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1bf66006c9
feat: add new optimized tutorial kernels MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add 01_naive_gemm baseline implementation - Add 02_padding_k_first with PADDING_K_FIRST + MFMA_32x32x16 - Add 03_mfma_16x16x16 with PADDING_K_FIRST + MFMA_16x16x16 - Share common reference_gemm.hpp in parent gemm/ directory ## Proposed changes Please describe the motivation behind the pull request, whether it enables a new feature or fixes a bug. If there are associated pull requests or issues, please link them to the pull request. ## Checklist Please put an `x` into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask. - [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally - [ ] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more than 30 seconds to run. - [ ] I have added inline documentation which enables the maintainers with understanding the motivation - [ ] I have removed the stale documentation which is no longer relevant after this pull request - [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request - [ ] I have run `clang-format` on all changed files - [ ] Any dependent changes have been merged ## Discussion If this is a relatively large or complex change, feel free to start a discussion by explaining why you chose the solution you did and what alternatives you considered
38 lines
1.2 KiB
C++
38 lines
1.2 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/host_tensor.hpp"
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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void reference_basic_gemm(const ck_tile::HostTensor<ADataType>& a_m_k,
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const ck_tile::HostTensor<BDataType>& b_n_k,
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ck_tile::HostTensor<CDataType>& c_m_n)
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{
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const int N = b_n_k.mDesc.get_lengths()[0];
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const int K = b_n_k.mDesc.get_lengths()[1];
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auto f = [&](auto m) {
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for(int n = 0; n < N; ++n)
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{
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AccDataType v_acc = 0;
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for(int k = 0; k < K; ++k)
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{
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ADataType v_a = a_m_k(m, k);
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BDataType v_b = b_n_k(n, k);
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v_acc += ck_tile::type_convert<AccDataType>(v_a) *
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ck_tile::type_convert<AccDataType>(v_b);
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}
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(v_acc);
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}
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};
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ck_tile::make_ParallelTensorFunctor(f, c_m_n.mDesc.get_lengths()[0])(
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std::thread::hardware_concurrency());
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}
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