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sm100_blockscaled_mma_warpspecialized.hpp
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/***************************************************************************************************
* Copyright (c) 2023 - 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/detail/collective.hpp"
#include "cutlass/detail/cluster.hpp"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/numeric_types.h"
#include "cutlass/pipeline/pipeline.hpp"
#include "cutlass/gemm/gemm.h"
#include "cutlass/detail/sm100_blockscaled_layout.hpp"
#include "cutlass/trace.h"
#include "cutlass/kernel_hardware_info.hpp"
#include "cutlass/detail/collective.hpp"
#include "cutlass/detail/sm100_tmem_helper.hpp"
#include "cute/algorithm/functional.hpp"
#include "cute/arch/cluster_sm90.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cute/algorithm/gemm.hpp"
#include "cute/tensor_predicate.hpp"
#include "cute/numeric/arithmetic_tuple.hpp"
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace cutlass::gemm::collective {
using namespace cute;
/////////////////////////////////////////////////////////////////////////////////////////////////
// WarpSpecialized Mainloop
// Both DMA Load and MMA methods of this class must be run by a single thread that's picked by elect_one
template <
int Stages,
int SchedulerPipelineStageCount,
int AccumulatorPipelineStageCount,
class ClusterShape, // Static cluster shape or dynamic (int, int, _1)
class TileShape_, // (MmaAtomShapeM, MmaAtomShapeN, TileK)
class ElementPairA_,
class StridePairA_,
class ElementPairB_,
class StridePairB_,
class TiledMma_,
class GmemTiledCopyPairA_,
class SmemLayoutAtomPairA_,
class SmemCopyAtomA_,
class TransformA_,
class GmemTiledCopyPairB_,
class SmemLayoutAtomPairB_,
class SmemCopyAtomB_,
class TransformB_>
struct CollectiveMma<
MainloopSm100TmaUmmaWarpSpecializedBlockScaled<
Stages,
SchedulerPipelineStageCount,
AccumulatorPipelineStageCount,
ClusterShape>,
TileShape_,
ElementPairA_,
StridePairA_,
ElementPairB_,
StridePairB_,
TiledMma_,
GmemTiledCopyPairA_,
SmemLayoutAtomPairA_,
SmemCopyAtomA_,
TransformA_,
GmemTiledCopyPairB_,
SmemLayoutAtomPairB_,
SmemCopyAtomB_,
TransformB_>
{
//
// Type Aliases
//
using TiledMma = TiledMma_;
using AtomThrShapeMNK = Shape<decltype(shape<0>(typename TiledMma::ThrLayoutVMNK{})), _1, _1>;
using DispatchPolicy = MainloopSm100TmaUmmaWarpSpecializedBlockScaled<
Stages,
SchedulerPipelineStageCount,
AccumulatorPipelineStageCount,
ClusterShape>;
using TileShape = TileShape_;
using TiledMMA_SF = TiledMMA<MMA_Atom<typename TiledMma::MMA_ScaleFactor>,
Layout<Shape<_1,_1,_1>>,
Tile<Underscore,Underscore,Underscore>>;
static constexpr bool IsDynamicCluster = not cute::is_static_v<ClusterShape>;
static constexpr int SFVecSize = TiledMma::SFVecSize;
static constexpr bool IsOverlappingAccum = DispatchPolicy::IsOverlappingAccum;
CUTE_STATIC_ASSERT_V(evenly_divides(TileShape{}, tile_shape(TiledMma{})),
"Static cluster shape used: TileShape should be evenly divided by TiledMma");
using CtaShape_MNK = decltype(shape_div(TileShape{}, AtomThrShapeMNK{}));
static_assert(shape<1>(CtaShape_MNK{}) == 192 or shape<1>(CtaShape_MNK{}) == 128 or shape<1>(CtaShape_MNK{}) == 256,
"Cta N should be one of 128/192/256");
using ClusterTileShape = decltype(make_shape(get<0>(TileShape{})*get<0>(ClusterShape{}),get<1>(TileShape{})*get<1>(ClusterShape{}),get<2>(TileShape{})*get<2>(ClusterShape{})));
using Sm100BlkScaledConfig = cutlass::detail::Sm100BlockScaledConfig<SFVecSize>;
using Blk_MN = typename Sm100BlkScaledConfig::Blk_MN;
static constexpr int IsCtaN192 = shape<1>(CtaShape_MNK{}) == 192;
static int constexpr CTA_N_SF = cutlass::ceil_div(size<1>(CtaShape_MNK{}), Blk_MN{}) * Blk_MN{};
// Tile shape used for partitioning Scale Factor B.
