Files
composable_kernel/example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
Adam Osewski 39dc25a9b8 [CK-Tile] Enable vectorized reads on all layouts & improve perf. (#1835)
* 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>
2025-01-27 16:37:19 +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/grouped_gemm_kernel.hpp"
template <typename DataType>
struct GemmBasicTypeConfig;
template <>
struct GemmBasicTypeConfig<ck_tile::half_t>
{
using ADataType = ck_tile::half_t;
using BDataType = ck_tile::half_t;
using CDataType = ck_tile::half_t;
using AccDataType = float;
};
using Types = GemmBasicTypeConfig<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;
using grouped_gemm_kargs = ck_tile::GroupedGemmHostArgs;
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("Ms", "", "M dimensions - empty by default.")
.insert("Ns", "", "N dimensions - empty by default.")
.insert("Ks", "", "K dimensions - empty by default.")
.insert("stride_As", "", "Tensor A strides - it is empty by default.")
.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
.insert("a_layout", "R", "A tensor data layout - Row by default.")
.insert("b_layout", "C", "B tensor data layout - Row by default.")
.insert("c_layout", "R", "C tensor data layout - Row by default.")
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
.insert("warmup", "10", "number of iterations before benchmark the kernel.")
.insert("repeat", "100", "number of iterations to benchmark the kernel.")
.insert("group_count", "16", "group count.");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs);
float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_);