mirror of https://github.com/alibaba/MNN.git
505 lines
23 KiB
C++
505 lines
23 KiB
C++
//
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// LoopExecution.cpp
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// MNN
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//
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// Created by MNN on 2023/05/04.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/opencl/execution/image/LoopExecution.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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static void _TileTensor(Tensor *input, cl::Buffer *output, cl::Kernel& kernel, cl::NDRange &globalWorkSize,
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cl::NDRange &localWorkSize, const int Width, const int Height, const int Channel,
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const int Batch, OpenCLRuntime *runTime, std::set<std::string> buildOptions) {
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if (TensorUtils::getDescribe(input)->dimensionFormat == MNN::MNN_DATA_FORMAT_NHWC){
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buildOptions.emplace("-DMNN_NHWC");
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}
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kernel = runTime->buildKernel("loop", "tile", buildOptions);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(Width * Height), (uint32_t)(UP_DIV(Channel, 4)), (uint32_t)(Batch)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= kernel.setArg(index++, openCLImage(input));
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ret |= kernel.setArg(index++, *output);
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ret |= kernel.setArg(index++, Width);
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ret |= kernel.setArg(index++, Height);
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ret |= kernel.setArg(index++, Channel);
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MNN_CHECK_CL_SUCCESS(ret, "setArg Loop _PackTensor");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "tile", kernel).first;
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globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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recordKernel3d(kernel, mGlobalWorkSize, mLocalWorkSize, runTime);
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}
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static void _PackTensor(cl::Buffer *input, Tensor *output, cl::Kernel& kernel, cl::NDRange &globalWorkSize,
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cl::NDRange &localWorkSize, const int Width, const int Height, const int Channel,
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const int Batch, OpenCLRuntime *runTime, std::set<std::string> buildOptions) {
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if (TensorUtils::getDescribe(output)->dimensionFormat == MNN::MNN_DATA_FORMAT_NHWC){
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buildOptions.emplace("-DMNN_NHWC");
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}
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kernel = runTime->buildKernel("loop", "pack", buildOptions);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(Width * Height), (uint32_t)(UP_DIV(Channel, 4)), (uint32_t)(Batch)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= kernel.setArg(index++, *input);
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ret |= kernel.setArg(index++, openCLImage(output));
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ret |= kernel.setArg(index++, Width);
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ret |= kernel.setArg(index++, Height);
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ret |= kernel.setArg(index++, Channel);
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MNN_CHECK_CL_SUCCESS(ret, "setArg Loop _PackTensor");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "pack", kernel).first;
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globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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recordKernel3d(kernel, mGlobalWorkSize, mLocalWorkSize, runTime);
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}
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static void _setTensorStack(std::vector<Tensor *> &result, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs, const LoopParam *loop) {
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if (loop->inputIndexes() != nullptr) {
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for (int i = 0; i < loop->inputIndexes()->size(); ++i) {
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result[loop->inputIndexes()->data()[i]] = inputs[i];
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}
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}
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for (int i = 0; i < loop->outputIndexes()->size(); ++i) {
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result[loop->outputIndexes()->data()[i]] = outputs[i];
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}
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}
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LoopGatherExecution::LoopGatherExecution(const LoopParam *loop, const MNN::Op *op, Backend *bn)
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: CommonExecution(bn, op) {
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mLoop = loop;
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mTensors.resize(mLoop->tensorNumber());
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auto cmd = loop->commands()->GetAs<RegionCommand>(0);
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}
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ErrorCode LoopGatherExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto cmd = mLoop->commands()->GetAs<RegionCommand>(0);
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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startRecord(runTime, mRecording);
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auto bufferPool = mOpenCLBackend->getBufferPool();
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auto bufferUnitSize = runTime->isSupportedFP16() ? sizeof(half_float::half) : sizeof(float);
