mirror of https://github.com/alibaba/MNN.git
124 lines
5.1 KiB
C++
124 lines
5.1 KiB
C++
//
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// MatmulExecution.cpp
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// MNN
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//
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// Created by MNN on 2019/02/28.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/opencl/execution/image/MatmulExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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MatMulExecution::MatMulExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend,
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bool transposeA, bool transposeB) : CommonExecution(backend, op)
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, mTransposeA(transposeA), mTransposeB(transposeB){
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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mAreadySetArg = false;
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}
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ErrorCode MatMulExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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mUnits.resize(1);
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auto &unit = mUnits[0];
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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Tensor *input0 = inputs[0];
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Tensor *input1 = inputs[1];
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Tensor *output = outputs[0];
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std::vector<int> input0Shape = tensorShapeFormat(input0);
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std::vector<int> input1Shape = tensorShapeFormat(input1);
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std::vector<int> outputShape = tensorShapeFormat(output);
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std::vector<uint32_t> mGlobalWorkSize{1, 1};
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std::vector<uint32_t> mLocalWorkSize{1, 1, 1, 1};
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std::string kernelName;
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std::set<std::string> buildOptions;
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if(mTransposeA) {
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kernelName = mTransposeB ? "matmul_transA_transB":"matmul_transA";
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} else {
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kernelName = mTransposeB ? "matmul_transB":"matmul";
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}
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if(inputs.size() > 2) {
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buildOptions.emplace("-DBIAS");
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}
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unit.kernel = runtime->buildKernel("matmul", kernelName, buildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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//处理二维矩阵相乘，N C相当于H W
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//二维矩阵相乘
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if(mTransposeA) {
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const int height = input0Shape.at(3);
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const int outputChannel = input0Shape.at(0);
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const int width = mTransposeB ? input1Shape.at(0): input1Shape.at(3);
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const int outputChannelBlocks = UP_DIV(outputChannel, 4);
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const int widthblocks = UP_DIV(width, 4);
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const int heightblocks = UP_DIV(height, 4);
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mGlobalWorkSize = {static_cast<uint32_t>(widthblocks), static_cast<uint32_t>(heightblocks)};
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cl_int ret = CL_SUCCESS;
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int idx = 0;
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(idx++, openCLImage(input0));
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ret |= unit.kernel->get().setArg(idx++, openCLImage(input1));
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if(inputs.size() > 2) {
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ret |= unit.kernel->get().setArg(idx++, openCLImage(inputs[2]));
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}
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ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannel));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannelBlocks));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(height));
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MNN_CHECK_CL_SUCCESS(ret, "setArg MatMulExecution transposeA");
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mLocalWorkSize = {mMaxWorkGroupSize / 64, 64, 0};
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}
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else {
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const int height = input0Shape.at(0);
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const int outputChannel = input0Shape.at(3);
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const int width = mTransposeB ? input1Shape.at(0): input1Shape.at(3);
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const int outputChannelBlocks = UP_DIV(outputChannel, 4);
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const int widthblocks = UP_DIV(width, 4);
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mGlobalWorkSize = {static_cast<uint32_t>(widthblocks), static_cast<uint32_t>(height)};
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int idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(idx++, openCLImage(input0));
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ret |= unit.kernel->get().setArg(idx++, openCLImage(input1));
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if(inputs.size() > 2) {
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ret |= unit.kernel->get().setArg(idx++, openCLImage(inputs[2]));
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}
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ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannel));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannelBlocks));
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MNN_CHECK_CL_SUCCESS(ret, "setArg MatMulExecution transposeA");
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mLocalWorkSize = {mMaxWorkGroupSize / 64, 64, 0};
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}
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mOpenCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1]};
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return NO_ERROR;
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}
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class MatMulCreator : public OpenCLBackend::Creator {
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public:
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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 param = op->main_as_MatMul();
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return new MatMulExecution(inputs, op, backend, param->transposeA(), param->transposeB());
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}
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};
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REGISTER_OPENCL_OP_CREATOR(MatMulCreator, OpType_MatMul, IMAGE);
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} // namespace OpenCL
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} // namespace MNN
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