/*************************************************************************************************** * Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, this * list of conditions and the following disclaimer. * * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * * 3. Neither the name of the copyright holder nor the names of its * contributors may be used to endorse or promote products derived from * this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * **************************************************************************************************/ #pragma once #include "cutlass/cutlass.h" #include "cutlass/gemm/gemm.h" #include "cutlass/pipeline/pipeline.hpp" #include "cutlass/gemm/dispatch_policy.hpp" #include "cutlass/detail/dependent_false.hpp" #include "cutlass/trace.h" #include "cutlass/numeric_types.h" #include "cute/arch/cluster_sm90.hpp" #include "cute/arch/copy_sm90.hpp" #include "cute/atom/mma_atom.hpp" #include "cute/algorithm/functional.hpp" #include "cute/algorithm/gemm.hpp" #include "cute/numeric/arithmetic_tuple.hpp" ///////////////////////////////////////////////////////////////////////////////////////////////// namespace cutlass::gemm::collective { using namespace cute; ///////////////////////////////////////////////////////////////////////////////////////////////// template < int Stages, int SchedulerPipelineStageCount, class ClusterShape, class KernelScheduleType, class TileShape_, class ElementA_, class StrideA_, class ElementB_, class StrideB_, class TiledMma_, class GmemTiledCopyA_, class SmemLayoutAtomA_, class SmemCopyAtomA_, class TransformA_, class GmemTiledCopyB_, class SmemLayoutAtomB_, class SmemCopyAtomB_, class TransformB_> struct CollectiveMma< MainloopSm120TmaWarpSpecialized, TileShape_, ElementA_, StrideA_, ElementB_, StrideB_, TiledMma_, GmemTiledCopyA_, SmemLayoutAtomA_, SmemCopyAtomA_, TransformA_, GmemTiledCopyB_, SmemLayoutAtomB_, SmemCopyAtomB_, TransformB_> { // // Type Aliases // using DispatchPolicy = MainloopSm120TmaWarpSpecialized; using TileShape = TileShape_; using ElementA = ElementA_; using StrideA = StrideA_; using ElementB = ElementB_; using StrideB = StrideB_; using TiledMma = TiledMma_; using CtaShape_MNK = decltype(shape_div(TileShape{}, ClusterShape{})); using ElementAccumulator = typename TiledMma::ValTypeC; using GmemTiledCopyA = GmemTiledCopyA_; using GmemTiledCopyB = GmemTiledCopyB_; using SmemLayoutAtomA = SmemLayoutAtomA_; using SmemLayoutAtomB = SmemLayoutAtomB_; using SmemCopyAtomA = SmemCopyAtomA_; using SmemCopyAtomB = SmemCopyAtomB_; using TransformA = TransformA_; using TransformB = TransformB_; using ArchTag = typename DispatchPolicy::ArchTag; using RuntimeDataTypeA = void*; using RuntimeDataTypeB = void*; static constexpr int ThreadCount = size(TiledMma{}); using MainloopPipeline = cutlass::PipelineTmaAsync; using PipelineParams = typename MainloopPipeline::Params; using PipelineState = typename cutlass::PipelineState; // One threads per CTA are producers (1 for operand tile) static constexpr int NumProducerThreadEvents = 