/******************************************************************************* * * MIT License * * Copyright (c) 2024 Advanced Micro Devices, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. * *******************************************************************************/ #ifndef GUARD_MIOPEN_REDUCEEXTREME_DRIVER_HPP #define GUARD_MIOPEN_REDUCEEXTREME_DRIVER_HPP #include "InputFlags.hpp" #include "driver.hpp" #include "tensor_driver.hpp" #include "timer.hpp" #include "random.hpp" #include #include #include #include #include #include #include #include #include <../test/tensor_holder.hpp> #include <../test/verify.hpp> #include "../src/kernels/MIOpenReduceExtreme.hpp" template bool compare_equal(T r1, T r2) { return r1 == r2; } template int32_t mloReduceExtremeForwardRunHost(miopenTensorDescriptor_t xDesc, miopenTensorDescriptor_t yDesc, miopenTensorDescriptor_t indiceDesc, Tgpu* x, Tcheck* yhost, int32_t* indicehost, int32_t dim) { auto x_dims = miopen::deref(xDesc).GetLengths(); std::vector indice_dims; if(yhost) indice_dims = miopen::deref(yDesc).GetLengths(); else indice_dims = miopen::deref(indiceDesc).GetLengths(); int32_t reduce_size = static_cast(x_dims[dim]); auto indice_numel = std::accumulate(indice_dims.begin(), indice_dims.end(), 1LL, std::multiplies()); auto inner_size = std::accumulate(x_dims.begin() + dim + 1, x_dims.end(), 1ULL, std::multiplies()); int32_t ret = miopenStatusSuccess; for(size_t o = 0; o < indice_numel; ++o) { size_t x_idx = (o / inner_size) * inner_size * reduce_size + o % inner_size; int32_t extreme_idx = 0; Tcheck extreme = static_cast(x[x_idx]); for(int32_t i = 1; i < reduce_size; ++i) { x_idx += inner_size; Tcheck val = static_cast(x[x_idx]); reduce_func{}.calculate(extreme, val, extreme_idx, i); } indicehost[o] = extreme_idx; if(yhost) yhost[o] = extreme; } return ret; } template class ReduceExtremeDriver : public Driver { public: ReduceExtremeDriver() : Driver() { miopenCreateTensorDescriptor(&xDesc); miopenCreateTensorDescriptor(&yDesc); miopenCreateTensorDescriptor(&indiceDesc); data_type = miopen_type{}; indice_data_type = miopen_type{}; } int AddCmdLineArgs() override; int ParseCmdLineArgs(int argc, char* argv[]) override; InputFlags& GetInputFlags() override { return inflags; } int GetandSetData() override; int AllocateBuffersAndCopy() override; int RunForwardGPU() override; int RunForwardCPU(); int RunBackwardGPU() override; Tref GetTolerance(); int VerifyBackward() override; int VerifyForward() override; ~ReduceExtremeDriver() override { miopenDestroyTensorDescriptor(xDesc); miopenDestroyTensorDescriptor(yDesc); miopenDestroyTensorDescriptor(indiceDesc); } private: InputFlags inflags; miopenTensorDescriptor_t xDesc; miopenTensorDescriptor_t yDesc; miopenTensorDescriptor_t indiceDesc; std::unique_ptr x_dev; std::unique_ptr indice_dev; std::unique_ptr y_dev; std::vector x; std::vector y; std::vector yhost; std::vector indice; std::vector indicehost; int dim; miopenReduceExtremeOp_t reduceExtremeOp; miopenDataType_t indice_data_type; }; template int ReduceExtremeDriver::ParseCmdLineArgs(int argc, char* argv[]) { inflags.Parse(argc, argv); if(inflags.GetValueInt("time") == 1) { miopenEnableProfiling(GetHandle(), true); } if((static_cast(inflags.GetValueInt("ReduceExtremeOp")) < ReduceExtremeOp_t::First_) || (static_cast(inflags.GetValueInt("ReduceExtremeOp")) > ReduceExtremeOp_t::Last_)) { std::cerr << "Error ReduceExtremeOp(1-4)" << std::endl; return