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_setTensorStack(mTensors, inputs, outputs, mLoop);
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mUnits.clear();
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mOffsetBuffers.clear();
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mTmpBuffers.resize(2);
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int x = cmd->size()->data()[0];
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int y = cmd->size()->data()[1];
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int z = cmd->size()->data()[2];
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int n = mLoop->loopNumber();
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auto srcStride = cmd->view()->GetAs<View>(1)->stride()->data();
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auto dstStride = cmd->view()->GetAs<View>(0)->stride()->data();
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for (int i = 0; i < 3; ++i) {
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mStride_src[i] = srcStride[i];
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mStride_dst[i] = dstStride[i];
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}
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mStride_src[3] = cmd->view()->GetAs<View>(1)->offset();
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mStride_dst[3] = cmd->view()->GetAs<View>(0)->offset();
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::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
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::memcpy(mIter, cmd->iterIndexes()->data(), cmd->iterIndexes()->size() * sizeof(int));
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// tile input
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{
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auto input = mTensors[cmd->indexes()->data()[1]];
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std::vector<int> Shape = tensorShapeFormat(input);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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mTmpBuffers[1] = bufferPool->alloc(input->elementSize() * bufferUnitSize);
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Unit unit;
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_TileTensor(mTensors[cmd->indexes()->data()[1]], mTmpBuffers[1], unit.kernel, unit.globalWorkSize, unit.localWorkSize, Width, Height,Channel, Batch, runTime, mBuildOptions);
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mUnits.emplace_back(unit);
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}
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for(int i = 0; i < cmd->iterIndexes()->size(); ++i){
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if (mIter[i] >= 0) {
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auto input = mTensors[cmd->iterIndexes()->data()[i]];
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std::vector<int> Shape = tensorShapeFormat(input);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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mOffsetBuffers.emplace_back(bufferPool->alloc(input->elementSize() * bufferUnitSize));
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Unit unit;
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_TileTensor(input, mOffsetBuffers.back(), unit.kernel, unit.globalWorkSize, unit.localWorkSize, Width, Height, Channel, Batch, runTime, mBuildOptions);
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mUnits.emplace_back(unit);
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}
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}
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// gather
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{
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mTmpBuffers[0] = bufferPool->alloc(n * z * y * x * bufferUnitSize);
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int offset_index = 0;
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Unit unit;
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std::string KernelName = "batch_gather";
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unit.kernel = runTime->buildKernel("loop", KernelName, mBuildOptions);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(x * y), (uint32_t)(z), (uint32_t)(n)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[0]);
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[1]);
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for (int i = 0; i < cmd->iterIndexes()->size(); ++i) {
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if (mIter[i] >= 0) {
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ret |= unit.kernel.setArg(index++, *mOffsetBuffers[offset_index++]);
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} else {
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[0]);
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}
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}
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ret |= unit.kernel.setArg(index++, x);
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ret |= unit.kernel.setArg(index++, sizeof(mStride_src), mStride_src);
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ret |= unit.kernel.setArg(index++, sizeof(mStride_dst), mStride_dst);
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ret |= unit.kernel.setArg(index++, sizeof(mStep), mStep);
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ret |= unit.kernel.setArg(index++, sizeof(mIter), mIter);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopGatherExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, KernelName, unit.kernel).first;
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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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recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize, runTime);
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mUnits.emplace_back(unit);
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}
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//pack output
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{
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auto output = mTensors[cmd->indexes()->data()[0]];
