mirror of https://github.com/alibaba/MNN.git
104 lines
3.6 KiB
C++
104 lines
3.6 KiB
C++
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//
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// SplitGeluBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2024/06/26.
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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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#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
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#include "backend/opencl/execution/buffer/SplitGeluBufExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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SplitGeluBufExecution::SplitGeluBufExecution(const MNN::Op* op, Backend* backend) : CommonExecution(backend, op) {
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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}
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ErrorCode SplitGeluBufExecution::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto runtime = static_cast<OpenCLBackend*>(backend())->getOpenCLRuntime();
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MNN_ASSERT(outputs.size() == 1);
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auto input = inputs[0];
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auto output = outputs[0];
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MNN_ASSERT(input->dimensions() == 3);
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MNN_ASSERT(output->dimensions() == 3);
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if(inputs.size() > 1) {
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MNN_ASSERT(inputs[1]->dimensions() == 1);
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MNN_ASSERT(inputs[1]->length(0) == inputs[0]->length(2));
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}
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mUnits.clear();
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mUnits.resize(1);
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std::vector<int> outputShape = tensorShapeFormat(output);
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int shape[4] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};
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std::set<std::string> buildOptions;
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if(inputs.size() > 1) {
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buildOptions.emplace("-DDOUBLE_INPUTS");
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}
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int pack_wh = 1;
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if(shape[2] % 4 == 0) {
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pack_wh = 4;
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buildOptions.emplace("-DWH_4");
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}
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auto &unit = mUnits[0];
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std::string kernelName = "splitgelu_buf";
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unit.kernel = runtime->buildKernel("splitgelu_buf", kernelName, buildOptions);
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auto maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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mGWS = {static_cast<uint32_t>(shape[0]),
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static_cast<uint32_t>(UP_DIV(shape[1], 4)),
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static_cast<uint32_t>(UP_DIV(shape[2],pack_wh))};
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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++, mGWS[0]);
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ret |= unit.kernel->get().setArg(idx++, mGWS[1]);
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ret |= unit.kernel->get().setArg(idx++, mGWS[2]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input));
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if(inputs.size() > 1) {
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[1]));
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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++, sizeof(shape), shape);
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MNN_CHECK_CL_SUCCESS(ret, "setArg SplitGeluBufExecution");
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mLWS = localWS3DDefault(mGWS, maxWorkGroupSize, runtime, "splitgelu_buf", unit.kernel).first;
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unit.globalWorkSize = {mGWS[0], mGWS[1], mGWS[2]};
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unit.localWorkSize = {mLWS[0], mLWS[1], mLWS[2]};
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mOpenCLBackend->recordKernel3d(unit.kernel, mGWS, mLWS);
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mOpenCLBackend->endRecord(mRecording);
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return NO_ERROR;
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}
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class SplitGeluBufCreator : public OpenCLBackend::Creator {
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public:
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virtual ~SplitGeluBufCreator() = default;
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const override {
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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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return new SplitGeluBufExecution(op, backend);
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}
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};
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REGISTER_OPENCL_OP_CREATOR(SplitGeluBufCreator, OpType_SplitGeLU, BUFFER);
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} // namespace OpenCL
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} // namespace MNN
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#endif/* MNN_SUPPORT_TRANSFORMER_FUSE */
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#endif/* MNN_OPENCL_BUFFER_CLOSED */
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