提交 84c3523c 编写于 作者: H hedaoyuan 提交者: GitHub

Merge pull request #2625 from hedaoyuan/nnpack_lib

NNPACKConvFunction
...@@ -49,6 +49,7 @@ option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF) ...@@ -49,6 +49,7 @@ option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option(ON_TRAVIS "Exclude special unit test on Travis CI" OFF) option(ON_TRAVIS "Exclude special unit test on Travis CI" OFF)
option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF) option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF)
option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF) option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
# CMAKE_BUILD_TYPE # CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE) if(NOT CMAKE_BUILD_TYPE)
...@@ -129,6 +130,10 @@ if(WITH_GPU) ...@@ -129,6 +130,10 @@ if(WITH_GPU)
endif(NOT WITH_DSO) endif(NOT WITH_DSO)
endif(WITH_GPU) endif(WITH_GPU)
if(USE_NNPACK)
list(APPEND EXTERNAL_LIBS ${NNPACK_LIB} ${PTHREADPOOL_LIB} "rt")
endif(USE_NNPACK)
add_subdirectory(proto) add_subdirectory(proto)
# "add_subdirectory(paddle)" and "add_subdirectory(python)" should be # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be
......
...@@ -10,6 +10,14 @@ if(WITH_GPU) ...@@ -10,6 +10,14 @@ if(WITH_GPU)
cuda_compile(cu_objs ${cu_files}) cuda_compile(cu_objs ${cu_files})
endif() endif()
if(USE_NNPACK)
include(nnpack/nnpack.cmake)
list(APPEND cpp_files nnpack/NNPACKConvOp.cpp)
if(WITH_TESTING)
add_unittest(NNPACKConvOpTest nnpack/NNPACKConvOpTest.cpp)
endif()
endif()
add_library(paddle_function STATIC ${cpp_files} ${cu_objs}) add_library(paddle_function STATIC ${cpp_files} ${cu_objs})
add_dependencies(paddle_function ${external_project_dependencies}) add_dependencies(paddle_function ${external_project_dependencies})
add_dependencies(paddle_function paddle_proto) add_dependencies(paddle_function paddle_proto)
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "nnpack.h"
#include "paddle/function/ConvOp.h"
DEFINE_bool(nnpack_allocate_outside,
false,
"Allocate and free workspace memory outside the NNPACK interface.");
DEFINE_int32(nnpack_num_threads,
0,
"The number of nnpack threads"
"default: 0; 0 to disable threadpool.");
namespace paddle {
nnp_convolution_algorithm get_nnp_convolution_algorithm(
const std::string& algorithm) {
if (algorithm == "auto") {
return nnp_convolution_algorithm_auto;
} else if (algorithm == "ft8x8") {
return nnp_convolution_algorithm_ft8x8;
} else if (algorithm == "ft16x16") {
return nnp_convolution_algorithm_ft16x16;
} else if (algorithm == "wt8x8") {
return nnp_convolution_algorithm_wt8x8;
} else if (algorithm == "implicit-gemm") {
return nnp_convolution_algorithm_implicit_gemm;
} else if (algorithm == "direct") {
return nnp_convolution_algorithm_direct;
} else {
return nnp_convolution_algorithm_auto;
}
}
template <DeviceType Device>
class NNPACKConvFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
CHECK_EQ(groups_, (size_t)1);
algorithm_ = get_nnp_convolution_algorithm(config.get<std::string>("algo"));
// algorithm_ = nnp_convolution_algorithm_auto;
transform_strategy_ = nnp_convolution_transform_strategy_compute;
nnp_status status = nnp_initialize();
CHECK_EQ(status, nnp_status_success);
workspaceBuffer_ = nullptr;
workspaceSize_ = 0;
threadpool_ = nullptr;
if (FLAGS_nnpack_num_threads) {
threadpool_ = pthreadpool_create(FLAGS_nnpack_num_threads);
VLOG(3) << "Number of threads "
<< pthreadpool_get_threads_count(threadpool_);
}
}
~NNPACKConvFunction() {
if (threadpool_) {
pthreadpool_destroy(threadpool_);
}
if (workspaceBuffer_) {
free(workspaceBuffer_);
}
}