// The M-dim does not affect the SFB, so just set it as the original TileShape;
using TileShape_SF = decltype(make_shape(get<0>(CtaShape_MNK{}),
Int<CTA_N_SF>{} * shape<2>(typename TiledMma::ThrLayoutVMNK()),
get<2>(TileShape{})));
// Define A and B block shapes for reduced size TMA_LOADs
using MmaShapeA_MK = decltype(partition_shape_A(TiledMma{}, make_shape(size<0>(TileShape{}), size<2>(TileShape{}))));
using MmaShapeB_NK = decltype(partition_shape_B(TiledMma{}, make_shape(size<1>(TileShape{}), size<2>(TileShape{}))));
using ElementPairA = ElementPairA_;
using ElementPairB = ElementPairB_;
using ElementAMma = typename TiledMma::ValTypeA;
using ElementBMma = typename TiledMma::ValTypeB;
using StridePairA = StridePairA_;
using StridePairB = StridePairB_;
using SmemLayoutAtomPairA = SmemLayoutAtomPairA_;
using SmemLayoutAtomPairB = SmemLayoutAtomPairB_;
static_assert(cute::is_same_v<remove_cvref_t<decltype(get<1>(ElementPairA{}))>,
remove_cvref_t<decltype(get<1>(ElementPairB{}))>>, "SFA and SFB data types should be the same");
// A and B matrices
using ElementA = remove_cvref_t<decltype(get<0>(ElementPairA{}))>;
using StrideA = remove_cvref_t<decltype(get<0>(StridePairA{}))>;
using ElementB = remove_cvref_t<decltype(get<0>(ElementPairB{}))>;
using StrideB = remove_cvref_t<decltype(get<0>(StridePairB{}))>;
static constexpr bool IsRuntimeDataTypeA = cutlass::gemm::collective::detail::is_sm10x_runtime_f8f6f4<ElementA>();
static constexpr bool IsRuntimeDataTypeB = cutlass::gemm::collective::detail::is_sm10x_runtime_f8f6f4<ElementB>();
static_assert((IsRuntimeDataTypeA && IsRuntimeDataTypeB) ||
(!IsRuntimeDataTypeA && !IsRuntimeDataTypeB),
"ElementA and ElementB should be both runtime or both static.");
static constexpr bool IsRuntimeDataType = IsRuntimeDataTypeA && IsRuntimeDataTypeB;
// SFA and SFB
using ElementSF = remove_cvref_t<decltype(get<1>(ElementPairA{}))>;
using LayoutSFA = remove_cvref_t<decltype(get<1>(StridePairA{}))>;
using LayoutSFB = remove_cvref_t<decltype(get<1>(StridePairB{}))>;
using ElementAccumulator = typename TiledMma::ValTypeC;
using GmemTiledCopyPairA = GmemTiledCopyPairA_;
using GmemTiledCopyPairB = GmemTiledCopyPairB_;
using GmemTiledCopyA = remove_cvref_t<decltype(get<0>(GmemTiledCopyPairA{}))>;
using GmemTiledCopySFA = remove_cvref_t<decltype(get<1>(GmemTiledCopyPairA{}))>;
using GmemTiledCopyB = remove_cvref_t<decltype(get<0>(GmemTiledCopyPairB{}))>;
using GmemTiledCopySFB = remove_cvref_t<decltype(get<1>(GmemTiledCopyPairB{}))>;
using SmemLayoutAtomA = remove_cvref_t<decltype(get<0>(SmemLayoutAtomPairA{}))>;
using SmemLayoutAtomSFA = remove_cvref_t<decltype(get<1>(SmemLayoutAtomPairA{}))>;
using SmemLayoutAtomB = remove_cvref_t<decltype(get<0>(SmemLayoutAtomPairB{}))>;
using SmemLayoutAtomSFB = remove_cvref_t<decltype(get<1>(SmemLayoutAtomPairB{}))>;
using SmemCopyAtomA = SmemCopyAtomA_;
using SmemCopyAtomB = SmemCopyAtomB_;
using TransformA = TransformA_;
using TransformB = TransformB_;
using ArchTag = typename DispatchPolicy::ArchTag;
using MainloopPipeline = cutlass::PipelineTmaUmmaAsync<
DispatchPolicy::Stages,
ClusterShape,
AtomThrShapeMNK>;
using MainloopPipelineState = typename MainloopPipeline::PipelineState;
static_assert(rank(SmemLayoutAtomA{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
static_assert((size<0>(TileShape{}) % size<0>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtomA must evenly divide the tile shape.");
static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtomA must evenly divide the tile shape.");
static_assert(cute::is_void_v<SmemCopyAtomA>,
"SM100 UMMA cannot have a non-void copy atom for smem sourced instructions.");
static_assert(rank(SmemLayoutAtomB{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
static_assert((size<1>(TileShape{}) % size<0>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtomB must evenly divide the tile shape.");
static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtomB must evenly divide the tile shape.");
static_assert(cute::is_void_v<SmemCopyAtomB>,
"SM100 UMMA cannot have a non-void copy atom for smem sourced instructions.");
// Tile along K mode first before tiling over MN. PIPE mode last as usual.
