chore: import upstream snapshot with attribution
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wehub-resource-sync
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//
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// PoolExecution.cpp
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// MNN
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//
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// Created by MNN on 2019/02/28.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/opencl/execution/image/PoolExecution.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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#include "backend/opencl/core/OpenCLBackend.hpp"
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namespace MNN {
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namespace OpenCL {
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std::vector<uint32_t> PoolExecution::poolLocalWS(const std::vector<uint32_t> &gws, const uint32_t maxWorkGroupSize) {
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std::vector<uint32_t> lws(3, 0);
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auto maxWorkItemSizes = mOpenCLBackend->getOpenCLRuntime()->getMaxWorkItemSizes();
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uint32_t deviceComputeUnits = mOpenCLBackend->getOpenCLRuntime()->deviceComputeUnits();
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int coreNum = deviceComputeUnits;
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for (int i = 0, totalSizeNow = 1; i < gws.size(); ++i) {
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int remain = gws[i] % coreNum, groupSize = gws[i] / coreNum;
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if (remain == 0) {
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lws[i] = groupSize;
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} else {
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while(groupSize) {
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int remain = gws[i] % groupSize;
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if (remain == 0 && (i > 0 || groupSize <= maxWorkGroupSize)) {
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lws[i] = groupSize;
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break;
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}
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--groupSize;
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}
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}
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int limit = std::min<uint32_t>(maxWorkGroupSize / totalSizeNow, maxWorkItemSizes[i]);
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lws[i] = std::max<uint32_t>(std::min<uint32_t>(lws[i], limit), 1);
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totalSizeNow *= lws[i];
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}
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return lws;
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}
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PoolExecution::PoolExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend)
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: CommonExecution(backend, op) {
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mUnits.resize(1);
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auto &unit = mUnits[0];
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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mPoolParams = op->main_as_Pool();
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mPoolType = mPoolParams->type();
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mStrides[0] = mPoolParams->strideY();
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mStrides[1] = mPoolParams->strideX();
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mKernels[0] = mPoolParams->kernelY();
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mKernels[1] = mPoolParams->kernelX();
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mPaddings[0] = mPoolParams->padY() * 2;
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mPaddings[1] = mPoolParams->padX() * 2;
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mPadType = mPoolParams->padType();
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unit.kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("pooling", "global_pooling", {"-DLOCAL_SIZE=512"}, mOpenCLBackend->getPrecision());
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OPENCL_CHECK_KERNEL_CTOR(unit.kernel);
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mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(unit.kernel));
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}
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int PoolExecution::getLocalSize(int size, int maxGroupSize){
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int local_size = 1;
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while(local_size * 2 <= maxGroupSize && local_size * 2 <= size){
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local_size *= 2;
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}
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return local_size;
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}
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ErrorCode PoolExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start PoolExecution onResize !\n");
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#endif
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auto &unit = mUnits[0];
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auto input = inputs[0];
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auto output = outputs[0];
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bool returnRedice = outputs.size() == 2;
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auto redice = returnRedice ? outputs[1] : outputs[0];
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std::set<std::string> buildOptions;
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std::string kernelName = "pooling";
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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int local_size = 1;
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if (mPoolParams->isGlobal()) {
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std::vector<int> inputShape = tensorShapeFormat(inputs[0]);
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mKernels = {inputShape.at(1), inputShape.at(2)};
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mStrides = {inputShape.at(1), inputShape.at(2)};
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mPaddings = {0, 0};
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kernelName = "global_pooling";
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auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize);
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local_size = getLocalSize(inputShape.at(1) * inputShape.at(2), MaxLocalSize);
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}
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buildOptions.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
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if (mPadType == PoolPadType_SAME) {
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int padNeededHeight = std::max(0, (output->height() - 1) * mStrides[0] + mKernels[0] - input->height());
