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https://github.com/ROCm/composable_kernel.git
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ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
212 lines
8.3 KiB
C++
212 lines
8.3 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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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/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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/check_err.hpp"
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using ::ck::DeviceMem;
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using ::ck::HostTensorDescriptor;
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using ::ck::Tensor;
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using I8 = int8_t;
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using I32 = int32_t;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ActivationOp = PassThrough;
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using CDEElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
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using ADataType = I8;
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using BDataType = I8;
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using AccDataType = I32;
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using CShuffleDataType = I32;
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using DsDataType = ck::Tuple<>;
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using EDataType = I8;
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using ALayout = Row;
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using BLayout = Col;
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using DsLayout = ck::Tuple<>;
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using ELayout = Row;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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// clang-format off
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<
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ALayout,
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BLayout,
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DsLayout,
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ELayout,
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ADataType,
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BDataType,
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AccDataType,
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CShuffleDataType,
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DsDataType,
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EDataType,
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PassThrough, // AElementwiseOperation,
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PassThrough, // BElementwiseOperation,
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CDEElementOp, // CDEElementwiseOperation,
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GemmDefault, // GemmSpecialization GemmSpec,
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1, // NumGemmKPrefetchStage,
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256, // BlockSize,
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256, // MPerBlock,
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128, // NPerBlock,
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64, // KPerBlock,
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16, // AK1,
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16, // BK1,
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16, // MPerXDL,
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16, // NPerXDL,
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8, // MXdlPerWave,
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4, // NXdlPerWave,
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S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1,
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S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
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S<1, 0, 2>, // ABlockTransferSrcAccessOrder,
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2, // index_t ABlockTransferSrcVectorDim,
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16, // index_t ABlockTransferSrcScalarPerVector,
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16, // index_t ABlockTransferDstScalarPerVector_AK1,
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1, // bool ABlockLdsExtraM,
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S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder,
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S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder,
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2, // index_t BBlockTransferSrcVectorDim,
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8, // index_t BBlockTransferSrcScalarPerVector,
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8, // index_t BBlockTransferDstScalarPerVector_BK1,
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1, // bool BBlockLdsExtraN,
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1, // index_t CShuffleMXdlPerWavePerShuffle,
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1, // index_t CShuffleNXdlPerWavePerShuffle,
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S<1, 32, 1, 8>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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4>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::
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ReferenceGemm<ADataType, BDataType, EDataType, float, PassThrough, PassThrough, CDEElementOp>;
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int main()
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{
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bool do_verification = true;
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bool time_kernel = false;
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// GEMM shape
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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ck::index_t K = 1024;
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ck::index_t StrideA = 1024;
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ck::index_t StrideB = 1024;
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ck::index_t StrideE = 1024;
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float requant_scale = 0.03;
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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(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1_uz}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1_uz, stride}));
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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<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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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 << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-128, 127});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-128, 127});
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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 e_device_buf(sizeof(EDataType) * e_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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auto a_element_op = PassThrough{};
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auto b_element_op = PassThrough{};
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auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
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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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auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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{},
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e_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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{},
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StrideE,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!gemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_gemm with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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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(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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< gemm.GetTypeString() << std::endl;
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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if(do_verification)
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{
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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, e_m_n_host_result, a_element_op, b_element_op, cde_element_op);
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ref_invoker.Run(ref_argument);
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return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
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}
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return 0;
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}
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