// This maximizes TMA boxes due to better smem-K vectorization, reducing total issued TMAs.
// (MMA_TILE_M,MMA_TILE_K),MMA_M,MMA_K,PIPE)
using SmemLayoutA = decltype(UMMA::tile_to_mma_shape(
SmemLayoutAtomA{},
append(MmaShapeA_MK{}, Int<DispatchPolicy::Stages>{}),
cute::conditional_t<cutlass::gemm::detail::is_mn_major<StrideA>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
// (MMA_TILE_N,MMA_TILE_K),MMA_N,MMA_K,PIPE)
using SmemLayoutB = decltype(UMMA::tile_to_mma_shape(
SmemLayoutAtomB{},
append(MmaShapeB_NK{}, Int<DispatchPolicy::Stages>{}),
cute::conditional_t<cutlass::gemm::detail::is_mn_major<StrideB>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
// SmemLayoutAtomSFA and SmemLayoutAtomSFB are for whole CTA tiles. We add the number of pipeline stages here.
// The number of pipeline stages is the same as the number of pipeline stages from AB Load <-> MainLoop
using SmemLayoutSFA = decltype(make_layout(
append(shape(SmemLayoutAtomSFA{}), Int<DispatchPolicy::Stages>{}),
append(stride(SmemLayoutAtomSFA{}), size(filter_zeros(SmemLayoutAtomSFA{})))
));
using SmemLayoutSFB = decltype(make_layout(
append(shape(SmemLayoutAtomSFB{}), Int<DispatchPolicy::Stages>{}),
append(stride(SmemLayoutAtomSFB{}), size(filter_zeros(SmemLayoutAtomSFB{})))
));
static_assert(cute::is_base_of<cute::UMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value &&
cute::is_base_of<cute::UMMA::DescriptorIterator, typename TiledMma::FrgTypeB>::value,
"MMA atom must source both A and B operand from smem_desc for this mainloop.");
static_assert(
(size(AtomThrShapeMNK{}) == 1 &&
(cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>)) ||
(size(AtomThrShapeMNK{}) == 2 &&
(cute::is_same_v<GmemTiledCopyA, SM100_TMA_2SM_LOAD> || cute::is_same_v<GmemTiledCopyA, SM100_TMA_2SM_LOAD_MULTICAST>)),
"GmemTiledCopy - invalid TMA copy atom specified.");
static_assert(
(size(AtomThrShapeMNK{}) == 1 &&
(cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>)) ||
(size(AtomThrShapeMNK{}) == 2 &&
(cute::is_same_v<GmemTiledCopyB, SM100_TMA_2SM_LOAD> || cute::is_same_v<GmemTiledCopyB, SM100_TMA_2SM_LOAD_MULTICAST>)),
"GmemTiledCopy - invalid TMA copy atom specified.");
static constexpr bool IsF8F6F4 = detail::is_sm100_mma_f8f6f4<TiledMma, ElementA, ElementB>();
using TmaInternalElementA = cute::conditional_t<IsF8F6F4, ElementAMma, ElementA>;
using TmaInternalElementB = cute::conditional_t<IsF8F6F4, ElementBMma, ElementB>;
using SmemAllocTypeA = cute::conditional_t<IsF8F6F4 && cute::sizeof_bits_v<ElementAMma> < 8, uint8_t, ElementAMma>;
using SmemAllocTypeB = cute::conditional_t<IsF8F6F4 && cute::sizeof_bits_v<ElementBMma> < 8, uint8_t, ElementBMma>;
using BitTypeElementA = cute::uint_bit_t<cute::sizeof_bits_v<ElementA>>;
using BitTypeElementB = cute::uint_bit_t<cute::sizeof_bits_v<ElementB>>;
using ArrayElementA = cute::conditional_t<IsRuntimeDataTypeA, BitTypeElementA, ElementA>;
using ArrayElementB = cute::conditional_t<IsRuntimeDataTypeB, BitTypeElementB, ElementB>;
using RuntimeDataTypeA = typename detail::sm10x_block_scale_runtime_input_t<ElementAMma, IsRuntimeDataTypeA>::Type;
using RuntimeDataTypeB = typename detail::sm10x_block_scale_runtime_input_t<ElementBMma, IsRuntimeDataTypeB>::Type;
struct SharedStorage {
struct TensorStorage : cute::aligned_struct<128, _0> {
cute::ArrayEngine<SmemAllocTypeA, cute::cosize_v<SmemLayoutA>> smem_A;
cute::ArrayEngine<SmemAllocTypeB, cute::cosize_v<SmemLayoutB>> smem_B;
cute::ArrayEngine<ElementSF, cute::cosize_v<SmemLayoutSFA>> smem_SFA;
cute::ArrayEngine<ElementSF, cute::cosize_v<SmemLayoutSFB>> smem_SFB;
} tensors;
using PipelineStorage = typename MainloopPipeline::SharedStorage;
PipelineStorage pipeline;
};
// Expose shared storage for tensors/pipelines separately to allow kernel layer to reorder them.