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std::vector<int> Shape = tensorShapeFormat(output);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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Unit unit;
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_PackTensor(mTmpBuffers[0], mTensors[cmd->indexes()->data()[0]], unit.kernel, unit.globalWorkSize, unit.localWorkSize, Width, Height, Channel, Batch, runTime, mBuildOptions);
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mUnits.emplace_back(unit);
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}
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for (int i = 0; i < mTmpBuffers.size(); ++i) {
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bufferPool->recycle(mTmpBuffers[i]);
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}
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for (int i = 0; i < mOffsetBuffers.size(); ++i) {
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bufferPool->recycle(mOffsetBuffers[i]);
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}
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endRecord(runTime, mRecording);
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return NO_ERROR;
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}
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LoopBatchMatMulExecution::LoopBatchMatMulExecution(const LoopParam *loop, const MNN::Op *op, Backend *bn)
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: CommonExecution(bn, op) {
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mLoop = loop;
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mTensors.resize(mLoop->tensorNumber());
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auto cmd = loop->commands()->GetAs<RegionCommand>(0);
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mHasBias = cmd->indexes()->size() > 3;
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mTransposeA = cmd->op()->main_as_MatMul()->transposeA();
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mTransposeB = cmd->op()->main_as_MatMul()->transposeB();
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}
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ErrorCode LoopBatchMatMulExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto cmd = mLoop->commands()->GetAs<RegionCommand>(0);
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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startRecord(runTime, mRecording);
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auto bufferPool = mOpenCLBackend->getBufferPool();
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auto bufferUnitSize = runTime->isSupportedFP16() ? sizeof(half_float::half) : sizeof(float);
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_setTensorStack(mTensors, inputs, outputs, mLoop);
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mOffset[0] = cmd->view()->GetAs<View>(0)->offset();
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mOffset[1] = cmd->view()->GetAs<View>(1)->offset();
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mOffset[2] = cmd->view()->GetAs<View>(2)->offset();
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mUnits.clear();
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mOffsetBuffers.clear();
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mTmpBuffers.resize(3);
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if (mHasBias) {
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mTmpBuffers.resize(4);
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mOffset[3] = cmd->view()->GetAs<View>(3)->offset();
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}
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::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
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::memcpy(mIter, cmd->iterIndexes()->data(), cmd->iterIndexes()->size() * sizeof(int));
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int e = cmd->size()->data()[0];
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int l = cmd->size()->data()[1];
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int h = cmd->size()->data()[2];
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int n = mLoop->loopNumber();
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// tile input
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for (int i = 1; i < cmd->indexes()->size(); ++i) {
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auto input = mTensors[cmd->indexes()->data()[i]];
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std::vector<int> Shape = tensorShapeFormat(input);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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mTmpBuffers[i] = bufferPool->alloc(input->elementSize() * bufferUnitSize);
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Unit unit;
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_TileTensor(input, mTmpBuffers[i], unit.kernel, unit.globalWorkSize, unit.localWorkSize, Width, Height, Channel, Batch, runTime, mBuildOptions);
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mUnits.emplace_back(unit);
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}
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for(int i = 0; i < cmd->iterIndexes()->size(); ++i){
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if (mIter[i] >= 0) {
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auto input = mTensors[cmd->iterIndexes()->data()[i]];
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std::vector<int> Shape = tensorShapeFormat(input);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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mOffsetBuffers.emplace_back(bufferPool->alloc(input->elementSize() * bufferUnitSize));
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Unit unit;
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_TileTensor(input, mOffsetBuffers.back(), unit.kernel, unit.globalWorkSize, unit.localWorkSize, Width, Height, Channel, Batch, runTime, mBuildOptions);
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mUnits.emplace_back(unit);
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}
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}
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// matmul
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{