1; static_assert(rank(SmemLayoutAtomA{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)"); static_assert((size<0>(TileShape{}) % size<0>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape."); static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape."); static_assert(rank(SmemLayoutAtomB{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)"); static_assert((size<1>(TileShape{}) % size<0>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape."); static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape."); static_assert(not cute::is_void_v, "SM120 mainloop must specify a copy atom for A operand smem->rmem reads."); static_assert(not cute::is_void_v, "SM120 mainloop must specify a copy atom for B operand smem->rmem reads."); // Tile along modes in a way that maximizes the TMA box size. using SmemLayoutA = decltype(tile_to_shape( SmemLayoutAtomA{}, make_shape(shape<0>(TileShape{}), shape<2>(TileShape{}), Int{}), conditional_t< ::cutlass::gemm::detail::is_major<0,StrideA>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{})); using SmemLayoutB = decltype(tile_to_shape( SmemLayoutAtomB{}, make_shape(shape<1>(TileShape{}), shape<2>(TileShape{}), Int{}), conditional_t< ::cutlass::gemm::detail::is_major<0,StrideB>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{})); static_assert(rank(SmemLayoutA{}) == 3, "Smem layout must be rank 3."); static_assert(rank(SmemLayoutB{}) == 3, "Smem layout must be rank 3."); static_assert(DispatchPolicy::Stages >= 2, "Specialization requires Stages set to value 2 or more."); static_assert(not cute::is_base_of::value && not cute::is_base_of::value, "MMA atom must source both A and B operands from rmem for this mainloop."); static_assert(cute::is_same_v || cute::is_same_v, "GmemTiledCopy - invalid SM90 TMA copy atom specified."); static_assert(cute::is_same_v || cute::is_same_v, "GmemTiledCopy - invalid SM90 TMA copy atom specified."); static constexpr bool IsF8F6F4 = detail::is_sm120_f8f6f4(); // TMA converts f32 input to tf32 when copying from GMEM to SMEM // For all other types, cast to size equivalent uint type to avoid any rounding by TMA. using TmaInternalElementA = cute::conditional_t, cutlass::tfloat32_t, cute::conditional_t, cutlass::detail::float_e2m1_unpacksmem_t, cute::conditional_t, cutlass::detail::float_e2m3_unpacksmem_t, cute::conditional_t, cutlass::detail::float_e3m2_unpacksmem_t, uint_bit_t>>>>>; using TmaInternalElementB = cute::conditional_t, cutlass::tfloat32_t, cute::conditional_t, cutlass::detail::float_e2m1_unpacksmem_t, cute::conditional_t, cutlass::detail::float_e2m3_unpacksmem_t, cute::conditional_t, cutlass::detail::float_e3m2_unpacksmem_t, uint_bit_t>>>>>; using SmemAllocTypeA = cute::conditional_t; using SmemAllocTypeB = cute::conditional_t; // Set the bytes transferred in this TMA transaction (may involve multiple issues) static constexpr uint32_t TmaTransactionBytesMK = static_cast( cutlass::bits_to_bytes(size(take<0,2>(SmemLayoutA{})) * sizeof_bits::value)); static constexpr uint32_t TmaTransactionBytesNK = static_cast( cutlass::bits_to_bytes(size(take<0,2>(SmemLayoutB{})) * sizeof_bits::value)); static constexpr uint32_t TmaTransactionBytes = TmaTransactionBytesMK + TmaTransactionBytesNK; struct SharedStorage { struct TensorStorage : cute::aligned_struct<128, _0> { alignas(1024) cute::array_aligned> smem_A; alignas(1024) cute::array_aligned> smem_B; } tensors; using PipelineStorage = typename