// SPDX-License-Identifier: MIT // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. #include #include #include #include #include #include "ck/ck.hpp" #include "ck/tensor_operation/gpu/device/device_reduce.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/library/tensor_operation_instance/gpu/reduce/reduce.hpp" using InDataType = float; using OutDataType = float; using AccDataType = float; using ReduceAdd = ck::reduce::Add; using PassThrough = ck::tensor_operation::element_wise::PassThrough; using UnaryDivide = ck::tensor_operation::element_wise::UnaryDivide; constexpr bool PropagateNan = false; constexpr bool OutputIndex = false; constexpr int Rank = 4; constexpr int NumReduceDim = 3; struct SimpleDeviceMem { SimpleDeviceMem() = delete; SimpleDeviceMem(std::size_t mem_size) : p_mem_{} { (void)hipMalloc(static_cast(&p_mem_), mem_size); } void* GetDeviceBuffer() { return p_mem_; } ~SimpleDeviceMem() { (void)hipFree(p_mem_); } void* p_mem_; }; int main(int argc, char* argv[]) { std::array in_lengths{16, 8, 128, 256}; std::array in_strides{8 * 128 * 256, 128 * 256, 256, 1}; std::array out_lengths{256}; std::array out_strides{1}; std::array reduce_dims{0, 1, 2}; ck::index_t num_in_elements = std::accumulate(in_lengths.begin(), in_lengths.end(), 1, std::multiplies()); ck::index_t num_out_elements = std::accumulate(out_lengths.begin(), out_lengths.end(), 1, std::multiplies()); ck::index_t reduce_length = 1; for(auto dim : reduce_dims) reduce_length *= in_lengths[dim]; double alpha{1.0}; double beta{0.0}; SimpleDeviceMem in(sizeof(InDataType) * num_in_elements); SimpleDeviceMem out(sizeof(OutDataType) * num_out_elements); using DeviceOp = ck::tensor_operation::device::DeviceReduce; const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory< DeviceOp>::GetInstances(); std::cout << "found " << op_ptrs.size() << " instances" << std::endl; std::string best_op_name; bool found = false; int best_op_id = -1; float best_ave_time = std::numeric_limits::max(); float best_gb_per_sec = 0; // profile device operation instances std::cout << "Run all instances and do timing" << std::endl; for(int i = 0; i < op_ptrs.size(); ++i) { auto& op_ptr = op_ptrs[i]; auto argument_ptr = op_ptr->MakeArgumentPointer(in_lengths, in_strides, out_lengths, out_strides, reduce_dims, alpha, beta, in.GetDeviceBuffer(), nullptr, out.GetDeviceBuffer(), nullptr, PassThrough{}, UnaryDivide{reduce_length}); auto invoker_ptr = op_ptr->MakeInvokerPointer(); std::string op_name = op_ptr->GetTypeString(); if(op_ptr->IsSupportedArgument(argument_ptr.get())) { float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true}); std::size_t num_bytes = num_in_elements * sizeof(InDataType) + (beta == 0.0f ? 1 : 2) * num_out_elements * sizeof(OutDataType); float gb_per_sec = num_bytes / 1.E6 / ave_time; std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, " << op_name << std::endl; if(ave_time < best_ave_time) { found = true; best_op_id = i; best_op_name = op_name; best_ave_time = ave_time; best_gb_per_sec = gb_per_sec; } } else { std::cout << op_name << " does not support this problem" << std::endl; } } std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl; // run the best intance if(found) { auto& op_ptr = op_ptrs[best_op_id]; std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString() << std::endl; auto argument_ptr = op_ptr->MakeArgumentPointer(in_lengths, in_strides, out_lengths, out_strides, reduce_dims, alpha, beta, in.GetDeviceBuffer(), nullptr, out.GetDeviceBuffer(), nullptr, PassThrough{}, UnaryDivide{reduce_length}); auto invoker_ptr = op_ptr->MakeInvokerPointer(); if(op_ptr->IsSupportedArgument(argument_ptr.get())) { invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false}); } std::cout << "Done" << std::endl; } return 0; }