2019-04-17 10:49:11 +08:00
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//
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// UnaryExecution.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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2021-03-12 18:41:50 +08:00
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#include "backend/opencl/execution/image/UnaryExecution.hpp"
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2019-12-27 22:16:57 +08:00
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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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2019-04-17 10:49:11 +08:00
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namespace MNN {
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namespace OpenCL {
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UnaryExecution::UnaryExecution(const std::string& compute, Backend* backend) : Execution(backend) {
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auto openCLBackend = static_cast<OpenCLBackend*>(backend);
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std::set<std::string> buildOptions;
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buildOptions.emplace(" -DOPERATOR=" + compute);
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// FUNC_PRINT_ALL(buildOptions.begin()->c_str(), s);
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auto runtime = openCLBackend->getOpenCLRuntime();
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mKernel = runtime->buildKernel("unary", "unary", buildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mKernel));
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}
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2019-12-27 22:16:57 +08:00
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ErrorCode UnaryExecution::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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2019-04-17 10:49:11 +08:00
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Tensor* input = inputs[0];
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Tensor* output = outputs[0];
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auto openCLBackend = static_cast<OpenCLBackend*>(backend());
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2023-06-16 09:42:45 +08:00
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startRecord(openCLBackend->getOpenCLRuntime(), mRecording);
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2019-04-17 10:49:11 +08:00
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std::vector<int> inputShape = tensorShapeFormat(input);
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std::vector<int> outputShape = tensorShapeFormat(output);
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2019-12-27 22:16:57 +08:00
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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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2019-04-17 10:49:11 +08:00
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2019-12-27 22:16:57 +08:00
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int channelBlocks = (channels + 3) / 4;
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2019-04-17 10:49:11 +08:00
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2019-12-27 22:16:57 +08:00
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mGlobalWorkSize = {
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static_cast<uint32_t>(channelBlocks),
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static_cast<uint32_t>(outputWidth),
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static_cast<uint32_t>(batch * outputHeight),
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};
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2019-04-17 10:49:11 +08:00
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2019-12-27 22:16:57 +08:00
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uint32_t idx = 0;
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mKernel.setArg(idx++, mGlobalWorkSize[0]);
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mKernel.setArg(idx++, mGlobalWorkSize[1]);
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mKernel.setArg(idx++, mGlobalWorkSize[2]);
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mKernel.setArg(idx++, openCLImage(input));
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mKernel.setArg(idx++, openCLImage(output));
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2019-04-17 10:49:11 +08:00
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2020-11-05 16:41:56 +08:00
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std::string name = "unary";
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const std::vector<uint32_t> lws =
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localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), name, mKernel).first;
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mLocalSize = lws;
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recordKernel3d(mKernel, mGlobalWorkSize, mLocalSize, openCLBackend->getOpenCLRuntime());
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endRecord(openCLBackend->getOpenCLRuntime(), mRecording);
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2019-12-27 22:16:57 +08:00
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return NO_ERROR;
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}
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ErrorCode UnaryExecution::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 UnaryExecution onExecute...");
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#endif
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2020-05-28 19:04:27 +08:00
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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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run3DKernelDefault(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 Unary\n",costTime);
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#else
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auto openCLBackend = static_cast<OpenCLBackend*>(backend());
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if(openCLBackend->getOpenCLRuntime()->isUseRecordQueue()){
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mOpenCLBackend->getOpenCLRuntime()->getRecordings()->emplace_back(mRecording);
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#ifdef LOG_VERBOSE
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MNN_PRINT("End UnaryExecution onExecute... \n");
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#endif
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return NO_ERROR;
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}
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2020-05-28 19:04:27 +08:00
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run3DKernelDefault(mKernel, mGlobalWorkSize, mLocalSize,
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mOpenCLBackend->getOpenCLRuntime());
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#endif
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2019-04-17 10:49:11 +08:00
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#ifdef LOG_VERBOSE
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MNN_PRINT("end UnaryExecution onExecute...");
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#endif
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return NO_ERROR;
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}
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2019-12-27 22:16:57 +08:00
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2019-04-17 10:49:11 +08:00
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class UnaryCreator : 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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if (op->type() == OpType_UnaryOp) {
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switch (op->main_as_UnaryOp()->opType()) {
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2021-04-08 15:34:23 +08:00
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case UnaryOpOperation_ABS:
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return new UnaryExecution("fabs(convert_float4(in))", backend);
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2020-02-21 18:42:39 +08:00
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case UnaryOpOperation_SQUARE:
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return new UnaryExecution("in*in", backend);
