90d3b0fb18
* Updates for 3.2.1 release. * Minor fix in gemm op profiler for raster order. * Add scheduler mapping for raster order in the kernels.
267 lines
10 KiB
C++
267 lines
10 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2023 - 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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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/kernel_hardware_info.hpp"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/dispatch_policy.hpp"
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#include "cute/tensor.hpp"
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#include "gather_tensor.hpp"
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namespace cutlass::gemm::kernel {
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///////////////////////////////////////////////////////////////////////////////
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template <
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class ProblemShape_,
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class CollectiveMainloop_,
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class CollectiveEpilogue_,
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class GatherA_,
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class GatherB_,
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class TileScheduler_ = void
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>
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class GemmGather
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{
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public:
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//
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// Type Aliases
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//
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using ProblemShape = ProblemShape_;
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using TileSchedulerTag = TileScheduler_;
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using TileScheduler = TileScheduler_;
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static_assert(rank(ProblemShape{}) == 3 or rank(ProblemShape{}) == 4,
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"ProblemShape{} should be <M,N,K> or <M,N,K,L>");
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// Mainloop derived types
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using CollectiveMainloop = CollectiveMainloop_;
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using TileShape = typename CollectiveMainloop::TileShape;
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using TiledMma = typename CollectiveMainloop::TiledMma;
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using ArchTag = typename CollectiveMainloop::ArchTag;
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using ElementA = typename CollectiveMainloop::ElementA;
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using StrideA = typename CollectiveMainloop::StrideA;
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using ElementB = typename CollectiveMainloop::ElementB;
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using StrideB = typename CollectiveMainloop::StrideB;
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using DispatchPolicy = typename CollectiveMainloop::DispatchPolicy;
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using ElementAccumulator = typename CollectiveMainloop::ElementAccumulator;
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using MainloopArguments = typename CollectiveMainloop::Arguments;
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using MainloopParams = typename CollectiveMainloop::Params;
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// Epilogue derived types
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using CollectiveEpilogue = CollectiveEpilogue_;
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using ElementC = typename CollectiveEpilogue::ElementC;
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using StrideC = typename CollectiveEpilogue::StrideC;
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using ElementD = typename CollectiveEpilogue::ElementD;
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using StrideD = typename CollectiveEpilogue::StrideD;
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using EpilogueArguments = typename CollectiveEpilogue::Arguments;
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using EpilogueParams = typename CollectiveEpilogue::Params;
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static_assert(std::is_same_v<ElementAccumulator, typename CollectiveEpilogue::ElementAccumulator>,
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"Mainloop and epilogue do not agree on accumulator value type.");
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using GatherA = GatherA_;
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using GatherB = GatherB_;
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static constexpr int SharedStorageSize = static_cast<int>(cute::max(
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sizeof(typename CollectiveMainloop::SharedStorage),
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sizeof(typename CollectiveEpilogue::SharedStorage)));
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static constexpr uint32_t MaxThreadsPerBlock = cute::size(TiledMma{});
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static constexpr uint32_t MinBlocksPerMultiprocessor = 1;
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// Device side arguments
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struct Arguments {
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GemmUniversalMode mode{};
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ProblemShape problem_shape{};
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MainloopArguments mainloop{};
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EpilogueArguments epilogue{};
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KernelHardwareInfo hw_info{};
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GatherA gather_A{};
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GatherB gather_B{};
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};
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// Kernel entry point API
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struct Params {
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GemmUniversalMode mode;
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ProblemShape problem_shape;
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MainloopParams mainloop;
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EpilogueParams epilogue;
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GatherA gather_A{};
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GatherB gather_B{};
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};
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//
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// Methods
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//
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// Convert to underlying arguments.
