mirror of https://github.com/alibaba/MNN.git
104 lines
3.7 KiB
C++
104 lines
3.7 KiB
C++
//
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// SelectBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2023/08/11.
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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/SelectBufExecution.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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#include "backend/opencl/core/OpenCLBackend.hpp"
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namespace MNN {
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namespace OpenCL {
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SelectBufExecution::SelectBufExecution(Backend* backend) : Execution(backend) {
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// Do nothing
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}
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ErrorCode SelectBufExecution::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto inSize1 = inputs[1]->elementSize();
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auto inSize2 = inputs[2]->elementSize();
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auto openCLBackend = static_cast<OpenCLBackend*>(backend());
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auto runtime = openCLBackend->getOpenCLRuntime();
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if(inSize1 == 1)
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mBuildOptions.emplace("-DINSIZE1_EUQAL_1");
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if(inSize2 == 1)
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mBuildOptions.emplace("-DINSIZE2_EUQAL_1");
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mKernel = runtime->buildKernel("select_buf", "select_buf", mBuildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mKernel));
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std::vector<int> outputShape = tensorShapeFormat(outputs[0]);
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int batch = outputShape.at(0);
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int outputHeight = outputShape.at(1);
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int outputWidth = outputShape.at(2);
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int channels = outputShape.at(3);
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int channelBlocks = (channels + 3) / 4;
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int outSize = batch * channelBlocks * outputWidth * outputHeight * 4;
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mGlobalWorkSize = {
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static_cast<uint32_t>(outSize),
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1
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};
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= mKernel.setArg(idx++, mGlobalWorkSize[0]);
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ret |= mKernel.setArg(idx++, mGlobalWorkSize[1]);
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ret |= mKernel.setArg(idx++, openCLBuffer(inputs[0]));
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ret |= mKernel.setArg(idx++, openCLBuffer(inputs[1]));
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ret |= mKernel.setArg(idx++, openCLBuffer(inputs[2]));
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ret |= mKernel.setArg(idx++, openCLBuffer(outputs[0]));
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MNN_CHECK_CL_SUCCESS(ret, "setArg SelectBufExecution");
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std::string kernelName = "select_buf";
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mLocalSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), kernelName, mKernel).first;
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return NO_ERROR;
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}
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ErrorCode SelectBufExecution::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start SelectBufExecution onExecute...");
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#endif
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auto mOpenCLBackend = static_cast<OpenCLBackend*>(backend());
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#ifdef ENABLE_OPENCL_TIME_PROFILER
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cl::Event event;
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runKernel2D(mKernel, mGlobalWorkSize, mLocalSize,
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mOpenCLBackend->getOpenCLRuntime(), &event);
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int costTime = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
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MNN_PRINT("kernel cost:%d us Select\n",costTime);
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#else
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runKernel2D(mKernel, mGlobalWorkSize, mLocalSize,
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mOpenCLBackend->getOpenCLRuntime());
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#endif
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#ifdef LOG_VERBOSE
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MNN_PRINT("end SelectBufExecution onExecute...");
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#endif
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return NO_ERROR;
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}
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class SelectBufCreator : 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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return new SelectBufExecution(backend);
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}
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};
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OpenCLCreatorRegister<SelectBufCreator> __SelectBuf__(OpType_Select, 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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