mirror of https://github.com/alibaba/MNN.git
474 lines
24 KiB
C++
474 lines
24 KiB
C++
//
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// LoopBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2023/04/23.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifndef MNN_OPENCL_BUFFER_CLOSED
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#include "backend/opencl/execution/buffer/LoopBufExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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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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LoopGatherBufExecution::LoopGatherBufExecution(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 LoopGatherBufExecution::InitCommandOnEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs){
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auto cmd = mLoop->initCommand()->GetAs<RegionCommand>(0);
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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if (cmd->op() == nullptr){
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Unit unit;
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auto output = mTensors[cmd->indexes()->data()[0]];
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auto outputShape = tensorShapeFormat(output);
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auto outputDes = TensorUtils::getDescribe(output);
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int region[] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//nchw
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if(MNN_DATA_FORMAT_NC4HW4 == outputDes->dimensionFormat){
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region[1] = ROUND_UP(outputShape[3], 4);
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}
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unit.kernel = runTime->buildKernel("raster_buf", "buffer_set_zero", {}, mOpenCLBackend->getPrecision(), output, output);
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unit.localWorkSize = {8, 8};
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unit.globalWorkSize = {(uint32_t)UP_DIV((region[2] * region[3]), 8)*8,
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(uint32_t)UP_DIV((region[0] * region[1]), 8)*8};
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int global_dim0 = region[2] * region[3];
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int global_dim1 = region[0] * region[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++, global_dim0);
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ret |= unit.kernel->get().setArg(idx++, global_dim1);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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MNN_CHECK_CL_SUCCESS(ret, "setArg buffer_set_zero");
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mOpenCLBackend->recordKernel2d(unit.kernel, {(uint32_t)UP_DIV((region[2] * region[3]), 8)*8,
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(uint32_t)UP_DIV((region[0] * region[1]), 8)*8}, {8, 8});
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mUnits.emplace_back(unit);
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return NO_ERROR;
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}
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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 inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
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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] = 0;
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mStride_dst[3] = 0;
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::memset(mStep, 0, 2 * sizeof(int));
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// gather
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{
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Unit unit;
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auto input = mTensors[cmd->indexes()->data()[1]];
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auto output = mTensors[cmd->indexes()->data()[0]];
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std::set<std::string> buildOptions;
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unit.kernel = runTime->buildKernel("gather_buf", "batch_gather_buf", buildOptions, mOpenCLBackend->getPrecision(), input, output);
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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)(1)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(input));
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ret |= unit.kernel->get().setArg(index++, x);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_src), mStride_src);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_dst), mStride_dst);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
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ret |= unit.kernel->get().setArg(index++, sizeof(mIter), mIter);
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ret |= unit.kernel->get().setArg(index++, inputSize);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopInitGatherBufExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "batch_gather_buf", unit.kernel, mOpenCLBackend->getCLTuneLevel()).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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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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}
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return NO_ERROR;
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}
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ErrorCode LoopGatherBufExecution::onEncode(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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_setTensorStack(mTensors, inputs, outputs, mLoop);
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mUnits.clear();
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mOffsetTensors.clear();
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if(mLoop->initCommand() != nullptr){
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InitCommandOnEncode(inputs, outputs);
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}
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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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int inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
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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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// gather
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{
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Unit unit;
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auto input = mTensors[cmd->indexes()->data()[1]];
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auto output = mTensors[cmd->indexes()->data()[0]];
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std::set<std::string> buildOptions;
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if (mIter[0] >= 0) {
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buildOptions.emplace("-DOFFSET_DST");
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}
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if (mIter[1] >= 0) {
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buildOptions.emplace("-DOFFSET_SRC");
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}
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unit.kernel = runTime->buildKernel("gather_buf", "batch_gather_buf", buildOptions, mOpenCLBackend->getPrecision(), input, output);