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mTmpBuffers[0] = bufferPool->alloc(n * e * h * bufferUnitSize);
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int offset_index = 0;
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Unit unit;
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std::string KernelName = "batch_matmul";
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if (mHasBias) {
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mBuildOptions.emplace("-DBIAS");
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}
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if (mTransposeA) {
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mBuildOptions.emplace("-DTRANSPOSE_A");
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}
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if (mTransposeB) {
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mBuildOptions.emplace("-DTRANSPOSE_B");
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}
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unit.kernel = runTime->buildKernel("loop", KernelName, mBuildOptions);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(h), (uint32_t)(e),(uint32_t)(n)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[0]);
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[1]);
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[2]);
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if (mHasBias) {
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[3]);
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}
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for (int i = 0; i < cmd->iterIndexes()->size(); ++i) {
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if (mIter[i] >= 0) {
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ret |= unit.kernel.setArg(index++, *mOffsetBuffers[offset_index++]);
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} else {
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ret |= unit.kernel.setArg(index++, *mTmpBuffers[0]);
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}
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}
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ret |= unit.kernel.setArg(index++, e);
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ret |= unit.kernel.setArg(index++, l);
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ret |= unit.kernel.setArg(index++, h);
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ret |= unit.kernel.setArg(index++, sizeof(mOffset), mOffset);
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ret |= unit.kernel.setArg(index++, sizeof(mIter), mIter);
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ret |= unit.kernel.setArg(index++, sizeof(mStep), mStep);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBatchMatMulExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, KernelName, unit.kernel).first;
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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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mUnits.emplace_back(unit);
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recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize, runTime);
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}
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//pack output
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{
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auto output = mTensors[cmd->indexes()->data()[0]];
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std::vector<int> Shape = tensorShapeFormat(output);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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Unit unit;
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_PackTensor(mTmpBuffers[0], output, unit.kernel, unit.globalWorkSize, unit.localWorkSize, Width, Height, Channel, Batch, runTime, mBuildOptions);
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mUnits.emplace_back(unit);
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}
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for (int i = 0; i < mTmpBuffers.size(); ++i) {
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bufferPool->recycle(mTmpBuffers[i]);
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}
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for (int i = 0; i < mOffsetBuffers.size(); ++i) {
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bufferPool->recycle(mOffsetBuffers[i]);
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}
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endRecord(runTime, mRecording);
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return NO_ERROR;
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}
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LoopBinaryExecution::LoopBinaryExecution(const LoopParam *loop, const std::string &compute, const MNN::Op *op, Backend *bn)
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: CommonExecution(bn, op) {
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mLoop = loop;
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mTensors.resize(mLoop->tensorNumber());
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auto cmd = loop->commands()->GetAs<RegionCommand>(0);
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mBuildOptions.emplace("-DLOOP_BINARY_OPERATOR=" + compute);
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}
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ErrorCode LoopBinaryExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto cmd = mLoop->commands()->GetAs<RegionCommand>(0);
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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startRecord(runTime, mRecording);
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_setTensorStack(mTensors, inputs, outputs, mLoop);
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mUnits.clear();
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Unit unit;
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auto input0 = mTensors[cmd->indexes()->data()[1]];
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std::vector<int> Input0Shape = tensorShapeFormat(input0);
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int Input0Size[4] = {Input0Shape.at(2), Input0Shape.at(1),Input0Shape.at(3),Input0Shape.at(0)};
