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MNN/source/backend/opencl/execution/buffer/PoolBufExecution.cpp

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//
// PoolBufExecution.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#ifndef MNN_OPENCL_BUFFER_CLOSED
#include "backend/opencl/execution/buffer/PoolBufExecution.hpp"
namespace MNN {
namespace OpenCL {
PoolBufExecution::PoolBufExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend)
: CommonExecution(backend, op) {
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
mPoolParams = op->main_as_Pool();
mPoolType = mPoolParams->type();
mStrides[0] = mPoolParams->strideY();
mStrides[1] = mPoolParams->strideX();
mKernels[0] = mPoolParams->kernelY();
mKernels[1] = mPoolParams->kernelX();
mPaddings[0] = mPoolParams->padY() * 2;
mPaddings[1] = mPoolParams->padX() * 2;
mPadType = mPoolParams->padType();
auto kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("pooling_buf", "global_pooling_buf", {"-DLOCAL_SIZE=512"}, mOpenCLBackend->getPrecision());
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel));
}
int PoolBufExecution::getLocalSize(int size, int maxGroupSize){
int local_size = 1;
while(local_size * 2 <= maxGroupSize && local_size * 2 <= size){
local_size *= 2;
}
return local_size;
}
ErrorCode PoolBufExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
#ifdef LOG_VERBOSE
MNN_PRINT("start PoolBufExecution onResize !\n");
#endif
mUnits.resize(1);
auto &unit = mUnits[0];
auto input = inputs[0];
auto output = outputs[0];
bool returnRedice = outputs.size() == 2;
auto redice = returnRedice ? outputs[1] : outputs[0];
auto runtime = mOpenCLBackend->getOpenCLRuntime();
#ifdef MNN_SUPPORT_INTEL_SUBGROUP
if (runtime->isSupportedIntelSubgroup()) {
return SubgrouponResize(inputs, outputs);
}
#endif /* MNN_SUPPORT_INTEL_SUBGROUP */
std::set<std::string> buildOptions;
std::string kernelName = "pooling";
int local_size;
if (mPoolParams->isGlobal()) {
std::vector<int> inputShape = tensorShapeFormat(inputs[0]);
mKernels = {inputShape.at(1), inputShape.at(2)};
mStrides = {inputShape.at(1), inputShape.at(2)};
mPaddings = {0, 0};
kernelName = "global_pooling_buf";
auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize);
local_size = getLocalSize(inputShape.at(1) * inputShape.at(2), MaxLocalSize);
buildOptions.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
}
if (mPadType == PoolPadType_SAME) {
int padNeededHeight = std::max(0, (output->height() - 1) * mStrides[0] + mKernels[0] - input->height());
int padNeededWidth = std::max(0, (output->width() - 1) * mStrides[1] + mKernels[1] - input->width());
mPaddings[0] = padNeededHeight;
mPaddings[1] = padNeededWidth;
}else if (mPoolParams->padType() == PoolPadType_VALID) {
mPaddings[0] = mPaddings[1] = 0;
}
auto countType = mPoolParams->countType();
if (mPoolParams->pads() != nullptr && mPadType == PoolPadType_CAFFE) {
mPadType = PoolPadType_VALID;
}
if (countType == MNN::AvgPoolCountType_DEFAULT) {
if (mPadType == MNN::PoolPadType_CAFFE) {
countType = MNN::AvgPoolCountType_INCLUDE_PADDING;
} else {
countType = MNN::AvgPoolCountType_EXCLUDE_PADDING;
}
}
MNN_ASSERT(mDilations[0] == 1 && mDilations[1] == 1);
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
const int batch = outputShape.at(0);
const int outputHeight = outputShape.at(1);
const int outputWidth = outputShape.at(2);
const int channels = outputShape.at(3);
const int inputHeight = inputShape.at(1);
const int inputWidth = inputShape.at(2);
int channelBlocks = (channels + 3) / 4;
if (mPoolType == PoolType_AVEPOOL) {
buildOptions.emplace("-DPOOL_AVG");
if(countType == MNN::AvgPoolCountType_INCLUDE_PADDING){
buildOptions.emplace("-DCOUNT_INCLUDE_PADDING");
}
}
if(returnRedice){
buildOptions.emplace("-DRETURN_REDICE");
}
unit.kernel = runtime->buildKernel("pooling_buf", kernelName, buildOptions, mOpenCLBackend->getPrecision());
mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
mGlobalWorkSize = {
static_cast<uint32_t>(outputWidth),
static_cast<uint32_t>(batch * outputHeight),
static_cast<uint32_t>(channelBlocks),
};
if (mPoolParams->isGlobal()) {
mGlobalWorkSize = {
static_cast<uint32_t>(local_size),
static_cast<uint32_t>(channelBlocks),
static_cast<uint32_t>(batch),
};
mLocalWorkSize = {
static_cast<uint32_t>(local_size),
static_cast<uint32_t>(1),
static_cast<uint32_t>(1),
};
}
int inputImageShape[2] = {inputHeight, inputWidth};
int outputImageShape[2] = {outputHeight, outputWidth};
int paddingShape[2] = {mPaddings[0] / 2, mPaddings[1] / 2};
int strideShape[2] = {mStrides[0], mStrides[1]};
int kernelShape[2] = {mKernels[0], mKernels[1]};
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input));
ret |= unit.kernel->get().setArg(idx++, sizeof(inputImageShape), inputImageShape);
ret |= unit.kernel->get().setArg(idx++, sizeof(outputImageShape), outputImageShape);
ret |= unit.kernel->get().setArg(idx++, sizeof(paddingShape), paddingShape);
ret |= unit.kernel->get().setArg(idx++, sizeof(strideShape), strideShape);
ret |= unit.kernel->get().setArg(idx++, sizeof(kernelShape), kernelShape);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(redice));
ret |= unit.kernel->get().setArg(idx++, batch);
MNN_CHECK_CL_SUCCESS(ret, "setArg PoolBufExecution");
std::string kernelNameTune = "pooling_buf";
if (!mPoolParams->isGlobal()){
mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelNameTune, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "pooling_buf").first;
}
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
#ifdef LOG_VERBOSE
MNN_PRINT("end PoolBufExecution onResize !\n");
#endif
return NO_ERROR;
}
#ifdef MNN_SUPPORT_INTEL_SUBGROUP
ErrorCode PoolBufExecution::SubgrouponResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
#ifdef LOG_VERBOSE
MNN_PRINT("start PoolBufExecution onResize !\n");
#endif
auto &unit = mUnits[0];
auto input = inputs[0];
auto output = outputs[0];
bool returnRedice = outputs.size() == 2;
auto redice = returnRedice ? outputs[1] : outputs[0];
auto runtime = mOpenCLBackend->getOpenCLRuntime();
if (mPoolParams->isGlobal()) {
std::vector<int> inputShape = tensorShapeFormat(inputs[0]);
mKernels = {inputShape.at(1), inputShape.at(2)};
mStrides = {inputShape.at(1), inputShape.at(2)};
mPaddings = {0, 0};
}
if (mPadType == PoolPadType_SAME) {
int padNeededHeight = std::max(0, (output->height() - 1) * mStrides[0] + mKernels[0] - input->height());
int padNeededWidth = std::max(0, (output->width() - 1) * mStrides[1] + mKernels[1] - input->width());
mPaddings[0] = padNeededHeight;
mPaddings[1] = padNeededWidth;
} else if (mPoolParams->padType() == PoolPadType_VALID) {
mPaddings[0] = mPaddings[1] = 0;
}
auto countType = mPoolParams->countType();
if (mPoolParams->pads() != nullptr && mPadType == PoolPadType_CAFFE) {
mPadType = PoolPadType_VALID;
}
if (countType == MNN::AvgPoolCountType_DEFAULT) {
if (mPadType == MNN::PoolPadType_CAFFE) {
countType = MNN::AvgPoolCountType_INCLUDE_PADDING;
} else {
countType = MNN::AvgPoolCountType_EXCLUDE_PADDING;
}
}
MNN_ASSERT(mDilations[0] == 1 && mDilations[1] == 1);
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
const int batch = outputShape.at(0);
const int outputHeight = outputShape.at(1);
const int outputWidth = outputShape.at(2);
const int channels = outputShape.at(3);
const int inputHeight = inputShape.at(1);
const int inputWidth = inputShape.at(2);
int input_c_pack = TensorUtils::getTensorChannelPack(input);
int output_c_pack = TensorUtils::getTensorChannelPack(output);
auto inputpad = TensorUtils::getDescribe(input)->mPads;
auto outputpad = TensorUtils::getDescribe(output)->mPads;
int inputImageShape[2] = {inputHeight, inputWidth};
int outputImageShape[2] = {outputHeight, outputWidth};
int paddingShape[2] = {mPaddings[0] / 2, mPaddings[1] / 2};