MainloopPipeline::SharedStorage; alignas(16) PipelineStorage pipeline_storage; }; using TensorStorage = typename SharedStorage::TensorStorage; using PipelineStorage = typename SharedStorage::PipelineStorage; // Host side kernel arguments struct Arguments { ElementA const* ptr_A{nullptr}; StrideA dA{}; ElementB const* ptr_B{nullptr}; StrideB dB{}; }; // Device side kernel params struct Params { // Assumption: StrideA is congruent with Problem_MK using TMA_A = decltype(make_tma_copy( GmemTiledCopyA{}, make_tensor(recast_ptr(nullptr), repeat_like(StrideA{}, int32_t(0)), StrideA{}), SmemLayoutA{}(_,_,0), make_shape(shape<0>(TileShape{}), shape<2>(TileShape{})), size<1>(ClusterShape{}))); // mcast along N mode for this M load, if any // Assumption: StrideB is congruent with Problem_NK using TMA_B = decltype(make_tma_copy( GmemTiledCopyB{}, make_tensor(recast_ptr(nullptr), repeat_like(StrideB{}, int32_t(0)), StrideB{}), SmemLayoutB{}(_,_,0), make_shape(shape<1>(TileShape{}), shape<2>(TileShape{})), size<0>(ClusterShape{}))); // mcast along M mode for this N load, if any TMA_A tma_load_a; TMA_B tma_load_b; uint32_t tma_transaction_bytes = TmaTransactionBytes; uint32_t tma_transaction_bytes_mk = TmaTransactionBytesMK; uint32_t tma_transaction_bytes_nk = TmaTransactionBytesNK; }; // // Methods // template static constexpr Params to_underlying_arguments(ProblemShape const& problem_shape, Arguments const& args, void* workspace) { (void) workspace; // Optionally append 1s until problem shape is rank-4 (MNKL), in case it is only rank-3 (MNK) auto problem_shape_MNKL = append<4>(problem_shape, 1); auto [M, N, K, L] = problem_shape_MNKL; auto ptr_A = recast_ptr(args.ptr_A); auto ptr_B = recast_ptr(args.ptr_B); Tensor tensor_a = make_tensor(ptr_A, make_layout(make_shape(M,K,L), args.dA)); Tensor tensor_b = make_tensor(ptr_B, make_layout(make_shape(N,K,L), args.dB)); typename Params::TMA_A tma_load_a = make_tma_copy( GmemTiledCopyA{}, tensor_a, SmemLayoutA{}(_,_,cute::Int<0>{}), make_shape(shape<0>(TileShape{}), shape<2>(TileShape{})), size<1>(ClusterShape{})); // mcast along N mode for this M load, if any typename Params::TMA_B tma_load_b = make_tma_copy( GmemTiledCopyB{}, tensor_b, SmemLayoutB{}(_,_,cute::Int<0>{}), make_shape(shape<1>(TileShape{}), shape<2>(TileShape{})), size<0>(ClusterShape{})); // mcast along M mode for this N load, if any return { tma_load_a, tma_load_b, TmaTransactionBytes, TmaTransactionBytesMK, TmaTransactionBytesNK }; } template static bool can_implement( ProblemShape const& problem_shape, [[maybe_unused]] Arguments const& args) { auto problem_shape_MNKL = append<4>(problem_shape, 1); auto [M, N, K, L] = problem_shape_MNKL; constexpr int tma_alignment_bits_A = cutlass::detail::get_input_alignment_bits(); constexpr int tma_alignment_bits_B = cutlass::detail::get_input_alignment_bits(); bool implementable = true; constexpr int min_tma_aligned_elements_A = tma_alignment_bits_A / cutlass::sizeof_bits::value; implementable = implementable && cutlass::detail::check_alignment(cute::make_shape(M,K,L), StrideA{}); constexpr int min_tma_aligned_elements_B = tma_alignment_bits_B / cutlass::sizeof_bits::value; implementable = implementable && cutlass::detail::check_alignment(cute::make_shape(N,K,L), StrideB{}); if (!implementable) { CUTLASS_TRACE_HOST(" CAN IMPLEMENT: Problem Size doesn't meet the minimum alignment requirements for TMA.