](const typename ClassType::type& op) -> std::vector<std::string> {
return op.outputs_;
})
.def("__str__", &ClassType::type::DebugString);
}
PYBIND11_PLUGIN(core) {
py::module m("core", "C++ core of Paddle Paddle");
py::module m("core", "C++ core of PaddlePaddle");
py::class_<pd::Tensor>(m, "Tensor", py::buffer_protocol())
.def_buffer([](pd::Tensor& self) -> py::buffer_info {
......@@ -41,7 +59,7 @@ PYBIND11_PLUGIN(core) {
[](const pd::Tensor& self) { return pd::vectorize(self.dims()); })
.def("set_dims",
[](pd::Tensor& self, const std::vector<int>& dim) {
self.set_dims(pd::make_ddim(dim));
self.Resize(pd::make_ddim(dim));
})
.def("alloc_float",
[](pd::Tensor& self) {
......@@ -108,21 +126,37 @@ All parameter, weight, gradient are variables in Paddle.
return new paddle::platform::CPUDeviceContext();
});
py::class_<pd::OperatorBase, pd::OperatorPtr>(m, "Operator")
.def("__str__", &pd::OperatorBase::DebugString)
py::class_<pd::OperatorBase, pd::OperatorPtr> operator_base(m, "Operator");
operator_base.def_static("create", [](py::bytes protobin) -> pd::OperatorPtr {
pd::OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
return pd::OpRegistry::CreateOp(desc);
});
ExposeOperator(operator_base);
using PlainNetPtr = std::shared_ptr<pd::PlainNet>;
py::class_<pd::PlainNet, PlainNetPtr> plain_net(m, "PlainNet");
plain_net
.def_static("create",
[](py::bytes protobin) {
pd::OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
return pd::OpRegistry::CreateOp(desc);
[]() -> std::shared_ptr<pd::PlainNet> {
auto retv = std::make_shared<pd::PlainNet>();
retv->type_ = "plain_net";
return retv;
})
.def("infer_shape", &pd::OperatorBase::InferShape)
.def("run", &pd::OperatorBase::Run)
.def("outputs", [](const pd::OperatorPtr& op) { return op->outputs_; });
.def("add_op", &pd::PlainNet::AddOp)
.def("add_op",
[](PlainNetPtr& self, const PlainNetPtr& plain_net) -> void {
self->AddOp(std::static_pointer_cast<pd::OperatorBase>(plain_net));
})
.def("complete_add_op", &pd::PlainNet::CompleteAddOp)
.def("complete_add_op", [](PlainNetPtr& self) { self->CompleteAddOp(); });
ExposeOperator(plain_net);
return m.ptr();
}
paddle/pybind/tensor_bind.h
浏览文件 @ c110f565
......@@ -86,7 +86,7 @@ void PyTensorSetFromArray(
dims.push_back((int)array.shape()[i]);
}
self.set_dims(framework::make_ddim(dims));
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<T>(paddle::platform::CPUPlace());
std::memcpy(dst, array.data(), sizeof(T) * array.size());
}
......
python/paddle/trainer/config_parser.py
浏览文件 @ c110f565
......@@ -3219,6 +3219,10 @@ def ParameterHook(type, **kwargs):
if sparsity_ratio is not None:
hook.sparsity_ratio = sparsity_ratio
return hook
elif type == 'dpruning':
hook = ParameterUpdaterHookConfig()
hook.type = type
return hook
else:
return None
......