miopenStatusBadParm; } auto inTensorParam = inflags.GetValueTensor("input"); if((inflags.GetValueInt("DimToReduce") < 0) || (inflags.GetValueInt("DimToReduce") > inTensorParam.lengths.size() - 1)) { std::cerr << "Error DimToReduce(0-" << inTensorParam.lengths.size() - 1 << ")" << std::endl; return miopenStatusBadParm; } return miopenStatusSuccess; } template int ReduceExtremeDriver::GetandSetData() { auto inTensorParam = inflags.GetValueTensor("input"); auto in_len = inTensorParam.lengths; dim = inflags.GetValueInt("DimToReduce"); reduceExtremeOp = static_cast(inflags.GetValueInt("ReduceExtremeOp")); std::vector out_len; for(int i = 0; i < in_len.size(); ++i) { if(i != dim) { out_len.push_back(in_len[i]); } } if(out_len.empty()) out_len.push_back(1); if(SetTensorNd(xDesc, in_len, data_type) != miopenStatusSuccess) MIOPEN_THROW("Error parsing x tensor: " + inflags.GetValueStr("input") + "."); if(SetTensorNd(yDesc, out_len, data_type) != miopenStatusSuccess) MIOPEN_THROW("Error setting y tensor."); if(SetTensorNd(indiceDesc, out_len, indice_data_type) != miopenStatusSuccess) MIOPEN_THROW("Error setting indice tensor."); return 0; } template int ReduceExtremeDriver::AddCmdLineArgs() { inflags.AddInputFlag("forw", 'F', "1", "Run only Forward ReduceExtreme (Default=1)", "int"); inflags.AddTensorFlag("input", 'X', "21x500x375", "input tensor descriptor"); inflags.AddInputFlag( "DimToReduce", 'R', "0", "The indice of the dimensions to be reduced(Default=1)", "int"); inflags.AddInputFlag("ReduceExtremeOp", 'O', "1", "Reduce Extreme Operation Type (check the enum miopenReduceExtremeOp_t in " "miopen.h) (Default=1 to Find the the minimum index)", "int"); inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); inflags.AddInputFlag( "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); return miopenStatusSuccess; } template int ReduceExtremeDriver::AllocateBuffersAndCopy() { size_t in_sz = GetTensorSize(xDesc); size_t out_sz = GetTensorSize(yDesc); uint32_t ctx = 0; x_dev = std::unique_ptr(new GPUMem(ctx, in_sz, sizeof(Tgpu))); indice_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(int32_t))); x = std::vector(in_sz, static_cast(0)); indice = std::vector(out_sz, static_cast(0)); indicehost = std::vector(out_sz, static_cast(0)); for(int32_t i = 0; i < in_sz; ++i) { x[i] = prng::gen_A_to_B(static_cast(-1.0), static_cast(1.0)); } if(x_dev->ToGPU(GetStream(), x.data()) != 0) { std::cerr << "Error copying (x) to GPU, size: " << x_dev->GetSize() << std::endl; return miopenStatusAllocFailed; } if(indice_dev->ToGPU(GetStream(), indice.data()) != 0) { std::cerr << "Error copying (indice) to GPU, size: " << indice_dev->GetSize() << std::endl; return miopenStatusAllocFailed; } if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) { y_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(Tgpu))); y = std::vector(out_sz, static_cast(0)); yhost = std::vector(out_sz, static_cast(0)); if(y_dev->ToGPU(GetStream(), y.data()) != 0) { std::cerr << "Error copying (y) to GPU, size: " << y_dev->GetSize() << std::endl; return miopenStatusAllocFailed; } } return miopenStatusSuccess; } template int ReduceExtremeDriver::RunForwardGPU() { float kernel_total_time = 0; float kernel_first_time = 0; Timer t; START_TIME for(int32_t i = 0; i < inflags.GetValueInt("iter"); ++i) { if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) { miopenReduceExtremeForward(GetHandle(), xDesc, x_dev->GetMem(), dim, reduceExtremeOp, yDesc, y_dev->GetMem(), indiceDesc, indice_dev->GetMem()); } else { miopenReduceExtremeForward(GetHandle(), xDesc, x_dev->GetMem(), dim, reduceExtremeOp, nullptr, nullptr, indiceDesc, indice_dev->GetMem()); } float time = 0; miopenGetKernelTime(GetHandle(), &time); kernel_total_time += time; if(i == 0) kernel_first_time = time; } if(inflags.GetValueInt("time") == 1) { STOP_TIME int32_t iter = inflags.GetValueInt("iter"); if(WALL_CLOCK) std::cout << "Wall-clock Time Forward ReduceExtreme Elapsed: " << t.gettime_ms() / iter << " ms" << std::endl; float kernel_average_time = iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; std::cout << "GPU Kernel Time Forward ReduceExtreme Elapsed: " << kernel_average_time << " ms" << std::endl; } if(indice_dev->FromGPU(GetStream(), indice.data()) != 0) { std::cerr << "Error copying (indice_dev) from GPU, size: " << indice_dev->GetSize() << std::endl; return miopenStatusInternalError; } if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) { if(y_dev->FromGPU(GetStream(), y.data()) != 0) { std::cerr << "Error copying (y_dev) from GPU, size: " << y_dev->GetSize() << std::endl; return miopenStatusInternalError; } } return miopenStatusSuccess; } template int ReduceExtremeDriver::RunForwardCPU() { if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_ARGMIN) { return mloReduceExtremeForwardRunHost( xDesc, nullptr, indiceDesc, x.data(), nullptr, indicehost.data(), dim); } else if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_ARGMAX) { return mloReduceExtremeForwardRunHost( xDesc, nullptr, indiceDesc, x.data(), nullptr, indicehost.data(), dim); } else if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) { return mloReduceExtremeForwardRunHost( xDesc, yDesc, indiceDesc, x.data(), yhost.data(), indicehost.data(), dim); } else if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX) { return mloReduceExtremeForwardRunHost( xDesc, yDesc, indiceDesc, x.data(), yhost.data(), indicehost.data(), dim); } return miopenStatusInternalError; } template int ReduceExtremeDriver::RunBackwardGPU() { return miopenStatusSuccess; } template Tref ReduceExtremeDriver::GetTolerance() { // Computation error of fp16 is ~2^13 (=8192) bigger than // the one of fp32 because mantissa is shorter by 13 bits. auto tolerance = std::is_same::value ? 1.5e-6 : 8.2e-3; // bf16 mantissa has 7 bits, by 3 bits shorter than fp16. if(std::is_same::value) tolerance *= 8.0; return tolerance; } template int ReduceExtremeDriver::VerifyForward() { RunForwardCPU(); if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) { const Tref tolerance = GetTolerance(); auto error = miopen::rms_range(yhost, y); if(!std::isfinite(error) || error > tolerance) { std::cout << "Forward ReduceExtreme FAILED: " << error << " > " << tolerance << std::endl; return EC_VerifyFwd; } else { std::cout << "Forward ReduceExtreme Verifies on CPU (" << error << " < " << tolerance << ')' << std::endl; } } auto error_idx = miopen::mismatch_idx(indicehost, indice, compare_equal); if(error_idx < miopen::range_distance(indicehost)) { std::cout << "Forward ReduceExtreme FAILED: Indice does not equal at " << error_idx << std::endl; return EC_VerifyFwd; } else { std::cout << "Forward ReduceExtreme Incide Verifies on CPU and GPU" << std::endl; } return miopenStatusSuccess; } template int ReduceExtremeDriver::VerifyBackward() { return miopenStatusSuccess; } #endif // GUARD_MIOPEN_REDUCEEXTREME_DRIVER_HPP