mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-19 04:19:36 +00:00
4839132e99
* Add KBatch support for gemm_ab_scale
* Revert kernel parameters change
* Remove printing
* fix formatting
* fix check
* Use {} in if
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
[ROCm/composable_kernel commit: 9d4bfe3932]
366 lines
15 KiB
C++
366 lines
15 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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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 "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_multiple_d_xdl_cshuffle_v3_ab_scale.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_ab_scale.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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namespace ck {
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namespace profiler {
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template <typename A0DataType,
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typename A1DataType,
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typename B0DataType,
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typename B1DataType,
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typename ComputeDataType,
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typename AccDataType,
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typename EDataType,
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index_t ScaleBlockM,
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index_t ScaleBlockN,
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index_t ScaleBlockK,
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typename ALayout,
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typename BLayout,
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typename ELayout>
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bool profile_gemm_ab_scale_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 StrideE,
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int KBatch,
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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});
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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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ck::index_t Scale_Stride_AM = ck::is_same_v<ALayout, tensor_layout::gemm::RowMajor>
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? ((K + ScaleBlockK - 1) / ScaleBlockK)
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: ((M + ScaleBlockM - 1) / ScaleBlockM);
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ck::index_t Scale_Stride_BN = ck::is_same_v<BLayout, ck::tensor_layout::gemm::ColumnMajor>
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? ((K + ScaleBlockK - 1) / ScaleBlockK)
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: ((N + ScaleBlockN - 1) / ScaleBlockN);
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Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<A1DataType> a1_m_k(f_host_tensor_descriptor((M + ScaleBlockM - 1) / ScaleBlockM,
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(K + ScaleBlockK - 1) / ScaleBlockK,
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Scale_Stride_AM,
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ALayout{}));
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Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor((K + ScaleBlockK - 1) / ScaleBlockK,
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(N + ScaleBlockN - 1) / ScaleBlockN,
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Scale_Stride_BN,
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BLayout{}));
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Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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int total_gemm_needed =
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a0_m_k.GetElementSpaceSizeInBytes() + b0_k_n.GetElementSpaceSizeInBytes() +
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a1_m_k.GetElementSpaceSizeInBytes() + b1_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 << "a0_m_k: " << a0_m_k.mDesc << std::endl;
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std::cout << "a1_m_k: " << a1_m_k.mDesc << std::endl;
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std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
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std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
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std::cout << "e_m_n: " << e_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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a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
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b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
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a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
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b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
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break;
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default:
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a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{-0.5, 0.5});
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b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
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a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
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b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
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}
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CElementOp = 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 a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
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DeviceMem a1_device_buf(sizeof(A1DataType) * a1_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b1_device_buf(sizeof(B1DataType) * b1_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
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a0_device_buf.ToDevice(a0_m_k.mData.data());
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b0_device_buf.ToDevice(b0_k_n.mData.data());
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a1_device_buf.ToDevice(a1_m_k.mData.data());
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b1_device_buf.ToDevice(b1_k_n.mData.data());
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using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD_ABScale<ALayout,
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BLayout,
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ck::Tuple<>,
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ELayout,
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A0DataType,
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A1DataType,
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B0DataType,
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B1DataType,
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ck::Tuple<>,
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EDataType,
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ScaleBlockM,
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ScaleBlockN,
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ScaleBlockK,
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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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Tensor<AccDataType> c_m_n({M, N});
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Tensor<float> a_m_k({M, K});
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Tensor<float> b_k_n({K, N});
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for(int m = 0; m < M; m++)
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{
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for(int k = 0; k < K; k++)
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{
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a_m_k(m, k) = ck::type_convert<float>(a0_m_k(m, k)) *
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a1_m_k(m / ScaleBlockM, k / ScaleBlockK);
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}
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}
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for(int n = 0; n < N; n++)
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{
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for(int k = 0; k < K; k++)
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{
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b_k_n(k, n) = ck::type_convert<float>(b0_k_n(k, n)) *
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b1_k_n(k / ScaleBlockK, n / ScaleBlockN);
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}
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}
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<float,
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float,
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AccDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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PassThrough,
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float>;
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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 =
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ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
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ref_invoker.Run(ref_argument);
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for(int m = 0; m < M; ++m)
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{
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for(int n = 0; n < N; ++n)
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{
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e_m_n_host_result(m, n) = ck::type_convert<EDataType>(c_m_n(m, n));
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}
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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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// profile device GEMM 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<A0DataType*>(a0_device_buf.GetDeviceBuffer()),
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static_cast<B0DataType*>(b0_device_buf.GetDeviceBuffer()),
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std::array<const void*, 0>{},
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static_cast<EDataType*>(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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std::array<ck::index_t, 0>{},
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StrideE,
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a1_device_buf.GetDeviceBuffer(),
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b1_device_buf.GetDeviceBuffer(),
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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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op_ptr->SetKBatch(argument_ptr.get(), KBatch);
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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(), 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(e_m_n_device_result.mData.data());
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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<A0DataType, f8_t> || is_same_v<B0DataType, f8_t> ||
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is_same_v<EDataType, f8_t>)
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{
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std::string msg = "Error: Incorrect results!";
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double rtol = 5e-2;
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double atol = 5e-2;
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pass = pass & ck::utils::check_err(
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e_m_n_device_result, e_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(e_m_n_device_result, e_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(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a0_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b0_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "c_device: ", e_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(
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argument_ptr.get(),
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StreamConfig{
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nullptr, time_kernel, 0, n_warmup, n_iter, rotating_count > 1, 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 =
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sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * 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 << " 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_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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}
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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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}
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if constexpr(is_same<EDataType, 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<EDataType, 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<EDataType, 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<EDataType, 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 << " StrideE = " << StrideE << " : " << 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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