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ c110f565
add_python_test(test_framework test_protobuf.py test_scope.py
test_default_scope_funcs.py test_op_creation_methods.py
test_tensor.py test_fc_op.py test_add_two_op.py test_sgd_op.py)
add_python_test(test_framework
test_protobuf.py
test_scope.py
test_default_scope_funcs.py
test_op_creation_methods.py
test_plain_net.py
test_tensor.py
test_fc_op.py
test_add_two_op.py
test_sgd_op.py
test_cross_entropy_op.py
test_mul_op.py
test_sigmoid_op.py
test_softmax_op.py
test_rowwise_add_op.py)
python/paddle/v2/framework/tests/op_test_util.py
浏览文件 @ c110f565
......@@ -56,7 +56,10 @@ class OpTestMeta(type):
for out_name in func.all_output_args:
actual = numpy.array(scope.get_var(out_name).get_tensor())
expect = getattr(self, out_name)
numpy.testing.assert_almost_equal(actual, expect)
# TODO(qijun) The default decimal is 7, but numpy.dot and eigen.mul
# has some diff, and could not pass unittest. So I set decimal 3 here.
# And I will check this in future.
numpy.testing.assert_almost_equal(actual, expect, decimal=3)
obj.test_all = test_all
return obj
python/paddle/v2/framework/tests/test_cross_entropy_op.py 0 → 100644
浏览文件 @ c110f565
import unittest
import numpy
from op_test_util import OpTestMeta
class TestSGD(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "onehot_cross_entropy"
batch_size = 100
class_num = 10
self.X = numpy.random.random((batch_size, class_num)).astype("float32")
self.label = 5 * numpy.ones(batch_size).astype("int32")
Y = []
for i in range(0, batch_size):
Y.append(-numpy.log(self.X[i][self.label[i]]))
self.Y = numpy.array(Y).astype("float32")
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_mul_op.py 0 → 100644
浏览文件 @ c110f565
import unittest
from op_test_util import OpTestMeta
import numpy as np
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "mul"
self.X = np.random.random((32, 784)).astype("float32")
self.Y = np.random.random((784, 100)).astype("float32")
self.Out = np.dot(self.X, self.Y)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_plain_net.py 0 → 100644
浏览文件 @ c110f565
import paddle.v2.framework.core as core
from paddle.v2.framework.create_op_creation_methods import op_creations
import unittest
class TestNet(unittest.TestCase):
def test_net_all(self):
net = core.PlainNet.create()
op1 = op_creations.add_two(X="X", Y="Y", Out="Out")
net.add_op(op1)
net2 = core.PlainNet.create()
net2.add_op(op_creations.fc(X="X", W="w", Y="fc.out"))
net2.complete_add_op(True)
net.add_op(net2)
net.complete_add_op(True)
expected = '''
Op(plain_net), inputs:(@EMPTY@, X, Y, w), outputs:(@TEMP@fc@0, Out, fc.out).
Op(add_two), inputs:(X, Y), outputs:(Out).
Op(plain_net), inputs:(@EMPTY@, X, w), outputs:(@TEMP@fc@0, fc.out).
Op(fc), inputs:(X, w, @EMPTY@), outputs:(fc.out, @TEMP@fc@0).
Op(mul), inputs:(X, w), outputs:(@TEMP@fc@0).
Op(sigmoid), inputs:(@TEMP@fc@0), outputs:(fc.out).
'''
self.assertEqual(expected, "\n" + str(net))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_rowwise_add_op.py 0 → 100644
浏览文件 @ c110f565
import unittest
from op_test_util import OpTestMeta
import numpy as np
class TestRowwiseAddOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "rowwise_add"
self.X = np.random.random((32, 784)).astype("float32")
self.b = np.random.random(784).astype("float32")
self.Out = np.add(self.X, self.b)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_sigmoid_op.py 0 → 100644
浏览文件 @ c110f565
import unittest
from op_test_util import OpTestMeta
import numpy as np
class TestSigmoidOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "sigmoid"
self.X = np.random.random((32, 100)).astype("float32")
self.Y = 1 / (1 + np.exp(-self.X))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_softmax_op.py 0 → 100644
浏览文件 @ c110f565
import unittest
from op_test_util import OpTestMeta
import numpy as np
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx = x - np.max(x)
exps = np.exp(shiftx)
return exps / np.sum(exps)
class TestSoftmaxOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "softmax"
self.X = np.random.random((32, 100)).astype("float32")
self.Y = np.apply_along_axis(stable_softmax, 1, self.X)
if __name__ == '__main__':
unittest.main()
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