using TensorStorage = typename SharedStorage::TensorStorage;
using PipelineStorage = typename SharedStorage::PipelineStorage;
static constexpr uint32_t SFTransactionBytes =
cutlass::bits_to_bytes(size(AtomThrShapeMNK{}) * cosize(take<0,3>(SmemLayoutSFA{})) * cute::sizeof_bits_v<ElementSF>) +
cutlass::bits_to_bytes(size(AtomThrShapeMNK{}) * cosize(take<0,3>(SmemLayoutSFB{})) * cute::sizeof_bits_v<ElementSF>);
// Only one thread issues the TMA and updates the barriers in a 2SM MMA, adjust bytes accordingly
static constexpr uint32_t ABTmaTransactionBytes =
cutlass::bits_to_bytes(size(AtomThrShapeMNK{}) * cosize(take<0,3>(SmemLayoutA{})) * cute::sizeof_bits_v<ElementA>) +
cutlass::bits_to_bytes(size(AtomThrShapeMNK{}) * cosize(take<0,3>(SmemLayoutB{})) * cute::sizeof_bits_v<ElementB>);
static constexpr uint32_t TmaTransactionBytes = ABTmaTransactionBytes + SFTransactionBytes;
template<class AccTensor, class SfaTensor, class SfbTensor>
struct TmemStorage {
AccTensor accumulators;
SfaTensor tCtSFA;
SfbTensor tCtSFB;
};
template<
class KTileCount,
class GTensorPartitionedA, class GTensorPartitionedB,
class STensorA, class STensorB,
class GTensorPartitionedSFA, class GTensorPartitionedSFB,
class STensorSFA, class STensorSFB
>
struct LoadParams {
// for scheduler
KTileCount k_tiles;
// for input tensor values
GTensorPartitionedA tAgA_mkl;
GTensorPartitionedB tBgB_nkl;
STensorA tAsA;
STensorB tBsB;
// for scale factor tensor values
GTensorPartitionedSFA tAgSFA_mkl;
GTensorPartitionedSFB tBgSFB_nkl;
STensorSFA tAsSFA;
STensorSFB tBsSFB;
// the TMA multicast masks
uint16_t mcast_mask_a;
uint16_t mcast_mask_b;
uint16_t mcast_mask_sfa;
uint16_t mcast_mask_sfb;
CUTLASS_DEVICE
LoadParams (
KTileCount k_tiles_,
GTensorPartitionedA tAgA_mkl_, GTensorPartitionedB tBgB_nkl_,
STensorA tAsA_, STensorB tBsB_,
GTensorPartitionedSFA tAgSFA_mkl_, GTensorPartitionedSFB tBgSFB_nkl_,
STensorSFA tAsSFA_, STensorSFB tBsSFB_,
uint16_t mcast_mask_a_, uint16_t mcast_mask_b_,
uint16_t mcast_mask_sfa_, uint16_t mcast_mask_sfb_)
: k_tiles(k_tiles_)
, tAgA_mkl(tAgA_mkl_), tBgB_nkl(tBgB_nkl_)
, tAsA(tAsA_), tBsB(tBsB_)
, tAgSFA_mkl(tAgSFA_mkl_), tBgSFB_nkl(tBgSFB_nkl_)
, tAsSFA(tAsSFA_), tBsSFB(tBsSFB_)
, mcast_mask_a(mcast_mask_a_), mcast_mask_b(mcast_mask_b_)
, mcast_mask_sfa(mcast_mask_sfa_), mcast_mask_sfb(mcast_mask_sfb_) {}
};
template<
class FragmentA, class FragmentB,
class FragmentSFA, class FragmentSFB,
class SFATiledCopy, class SmemFrgSFA, class TmemFrgSFA,
class SFBTiledCopy, class SmemFrgSFB, class TmemFrgSFB
>
struct MmaParams {
TiledMma tiled_mma;
FragmentA tCrA;
FragmentB tCrB;
FragmentSFA tCtSFA;
FragmentSFB tCtSFB;
SFATiledCopy tiled_copy_s2t_SFA;
