mirror of
https://github.com/ROCm/composable_kernel.git
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4cbde83cb6
* 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]
422 lines
17 KiB
C++
422 lines
17 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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#include <hip/hip_runtime.h>
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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/impl/device_gemm_xdl_cshuffle_streamk_v3.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_universal_streamk.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/reference_tensor_operation/gpu/reference_gemm.hpp"
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namespace ck {
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namespace profiler {
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template <typename ADataType,
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typename BDataType,
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typename ComputeDataType,
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typename AccDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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bool profile_gemm_universal_streamk_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 Streamk_sel,
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int Grid_size,
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int n_warmup,
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int n_iter,
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uint64_t rotating = 0)
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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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Tensor<CDataType> c_m_n_device_ref_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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int total_gemm_needed = a_m_k.GetElementSpaceSizeInBytes() + b_k_n.GetElementSpaceSizeInBytes();
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int rotating_count = std::max(
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1,
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std::min(n_iter,
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static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
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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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std::cout << "rotating count: " << rotating_count << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-1, 2});
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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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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DeviceMem c_m_n_device_ref_buf(sizeof(CDataType) *
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c_m_n_device_ref_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_Streamk_V2<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 GEMM
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if(do_verification)
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{
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// Use GPU validation
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using ReferenceGemmInstanceGPU =
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ck::tensor_operation::device::ReferenceGemm<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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AccDataType,
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AElementOp,
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BElementOp,
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CElementOp,
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ComputeDataType,
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ComputeDataType>;
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auto ref_gemm_gpu = ReferenceGemmInstanceGPU{};
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auto ref_invoker_gpu = ref_gemm_gpu.MakeInvoker();
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auto ref_argument_gpu = ref_gemm_gpu.MakeArgument(
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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_m_n_device_ref_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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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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if(ref_gemm_gpu.IsSupportedArgument(&ref_argument_gpu))
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{
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ref_invoker_gpu.Run(ref_argument_gpu, StreamConfig{nullptr, true});
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c_m_n_device_ref_buf.FromDevice(c_m_n_host_result.mData.data());
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}
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else
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{
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std::cerr << "GPU reference GEMM does not support this problem configuration so does "
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"CPU validation."
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<< std::endl;
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// Use CPU validation
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using ReferenceGemmInstanceCPU =
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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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ComputeDataType>;
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auto ref_gemm_cpu = ReferenceGemmInstanceCPU{};
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auto ref_invoker_cpu = ref_gemm_cpu.MakeInvoker();
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auto ref_argument_cpu = ref_gemm_cpu.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_cpu.Run(ref_argument_cpu);
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}
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}
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std::string best_op_name;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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float best_grid_size = 0;
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float best_streamk_sel = 0;
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// Get number of SMs on the current GPU
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int device_id;
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hipError_t err = hipGetDevice(&device_id);
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if(err != hipSuccess)
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{
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std::cerr << "hipGetDevice failed: " << hipGetErrorString(err) << std::endl;
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return false;
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}
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hipDeviceProp_t props;
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err = hipGetDeviceProperties(&props, device_id);
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if(err != hipSuccess)
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{
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std::cerr << "hipGetDeviceProperties failed: " << hipGetErrorString(err) << std::endl;
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return false;
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}
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int num_sms = props.multiProcessorCount;
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// Generate grid sizes based on SM count with multipliers
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std::vector<float> multipliers = {0.2f, 0.4f, 0.6f, 0.8f, 1.0f, 1.2f, 1.4f, 1.6f, 2.0f};
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std::vector<int> grid_size_list;
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for(float mult : multipliers)
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{
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int grid_size = static_cast<int>(num_sms * mult);
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if(grid_size > 0)
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{
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grid_size_list.push_back(grid_size);
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}
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}
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std::cout << "Number of SMs: " << num_sms << std::endl;
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std::cout << "Grid sizes to test: ";
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for(auto gs : grid_size_list)
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{
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std::cout << gs << " ";
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}
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std::cout << std::endl;
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// profile device GEMM instances
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for(auto& op_ptr : op_ptrs)
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{
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std::vector<int> streamk_sel_list = {
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0, 1, 2, 3, 4}; // 0: Data Parallel (DP) mode (Stream-K OFF), 1: 1-tile Stream-K+ DP,
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// 2:2-tile Stream-K + DP
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if(Grid_size == -1)
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{
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grid_size_list = {Grid_size};
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}
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if(Streamk_sel != -1)
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{
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streamk_sel_list = {Streamk_sel};
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}
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for(std::size_t j = 0; j < streamk_sel_list.size(); j++)
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{
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for(std::size_t i = 0; i < grid_size_list.size(); i++)
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{
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auto grid_size_curr = grid_size_list[i];
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index_t streamk_sel_curr = streamk_sel_list[j];
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printf("streamk_sel_curr=%0d\n", streamk_sel_curr);
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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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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streamk_sel_curr,
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grid_size_curr,
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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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invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr, false, 0, n_warmup, n_iter});
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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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// Always compare against CPU reference results computed earlier
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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, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_host : ", c_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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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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std::string op_name = op_ptr->GetTypeString();
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float ave_time = invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr,
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time_kernel,
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0,
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n_warmup,
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n_iter,
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rotating_count > 1,
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rotating_count});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
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<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", Grid_size "
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<< grid_size_curr << ", streamk selection strategy"
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<< streamk_sel_curr << std::endl;
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#if defined CK_ENABLE_FP8
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// set softer tolerances for fp8
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if constexpr(is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
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is_same_v<CDataType, f8_t>)
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{
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std::string msg = "Error: Incorrect results!";
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double rtol = 1e-1;
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double atol = 1e-1;
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pass = pass & ck::utils::check_err(
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c_m_n_device_result, c_m_n_host_result, msg, rtol, atol);
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}
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else
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{
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#endif
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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 defined CK_ENABLE_FP8
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}
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#endif
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if(tflops > best_tflops)
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{
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best_op_name = op_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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best_grid_size = grid_size_curr;
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best_streamk_sel = streamk_sel_curr;
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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"
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<< std::endl;
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}
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}
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}
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}
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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 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
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<< " Grid_size = " << best_grid_size
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<< " Stream-K selection strategy = " << best_streamk_sel << " : " << best_ave_time
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<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
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<< best_op_name << std::endl;
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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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