mirror of https://github.com/alibaba/MNN.git
265 lines
10 KiB
C++
265 lines
10 KiB
C++
//
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// ReductionExecution.cpp
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// MNN
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//
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// Created by MNN on 2019/10/25.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/opencl/execution/image/ReductionExecution.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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namespace OpenCL {
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ReductionExecution::ReductionExecution(const MNN::Op* op, Backend* backend) : CommonExecution(backend, op) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start ReductionExecution init !\n");
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#endif
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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mAxis = op->main_as_ReductionParam()->dim()->data()[0];
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switch (op->main_as_ReductionParam()->operation()) {
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case ReductionType_MEAN:
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mReductType = 0;
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break;
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case ReductionType_MAXIMUM:
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mReductType = 1;
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break;
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case ReductionType_MINIMUM:
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mReductType = 2;
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break;
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case ReductionType_PROD:
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mReductType = 3;
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break;
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case ReductionType_SUM:
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mReductType = 4;
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break;
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default:
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MNN_ASSERT(false);
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break;
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}
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#ifdef LOG_VERBOSE
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MNN_PRINT("end ReductionExecution init !\n");
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#endif
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}
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int ReductionExecution::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 ReductionExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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startRecord(runtime, mRecording);
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auto input = inputs[0];
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auto output = outputs[0];
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if(mAxis < 0){
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mAxis = input->dimensions() + mAxis;
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}
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int inside = 1;
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int outside = 1;
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for(int i = 0; i < mAxis; ++i){
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outside *= input->length(i);
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}
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for(int i = mAxis + 1; i < input->dimensions(); ++i){
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inside *= input->length(i);
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}
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int dim = input->length(mAxis);
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int local_size = 0;
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auto MaxWorkItems = runtime->getMaxWorkItemSizes();
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if(dim >= 16){
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mUseLocal = true;
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}
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std::vector<int> inputShape = tensorShapeFormat(input);
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std::vector<int> outputShape = tensorShapeFormat(output);
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int batch = inputShape.at(0);
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int inputHeight = inputShape.at(1);
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int inputWidth = inputShape.at(2);
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int inputChannels = inputShape.at(3);
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int inputChannelBlocks = (inputChannels + 3) / 4;
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int outputBatch = outputShape.at(0);
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int outputHeight = outputShape.at(1);
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int outputWidth = outputShape.at(2);
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int outputChannels = outputShape.at(3);
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int outputChannelBlocks = (outputChannels + 3) / 4;
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std::set<std::string> buildOption;
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switch (mReductType) {
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case 0:
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buildOption.emplace("-DOPERATE(a,b)=(a+b)");
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buildOption.emplace("-DGET_AVG");
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buildOption.emplace("-DVALUE=0");
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break;
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case 1:
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buildOption.emplace("-DOPERATE(a,b)=max(a,b)");
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buildOption.emplace("-DVALUE=-FLT_MAX");
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break;
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case 2:
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buildOption.emplace("-DOPERATE(a,b)=min(a,b)");
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buildOption.emplace("-DVALUE=FLT_MAX");
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break;
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case 3:
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buildOption.emplace("-DOPERATE(a,b)=(a*b)");
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buildOption.emplace("-DVALUE=1");
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break;
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case 4:
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buildOption.emplace("-DOPERATE(a,b)=(a+b)");
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buildOption.emplace("-DVALUE=0");
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break;
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default:
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MNN_ASSERT(false);
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break;
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}
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mGlobalWorkSize = {
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static_cast<uint32_t>(outputWidth),
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static_cast<uint32_t>(outputHeight),
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static_cast<uint32_t>(outputBatch * outputChannelBlocks)
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};
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if(mUseLocal){
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if(batch * inputHeight * inputChannels == outside && 1 == inside && dim == inputWidth){
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local_size = getLocalSize(inputWidth, MaxWorkItems[0]);
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buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_width", buildOption);
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}else if(batch * inputChannels == outside && inputWidth == inside && dim == inputHeight){
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local_size = getLocalSize(inputHeight, MaxWorkItems[0]);
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buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_height", buildOption);
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}else if(batch == outside && inputWidth * inputHeight == inside && dim == inputChannels){
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local_size = getLocalSize(inputChannelBlocks - 1, MaxWorkItems[0]);
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buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_channel", buildOption);
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mGlobalWorkSize[2] = static_cast<uint32_t>(outputBatch * outputChannels);
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}else if(1 == outside && inputWidth * inputHeight * inputChannels == inside && dim == batch){
