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7589116121
* add scripts * fixed splitK_gemm_fp32 * clean * clean * use gemm_xdl_splitK_c_shuffle into profiler * remove device_gemm_xdl_splitk.hpp
263 lines
8.8 KiB
C++
263 lines
8.8 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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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/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_splitk.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/reference_tensor_operation/cpu/reference_gemm.hpp"
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#include "ck/library/utility/host_gemm.hpp"
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enum struct GemmMatrixLayout
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{
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MK_KN_MN, // 0
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MK_NK_MN, // 1
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KM_KN_MN, // 2
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KM_NK_MN, // 3
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};
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template <typename T>
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static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
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{
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float max_diff = 1e-6;
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for(std::size_t i = 0; i < ref.mData.size(); ++i)
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{
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float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
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if(max_diff < diff)
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{
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return false;
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}
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}
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return true;
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}
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struct gemmArgs
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{
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GemmMatrixLayout layout;
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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 KBatch;
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};
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int test_gemm(const gemmArgs& args)
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{
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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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bool a_row_major, b_row_major, c_row_major;
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switch(args.layout)
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{
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case GemmMatrixLayout::MK_KN_MN:
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a_row_major = true;
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b_row_major = true;
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c_row_major = true;
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break;
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case GemmMatrixLayout::MK_NK_MN:
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a_row_major = true;
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b_row_major = false;
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c_row_major = true;
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break;
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case GemmMatrixLayout::KM_KN_MN:
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a_row_major = false;
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b_row_major = true;
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c_row_major = true;
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break;
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case GemmMatrixLayout::KM_NK_MN:
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a_row_major = false;
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b_row_major = false;
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c_row_major = true;
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break;
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default: printf("not supported layout"); return 1;
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}
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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, bool row_major) {
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if(row_major)
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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}));
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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, stride}));
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}
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};
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Tensor<float> a_m_k(f_host_tensor_descriptor(args.M, args.K, args.StrideA, a_row_major));
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Tensor<float> b_k_n(f_host_tensor_descriptor(args.K, args.N, args.StrideB, b_row_major));
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Tensor<float> c_m_n_host_result(
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f_host_tensor_descriptor(args.M, args.N, args.StrideC, c_row_major));
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Tensor<float> c_m_n_device_result(
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f_host_tensor_descriptor(args.M, args.N, args.StrideC, c_row_major));
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// init data
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std::size_t num_thread = 1;
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a_m_k.GenerateTensorValue(GeneratorTensor_2<float>{-5, 5}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_2<float>{-5, 5}, num_thread);
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// set zero to c_device_buf
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c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<float>{}, num_thread);
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host_gemm_mk_kn_mn(a_m_k,
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b_k_n,
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c_m_n_host_result,
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ck::tensor_operation::element_wise::PassThrough{},
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ck::tensor_operation::element_wise::PassThrough{},
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ck::tensor_operation::element_wise::PassThrough{});
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DeviceMem a_device_buf(sizeof(float) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(float) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(float) * c_m_n_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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c_device_buf.ToDevice(c_m_n_device_result.mData.data());
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auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
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bool success = false;
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using DeviceOp = ck::tensor_operation::device::DeviceGemmSplitK<decltype(a_layout),
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decltype(b_layout),
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decltype(c_layout),
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float,
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float,
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float,
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PassThrough,
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PassThrough,
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PassThrough>;
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const auto gemm_ptrs =
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ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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for(auto& gemm_ptr : gemm_ptrs)
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{
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auto argument_ptr =
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gemm_ptr->MakeArgumentPointer(static_cast<float*>(a_device_buf.GetDeviceBuffer()),
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static_cast<float*>(b_device_buf.GetDeviceBuffer()),
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static_cast<float*>(c_device_buf.GetDeviceBuffer()),
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args.M,
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args.N,
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args.K,
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args.StrideA,
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args.StrideB,
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args.StrideC,
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ck::tensor_operation::element_wise::PassThrough{},
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ck::tensor_operation::element_wise::PassThrough{},
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ck::tensor_operation::element_wise::PassThrough{},
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args.KBatch);
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auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
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if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get());
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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if(!check_out(c_m_n_host_result, c_m_n_device_result))
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{
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success = false;
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break;
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}
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success = true;
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}
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}
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return success;
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};
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bool success = false;
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if(args.layout == GemmMatrixLayout::MK_KN_MN)
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{
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success = test(Row{}, Row{}, Row{});
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}
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else if(args.layout == GemmMatrixLayout::MK_NK_MN)
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{
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success = test(Row{}, Col{}, Row{});
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}
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else if(args.layout == GemmMatrixLayout::KM_KN_MN)
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{
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success = test(Col{}, Row{}, Row{});
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}
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else
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{
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success = test(Col{}, Col{}, Row{});
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}
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auto error_code = 0;
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if(success)
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{
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std::cout << "test split k : Pass" << std::endl;
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}
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else
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{
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std::cout << "test split k: Fail " << std::endl;
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error_code = -1; // test needs to report failure
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}
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return error_code;
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}
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int main(int argc, char* argv[])
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{
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std::vector<gemmArgs> test_cases;
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if(argc == 1)
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{
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test_cases = {{GemmMatrixLayout::MK_KN_MN, 3, 3, 3, 3, 3, 3, 1}};
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// JD: Populate with more and meaningful
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return 0;
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}
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else if(argc == 9)
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{
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const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[1]));
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const int M = std::stoi(argv[2]);
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const int N = std::stoi(argv[3]);
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const int K = std::stoi(argv[4]);
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const int StrideA = std::stoi(argv[5]);
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const int StrideB = std::stoi(argv[6]);
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const int StrideC = std::stoi(argv[7]);
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const int KBatch = std::stoi(argv[8]);
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test_cases = {{layout, M, N, K, StrideA, StrideB, StrideC, KBatch}};
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}
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else
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{
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printf("arg1: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
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printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
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printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
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printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
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printf("arg2 to 7: M, N, K, StrideA, StrideB, StrideC KBatch\n");
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return -1;
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}
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for(const auto& kinder : test_cases)
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{
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const auto res = test_gemm(kinder);
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if(!res)
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return -1;
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}
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return 0;
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}
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