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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->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(input));
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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->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[i]]));
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}
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}
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ret |= unit.kernel->get().setArg(index++, x);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_src), mStride_src);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_dst), mStride_dst);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
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ret |= unit.kernel->get().setArg(index++, sizeof(mIter), mIter);
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ret |= unit.kernel->get().setArg(index++, inputSize);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopGatherBufExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "batch_gather_buf", unit.kernel, mOpenCLBackend->getCLTuneLevel()).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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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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}
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return NO_ERROR;
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}
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LoopBatchMatMulBufExecution::LoopBatchMatMulBufExecution(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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}
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ErrorCode LoopBatchMatMulBufExecution::onEncode(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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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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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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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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if (mHasBias) {
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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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{
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// matmul
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Unit unit;
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std::string KernelName = "batch_matmul";
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std::set<std::string> buildOptions = mBuildOptions;
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if (mHasBias) {
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buildOptions.emplace("-DBIAS");
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}
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if (mTransposeA) {
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buildOptions.emplace("-DTRANSPOSE_A");
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}
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if (mTransposeB) {
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buildOptions.emplace("-DTRANSPOSE_B");
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}
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buildOptions.emplace("-DH_LEAVES=" + std::to_string(h % 4));
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unit.kernel = runTime->buildKernel("loop", KernelName, buildOptions, mOpenCLBackend->getPrecision(), mTensors[cmd->indexes()->data()[1]], mTensors[cmd->indexes()->data()[0]]);
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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)(UP_DIV(h, 4)), (uint32_t)(UP_DIV(e, 4)),(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->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[0]]));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[1]]));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[2]]));
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if (mHasBias) {
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[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->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[i]]));
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} else {
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[1]]));
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}
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}
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ret |= unit.kernel->get().setArg(index++, e);
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ret |= unit.kernel->get().setArg(index++, l);
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ret |= unit.kernel->get().setArg(index++, h);
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ret |= unit.kernel->get().setArg(index++, sizeof(mOffset), mOffset);
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ret |= unit.kernel->get().setArg(index++, sizeof(mIter), mIter);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBatchMatMulBufExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, KernelName, unit.kernel, mOpenCLBackend->getCLTuneLevel()).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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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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}
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return NO_ERROR;
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}
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LoopBinaryBufExecution::LoopBinaryBufExecution(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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mBuildOptions.emplace("-DOPERATOR=" + compute);
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}
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ErrorCode LoopBinaryBufExecution::onEncode(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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if(cmd->op()->main_as_BinaryOp()->opType() == BinaryOpOperation_MOD && (outputs[0]->getType().code == halide_type_int || outputs[0]->getType().code == halide_type_uint)){
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mBuildOptions.emplace("-DINT_COMPUTE_MOD");
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}
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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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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int z = cmd->size()->data()[0];
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int y = cmd->size()->data()[1];
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int x = cmd->size()->data()[2];
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int n = mLoop->loopNumber();
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int inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
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auto src0Stride = cmd->view()->GetAs<View>(1)->stride()->data();
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auto src1Stride = cmd->view()->GetAs<View>(2)->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_src0[i] = src0Stride[i];
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mStride_src1[i] = src1Stride[i];
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mStride_dst[i] = dstStride[i];
