128 lines
5.2 KiB
C++
128 lines
5.2 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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#include "cute/tensor.hpp"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass/gemm/kernel/gemm_universal.hpp"
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#include "cutlass/epilogue/collective/default_epilogue.hpp"
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namespace nvrtc {
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namespace thread {
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template<
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typename ElementA, typename ElementB, typename ElementC,
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typename TileShape, typename ClusterShape,
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bool kTransA, bool kTransB,
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int RANK_M, int RANK_N, int RANK_K, int RANK_L
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>
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struct ContractionKernel {
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using ElementScalar = float;
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using ElementAccum = float;
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using EpilogueThread = cutlass::epilogue::thread::LinearCombination<ElementC,
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1,
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ElementAccum,
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ElementScalar>;
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static constexpr cute::GMMA::Major majorA = ! kTransA ? cute::GMMA::Major::MN : cute::GMMA::Major::K;
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static constexpr cute::GMMA::Major majorB = ! kTransB ? cute::GMMA::Major::K : cute::GMMA::Major::MN;
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/// Kernel config
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typedef int64_t stride_type;
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typedef int32_t extent_type;
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static constexpr const stride_type* stride_null = nullptr;
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static constexpr const extent_type* extent_null = nullptr;
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template <int Rank, bool IsMajor, class Indexable>
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static constexpr
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auto
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make_stride_tuple(Indexable const& t, int n, int64_t init_default = 0) {
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static_assert(Rank > 1);
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if constexpr (IsMajor) {
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return cute::transform(cute::make_seq<Rank>{}, [&](auto i) {
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if constexpr (i == 0) {
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return cute::Int<1>{};
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}
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else {
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return i < n ? t[i] : init_default;
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}
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});
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}
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else {
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return cute::make_int_tuple<Rank>(t, n, init_default);
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}
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}
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using StrideA = decltype(cute::make_stride(
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make_stride_tuple<RANK_M, majorA == cute::GMMA::Major::MN>(stride_null, 0, 0),
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make_stride_tuple<RANK_K, majorA == cute::GMMA::Major::K>(stride_null, 0, 0),
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cute::make_int_tuple<RANK_L>(stride_null, 0, 0)));
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using StrideB = decltype(cute::make_stride(
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make_stride_tuple<RANK_N, majorB == cute::GMMA::Major::MN>(stride_null, 0, 0),
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make_stride_tuple<RANK_K, majorB == cute::GMMA::Major::K>(stride_null, 0, 0),
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cute::make_int_tuple<RANK_L>(stride_null, 0, 0)));
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using StrideC = decltype(cute::make_stride(
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cute::make_int_tuple<RANK_M>(stride_null, 0, 0),
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cute::make_int_tuple<RANK_N>(stride_null, 0, 0),
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cute::make_int_tuple<RANK_L>(stride_null, 0, 0)));
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using ProblemShape = decltype(cute::make_shape(
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cute::make_int_tuple<RANK_M>(extent_null, 0, 0),
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cute::make_int_tuple<RANK_N>(extent_null, 0, 0),
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cute::make_int_tuple<RANK_K>(extent_null, 0, 0),
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cute::make_int_tuple<RANK_L>(extent_null, 0, 0)));
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using CollectiveOp = typename cutlass::gemm::collective::CollectiveBuilder<
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cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp,
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ElementA, StrideA, 16 / sizeof(ElementA),
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ElementB, StrideB, 16 / sizeof(ElementB),
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ElementAccum,
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TileShape, ClusterShape, cutlass::gemm::collective::StageCountAuto,
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cutlass::gemm::KernelTmaWarpSpecialized
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>::CollectiveOp;
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using EpilogueOutputOp = cutlass::epilogue::collective::DefaultEpilogue<StrideC, StrideC, EpilogueThread, cutlass::gemm::EpilogueDefault>;
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using CollectiveEpilogue = cutlass::epilogue::collective::detail::Sm90TmaWarpSpecializedAdapter<EpilogueOutputOp>;
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using Kernel = cutlass::gemm::kernel::GemmUniversal<
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ProblemShape,
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CollectiveOp,
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CollectiveEpilogue>;
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};
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} // namespace nvrtc
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} // namespace thread
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