\n"); } return implementable; } /// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance CUTLASS_DEVICE static void prefetch_tma_descriptors(Params const& mainloop_params) { cute::prefetch_tma_descriptor(mainloop_params.tma_load_a.get_tma_descriptor()); cute::prefetch_tma_descriptor(mainloop_params.tma_load_b.get_tma_descriptor()); } /// Set up the data needed by this collective for load and mma. /// Returns a tuple of tensors. The collective and the kernel layer have the contract /// Returned tuple must contain at least two elements, with the first two elements being: /// gA_mkl - The tma tensor, A after a local tile so it has shape (BLK_M,BLK_K,m,k,l) /// gB_nkl - The tma tensor, B after a local tile so it has shape (BLK_N,BLK_K,n,k,l) /// The rest of the tensors can be specified as needed by this collective. template CUTLASS_DEVICE auto load_init(ProblemShape_MNKL const& problem_shape_MNKL, Params const& mainloop_params) const { using X = Underscore; // Separate out problem shape for convenience auto [M, N, K, L] = problem_shape_MNKL; // TMA requires special handling of strides to deal with coord codomain mapping // Represent the full tensors -- get these from TMA Tensor mA_mkl = mainloop_params.tma_load_a.get_tma_tensor(make_shape(M,K,L)); // (m,k,l) Tensor mB_nkl = mainloop_params.tma_load_b.get_tma_tensor(make_shape(N,K,L)); // (n,k,l) // Make tiled views, defer the slice Tensor gA_mkl = local_tile(mA_mkl, TileShape{}, make_coord(_,_,_), Step<_1, X,_1>{}); // (BLK_M,BLK_K,m,k,l) Tensor gB_nkl = local_tile(mB_nkl, TileShape{}, make_coord(_,_,_), Step< X,_1,_1>{}); // (BLK_N,BLK_K,n,k,l) return cute::make_tuple(gA_mkl, gB_nkl); } /// Perform a collective-scoped matrix multiply-accumulate /// Producer Perspective template < class TensorA, class TensorB, class KTileIterator, class BlockCoord > CUTLASS_DEVICE void load( Params const& mainloop_params, MainloopPipeline pipeline, PipelineState smem_pipe_write, cute::tuple const& load_inputs, BlockCoord const& blk_coord, KTileIterator k_tile_iter, int k_tile_count, int thread_idx, uint32_t block_rank_in_cluster, TensorStorage& shared_tensors) { int lane_predicate = cute::elect_one_sync(); if (lane_predicate) { Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{}); // (BLK_M,BLK_K,PIPE) Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), SmemLayoutB{}); // (BLK_N,BLK_K,PIPE) // // Prepare the TMA loads for A and B // constexpr uint32_t cluster_shape_x = get<0>(typename DispatchPolicy::ClusterShape()); uint2 cluster_local_block_id = {block_rank_in_cluster % cluster_shape_x, block_rank_in_cluster / cluster_shape_x}; Tensor gA_mkl = get<0>(load_inputs); Tensor gB_nkl = get<1>(load_inputs); auto block_tma_a = mainloop_params.tma_load_a.get_slice(cluster_local_block_id.y); auto block_tma_b = mainloop_params.tma_load_b.get_slice(cluster_local_block_id.x); // Partition the inputs based on the current block coordinates. auto [m_coord, n_coord, k_coord, l_coord] = blk_coord; Tensor gA = gA_mkl(_,_,m_coord,_,l_coord); // (BLK_M,BLK_K,k) Tensor gB = gB_nkl(_,_,n_coord,_,l_coord); // (BLK_N,BLK_K,k) // Applies the mapping from block_tma_a Tensor tAgA = block_tma_a.partition_S(gA); // (TMA,TMA_M,TMA_K,k) Tensor tAsA = block_tma_a.partition_D(sA); // (TMA,TMA_M,TMA_K,PIPE) Tensor tBgB = block_tma_b.partition_S(gB); // (TMA,TMA_N,TMA_K,k) Tensor tBsB = block_tma_b.partition_D(sB); // (TMA,TMA_N,TMA_K,PIPE) uint16_t mcast_mask_a = 0; uint16_t mcast_mask_b = 0; // Issue TmaLoads // Maps the tile -> block, value if constexpr (cute::is_same_v) { auto block_layout = Layout{}; // (m,n) -> block_id for (int n = 0; n < size<1>(block_layout); ++n) { mcast_mask_a |= (uint16_t(1) << block_layout(cluster_local_block_id.x,n,Int<0>{})); } } if constexpr (cute::is_same_v) { auto block_layout = Layout{}; // (m,n) -> block_id for (int m = 0; m < size<0>(block_layout); ++m) { mcast_mask_b |= (uint16_t(1) << block_layout(m,cluster_local_block_id.y,Int<0>{})); } } // Mainloop CUTLASS_PRAGMA_NO_UNROLL for ( ; k_tile_count > 0; --k_tile_count) { // LOCK smem_pipe_write for _writing_ pipeline.producer_acquire(smem_pipe_write); // // Copy gmem to smem for *k_tile_iter // using BarrierType = typename MainloopPipeline::ProducerBarrierType; BarrierType* tma_barrier = pipeline.producer_get_barrier(smem_pipe_write); int write_stage = smem_pipe_write.index(); copy(mainloop_params.tma_load_a.with(*tma_barrier, mcast_mask_a), tAgA(_,_,_,*k_tile_iter), tAsA(_,_,_,write_stage)); copy(mainloop_params.tma_load_b.with(*tma_barrier, mcast_mask_b), tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage)); ++k_tile_iter; // Advance smem_pipe_write ++smem_pipe_write; } } } /// Perform a Producer Epilogue to prevent early exit of blocks in a Cluster CUTLASS_DEVICE void load_tail(MainloopPipeline pipeline, PipelineState smem_pipe_write) { int lane_predicate = cute::elect_one_sync(); // Issue the epilogue waits if (lane_predicate) { /* This helps avoid early exit of blocks in Cluster * Waits for all stages to either be released (all * Consumer UNLOCKs), or if the stage was never used * then would just be acquired since the phase was * still inverted from make_producer_start_state */ pipeline.producer_tail(smem_pipe_write); } } /// Perform a collective-scoped matrix multiply-accumulate /// Consumer Perspective template < class FrgTensorC > CUTLASS_DEVICE void mma(MainloopPipeline pipeline, PipelineState smem_pipe_read, FrgTensorC& accum, int k_tile_count, int thread_idx, TensorStorage& shared_tensors, Params const& mainloop_params) { using namespace cute; static_assert(is_rmem::value, "C tensor must be rmem resident."); clear(accum); Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{}); // (BLK_M,BLK_K,PIPE) Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), SmemLayoutB{}); // (BLK_N,BLK_K,PIPE) // // Define C accumulators and A/B partitioning // TiledMma tiled_mma; auto thread_mma = tiled_mma.get_thread_slice(thread_idx); // Allocate fragments and descriptors Tensor tCrA = thread_mma.partition_fragment_A(sA(_,_,Int<0>{})); // (MMA,MMA_M,MMA_K) Tensor tCrB = thread_mma.partition_fragment_B(sB(_,_,Int<0>{})); // (MMA,MMA_M,MMA_K) // // Copy Atom A and B retiling // auto smem_tiled_copy_A = make_tiled_copy_A(SmemCopyAtomA{}, tiled_mma); auto smem_thr_copy_A = smem_tiled_copy_A.get_thread_slice(thread_idx); Tensor tCsA = smem_thr_copy_A.partition_S( as_position_independent_swizzle_tensor(sA)); // (CPY,CPY_M,CPY_K,PIPE) Tensor