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}
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auto input0 = mTensors[cmd->indexes()->data()[1]];
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auto input1 = mTensors[cmd->indexes()->data()[2]];
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auto output = mTensors[cmd->indexes()->data()[0]];
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// cumsum
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// mTensors cmd->indexes()->data() = {2, 0, 1} -> {output, input0, input1}, output = input0
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if(!mLoop->parallel()){
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int loopNumber = mLoop->loopNumber();
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::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
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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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unit.kernel = runTime->buildKernel("loop_buf", "loop_cumsum_buf", mBuildOptions, mOpenCLBackend->getPrecision(), input0, output);
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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), (uint32_t)(y), (uint32_t)(z)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(input0));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(input1));
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ret |= unit.kernel->get().setArg(index++, mStride_src0[0]);
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ret |= unit.kernel->get().setArg(index++, mStride_src0[1]);
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ret |= unit.kernel->get().setArg(index++, mStride_src0[2]);
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ret |= unit.kernel->get().setArg(index++, mStride_src1[0]);
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ret |= unit.kernel->get().setArg(index++, mStride_src1[1]);
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ret |= unit.kernel->get().setArg(index++, mStride_src1[2]);
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ret |= unit.kernel->get().setArg(index++, mStride_dst[0]);
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ret |= unit.kernel->get().setArg(index++, mStride_dst[1]);
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ret |= unit.kernel->get().setArg(index++, mStride_dst[2]);
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ret |= unit.kernel->get().setArg(index++, loopNumber);
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ret |= unit.kernel->get().setArg(index++, sizeof(mOffset), mOffset);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopCumsumBufExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "loop_cumsum_buf", unit.kernel, mOpenCLBackend->getCLTuneLevel()).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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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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return NO_ERROR;
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}
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unit.kernel = runTime->buildKernel("loop_buf", "loop_binary_buf", mBuildOptions, mOpenCLBackend->getPrecision(), input0, output);
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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), (uint32_t)(y), (uint32_t)(z)};
|
|
|
|
uint32_t index = 0;
|
|
cl_int ret = CL_SUCCESS;
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input0));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input1));
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[2]);
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBinaryBufExecution");
|
|
|
|
std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "loop_binary_buf", unit.kernel, mOpenCLBackend->getCLTuneLevel()).first;
|
|
|
|
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
|
|
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
|
|
mUnits.emplace_back(unit);
|
|
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
|
|
return NO_ERROR;
|
|
}
|
|
|
|
class LoopBufCreator : 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 {
|
|
for (int i = 0; i < inputs.size(); ++i) {
|
|
TensorUtils::setTensorSupportPack(inputs[i], false);
|
|
}
|
|
for (int i = 0; i < outputs.size(); ++i) {
|
|
TensorUtils::setTensorSupportPack(outputs[i], false);
|
|
}
|
|
auto loop = op->main_as_LoopParam();
|
|
if (nullptr == loop || loop->commands() == nullptr) {
|
|
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 LoopGatherBufExecution(loop, op, backend);
|
|
}
|
|
if (OpType_MatMul == subop->type() && loop->parallel() && nullptr == loop->initCommand()) {
|
|
return new LoopBatchMatMulBufExecution(loop, op, backend);
|
|
}
|
|
if (OpType_BinaryOp == subop->type() && nullptr == loop->initCommand()) {
|
|
switch (subop->main_as_BinaryOp()->opType()) {
|
|
case BinaryOpOperation_MUL:
|
|
return new LoopBinaryBufExecution(loop, "in0*in1", op, backend);
|
|
case BinaryOpOperation_ADD:
|
|
return new LoopBinaryBufExecution(loop, "in0+in1", op, backend);
|
|
case BinaryOpOperation_SUB:
|
|
return new LoopBinaryBufExecution(loop, "in0-in1", op, backend);
|
|
case BinaryOpOperation_REALDIV:
|
|
return new LoopBinaryBufExecution(loop, "sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001))", op, backend);
|
|
case BinaryOpOperation_MINIMUM:
|
|
return new LoopBinaryBufExecution(loop, "in0>in1?in1:in0", op, backend);
|
|
case BinaryOpOperation_MAXIMUM:
|
|
return new LoopBinaryBufExecution(loop, "in0>in1?in0:in1", op, backend);
|
|
case BinaryOpOperation_GREATER:
|
|
return new LoopBinaryBufExecution(loop, "(float)(isgreater(in0,in1))", op, backend);
|
|
case BinaryOpOperation_LESS:
|
|
return new LoopBinaryBufExecution(loop, "(float)(isless(in0,in1))", op, backend);
|
|
case BinaryOpOperation_LESS_EQUAL:
|
|
return new LoopBinaryBufExecution(loop, "(float)(islessequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_GREATER_EQUAL:
|
|
return new LoopBinaryBufExecution(loop, "(float)(isgreaterequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_EQUAL:
|
|
return new LoopBinaryBufExecution(loop, "(float)(isequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_FLOORDIV:
|
|
return new LoopBinaryBufExecution(loop, "floor(sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001)))", op, backend);
|
|
case BinaryOpOperation_FLOORMOD:
|
|
return new LoopBinaryBufExecution(loop, "in0-floor(sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001)))*in1", op, backend);
|
|
case BinaryOpOperation_POW:
|
|
return new LoopBinaryBufExecution(loop, "pow(in0,in1)", op, backend);
|
|
case BinaryOpOperation_SquaredDifference:
|
|
return new LoopBinaryBufExecution(loop, "(in0-in1)*(in0-in1)", op, backend);
|
|
case BinaryOpOperation_ATAN2:
|
|
return new LoopBinaryBufExecution(loop, "(in1==(float)0?(sign(in0)*(float)(PI/2)):(atan(in0/in1)+(in1>(float)0?(float)0:sign(in0)*(float)PI)))", op, backend);
|
|
case BinaryOpOperation_NOTEQUAL:
|
|
return new LoopBinaryBufExecution(loop, "(float)(isnotequal(in0,in1))", op, backend);
|
|
case BinaryOpOperation_MOD:
|
|
return new LoopBinaryBufExecution(loop, "in0-floor(sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001)))*in1", op, backend);
|
|
default:
|
|
break;
|
|
}
|
|
return nullptr;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
};
|
|
|
|
REGISTER_OPENCL_OP_CREATOR(LoopBufCreator, OpType_While, BUFFER);
|
|
|
|
} // namespace OpenCL
|
|
} // namespace MNN
|
|
#endif /* MNN_OPENCL_BUFFER_CLOSED */
|