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case UnaryOpOperation_RSQRT:
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2020-11-05 16:41:56 +08:00
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return new UnaryExecution("rsqrt(convert_float4(in))", backend);
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2020-02-21 17:59:28 +08:00
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case UnaryOpOperation_NEG:
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return new UnaryExecution("-(in)", backend);
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case UnaryOpOperation_EXP:
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return new UnaryExecution("exp(convert_float4(in))", backend);
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case UnaryOpOperation_COS:
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return new UnaryExecution("cos(convert_float4(in))", backend);
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case UnaryOpOperation_SIN:
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return new UnaryExecution("sin(convert_float4(in))", backend);
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case UnaryOpOperation_TAN:
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return new UnaryExecution("tan(convert_float4(in))", backend);
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case UnaryOpOperation_ATAN:
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return new UnaryExecution("atan(convert_float4(in))", backend);
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case UnaryOpOperation_SQRT:
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return new UnaryExecution("sqrt(convert_float4(in))", backend);
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case UnaryOpOperation_CEIL:
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return new UnaryExecution("ceil(convert_float4(in))", backend);
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case UnaryOpOperation_RECIPROCAL:
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return new UnaryExecution("native_recip(convert_float4(in))", backend);
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2020-02-21 18:42:39 +08:00
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case UnaryOpOperation_LOG1P:
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return new UnaryExecution("log1p(convert_float4(in))", backend);
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case UnaryOpOperation_LOG:
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return new UnaryExecution("native_log(convert_float4(in)>(float4)(0.0000001)?convert_float4(in):(float4)(0.0000001))", backend);
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case UnaryOpOperation_FLOOR:
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return new UnaryExecution("floor(convert_float4(in))", backend);
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case UnaryOpOperation_BNLL:
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return new UnaryExecution("in>(FLOAT4)((FLOAT)0)?(in+native_log(exp(convert_float4(-(in)))+(float4)(1.0))):(native_log(exp(convert_float4(in))+(float4)(1.0)))", backend);
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case UnaryOpOperation_ACOSH:
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return new UnaryExecution("acosh(convert_float4(in))", backend);
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case UnaryOpOperation_SINH:
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return new UnaryExecution("sinh(convert_float4(in))", backend);
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case UnaryOpOperation_ASINH:
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return new UnaryExecution("asinh(convert_float4(in))", backend);
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case UnaryOpOperation_ATANH:
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return new UnaryExecution("atanh(convert_float4(in))", backend);
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case UnaryOpOperation_SIGN:
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return new UnaryExecution("sign(convert_float4(in))", backend);
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2020-02-21 17:59:28 +08:00
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case UnaryOpOperation_ROUND:
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return new UnaryExecution("round(convert_float4(in))", backend);
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case UnaryOpOperation_COSH:
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return new UnaryExecution("cosh(convert_float4(in))", backend);
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case UnaryOpOperation_ERF:
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return new UnaryExecution("erf(convert_float4(in))", backend);
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case UnaryOpOperation_ERFC:
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return new UnaryExecution("erfc(convert_float4(in))", backend);
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case UnaryOpOperation_EXPM1:
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return new UnaryExecution("expm1(convert_float4(in))", backend);
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2020-11-05 16:41:56 +08:00
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case UnaryOpOperation_SIGMOID:
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return new UnaryExecution("native_recip((float4)1+native_exp(convert_float4(-in)))", backend);
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case UnaryOpOperation_TANH:
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return new UnaryExecution("tanh(convert_float4(in))", backend);
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case UnaryOpOperation_HARDSWISH:
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2021-04-21 12:09:14 +08:00
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return new UnaryExecution("convert_float4(in)>(float4)(-3.0f)?(convert_float4(in)<(float4)(3.0f)?((convert_float4(in)*(convert_float4(in)+(float4)3.0f))/(float4)6.0f):convert_float4(in)):(float4)(0.0f)", backend);
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2023-02-28 10:41:24 +08:00
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case UnaryOpOperation_GELU:
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return new UnaryExecution("gelu(convert_float4(in))", backend);
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2021-04-21 12:09:14 +08:00
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default:
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2019-04-17 10:49:11 +08:00
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break;
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}
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return nullptr;
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}
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if (op->type() == OpType_Sigmoid) {
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return new UnaryExecution("native_recip((float4)(1.0)+native_exp(convert_float4(-(in))))", backend);
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2019-04-17 10:49:11 +08:00
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}
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if (op->type() == OpType_TanH) {
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2020-11-05 16:41:56 +08:00
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return new UnaryExecution("tanh(convert_float4(in))", backend);
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2019-04-17 10:49:11 +08:00
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}
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return nullptr;
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}
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};
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2021-03-12 18:41:50 +08:00
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OpenCLCreatorRegister<UnaryCreator> __UnaryExecution(OpType_UnaryOp, IMAGE);
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OpenCLCreatorRegister<UnaryCreator> __SigmoidExecution(OpType_Sigmoid, IMAGE);
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OpenCLCreatorRegister<UnaryCreator> __TanhExecution(OpType_TanH, IMAGE);
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2019-04-17 10:49:11 +08:00
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} // namespace OpenCL
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} // namespace MNN
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