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int padNeededWidth = std::max(0, (output->width() - 1) * mStrides[1] + mKernels[1] - input->width());
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mPaddings[0] = padNeededHeight;
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mPaddings[1] = padNeededWidth;
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}else if (mPadType == PoolPadType_VALID) {
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mPaddings[0] = mPaddings[1] = 0;
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}
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auto countType = mPoolParams->countType();
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if (mPoolParams->pads() != nullptr && mPadType == PoolPadType_CAFFE) {
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mPadType = PoolPadType_VALID;
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}
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if (countType == MNN::AvgPoolCountType_DEFAULT) {
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if (mPadType == MNN::PoolPadType_CAFFE) {
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countType = MNN::AvgPoolCountType_INCLUDE_PADDING;
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} else {
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countType = MNN::AvgPoolCountType_EXCLUDE_PADDING;
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}
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}
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if (mPoolType == PoolType_AVEPOOL) {
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buildOptions.emplace("-DPOOL_AVG");
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if(countType == MNN::AvgPoolCountType_INCLUDE_PADDING){
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buildOptions.emplace("-DCOUNT_INCLUDE_PADDING");
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}
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}
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if(returnRedice){
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buildOptions.emplace("-DRETURN_REDICE");
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}
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unit.kernel = runtime->buildKernel("pooling", kernelName, buildOptions, mOpenCLBackend->getPrecision());
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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MNN_ASSERT(mDilations[0] == 1 && mDilations[1] == 1);
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std::vector<int> inputShape = tensorShapeFormat(input);
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std::vector<int> outputShape = tensorShapeFormat(output);
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const int batch = outputShape.at(0);
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const int outputHeight = outputShape.at(1);
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const int outputWidth = outputShape.at(2);
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const int channels = outputShape.at(3);
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const int inputHeight = inputShape.at(1);
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const int inputWidth = inputShape.at(2);
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int channelBlocks = (channels + 3) / 4;
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std::vector<uint32_t> mGlobalWorkSize{1, 1, 1};
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std::vector<uint32_t> mLocalWorkSize{1, 1, 1, 1};
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if (mPoolParams->isGlobal()) {
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mGlobalWorkSize = {
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static_cast<uint32_t>(local_size),
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static_cast<uint32_t>(channelBlocks),
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static_cast<uint32_t>(batch),
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};
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mLocalWorkSize = {
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static_cast<uint32_t>(local_size),
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static_cast<uint32_t>(1),
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static_cast<uint32_t>(1),
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};
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}else{
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mGlobalWorkSize = {
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static_cast<uint32_t>(channelBlocks),
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static_cast<uint32_t>(outputWidth),
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static_cast<uint32_t>(batch * outputHeight),
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};
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mLocalWorkSize = poolLocalWS(mGlobalWorkSize, mMaxWorkGroupSize);
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}
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int inputImageShape[2] = {inputHeight, inputWidth};
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int paddingShape[2] = {mPaddings[0] / 2, mPaddings[1] / 2};
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int strideShape[2] = {mStrides[0], mStrides[1]};
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int kernelShape[2] = {mKernels[0], mKernels[1]};
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(idx++, openCLImage(input));
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ret |= unit.kernel->get().setArg(idx++, sizeof(inputImageShape), inputImageShape);
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ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(outputHeight));
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ret |= unit.kernel->get().setArg(idx++, sizeof(paddingShape), paddingShape);
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ret |= unit.kernel->get().setArg(idx++, sizeof(strideShape), strideShape);
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ret |= unit.kernel->get().setArg(idx++, sizeof(kernelShape), kernelShape);
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ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
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ret |= unit.kernel->get().setArg(idx++, openCLImage(redice));
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MNN_CHECK_CL_SUCCESS(ret, "setArg PoolExecution");
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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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#ifdef LOG_VERBOSE
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MNN_PRINT("end PoolExecution onResize !\n");
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#endif
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return NO_ERROR;
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}
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using PoolCreator = TypedCreator<PoolExecution>;
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REGISTER_OPENCL_OP_CREATOR(PoolCreator, OpType_Pooling, IMAGE);
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} // namespace OpenCL
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} // namespace MNN
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