Files
composable_kernel/example/ck_tile/16_batched_gemm/batched_gemm.hpp
aledudek 453ca37347 [CK TILE] Refactor GemmKernel to be reused by other GEMM related operators (#1730)
* Gemm Kernel Refactor part1

* Gemm Kernel Refactor common gemm pipeline part2

* [CK TILE] Refactor batched gemm to reuse GemmKernel

* [CK TILE] Refactor GemmKernel - review changes part1

* [CK TILE] Refactor GemmKernel - references fix

* [CK TILE] Refactor GemmKernel - naming changes, add problem

* [CK_TILE] Refactor GemmKernel - update tests

* [CK_TILE] Refactor GemmKernel - review changes

* [CK_TILE] Refactor GemmKernel - update test

* [CK_TILE] Refactor GemmKernel - constness fixes

* [CK_TILE] Refactor GemmKernel - update tests
2024-12-18 17:52:46 +01:00

60 lines
2.2 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp"
template <typename DataType>
struct BatchedGemmTypeConfig;
template <>
struct BatchedGemmTypeConfig<ck_tile::half_t>
{
using ADataType = ck_tile::half_t;
using BDataType = ck_tile::half_t;
using AccDataType = float;
using CDataType = ck_tile::half_t;
};
using Types = BatchedGemmTypeConfig<ck_tile::half_t>;
// Specific type aliases for easy access
using ADataType = Types::ADataType;
using BDataType = Types::BDataType;
using AccDataType = Types::AccDataType;
using CDataType = Types::CDataType;
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "256", "m dimension")
.insert("n", "128", "n dimension")
.insert("k", "128", "k dimension")
.insert("stride_a", "0", "Tensor A stride")
.insert("stride_b", "0", "Tensor B stride")
.insert("stride_c", "0", "Tensor C stride")
.insert("a_layout", "R", "A tensor data layout - Row by default")
.insert("b_layout", "R", "B tensor data layout - Row by default")
.insert("c_layout", "R", "C tensor data layout - Row by default")
.insert("batch_stride_a", "32768", "Batch A stride")
.insert("batch_stride_b", "16384", "Batch B stride")
.insert("batch_stride_c", "32768", "Batch C stride")
.insert("batch_count", "16", "Batch count")
.insert("v", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
.insert("warmup", "50", "number of iterations before benchmark the kernel")
.insert("repeat", "100", "number of iterations to benchmark the kernel")
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
// host API
float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stream_config& s);