mirror of
https://github.com/ROCm/composable_kernel.git
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c0de51533d
* CK-Tile GEMM with memory bound pipeline.
* Memory bound gemm pipeline.
* Fix not closed namespace.
* Block gemm mem pipeline draft.
* Do not use ck_tile:: within ck_tile namespace.
* Refactoring & Move Layout info to pipeline problem.
* Get hot loop and TailNum information before lunching kernel.
* Fixes in pipeline.
* Add comment to load_tile_raw and change variable naming style.
* Few small changes & formatting.
* Do not use macro.
* Add gtests.
* Use AccDataType for Output of MFMA instruction.
* Formatting.
* Refactor gemm examples.
* Switch over to current block gemm.
* Use currently available pipeline policy.
* Refactoring and review comment.s
* Fixes after merge.
* Add missing include.
* Add load tile overload which accepts output tensor as parameter.
* This give 8% perf boost at the cost of using more registers.
* Rename example.
* Small changes.
* Fix compilation err and lower K.
* Support different layouts for A/B
* Fix vector size for different layouts.
* Rename Alignment into VectorSize
* Unblock tests.
[ROCm/composable_kernel commit: 24d996aae1]
93 lines
2.6 KiB
C++
93 lines
2.6 KiB
C++
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <string>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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template <typename DataType>
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struct GemmBasicTypeConfig;
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template <>
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struct GemmBasicTypeConfig<ck_tile::half_t>
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{
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using ADataType = ck_tile::half_t;
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using BDataType = ck_tile::half_t;
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using AccDataType = float;
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using CDataType = ck_tile::half_t;
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// ToDo: Add more bias config to support different categories of GEMM.
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};
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template <typename T>
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struct DataTypeTraits;
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template <>
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struct DataTypeTraits<float>
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{
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static constexpr const char* name = "fp32";
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};
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template <>
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struct DataTypeTraits<double>
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{
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static constexpr const char* name = "fp64";
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};
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template <>
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struct DataTypeTraits<ck_tile::half_t>
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{
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static constexpr const char* name = "fp16";
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};
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using Types = GemmBasicTypeConfig<ck_tile::half_t>;
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// Specific type aliases for easy access
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using ADataType = Types::ADataType;
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using BDataType = Types::BDataType;
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using AccDataType = Types::AccDataType;
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using CDataType = Types::CDataType;
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struct gemm_basic_args
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{
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const void* p_a;
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const void* p_b;
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void* p_c;
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ck_tile::index_t kbatch;
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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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};
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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("b", "1", "batch size")
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.insert("m", "3840", "m dimension")
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.insert("n", "4096", "n dimension")
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.insert("k", "2048", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "R", "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", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
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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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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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// host API
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float gemm_calc(gemm_basic_args args, const ck_tile::stream_config& s);
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