mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 21:21:22 +00:00
aa932445ea
* Add a gpu gemm reference kernel * Switch to gpu reference in gemm examples * Remove redundant arguments * Update all related examples * Update more examples * Try less threads per block * Try even less threads per block * Add support for all matrix layouts * Increase block size * Clean up * Remove hardcoded strides * Clean up * Try a column-major case * Revert back to row-major * Run both CPU and GPU veriffication --------- Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
409 lines
14 KiB
C++
409 lines
14 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/tensor_operation/gpu/device/device_gemm_streamk.hpp"
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template <typename DataType>
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inline __host__ __device__ constexpr double get_rtol()
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{
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if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 2e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 2e-1;
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}
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else
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{
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return 1e-3;
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}
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}
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template <typename DataType>
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inline __host__ __device__ constexpr double get_atol()
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{
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if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 2e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 2e-1;
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}
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else
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{
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return 1e-3;
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}
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}
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template <typename ProblemType>
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bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
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{
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#if defined(BUILD_INT4_EXAMPLE) && defined(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
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static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
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#endif
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using namespace ck::literals;
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auto M = problem_size.M;
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auto N = problem_size.N;
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auto K = problem_size.K;
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auto StrideA = problem_size.StrideA;
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auto StrideB = problem_size.StrideB;
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auto StrideC = problem_size.StrideC;
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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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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto f_get_default_stride =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(stride == 0)
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{
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// give a chance if stride is zero, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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{
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return col;
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}
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else
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{
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return row;
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}
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}
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else
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return stride;
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};
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StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
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StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
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StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
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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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switch(config.init_method)
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{
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case 0:
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ck::utils::FillConstant<ADataType>{static_cast<ADataType>(1.f)}(a_m_k);
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ck::utils::FillConstant<BDataType>{static_cast<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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case 2:
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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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break;
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case 3:
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ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.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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case 4:
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ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
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ck::utils::FillUniformDistributionIntegerValue<BDataType>{1.f, 1.f}(b_k_n);
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break;
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case 5:
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ck::utils::FillUniformDistributionIntegerValue<ADataType>{-2.f, 2.f}(a_m_k);
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ck::utils::FillUniformDistributionIntegerValue<BDataType>{-2.f, 2.f}(b_k_n);
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break;
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default:
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ck::utils::FillUniformDistribution<ADataType>{-0.1f, 0.1f}(a_m_k);
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ck::utils::FillUniformDistribution<BDataType>{-0.1f, 0.1f}(b_k_n);
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}
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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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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_host_result.mDesc << std::endl;
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#ifdef BUILD_INT4_EXAMPLE
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DeviceMem a_m_k_device_buf(sizeof(KernelADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_device_buf(sizeof(KernelBDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_device_buf(sizeof(KernelCDataType) *
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c_m_n_device_result.mDesc.GetElementSpaceSize());
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const Tensor<KernelADataType> a_m_k_converted(a_m_k);
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const Tensor<KernelBDataType> b_k_n_converted(b_k_n);
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a_m_k_device_buf.ToDevice(a_m_k_converted.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n_converted.mData.data());
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#else
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_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_m_k_device_buf.ToDevice(a_m_k.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n.mData.data());
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#endif
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DeviceMem workspace;
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto c_element_op = CElementOp{};
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using BaseStreamK = ck::tensor_operation::device::DeviceGemmStreamK<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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// do GEMM
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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float ave_time = 0;
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if constexpr(std::is_same<ProblemType, ProblemSize>::value &&
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!std::is_base_of<BaseStreamK, DeviceGemmInstance>::value)
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{
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auto argument = gemm.MakeArgument(
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#ifdef BUILD_INT4_EXAMPLE
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static_cast<KernelADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<KernelBDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<KernelCDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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#else
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static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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#endif
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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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if(!gemm.IsSupportedArgument(argument))
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{
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std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
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return true;
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}
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ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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}
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else if constexpr(std::is_same<ProblemType, ProblemSizeStreamK>::value &&
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std::is_base_of<BaseStreamK, DeviceGemmInstance>::value)
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{
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auto argument = gemm.MakeArgument(
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#ifdef BUILD_INT4_EXAMPLE
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static_cast<KernelADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<KernelBDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<KernelCDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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#else
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static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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#endif
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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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problem_size.NumSKBlocks);
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if(!gemm.IsSupportedArgument(argument))
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{
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std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
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return true;
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}
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std::size_t workspace_size = gemm.GetWorkSpaceSize(&argument);
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if(workspace_size != 0)
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{
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workspace.Realloc(workspace_size);
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gemm.SetWorkSpacePointer(&argument, workspace.GetDeviceBuffer());
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}
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ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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#if 0
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// TODO!!!!!
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if(workspace_size != 0){
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float * ws_ptr = reinterpret_cast<float*>(malloc(workspace_size));
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size_t ws_dwords = workspace_size / sizeof(float);
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workspace.FromDevice(ws_ptr);
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for(size_t i = 0; i < ws_dwords; i++) {
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uint32_t rere = reinterpret_cast<uint32_t*>(ws_ptr)[i];
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printf("%4lu : %f(0x%08x)\n", i, ws_ptr[i], rere);
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}
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free(ws_ptr);
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}
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#endif
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}
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else
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{
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// When the Problem Type and Problem Size does not fit.
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std::cerr << gemm.GetTypeString() << ": the instance does not support the problem config."
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<< std::endl;
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return true;
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}
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std::size_t flop = 2_uz * 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 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< gemm.GetTypeString() << std::endl;
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bool pass = true;
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if(config.do_verification)
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{
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// CPU verification
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.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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std::cout << "Running verification on CPU." << std::endl;
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ref_invoker.Run(ref_argument);
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#ifdef BUILD_INT4_EXAMPLE
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Tensor<CDataType> c_m_n_device_result_converted(c_m_n_host_result.mDesc);
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c_m_n_device_buf.FromDevice(c_m_n_device_result_converted.mData.data());
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c_m_n_device_result = c_m_n_device_result_converted.CopyAsType<CDataType>();
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return ck::utils::check_err(c_m_n_device_result_converted, c_m_n_host_result);
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#else
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c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
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pass &= !ck::utils::check_err(c_m_n_device_result,
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c_m_n_host_result,
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"Error: Incorrect results!",
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get_rtol<CDataType>(),
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get_atol<CDataType>());
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#endif
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// GPU verification
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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_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_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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std::cout << "Running verification on GPU." << std::endl;
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ref_invoker_gpu.Run(ref_argument_gpu, StreamConfig{});
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c_m_n_device_ref_buf.FromDevice(c_m_n_device_ref_result.mData.data());
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c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
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pass &= !ck::utils::check_err(c_m_n_device_result,
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c_m_n_device_ref_result,
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"Error: Incorrect results!",
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get_rtol<CDataType>(),
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get_atol<CDataType>());
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}
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return !pass;
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}
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bool run_gemm_example(int argc, char* argv[])
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{
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ProblemSize problem_size;
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ExecutionConfig config;
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return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
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}
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bool run_gemm_streamk_example(int argc, char* argv[])
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{
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ProblemSizeStreamK problem_size;
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ExecutionConfig config;
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return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
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}
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