-
Couldn't load subscription status.
- Fork 5.9k
NNPACKConvFunction #2625
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Merged
Merged
NNPACKConvFunction #2625
Changes from 5 commits
Commits
Show all changes
7 commits
Select commit
Hold shift + click to select a range
0e45f95
Add a NNPACKConvFunction.
hedaoyuan 2e02952
Merge branch 'develop' of https://github.com/baidu/Paddle into nnpack…
hedaoyuan 7062be0
Add cmake for compile NNPACKConvOp.cpp.
hedaoyuan 2d9113d
Add test for NNPACKConvFunc.
hedaoyuan cdf8d99
Bug fix.
hedaoyuan b551460
Add the use_nnpack parameter in ExpandConvLayer, so that the convolut…
hedaoyuan 47f1031
Modify the type of alog parameter.
hedaoyuan File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,235 @@ | ||
| /* 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_); | ||
| } | ||
| } | ||
|
|
||
| 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); | ||
| } | ||
|
|
||
| LOG(INFO) << "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 |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| /* 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 |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| # 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() | ||
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
It would be great if we add a
/cmake/external/nnpack.cmaketo download the source code and build nnpack, like whatcmake/external/{glog,gflags,gtest}.cmakedo. In this way,cc_{library,binary,test}defined incmake/generic.cmakecan also make use of NNPACK.There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
But we don't have to complete it in this PR. I think an alternative is that we mark this in an issue and open subsequent PRs to fix it.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thank you for your comments. I also think automatically download the source code and compile it is better.