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Ck tile engine gemm (#2982)
* Partial Progress : CK Tile Engine GEMM * Partial Progress : CK Tile Engine GEMM * Partial Progress : Working GEMM Code * Partial Progress : Working GEMM Code * Changinf jenkins to remove preshuffle * Partial Progress : CK TILE ENGINE GEMM Debugging * Partial Progress : Removing changes that are not GEMM * Partial Progress : Validation of full block size in GEMM * Changes in Jenkins to run only fp16 and bf16 * Addressing Review Comments * Partial Progress : Addressing CI issues * Partial Progress - Runing GEMM for fp16,bf16 and rcr * Clang * Adding fp8 and bf8 * Adding fp8 and bf8 * Adding additional architrcture * Limited datatypes and layouts * Adding k_block_per_cu in test config * Changes to faling CI errors * Changes to faling CI errors * Validation for GEMM * Adding Layout support * Adding Validations * Adding layout in jenkins * Update on Jenkins * Distribution validation for GEMM * Resolving merge conflicts * Solving merge conflicts
This commit is contained in:
Thrupti Raj Lakshmana Gowda
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GitHub
GitHub
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b11f53a484
commit
7fc0a38e90
160
tile_engine/ops/gemm/gemm_benchmark_single.cpp
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160
tile_engine/ops/gemm/gemm_benchmark_single.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <functional>
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#include <tuple>
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#include <exception>
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#include <sstream>
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#include <vector>
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#include <string>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host.hpp"
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#include "gemm_profiler.hpp"
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#include "gemm_common.hpp"
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// The kernel header is included via the compile command line with -include flag
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// It defines SelectedKernel struct and KERNEL_NAME
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// DataTypeTraits are now defined in gemm_common.hpp
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// Create argument parser
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inline 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", "3840", "The value for m dimension. Default is 3840.")
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.insert("n", "4096", "The value for n dimension. Default is 4096.")
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.insert("k", "2048", "The value for k dimension. Default is 2048.")
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.insert("stride_a", "0", "The stride value for tensor A. Default is 0.")
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.insert("stride_b", "0", "The stride value for tensor B. Default is 0.")
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.insert("stride_c", "0", "The stride value for tensor C. Default is 0.")
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.insert("split_k", "1", "The split value for k dimension. Default is 1.")
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.insert("verify",
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"2",
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"The type of validation. Set to 0 for no validation, 1 for validation on CPU, or 2 "
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"for validation on GPU. Default is 2, GPU validation.")
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.insert("log",
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"false",
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"Whether output kernel instance information or not. Possible values are true or "
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"false. Default is false")
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.insert(
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"warmup", "50", "The number of iterations before benchmark the kernel. Default is 50.")
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.insert(
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"repeat", "100", "The number of iterations to benchmark the kernel. Default is 100.")
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.insert("timer",
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"true",
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"Whether if the timer is gpu timer or not. Possible values are false or true. "
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"Default is true.")
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.insert("init",
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"0",
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"The method of tensor initialization. Set to 0 for random, to 1 for linear, or 2 "
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"for constant(1). Default is 0, random.")
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.insert("flush_cache",
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"true",
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"To flush cache, possible values are true or false. "
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"Default is false.")
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.insert("rotating_count", "1000", "number of iterations to rotate the cache. default is 5.")
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.insert("metric",
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"0",
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"Metric with which to measure kernel performance. Set to 0 for latency, 1 for "
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"tflops, or 2 for bandwidth. Default is 0, latency.")
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.insert("csv_filename",
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"",
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"The filename of benchmark result. Default is empty (no CSV output).")
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.insert("structured_sparsity",
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"false",
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"Whether use sparsity kernel or not. Possible values are true or false. Default is "
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"false")
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.insert("json_output",
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"false",
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"Whether to output results in JSON format only. Possible values are true or false. "
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"Default is "
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"false");
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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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void benchmark_single(const ck_tile::ArgParser& arg_parser)
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{
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// Use DataTypeTraits to get the actual type names from the generated header
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// The generated header defines ADataType, BDataType, AccDataType, CDataType
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std::string dtype_a = DataTypeTraits<ADataType>::name;
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std::string dtype_b = DataTypeTraits<BDataType>::name;
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std::string dtype_acc = DataTypeTraits<AccDataType>::name;
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std::string dtype_c = DataTypeTraits<CDataType>::name;
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// Layout names from the layout types
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std::string layout_a = ALayout::name;
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std::string layout_b = BLayout::name;
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std::string layout_c = CLayout::name;
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// Create GemmProblem struct
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GemmProblem gemm_problem{arg_parser.get_int("split_k"),
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arg_parser.get_int("m"),
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arg_parser.get_int("n"),
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arg_parser.get_int("k"),
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arg_parser.get_int("stride_a"),
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arg_parser.get_int("stride_b"),
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arg_parser.get_int("stride_c"),
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dtype_a,
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dtype_b,
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dtype_acc,
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dtype_c,
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layout_a,
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layout_b,
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layout_c,
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arg_parser.get_bool("structured_sparsity")};
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// Create Setting struct
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Setting setting{arg_parser.get_int("warmup"),
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arg_parser.get_int("repeat"),
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arg_parser.get_bool("timer"),
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arg_parser.get_int("verify"),
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arg_parser.get_int("init"),
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arg_parser.get_bool("log"),
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arg_parser.get_str("csv_filename"),
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arg_parser.get_bool("flush_cache"),
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arg_parser.get_int("rotating_count"),
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arg_parser.get_bool("json_output")};
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// Get the profiler instance
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auto& profiler = GemmProfiler::instance(setting);
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try
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{
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// Create a lambda that wraps the kernel launch
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auto kernel_func = [](const ck_tile::GemmHostArgs& args,
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const ck_tile::stream_config& stream) {
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return SelectedKernel::launch(args, stream);
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};
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// Benchmark the kernel
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profiler.benchmark(gemm_problem, kernel_func);
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// Select best instance based on metric
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profiler.select_best_instance(static_cast<Metric>(arg_parser.get_int("metric")));
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}
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catch(const std::exception& e)
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{
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std::cerr << "Benchmark failed: " << e.what() << std::endl;
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}
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}
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int main(int argc, char* argv[])
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{
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try
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{
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auto [result, parser] = create_args(argc, argv);
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if(!result)
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return EXIT_FAILURE;
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benchmark_single(parser);
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return 0;
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}
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catch(const std::exception& e)
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{
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std::cerr << "Error: " << e.what() << "\n";
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return EXIT_FAILURE;
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}
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}
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