virtual 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_EQ(outputs[0].getArgType(), ASSIGN_TO);
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];
nnp_size inputSize = {.width = inputWidth, .height = inputHeight};
nnp_padding padding = {.top = (size_t)paddingH(),
.right = (size_t)paddingW(),
.bottom = (size_t)paddingH(),
.left = (size_t)paddingW()};
nnp_size kernelSize = {.width = filterWidth, .height = filterHeight};
nnp_size outputSubsampling = {.width = (size_t)strideW(),
.height = (size_t)strideH()};
float* inputData = inputs[0].data<float>();
float* filterData = inputs[1].data<float>();
float* outputData = outputs[0].data<float>();
void* bufferPtr = nullptr;
size_t* sizePtr = nullptr;
size_t needSize;
if (FLAGS_nnpack_allocate_outside) {
if (batchSize == 1) {
nnp_status status = nnp_convolution_inference(algorithm_,
transform_strategy_,
inputChannels,
outputChannels,
inputSize,
padding,
kernelSize,
outputSubsampling,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
&needSize,
nnp_activation_identity,
nullptr,
nullptr,
nullptr);
CHECK_EQ(status, nnp_status_success);
} else {
// only supports stride = 1
CHECK_EQ(strideH(), 1);
CHECK_EQ(strideW(), 1);
nnp_status status = nnp_convolution_output(algorithm_,
batchSize,
inputChannels,
outputChannels,
inputSize,
padding,
kernelSize,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
&needSize,
nnp_activation_identity,
nullptr,
nullptr,
nullptr);
CHECK_EQ(status, nnp_status_success);
}
VLOG(3) << "workspace size is " << needSize;
if (needSize > workspaceSize_) {
workspaceSize_ = needSize;
if (workspaceBuffer_) {
free(workspaceBuffer_);
} else {
posix_memalign(&workspaceBuffer_, 64, needSize);
}
}
if (needSize) {
bufferPtr = workspaceBuffer_;
sizePtr = &needSize;
}
}
if (batchSize == 1) {
nnp_status status =
nnp_convolution_inference(algorithm_,
transform_strategy_,
inputChannels,
outputChannels,
inputSize,
padding,
kernelSize,
outputSubsampling,
inputData,
filterData,
nullptr, /* bias */
outputData,
bufferPtr,
sizePtr,
nnp_activation_identity,
nullptr,
threadpool_, /* threadpool */
nullptr);
CHECK_EQ(status, nnp_status_success);
} else {
// only supports stride = 1
CHECK_EQ(strideH(), 1);
CHECK_EQ(strideW(), 1);
nnp_status status = nnp_convolution_output(algorithm_,
batchSize,
inputChannels,
outputChannels,
inputSize,
padding,
kernelSize,
inputData,
filterData,
nullptr, /* bias */
outputData,
bufferPtr,
sizePtr,
nnp_activation_identity,
nullptr,
threadpool_, /* threadpool */
nullptr);
CHECK_EQ(status, nnp_status_success);
}
}
private:
nnp_convolution_algorithm algorithm_;
nnp_convolution_transform_strategy transform_strategy_;
void* workspaceBuffer_;
size_t workspaceSize_;
pthreadpool_t threadpool_;
};
REGISTER_TYPED_FUNC(NNPACKConv, CPU, NNPACKConvFunction);
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/function/Function.h"
#include "paddle/function/FunctionTest.h"
DEFINE_string(algo,
"auto",
"The algorithm (auto, ft8x8, ft16x16, wt8x8, "
"implicit-gemm, or direct) for computing convolution of NNPACK.");
namespace paddle {
#define IS_NNPACK_SUPPORT(algo, filterSize, stride) \
if (algo == "direct" && filterSize != 1) continue; \
if (algo == "direct" && batchSize != 1) continue; \
if (algo == "wt8x8" && filterSize != 3) continue; \
if (algo == "implicit-gemm" && batchSize != 1) continue; \
if (algo != "auto" && algo != "implicit-gemm" && stride > 1) continue;
class ConvolutionTest {
public:
ConvolutionTest(const std::string& conv1,
const std::string& conv2,
std::string algo = "auto") {
for (size_t batchSize : {1, 32}) {
for (size_t inputSize : {7, 14, 54}) {