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static
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Params
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to_underlying_arguments(Arguments const& args, void* workspace) {
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(void) workspace;
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return {
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args.mode,
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args.problem_shape,
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CollectiveMainloop::to_underlying_arguments(args.problem_shape, args.mainloop, workspace),
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CollectiveEpilogue::to_underlying_arguments(args.problem_shape, args.epilogue, workspace),
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args.gather_A,
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args.gather_B
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};
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}
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static
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Status
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initialize_workspace(Arguments const& args, void* workspace = nullptr, cudaStream_t stream = nullptr) {
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return Status::kSuccess;
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}
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static
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bool
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can_implement(Arguments const& args) {
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return args.mode == GemmUniversalMode::kGemm or
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(args.mode == GemmUniversalMode::kBatched && rank(ProblemShape{}) == 4);
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}
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static
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int
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get_workspace_size(Arguments const& args) {
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return 0;
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}
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static constexpr
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dim3
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get_grid_shape(Params const& params) {
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int batch_count = 1;
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if constexpr (rank(ProblemShape{}) == 4) {
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batch_count = cute::size<3>(params.problem_shape);
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}
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return dim3(
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cute::size(cute::ceil_div(cute::shape<0>(params.problem_shape), cute::shape<0>(TileShape{}))),
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cute::size(cute::ceil_div(cute::shape<1>(params.problem_shape), cute::shape<1>(TileShape{}))),
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batch_count
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);
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}
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static constexpr
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dim3
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get_block_shape() {
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return dim3(MaxThreadsPerBlock, 1, 1);
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}
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CUTLASS_DEVICE
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void
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operator()(Params const& params, char* smem_buf) {
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using namespace cute;
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using X = Underscore;
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// Preconditions
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CUTE_STATIC_ASSERT(is_static<TileShape>::value);
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// Separate out problem shape for convenience
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// Optionally append 1s until problem shape is rank-4 in case its is only rank-3 (MNK)
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auto problem_shape_MNKL = append<4>(params.problem_shape, Int<1>{});
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auto M = get<0>(problem_shape_MNKL);
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auto N = get<1>(problem_shape_MNKL);
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auto K = get<2>(problem_shape_MNKL);
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auto L = get<3>(problem_shape_MNKL);
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// Preconditions
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static_assert(rank(StrideA{}) == 3, "StrideA must be rank-3: [M, K, L]. If batch mode is not needed, set L stride to Int<0>.");
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static_assert(rank(StrideB{}) == 3, "StrideB must be rank-3: [N, K, L]. If batch mode is not needed, set L stride to Int<0>.");
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static_assert(rank(StrideC{}) == 3, "StrideC must be rank-3: [M, N, L]. If batch mode is not needed, set L stride to Int<0>.");
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static_assert(rank(StrideD{}) == 3, "StrideD must be rank-3: [M, N, L]. If batch mode is not needed, set L stride to Int<0>.");
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// Get the appropriate blocks for this thread block -- potential for thread block locality
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int thread_idx = int(threadIdx.x);
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auto blk_shape = TileShape{}; // (BLK_M,BLK_N,BLK_K)
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auto [m_coord, n_coord, l_coord] = blockIdx;
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auto blk_coord_mnkl = make_coord(m_coord, n_coord, _, l_coord); // (m,n,k,l)
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// Represent the full tensors
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Tensor mA_mkl = make_gather_tensor(make_gmem_ptr(params.mainloop.ptr_A), make_shape(M,K,L), params.mainloop.dA, params.gather_A); //(m,k,l)
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Tensor mB_nkl = make_gather_tensor(make_gmem_ptr(params.mainloop.ptr_B), make_shape(N,K,L), params.mainloop.dB, params.gather_B); //(n,k,l)
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// Get batch slice
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Tensor mA_mk = mA_mkl(_,_,l_coord); // (m,k)
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Tensor mB_nk = mB_nkl(_,_,l_coord); // (n,k)
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// Slice to get the tiles this thread block is responsible for
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Tensor gA = local_tile(mA_mk, blk_shape, take<0,3>(blk_coord_mnkl), Step<_1, X,_1>{}); // (BLK_M,BLK_K,k)
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Tensor gB = local_tile(mB_nk, blk_shape, take<0,3>(blk_coord_mnkl), Step< X,_1,_1>{}); // (BLK_N,BLK_K,k)
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// Compute tile residues for predication
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auto m_max_coord = M - size<0>(gA) * get<0>(blk_coord_mnkl); // M - BLK_M * m_coord
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auto n_max_coord = N - size<0>(gB) * get<1>(blk_coord_mnkl); // N - BLK_N * n_coord
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auto k_residue = K - size<1>(gA) * size<2>(gA); // K - BLK_K * k_coord_max
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auto residue_mnk = make_tuple(m_max_coord, n_max_coord, k_residue);
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// Allocate the tiled_mma and the accumulators for the (M,N) blk_shape
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TiledMma tiled_mma;
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Tensor accumulators = partition_fragment_C(tiled_mma, take<0,2>(blk_shape)); // (MMA,MMA_M,MMA_N)
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clear(accumulators);
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auto k_tile_iter = cute::make_coord_iterator(shape<2>(gA));
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int k_tile_count = size<2>(gA);
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// Perform the collective scoped MMA
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CollectiveMainloop collective_mma;
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collective_mma(
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accumulators,
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gA,
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gB,
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accumulators,
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k_tile_iter, k_tile_count,
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residue_mnk,
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thread_idx,
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smem_buf
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);
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// Epilogue and write to gD
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CollectiveEpilogue epilogue{params.epilogue};
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epilogue(
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problem_shape_MNKL,
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blk_shape,
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blk_coord_mnkl,
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accumulators,
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tiled_mma,
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residue_mnk,
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thread_idx,
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smem_buf
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);
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}
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};
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///////////////////////////////////////////////////////////////////////////////
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} // namespace cutlass::gemm::kernel
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