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https://github.com/ROCm/composable_kernel.git
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38fd0bbc6f
* Refactor universal gemm policy.
* Adapt example to refactor changes.
* Introduce static encoding pattern
* Adding shuffled encoding patterns.
* Fix err in reverse tuple.
* Add transpose_tile2d
* Small refactoring + doc
* Enable reading on contiguous dimension in all layouts.
* Transpose A/B register tile if needed for comp v3 pipeline.
* Take contiguous dim size when calculating dram vector load size.
* A/B smem pack size taken from WarpGemm attributes
* Update B LDS layout and setup tile distribution pattern at class level.
* Fix static assert.
* Fix errors in examples.
* Formatting & fix IsTranspose
* Fix VectorSize & refactor.
* Add error loging messages.
* Fix VecLoadSize and TranspseC for mem pipeline.
* Update unit-tests & disable mem pipeline.
* Clang format
* Update include/ck_tile/core/tensor/tile_window.hpp
Co-authored-by: jakpiase <jakub.piasecki@amd.com>
* Fix compilation and reviewers comments.
* Refactor unit-test. Fallback to non-universal gemm.
Need to use GemmPipelineAGmemBGmemCRegV1 for now,
since GemmKernel is now supporting also non-K major vector reads.
---------
Co-authored-by: jakpiase <jakub.piasecki@amd.com>
[ROCm/composable_kernel commit: 39dc25a9b8]
61 lines
2.2 KiB
C++
61 lines
2.2 KiB
C++
// 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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#include "ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp"
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template <typename DataType>
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struct BatchedGemmTypeConfig;
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template <>
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struct BatchedGemmTypeConfig<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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};
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using Types = BatchedGemmTypeConfig<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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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", "256", "m dimension")
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.insert("n", "128", "n dimension")
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.insert("k", "128", "k dimension")
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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("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("batch_stride_a", "32768", "Batch A stride")
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.insert("batch_stride_b", "16384", "Batch B stride")
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.insert("batch_stride_c", "32768", "Batch C stride")
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.insert("batch_count", "16", "Batch count")
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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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.insert("split_k", "1", "splitK value");
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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 batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stream_config& s);
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