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[rocm-libraries] ROCm/rocm-libraries#4594 (commit 1fce4cb)
[CK_TILE] MX GEMM non-preshuffled RCR layout ## Motivation Implements a GEMM with MX scaling for fp4 and fp8 in non-preshuffled layouts using async pipeline. ## Technical Details <!-- Explain the changes along with any relevant GitHub links. --> ## Test Plan <!-- Explain any relevant testing done to verify this PR. --> ## Test Result <!-- Briefly summarize test outcomes. --> ## Submission Checklist - [ ] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
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128
example/ck_tile/42_mx_gemm/mx_gemm.cpp
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128
example/ck_tile/42_mx_gemm/mx_gemm.cpp
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <hip/hip_runtime.h>
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#include <cstring>
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#include <iostream>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include <type_traits>
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#include "ck_tile/host.hpp"
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#include "mx_gemm.hpp"
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#include "mx_gemm_instance.hpp"
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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 GemmConfig,
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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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typename ScaleM,
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typename ScaleN,
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bool UsePersistentKernel = false>
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float invoke_mx_gemm(ck_tile::DeviceMem& a_dev_buf,
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ck_tile::DeviceMem& b_dev_buf,
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ck_tile::DeviceMem& c_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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ScaleM scale_m,
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ScaleN scale_n,
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int n_warmup,
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int n_repeat)
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{
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MXGemmHostArgs<ScaleM, ScaleN> args(a_dev_buf.GetDeviceBuffer(),
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b_dev_buf.GetDeviceBuffer(),
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c_dev_buf.GetDeviceBuffer(),
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kbatch,
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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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scale_m,
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scale_n);
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// Simplified invocation - comp_async handles hot loop and tail internally
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auto invoke_splitk_path = [&](auto split_k_) {
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return mx_gemm_calc<GemmConfig,
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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,
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ScaleM,
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ScaleN,
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UsePersistentKernel,
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split_k_.value>(
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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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float ave_time = (args.k_batch == 1) ? invoke_splitk_path(std::false_type{})
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: invoke_splitk_path(std::true_type{});
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constexpr int APackedSize = ck_tile::numeric_traits<ADataType>::PackedSize;
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constexpr int BPackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;
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std::size_t flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / 32;
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std::size_t num_byte = sizeof(ADataType) * M * K / APackedSize +
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sizeof(BDataType) * N * K / BPackedSize + sizeof(CDataType) * M * N +
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sizeof(ck_tile::e8m0_t) * M * K / 32 +
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sizeof(ck_tile::e8m0_t) * N * K / 32;
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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 " << ck_tile::gemm_prec_str<ADataType, BDataType>() << " MX GEMM kernel " //
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<< " M = " << M << " N = " << N << " K = " << K << " StrideA = " << stride_A
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<< " StrideB = " << stride_B << " StrideC = " << stride_C << " : " << ave_time
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<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
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return ave_time;
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}
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "4096", "m dimension")
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.insert("n", "4096", "n dimension")
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.insert("k", "4096", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Row by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert(
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"mx_prec", "fp4xfp4", "data type for activation and weight, support: fp4xfp4, fp8xfp8")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("split_k", "1", "splitK value")
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.insert("init", "0", "0:random, 1:constant(1)");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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#include "run_mx_gemm.inc"
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int main(int argc, char* argv[]) { return run_mx_gemm_example(argc, argv); }
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