mirror of
https://github.com/ROCm/composable_kernel.git
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0577b5dd78
* chore(copyright): update copyright header for tile_engine directory
* chore(copyright): update copyright header for script directory
* chore(copyright): update copyright header for test_data directory
* chore(copyright): update copyright header for python directory
* chore(copyright): update copyright header for profiler directory
[ROCm/composable_kernel commit: 0aadb4b2c4]
310 lines
12 KiB
C++
310 lines
12 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <iomanip>
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#include <iostream>
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#include <typeinfo>
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#if defined(__unix__)
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#include <unistd.h>
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#endif
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
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#include "ck/library/utility/fill.hpp"
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namespace ck {
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namespace profiler {
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template <typename ALayout,
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typename BLayout,
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typename CLayout,
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typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType>
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int profile_gemm_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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int M,
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int N,
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int K,
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int StrideA,
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int StrideB,
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int StrideC,
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int n_warmup,
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int n_iter)
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{
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bool pass = true;
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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using namespace ck::literals;
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if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz}, layout);
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride}, layout);
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}
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};
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_device_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0:
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ck::utils::FillConstant<ADataType>{type_convert<ADataType>(1.f)}(a_m_k);
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ck::utils::FillConstant<BDataType>{type_convert<BDataType>(1.f)}(b_k_n);
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break;
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case 1:
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ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
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ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
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break;
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default:
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ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
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ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
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}
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{};
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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using DeviceOp = ck::tensor_operation::device::DeviceGemm<ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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CDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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// Run reference op
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if(do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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auto ref_op = ReferenceGemmInstance{};
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auto ref_invoker = ref_op.MakeInvoker();
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auto ref_argument = ref_op.MakeArgument(
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a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
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ref_invoker.Run(ref_argument);
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}
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float best_tflops = 0;
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int best_instance_id = 0;
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int instance_id = 0;
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// profile device op instances
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr =
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op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// re-init C to zero before profiling next kernel
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c_device_buf.SetZero();
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std::string op_name = op_ptr->GetTypeString();
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float avg_time = invoker_ptr->Run(
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argument_ptr.get(), StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_btype / 1.E6 / avg_time;
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_instance_id = instance_id;
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best_tflops = tflops;
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}
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if(do_verification)
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{
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host : ", c_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
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<< std::endl;
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}
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}
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}
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else
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{
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std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
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}
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instance_id++;
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}
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#if defined(__unix__)
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sleep(2);
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#endif
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// Run the best instance again
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{
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auto& op_ptr = op_ptrs[best_instance_id];
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auto argument_ptr =
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op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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std::string op_name = op_ptr->GetTypeString();
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float avg_time = invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr, time_kernel, 0, 50, 200});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_btype / 1.E6 / avg_time;
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if constexpr(is_same<CDataType, float>::value)
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{
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std::cout << "Best Perf for datatype = f32";
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}
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else if constexpr(is_same<CDataType, half_t>::value)
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{
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std::cout << "Best Perf for datatype = f16";
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}
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else if constexpr(is_same<CDataType, bhalf_t>::value)
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{
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std::cout << "Best Perf for datatype = bf16";
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}
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else if constexpr(is_same<CDataType, int8_t>::value)
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{
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std::cout << "Best Perf for datatype = int8";
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}
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#if defined CK_ENABLE_FP8
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else if constexpr(is_same<CDataType, f8_t>::value)
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{
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std::cout << "Best Perf for datatype = fp8";
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}
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#endif
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if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
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{
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std::cout << " ALayout = RowMajor";
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
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{
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std::cout << " ALayout = ColumnMajor";
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}
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if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
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{
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std::cout << " BLayout = RowMajor";
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}
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else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
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{
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std::cout << " BLayout = ColumnMajor";
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}
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std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
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<< " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << avg_time
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<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << op_name
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<< std::endl;
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}
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}
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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