for (size_t filterSize : {1, 3, 5}) {
for (size_t inputChannels : {3, 64}) {
for (size_t outputChannels : {3, 64, 128}) {
if (inputChannels < outputChannels) break;
for (size_t stride : {1, 2}) {
// if batchSize > 1 NNPACKConv only supports stride = 1
if (batchSize > 1 && stride > 1) break;
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
IS_NNPACK_SUPPORT(algo, filterSize, stride);
LOG(INFO) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize
<< " stride=" << stride << " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("algo", algo));
TensorShape shape0{
batchSize, inputChannels, inputSize, inputSize};
TensorShape shape1{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape shape2{
batchSize, outputChannels, outputSize, outputSize};
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape0));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape1));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape2));
test.run();
}
}
}
}
}
}
}
}
};
TEST(Convolution, NNPACK) {
// NNPACK only supports stride = 1
ConvolutionTest test("GemmConv-CPU", "NNPACKConv-CPU", FLAGS_algo);
}
} // namespace paddle
# Find the NNPACK library
# NNPACK_ROOT - where to find NNPACK include and library.
#
set(NNPACK_FOUND OFF)
set(NNPACK_ROOT $ENV{NNPACK_ROOT} CACHE PATH "Folder contains NNPACK")
find_path(NNPACK_INC_DIR nnpack.h PATHS ${NNPACK_ROOT}/include)
find_library(NNPACK_LIB NAMES nnpack PATHS ${NNPACK_ROOT}/lib)
find_library(PTHREADPOOL_LIB NAMES pthreadpool PATHS ${NNPACK_ROOT}/lib)
if(NNPACK_INC_DIR AND NNPACK_LIB AND PTHREADPOOL_LIB)
set(NNPACK_FOUND ON)
INCLUDE_DIRECTORIES(${NNPACK_INC_DIR})
else()
message(FATAL_ERROR "Cannot find NNPACK in (${NNPACK_ROOT})")
endif()
...@@ -16,6 +16,10 @@ limitations under the License. */ ...@@ -16,6 +16,10 @@ limitations under the License. */
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
DEFINE_bool(use_nnpack,
false,
"Whether to use nnpack for convolution calculation.");
namespace paddle { namespace paddle {
/* /*
...@@ -37,26 +41,38 @@ bool ExpandConvLayer::init(const LayerMap &layerMap, ...@@ -37,26 +41,38 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
for (int i = 0; i < config_.inputs_size(); i++) { 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> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]}; std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
createFunction(forward_,
!isDeconv_ ? "GemmConv" : "GemmConvGradInput", if (FLAGS_use_nnpack) {
FuncConfig() CHECK_EQ(isDeconv_, false);
.set("paddings", paddings) createFunction(forward_,
.set("strides", strides) "NNPACKConv",
.set("groups", (size_t)groups_[i])); FuncConfig()
.set("paddings", paddings)
createFunction(backward_, .set("strides", strides)
!isDeconv_ ? "GemmConvGradInput" : "GemmConv", .set("groups", (size_t)groups_[i])
FuncConfig() .set("algo", std::string("auto")));
.set("paddings", paddings) } else {
.set("strides", strides) createFunction(forward_,
.set("groups", (size_t)groups_[i])); !isDeconv_ ? "GemmConv" : "GemmConvGradInput",
FuncConfig()
createFunction(backward_, .set("paddings", paddings)
"GemmConvGradFilter", .set("strides", strides)
FuncConfig() .set("groups", (size_t)groups_[i]));
.set("paddings", paddings)
.set("strides", strides) createFunction(backward_,
.set("groups", (size_t)groups_[i])); !isDeconv_ ? "GemmConvGradInput" : "GemmConv",
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
"GemmConvGradFilter",
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)groups_[i]));
}
} }
return true; return true;
} }
......
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