int strideShape[2] = {mStrides[0], mStrides[1]};
int kernelShape[2] = {mKernels[0], mKernels[1]};
int in_channel_block = UP_DIV(channels, input_c_pack);
int out_channel_block = UP_DIV(channels, output_c_pack);
std::set<std::string> buildOptions;
std::string KernelName = "pooling_c" + std::to_string(input_c_pack) + "_c" + std::to_string(output_c_pack);
if (mPoolType == PoolType_AVEPOOL) {
buildOptions.emplace("-DPOOL_AVG");
if (countType == MNN::AvgPoolCountType_INCLUDE_PADDING) {
buildOptions.emplace("-DCOUNT_INCLUDE_PADDING");
}
}
if(returnRedice){
buildOptions.emplace("-DRETURN_REDICE");
}
int input_line_size = mStrides[1] * (8 - 1) + mKernels[1];
buildOptions.emplace("-DINPUT_LINE_SIZE=" + std::to_string(input_line_size));
if (channels % 16 != 0) {
buildOptions.emplace("-DOUTPUT_LEFTOVERS=" + std::to_string(1));
}
buildOptions.emplace("-DSTRIDE_Y=" + std::to_string(strideShape[0]));
buildOptions.emplace("-DSTRIDE_X=" + std::to_string(strideShape[1]));
buildOptions.emplace("-DKERNEL_Y=" + std::to_string(kernelShape[0]));
buildOptions.emplace("-DKERNEL_X=" + std::to_string(kernelShape[1]));
unit.kernel = runtime->buildKernel("pooling_subgroup_buf", KernelName, buildOptions, mOpenCLBackend->getPrecision());
mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
mGlobalWorkSize = {
static_cast<uint32_t>(ROUND_UP(channels, 16)),
static_cast<uint32_t>(UP_DIV(outputWidth, 8)),
static_cast<uint32_t>(batch * outputHeight),
};
if (input_c_pack == 4) {
mGlobalWorkSize = {
static_cast<uint32_t>(outputWidth),
static_cast<uint32_t>(batch * outputHeight),
static_cast<uint32_t>(UP_DIV(channels, 4)),
};
}
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input));
ret |= unit.kernel->get().setArg(idx++, sizeof(inputImageShape), inputImageShape);
ret |= unit.kernel->get().setArg(idx++, sizeof(outputImageShape), outputImageShape);
ret |= unit.kernel->get().setArg(idx++, sizeof(paddingShape), paddingShape);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(redice));
ret |= unit.kernel->get().setArg(idx++, channels);
ret |= unit.kernel->get().setArg(idx++, batch);
ret |= unit.kernel->get().setArg(idx++, in_channel_block);
ret |= unit.kernel->get().setArg(idx++, out_channel_block);
ret |= unit.kernel->get().setArg(idx++, static_cast<uint32_t>(inputpad.left));
ret |= unit.kernel->get().setArg(idx++, static_cast<uint32_t>(inputpad.right));
ret |= unit.kernel->get().setArg(idx++, static_cast<uint32_t>(outputpad.left));
ret |= unit.kernel->get().setArg(idx++, static_cast<uint32_t>(outputpad.right));
MNN_CHECK_CL_SUCCESS(ret, "setArg PoolBufExecution SubGroup");
std::string kernelNameTune = "pooling_subgroup_buf";
if (input_c_pack == 16) {
mLocalWorkSize = {16, 1, 1};
} else {
mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelNameTune, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "pooling_subgroup_buf").first;
}
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
#ifdef LOG_VERBOSE
MNN_PRINT("end PoolBufExecution onResize !\n");
#endif
return NO_ERROR;
}
#endif /* MNN_SUPPORT_INTEL_SUBGROUP */
class PoolBufCreator : public OpenCLBackend::Creator {
public:
virtual ~PoolBufCreator() = default;
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
const MNN::Op *op, Backend *backend) const override {
#ifdef MNN_SUPPORT_INTEL_SUBGROUP
for (int i = 0; i < inputs.size(); ++i) {
int channel = inputs[i]->channel();
if (channel >= 16 && static_cast<OpenCLBackend *>(backend)->getOpenCLRuntime()->isSupportedIntelSubgroup()) {
TensorUtils::setTensorChannelPack(inputs[i], 16);
}
}
#endif /* MNN_SUPPORT_INTEL_SUBGROUP */
return new PoolBufExecution(inputs, op, backend);
}
};
REGISTER_OPENCL_OP_CREATOR(PoolBufCreator, OpType_Pooling, BUFFER);
} // namespace OpenCL
} // namespace MNN
#endif /* MNN_OPENCL_BUFFER_CLOSED */
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