mirror of https://github.com/alibaba/MNN.git
128 lines
5.9 KiB
C++
128 lines
5.9 KiB
C++
//
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// MatmulBufExecution.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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#ifndef MNN_OPENCL_BUFFER_CLOSED
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#include "backend/opencl/execution/buffer/MatmulBufExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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MatMulBufExecution::MatMulBufExecution(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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}
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ErrorCode MatMulBufExecution::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::set<std::string> buildOptions;
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if(mTransposeA) {
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mKernelName = mTransposeB ? "matmul_transA_transB_buf":"matmul_transA_buf";
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} else {
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mKernelName = mTransposeB ? "matmul_transB_buf":"matmul_buf";
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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_buf", mKernelName, 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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cl_int ret = CL_SUCCESS;
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if(mTransposeA) {
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const int height = input0Shape.at(3);//input0 H
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const int outputChannel = input0Shape.at(0);//input0 W
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const int width = mTransposeB ? input1Shape.at(0): input1Shape.at(3);//input1 WW
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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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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++, openCLBuffer(input0));
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input1));
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if(inputs.size() > 2) {
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[2]));
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}
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(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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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(heightblocks));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(widthblocks));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(width));
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MNN_CHECK_CL_SUCCESS(ret, "setArg MatMulBufExecution mTransposeA");
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mLocalWorkSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), mKernelName, unit.kernel).first;
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}
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else {
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const int height = input0Shape.at(0);//input0 H
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const int outputChannel = input0Shape.at(3);//input0 W
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const int width = mTransposeB ? input1Shape.at(0): input1Shape.at(3);//input1 W
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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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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++, openCLBuffer(input0));
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input1));
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if(inputs.size() > 2) {
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[2]));
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}
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(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>(widthblocks));
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ret |= unit.kernel->get().setArg(idx++, static_cast<int>(width));
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MNN_CHECK_CL_SUCCESS(ret, "setArg MatMulBufExecution");
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mLocalWorkSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), mKernelName, unit.kernel).first;
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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 MatMulBufCreator : 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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for (int i = 0; i < inputs.size(); ++i) {
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TensorUtils::setTensorSupportPack(inputs[i], false);
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}
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for (int i = 0; i < outputs.size(); ++i) {
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TensorUtils::setTensorSupportPack(outputs[i], false);
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}
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auto param = op->main_as_MatMul();
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return new MatMulBufExecution(inputs, op, backend, param->transposeA(), param->transposeB());
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}
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};
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REGISTER_OPENCL_OP_CREATOR(MatMulBufCreator, OpType_MatMul, BUFFER);
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} // namespace OpenCL
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} // namespace MNN
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#endif /* MNN_OPENCL_BUFFER_CLOSED */
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