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auto input1 = mTensors[cmd->indexes()->data()[2]];
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std::vector<int> Input1Shape = tensorShapeFormat(input1);
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int Input1Size[4] = {Input1Shape.at(2), Input1Shape.at(1),Input1Shape.at(3),Input1Shape.at(0)};
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auto output = mTensors[cmd->indexes()->data()[0]];
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std::vector<int> Shape = tensorShapeFormat(output);
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const int Channel = Shape.at(3);
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const int Width = Shape.at(2);
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const int Height = Shape.at(1);
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const int Batch = Shape.at(0);
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const int ChannelBlock = UP_DIV(Channel, 4);
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auto BuildOptions = mBuildOptions;
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if(Input0Size[2] != Input1Size[2]){
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BuildOptions.emplace("-DBROADCAST_CHANNEL");
|
|
}
|
|
std::string KernelName = "broadcast_binary";
|
|
unit.kernel = runTime->buildKernel("loop", KernelName, BuildOptions);
|
|
uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
|
|
|
|
|
|
std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(Width), (uint32_t)(Height), (uint32_t)(Batch * ChannelBlock)};
|
|
|
|
uint32_t index = 0;
|
|
cl_int ret = CL_SUCCESS;
|
|
ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
|
|
ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
|
|
ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
|
|
ret |= unit.kernel.setArg(index++, openCLImage(output));
|
|
ret |= unit.kernel.setArg(index++, openCLImage(input0));
|
|
ret |= unit.kernel.setArg(index++, openCLImage(input1));
|
|
ret |= unit.kernel.setArg(index++, sizeof(Input0Size), Input0Size);
|
|
ret |= unit.kernel.setArg(index++, sizeof(Input1Size), Input1Size);
|
|
ret |= unit.kernel.setArg(index++, Width);
|
|
ret |= unit.kernel.setArg(index++, Height);
|
|
ret |= unit.kernel.setArg(index++, ChannelBlock);
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBinaryExecution");
|
|
|
|
std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, KernelName, unit.kernel).first;
|
|
|
|
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
|
|
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
|
|
recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize, runTime);
|
|
mUnits.emplace_back(unit);
|
|
|
|
endRecord(runTime, mRecording);
|
|
|
|
return NO_ERROR;
|
|
}
|
|
|
|
|
|
class LoopCreator : public OpenCLBackend::Creator {
|
|
public:
|
|
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
|
|
const MNN::Op *op, Backend *backend) const override {
|
|
auto loop = op->main_as_LoopParam();
|
|
if (nullptr == loop || loop->commands() == nullptr) {
|
|
return nullptr;
|
|
}
|
|
if (nullptr != loop->initCommand()) {
|
|
return nullptr;
|
|
}
|
|
// Make Tensor Stack
|
|
if (1 == loop->commands()->size()) {
|
|
auto cmd = loop->commands()->GetAs<RegionCommand>(0);
|
|
auto subop = cmd->op();
|
|
if (OpType_UnaryOp == subop->type() && nullptr == subop->main() && cmd->fuse() < 0) {
|
|
return new LoopGatherExecution(loop, op, backend);
|
|
}
|
|
if (OpType_MatMul == subop->type() && loop->parallel()) {
|
|
return new LoopBatchMatMulExecution(loop, op, backend);
|
|
}
|
|
if (OpType_BinaryOp == subop->type() && loop->parallel()) {
|
|
switch (subop->main_as_BinaryOp()->opType()) {
|
|
case BinaryOpOperation_MUL:
|
|
return new LoopBinaryExecution(loop, "in0*in1", op, backend);
|
|
case BinaryOpOperation_ADD:
|
|
return new LoopBinaryExecution(loop, "in0+in1", op, backend);
|
|
case BinaryOpOperation_SUB:
|
|
return new LoopBinaryExecution(loop, "in0-in1", op, backend);
|
|
case BinaryOpOperation_REALDIV:
|
|
return new LoopBinaryExecution(loop, "sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001))", op, backend);
|
|
case BinaryOpOperation_MINIMUM:
|
|
return new LoopBinaryExecution(loop, "in0>in1?in1:in0", op, backend);
|
|
case BinaryOpOperation_MAXIMUM:
|
|
return new LoopBinaryExecution(loop, "in0>in1?in0:in1", op, backend);
|
|
case BinaryOpOperation_GREATER:
|
|
return new LoopBinaryExecution(loop, "convert_float4(-isgreater(in0,in1))", op, backend);
|
|
case BinaryOpOperation_LESS:
|
|
return new LoopBinaryExecution(loop, "convert_float4(-isless(in0,in1))", op, backend);
|
|
case BinaryOpOperation_LESS_EQUAL:
|
|
return new LoopBinaryExecution(loop, "convert_float4(-islessequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_GREATER_EQUAL:
|
|
return new LoopBinaryExecution(loop, "convert_float4(-isgreaterequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_EQUAL:
|
|
return new LoopBinaryExecution(loop, "convert_float4(-isequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_FLOORDIV:
|
|
return new LoopBinaryExecution(loop, "floor(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))", op, backend);
|
|
case BinaryOpOperation_FLOORMOD:
|
|
return new LoopBinaryExecution(loop, "in0-floor(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))*in1", op, backend);
|
|
case BinaryOpOperation_POW:
|
|
return new LoopBinaryExecution(loop, "pow(in0,in1)", op, backend);
|
|
case BinaryOpOperation_SquaredDifference:
|
|
return new LoopBinaryExecution(loop, "(in0-in1)*(in0-in1)", op, backend);
|
|
case BinaryOpOperation_ATAN2:
|
|
return new LoopBinaryExecution(loop, "atan(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))", op, backend);
|
|
case BinaryOpOperation_NOTEQUAL:
|
|
return new LoopBinaryExecution(loop, "convert_float4(-isnotequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_MOD:
|
|
return new LoopBinaryExecution(loop, "in0-floor(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))*in1", op, backend);
|
|
default:
|
|
break;
|
|
}
|
|
return nullptr;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
};
|
|
|
|
OpenCLCreatorRegister<LoopCreator> __Loop_op(OpType_While, IMAGE);
|
|
|
|
} // namespace OpenCL
|
|
} // namespace MNN
|