mirror of https://github.com/alibaba/MNN.git
82 lines
3.3 KiB
C++
82 lines
3.3 KiB
C++
//
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// RangeBufExecution.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/RangeBufExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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RangeBufExecution::RangeBufExecution(const std::string &compute, const MNN::Op *Op, Backend* backend) : CommonExecution(backend, Op) {
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mBuildOptions.emplace(compute);
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// Do nothing
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}
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ErrorCode RangeBufExecution::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 openCLBackend = static_cast<OpenCLBackend*>(backend());
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auto runtime = openCLBackend->getOpenCLRuntime();
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std::vector<int> outputShape = tensorShapeFormat(outputs[0]);
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int totalSize = outputShape[0] * outputShape[1] * outputShape[2] * outputShape[3];
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mGlobalWorkSize = {
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static_cast<uint32_t>(UP_DIV(totalSize, 4)),
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static_cast<uint32_t>(1)
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};
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std::set<std::string> buildOption = mBuildOptions;
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if((totalSize % 4) != 0){
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buildOption.emplace("-DPACK_LEAVE");
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}
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unit.kernel = runtime->buildKernel("range_buf", "range_buf", buildOption, openCLBackend->getPrecision(), inputs[0], outputs[0]);
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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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++, 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(inputs[0]));
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[2]));
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(outputs[0]));
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ret |= unit.kernel->get().setArg(idx++, totalSize);
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MNN_CHECK_CL_SUCCESS(ret, "setArg RangeBufExecution");
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std::string kernelName = "range_buf";
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mLocalSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), kernelName, unit.kernel, openCLBackend->getCLTuneLevel(), "range_buf").first;
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openCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalSize);
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]};
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unit.localWorkSize = {mLocalSize[0], mLocalSize[1]};
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return NO_ERROR;
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}
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class RangeBufCreator : 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 code = inputs[0]->getType().code;
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switch (code) {
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case halide_type_int:
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return new RangeBufExecution("-DUSE_INT", op, backend);
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case halide_type_float:
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return new RangeBufExecution("-DUSE_FLOAT", op, backend);
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default:
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return nullptr;
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}
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}
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};
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REGISTER_OPENCL_OP_CREATOR(RangeBufCreator, OpType_Range, 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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