mirror of
https://github.com/ROCm/composable_kernel.git
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ab5d027866
* Support bf16/fb8/bf8 datatypes for ck_tile/gemm * remove commented out code. * Addressing code review comments and enabling universal_gemm for all the supported data types. * Merge conflict resolution. * Solve the memory pipeline compilation error. Merge with the new change of CShuffle * finish the feature, pass the tests * Fix the pipeline and add the benchmark script for other data types --------- Co-authored-by: ThomasNing <thomas.ning@amd.com>
237 lines
11 KiB
C++
237 lines
11 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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template <typename Layout>
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static constexpr inline auto is_row_major(Layout layout_)
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{
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return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
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ck_tile::tensor_layout::gemm::RowMajor>>{};
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}
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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auto calculate_rtol_atol(const ck_tile::index_t K,
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const ck_tile::index_t kbatch,
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const float max_accumulated_value)
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{
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using ComputeType =
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std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
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// Calculate thresholds
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const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
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ck_tile::integer_divide_ceil(K, kbatch));
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const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
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max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
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// Calculate error due to split_k accumulation
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const auto rtol_split_k =
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ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
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const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
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max_accumulated_value, kbatch);
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType,
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typename ALayout, typename BLayout, typename CLayout>
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float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
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ck_tile::DeviceMem& b_k_n_dev_buf,
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ck_tile::DeviceMem& c_m_n_dev_buf,
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ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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ck_tile::index_t stride_A,
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ck_tile::index_t stride_B,
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ck_tile::index_t stride_C,
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ck_tile::index_t kbatch,
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int n_warmup,
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int n_repeat)
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{
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ck_tile::GemmHostArgs args;
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args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
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args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
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args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
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args.k_batch = kbatch;
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args.M = M;
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args.N = N;
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args.K = K;
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args.stride_A = stride_A;
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args.stride_B = stride_B;
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args.stride_C = stride_C;
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float ave_time = gemm_calc<ADataType, BDataType, AccDataType, CDataType,
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ALayout, BLayout, CLayout>(
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args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_byte =
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sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + 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_byte / 1.E6 / ave_time;
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std::cout << "Run Gemm kernel with M =" << M << " N =" << N << " K =" << K
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<< " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideC =" << stride_C
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<< " A_Layout =" << ALayout::name
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<< " B_Layout =" << BLayout::name
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<< " C_Layout =" << CLayout::name
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<< " A Type = " << DataTypeTraits<ADataType>::name
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<< " B Type = " << DataTypeTraits<BDataType>::name
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<< " C Type = " << DataTypeTraits<CDataType>::name
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<< " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< std::endl;
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return ave_time;
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}
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template <typename PrecType, typename ALayout, typename BLayout, typename CLayout>
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int run_gemm_example_with_layouts(int argc,
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char* argv[],
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const ALayout a_layout = ALayout{},
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const BLayout b_layout = BLayout{},
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[[maybe_unused]] const CLayout c_layout = CLayout{})
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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using ADataType = typename GemmBasicTypeConfig<PrecType>::ADataType;
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using BDataType = typename GemmBasicTypeConfig<PrecType>::BDataType;
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using CDataType = typename GemmBasicTypeConfig<PrecType>::CDataType;
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using AccDataType = typename GemmBasicTypeConfig<PrecType>::AccDataType;
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ck_tile::index_t M = arg_parser.get_int("m");
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ck_tile::index_t N = arg_parser.get_int("n");
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ck_tile::index_t K = arg_parser.get_int("k");
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ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
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ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
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ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
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ck_tile::index_t kbatch = arg_parser.get_int("split_k");
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int n_warmup = arg_parser.get_int("warmup");
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int n_repeat = arg_parser.get_int("repeat");
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stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
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stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
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stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
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ck_tile::HostTensor<ADataType> a_m_k(
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ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
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ck_tile::HostTensor<BDataType> b_k_n(
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ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
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ck_tile::HostTensor<CDataType> c_m_n_dev_result(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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// TODO: add different init types
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ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);
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ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
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ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
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a_m_k_dev_buf.ToDevice(a_m_k.data());
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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c_m_n_dev_buf.SetZero();
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c_m_n_dev_result.SetZero();
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invoke_gemm<ADataType, BDataType, AccDataType, CDataType,
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ALayout, BLayout, CLayout>(a_m_k_dev_buf,
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b_k_n_dev_buf,
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c_m_n_dev_buf,
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M,
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N,
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K,
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stride_A,
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stride_B,
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stride_C,
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kbatch,
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n_warmup,
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n_repeat);
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c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
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bool pass = true;
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if(arg_parser.get_int("v") == 1)
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{
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ck_tile::HostTensor<CDataType> c_m_n_host_ref(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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c_m_n_host_ref.SetZero();
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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a_m_k, b_k_n, c_m_n_host_ref);
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const float max_accumulated_value =
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*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>
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(K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_m_n_dev_result,
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c_m_n_host_ref,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
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<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
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<< std::endl;
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std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
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}
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else if(arg_parser.get_int("v") == 2)
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{
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ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_gpu_ref.get_element_space_size_in_bytes());
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c_m_n_gpu_ref.SetZero();
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c_m_n_gpu_buf_ref.SetZero();
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ADataType* d_A;
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BDataType* d_B;
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CDataType* d_C;
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ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
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ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
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ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
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ck_tile::hip_check_error(hipMemcpy(d_A,
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a_m_k_dev_buf.GetDeviceBuffer(),
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M * K * sizeof(ADataType),
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hipMemcpyHostToDevice));
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ck_tile::hip_check_error(hipMemcpy(d_B,
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b_k_n_dev_buf.GetDeviceBuffer(),
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N * K * sizeof(BDataType),
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hipMemcpyHostToDevice));
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ck_tile::reference_gemm_gpu<ADataType,
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BDataType,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
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ck_tile::hip_check_error(hipMemcpy(c_m_n_gpu_buf_ref.GetDeviceBuffer(),
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d_C,
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M * N * sizeof(CDataType),
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hipMemcpyDeviceToHost));
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ck_tile::hip_check_error(hipFree(d_A));
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ck_tile::hip_check_error(hipFree(d_B));
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ck_tile::hip_check_error(hipFree(d_C));
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c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
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const float max_accumulated_value =
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*std::max_element(c_m_n_gpu_ref.mData.begin(), c_m_n_gpu_ref.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>
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(K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_m_n_dev_result,
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c_m_n_gpu_ref,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
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<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
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<< std::endl;
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std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
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}
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return pass;
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}
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