tCrA_copy_view = smem_thr_copy_A.retile_D(tCrA); // (CPY,CPY_M,CPY_K) auto smem_tiled_copy_B = make_tiled_copy_B(SmemCopyAtomB{}, tiled_mma); auto smem_thr_copy_B = smem_tiled_copy_B.get_thread_slice(thread_idx); Tensor tCsB = smem_thr_copy_B.partition_S( as_position_independent_swizzle_tensor(sB)); // (CPY,CPY_M,CPY_K,PIPE) Tensor tCrB_copy_view = smem_thr_copy_B.retile_D(tCrB); // (CPY,CPY_M,CPY_K) CUTE_STATIC_ASSERT_V(size<1>(tCsA) == size<1>(tCrA_copy_view)); CUTE_STATIC_ASSERT_V(size<2>(tCsA) == size<2>(tCrA_copy_view)); CUTE_STATIC_ASSERT_V(size<1>(tCrA) == size<1>(accum)); CUTE_STATIC_ASSERT_V(size<1>(tCrB) == size<2>(accum)); CUTE_STATIC_ASSERT_V(size<2>(tCsA) == size<2>(tCsB)); CUTE_STATIC_ASSERT_V(size<3>(tCsA) == size<3>(tCsB)); CUTE_STATIC_ASSERT_V(Int{} == size<2>(sA)); CUTE_STATIC_ASSERT_V(Int{} == size<2>(sB)); // // PIPELINED MAIN LOOP // // Size of the register pipeline auto K_BLOCK_MAX = size<2>(tCrA); int read_stage = smem_pipe_read.index(); auto tCsA_stage = tCsA(_,_,_,read_stage); auto tCsB_stage = tCsB(_,_,_,read_stage); auto copy_kblock = [&](auto k_block) { // copy smem->rmem for A/B operand copy(smem_tiled_copy_A, tCsA_stage(_,_,k_block), tCrA_copy_view(_,_,k_block)); copy(smem_tiled_copy_B, tCsB_stage(_,_,k_block), tCrB_copy_view(_,_,k_block)); // Left shift A,B for FP4 using MMAOp = typename TiledMma::MMA_Op; fp4_shift_A(MMAOp{}, tCrA_copy_view(_,_,k_block)); fp4_shift_B(MMAOp{}, tCrB_copy_view(_,_,k_block)); }; auto gemm_kblock = [&](auto k_block) { // (V,M) x (V,N) => (V,M,N) cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), accum); }; pipeline.consumer_wait(smem_pipe_read); copy_kblock(_0{}); CUTLASS_PRAGMA_NO_UNROLL for ( ; k_tile_count > 1; --k_tile_count) { // // Compute on k_tile // for_each(make_int_sequence{}, [&] (auto k_block) { auto k_block_next = ((k_block + 1) == K_BLOCK_MAX) ? 0 : (k_block + 1); if (k_block == K_BLOCK_MAX - 1) { cutlass::arch::NamedBarrier::sync( thr_size(tiled_mma), cutlass::arch::ReservedNamedBarriers::Sm120MainloopBarrier); // UNLOCK smem_pipe_read, done _computing_ on it pipeline.consumer_release(smem_pipe_read); ++smem_pipe_read; read_stage = smem_pipe_read.index(); tCsA_stage = tCsA(_,_,_,read_stage); tCsB_stage = tCsB(_,_,_,read_stage); pipeline.consumer_wait(smem_pipe_read); } copy_kblock(k_block_next); gemm_kblock(k_block); }); } // k_tile_count // // Hoist out last k_tile // for_each(make_int_sequence{}, [&] (auto k_block) { auto k_block_next = ((k_block + 1) == K_BLOCK_MAX) ? 0 : (k_block + 1); if (k_block == K_BLOCK_MAX - 1) { cutlass::arch::NamedBarrier::sync( thr_size(tiled_mma), cutlass::arch::ReservedNamedBarriers::Sm120MainloopBarrier); // UNLOCK smem_pipe_read, done _computing_ on it pipeline.consumer_release(smem_pipe_read); ++smem_pipe_read; } if (k_block_next > 0) { copy_kblock(k_block_next); } gemm_kblock(k_block); }); } /// Perform a Consumer Epilogue to release all buffers CUTLASS_DEVICE void mma_tail(MainloopPipeline, PipelineState, int) { } }; ///////////////////////////////////////////////////////////////////////////////////////////////// } // namespace cutlass::gemm::collective /////////////////////////////////////////////////////////////////////////////////////////////////