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local_size = getLocalSize(batch, MaxWorkItems[0]);
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buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_batch", buildOption);
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}
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mGlobalWorkSize[0] *= local_size;
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}else{
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buildOption.emplace("-DLOCAL_SIZE=0");
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if(batch * inputHeight * inputChannels == outside && 1 == inside && dim == inputWidth){
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_width", buildOption);
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}else if(batch * inputChannels == outside && inputWidth == inside && dim == inputHeight){
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_height", buildOption);
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}else if(batch == outside && inputWidth * inputHeight == inside && dim == inputChannels){
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_channel", buildOption);
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mGlobalWorkSize[2] = static_cast<uint32_t>(outputBatch * outputChannels);
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}else if(1 == outside && inputWidth * inputHeight * inputChannels == inside && dim == batch){
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mReduct1DKernel = runtime->buildKernel("reduction", "reduct_batch", buildOption);
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}
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}
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mUnits.resize(1);
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= mReduct1DKernel.setArg(idx++, mGlobalWorkSize[0]);
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ret |= mReduct1DKernel.setArg(idx++, mGlobalWorkSize[1]);
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ret |= mReduct1DKernel.setArg(idx++, mGlobalWorkSize[2]);
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ret |= mReduct1DKernel.setArg(idx++, openCLImage(input));
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ret |= mReduct1DKernel.setArg(idx++, openCLImage(output));
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ret |= mReduct1DKernel.setArg(idx++, inputWidth);
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ret |= mReduct1DKernel.setArg(idx++, inputHeight);
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ret |= mReduct1DKernel.setArg(idx++, inputChannels);
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ret |= mReduct1DKernel.setArg(idx++, batch);
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ret |= mReduct1DKernel.setArg(idx++, inputChannelBlocks);
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ret |= mReduct1DKernel.setArg(idx++, outputWidth);
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ret |= mReduct1DKernel.setArg(idx++, outputHeight);
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ret |= mReduct1DKernel.setArg(idx++, outputChannels);
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ret |= mReduct1DKernel.setArg(idx++, outputChannelBlocks);
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MNN_CHECK_CL_SUCCESS(ret, "setArg ReductionExecution");
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if(mUseLocal){
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mLocalWorkSize = {static_cast<uint32_t>(local_size), 1, 1};
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}else{
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auto MaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mReduct1DKernel));
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std::string kernelName = "reduct";
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mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, MaxWorkGroupSize, runtime, kernelName, mReduct1DKernel).first;
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}
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recordKernel3d(mReduct1DKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
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endRecord(runtime, mRecording);
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return NO_ERROR;
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}
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ErrorCode ReductionExecution::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start ReductionExecution onExecute !\n");
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#endif
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#ifdef ENABLE_OPENCL_TIME_PROFILER
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cl::Event event;
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run3DKernelDefault(mReduct1DKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime(), &event);
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int costTime = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
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MNN_PRINT("kernel cost:%d us Reduct1D\n",costTime);
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#else
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if(mOpenCLBackend->getOpenCLRuntime()->isUseRecordQueue()){
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if(mOpenCLBackend->getOpenCLRuntime()->isDevideOpRecord())
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mOpenCLBackend->getOpenCLRuntime()->getRecordings()->emplace_back(mRecording);
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#ifdef LOG_VERBOSE
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MNN_PRINT("End ReductionExecution onExecute... \n");
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#endif
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return NO_ERROR;
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}
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run3DKernelDefault(mReduct1DKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
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#endif
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#ifdef LOG_VERBOSE
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MNN_PRINT("end ReductionExecution onExecute !\n");
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#endif
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return NO_ERROR;
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}
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class ReductionCreator : public OpenCLBackend::Creator {
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public:
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virtual ~ReductionCreator() = default;
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const override {
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auto openCLBackend = static_cast<OpenCLBackend *>(backend);
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auto reduct = op->main_as_ReductionParam();
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if (nullptr == reduct->dim()) {
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return NULL;
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}
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if(reduct->dim()->size() != 1) {
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return NULL;
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}
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auto axis = reduct->dim()->data()[0];
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int dim = inputs[0]->length(axis);
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std::vector<int> inputShape = tensorShapeFormat(inputs[0]);
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if(dim == inputShape.at(3) && outputs[0]->buffer().dimensions == 1){
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return NULL;
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}
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switch (op->main_as_ReductionParam()->operation()) {
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case ReductionType_MEAN:
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break;
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case ReductionType_MAXIMUM:
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break;
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case ReductionType_MINIMUM:
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break;
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case ReductionType_PROD:
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break;
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case ReductionType_SUM:
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break;
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default:
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return NULL;
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break;
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}
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return new ReductionExecution(op, backend);
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return NULL;
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}
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};
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OpenCLCreatorRegister<ReductionCreator> __reduction_op(OpType_Reduction, IMAGE);
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} // namespace OpenCL
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} // namespace MNN
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