mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
ffb52783d0
* Implement tile loop persistent gemm kernel * Enable timing * Add tests for persistent gemm * Fix formatting * Fix gemm_basic * Rename True/False to Persistent/NonPersistent * Use only one set of layouts for persistent tests * Fix gemm example persistent template parameter * Fix formatting
438 lines
17 KiB
C++
438 lines
17 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 Tensor,
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typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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void permute_tensor_b(Tensor& tensor)
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{
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using GemmShape = ck_tile::TileGemmShape<
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ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
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ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
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ck_tile::
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sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>,
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GemmConfig::PermuteA,
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GemmConfig::PermuteB>;
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<GemmConfig::kPadM,
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GemmConfig::kPadN,
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GemmConfig::kPadK,
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GemmConfig::DoubleSmemBuffer,
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ALayout,
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BLayout,
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CLayout,
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GemmConfig::TransposeC,
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GemmConfig::UseStructuredSparsity>;
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using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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GEMM_PIPELINE_SCHEDULER,
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true,
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ck_tile::TailNumber::Full>;
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using GemmPipeline = GEMM_PIPELINE<UniversalGemmProblem>;
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const ck_tile::index_t K = tensor.get_length(0);
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const ck_tile::index_t N = tensor.get_length(1);
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const ck_tile::index_t K1 = GemmPipeline::GetSmemPackB();
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const ck_tile::index_t K0 = K / K1;
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Tensor tensor_copy = tensor;
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// int K0, N, K1
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for(int j = 0; j < K0; j++)
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{
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for(int i = 0; i < N; i++)
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{
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for(int jj = 0; jj < K1; jj++)
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{
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tensor(j * N * K1 + i * K1 + jj) = tensor_copy(i * K + (j * K1 + jj));
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}
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}
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}
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}
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template <typename Tensor>
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void permute_vectors_i4x4_b(Tensor& tensor)
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{
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const ck_tile::index_t K = tensor.get_length(0);
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const ck_tile::index_t N = tensor.get_length(1);
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// vector pk_i4x4 permute
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for(int i = 0; i < N; i++)
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{
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for(int j = 0; j < K; j += 8)
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{
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int8_t input[8];
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for(int k = 0; k < 4; k++)
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{
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int8_t i4x2 = tensor(j + k * 2, i).data;
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input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
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input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
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}
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// permute 01234567->20643175
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{
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int8_t hi = input[2];
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int8_t lo = input[0];
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int8_t i4x2 = (hi << 4) | lo;
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tensor(j + 0, i) = i4x2;
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}
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{
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int8_t hi = input[6];
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int8_t lo = input[4];
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int8_t i4x2 = (hi << 4) | lo;
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tensor(j + 2, i) = i4x2;
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}
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{
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int8_t hi = input[3];
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int8_t lo = input[1];
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int8_t i4x2 = (hi << 4) | lo;
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tensor(j + 4, i) = i4x2;
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}
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{
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int8_t hi = input[7];
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int8_t lo = input[5];
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int8_t i4x2 = (hi << 4) | lo;
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tensor(j + 6, i) = i4x2;
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}
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}
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}
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}
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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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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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bool persistent)
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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;
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if(persistent)
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{
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ave_time = gemm_calc<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,
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true>(
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args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
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}
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else
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{
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ave_time = gemm_calc<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,
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false>(
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args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
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}
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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 << " B_Layout =" << BLayout::name
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<< " C_Layout=" << CLayout::name << " 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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<< " StructuredSparsity=" << (GemmConfig::UseStructuredSparsity ? "on" : "off")
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<< " Persistent=" << (persistent ? "on" : "off") << " : " << ave_time << " ms, "
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<< tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
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return ave_time;
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}
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template <typename ADataType,
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typename BDataType = ADataType,
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typename CDataType = ADataType,
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typename ALayout,
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typename BLayout,
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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 AccDataType = typename GemmTypeConfig<ADataType, BDataType, CDataType>::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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ck_tile::index_t init_method = arg_parser.get_int("init");
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bool persistent = arg_parser.get_int("persistent");
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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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if(init_method == 0)
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{
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ck_tile::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
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}
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else if(init_method == 1)
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{
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ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k);
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ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n);
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}
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else if(init_method == 2)
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{
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ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n);
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}
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else
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{
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a_m_k.SetZero();
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b_k_n.SetZero();
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}
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if(GemmConfig::UseStructuredSparsity)
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{
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ck_tile::AdjustToStructuredSparsity<ADataType>{}(a_m_k);
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}
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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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static_assert(!GemmConfig::PermuteA, "Not implemented");
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if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
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{
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// Permute vector pk_i4x4 data for device implementation
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ck_tile::HostTensor<BDataType> b_k_n_dev = b_k_n;
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if constexpr(GemmConfig::PermuteB)
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{
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permute_tensor_b<decltype(b_k_n_dev),
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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>(b_k_n_dev);
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}
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permute_vectors_i4x4_b(b_k_n_dev);
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b_k_n_dev_buf.ToDevice(b_k_n_dev.data());
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}
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else
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{
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if constexpr(GemmConfig::PermuteB)
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{
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std::cout << "Permute for this DataType is not implemented." << std::endl;
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return false;
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}
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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}
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a_m_k_dev_buf.ToDevice(a_m_k.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, ALayout, BLayout, CLayout>(
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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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persistent);
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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 verification 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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if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
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{
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// Restore input for B for gpu reference
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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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, a_m_k.get_element_space_size_in_bytes()));
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ck_tile::hip_check_error(hipMalloc(&d_B, b_k_n.get_element_space_size_in_bytes()));
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ck_tile::hip_check_error(
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hipMalloc(&d_C, c_m_n_dev_result.get_element_space_size_in_bytes()));
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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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a_m_k.get_element_space_size_in_bytes(),
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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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b_k_n.get_element_space_size_in_bytes(),
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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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c_m_n_dev_result.get_element_space_size_in_bytes(),
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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>{}),
|
|
rtol_atol.at(ck_tile::number<1>{}));
|
|
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
|
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
|
<< std::endl;
|
|
std::cout << "The GPU verification result is: " << (pass ? "correct" : "fail") << std::endl;
|
|
}
|
|
|
|
return pass;
|
|
}
|