SmemFrgSFA thr_tCsSFA_s2t;
TmemFrgSFA thr_tCtSFA_s2t;
SFBTiledCopy tiled_copy_s2t_SFB;
SmemFrgSFB thr_tCsSFB_s2t;
TmemFrgSFB thr_tCtSFB_s2t;
CUTLASS_DEVICE
MmaParams (
TiledMma tiled_mma_,
FragmentA tCrA_, FragmentB tCrB_, FragmentSFA tCtSFA_, FragmentSFB tCtSFB_,
SFATiledCopy tiled_copy_s2t_SFA_, SmemFrgSFA thr_tCsSFA_s2t_, TmemFrgSFA thr_tCtSFA_s2t_,
SFBTiledCopy tiled_copy_s2t_SFB_, SmemFrgSFB thr_tCsSFB_s2t_, TmemFrgSFB thr_tCtSFB_s2t_)
: tiled_mma(tiled_mma_)
, tCrA(tCrA_), tCrB(tCrB_), tCtSFA(tCtSFA_), tCtSFB(tCtSFB_)
, tiled_copy_s2t_SFA(tiled_copy_s2t_SFA_), thr_tCsSFA_s2t(thr_tCsSFA_s2t_), thr_tCtSFA_s2t(thr_tCtSFA_s2t_)
, tiled_copy_s2t_SFB(tiled_copy_s2t_SFB_), thr_tCsSFB_s2t(thr_tCsSFB_s2t_), thr_tCtSFB_s2t(thr_tCtSFB_s2t_) {}
};
// Host side kernel arguments
struct Arguments {
ArrayElementA const* ptr_A{nullptr};
StrideA dA{};
ArrayElementB const* ptr_B{nullptr};
StrideB dB{};
ElementSF const* ptr_SFA{nullptr};
LayoutSFA layout_SFA{};
ElementSF const* ptr_SFB{nullptr};
LayoutSFB layout_SFB{};
RuntimeDataTypeA runtime_data_type_a{};
RuntimeDataTypeB runtime_data_type_b{};
};
// Device side kernel params
struct Params {
using ClusterLayout_VMNK =
decltype(tiled_divide(make_layout(conditional_return<IsDynamicCluster>(make_shape(uint32_t(0), uint32_t(0), Int<1>{}),
ClusterShape{})), make_tile(typename TiledMma::AtomThrID{})));
using ClusterLayoutSfb_VMNK =
decltype(tiled_divide(make_layout(conditional_return<IsDynamicCluster>(make_shape(uint32_t(0), uint32_t(0), Int<1>{}),
ClusterShape{})), make_tile(typename TiledMMA_SF::AtomThrID{})));
using TMA_A = decltype(make_tma_atom_A_sm100<TmaInternalElementA>(
GmemTiledCopyA{},
make_tensor(recast_ptr<TmaInternalElementA>(nullptr), repeat_like(StrideA{}, int32_t(0)), StrideA{}),
SmemLayoutA{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
ClusterLayout_VMNK{})
);
using TMA_B = decltype(make_tma_atom_B_sm100<TmaInternalElementB>(
GmemTiledCopyB{},
make_tensor(recast_ptr<TmaInternalElementB>(nullptr), repeat_like(StrideB{}, int32_t(0)), StrideB{}),
SmemLayoutB{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
ClusterLayout_VMNK{})
);
using TMA_SFA = decltype(make_tma_atom_A_sm100<uint16_t>(
GmemTiledCopySFA{},
make_tensor(static_cast<ElementSF const*>(nullptr), LayoutSFA{}),
SmemLayoutSFA{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
ClusterLayout_VMNK{})
);
using TMA_SFB = decltype(make_tma_atom_B_sm100<uint16_t>(
GmemTiledCopySFB{},
make_tensor(static_cast<ElementSF const*>(nullptr), LayoutSFB{}),
SmemLayoutSFB{}(_,_,_,cute::Int<0>{}),
TileShape_SF{},
TiledMMA_SF{},
ClusterLayoutSfb_VMNK{})
);
TMA_A tma_load_a;
TMA_B tma_load_b;
TMA_SFA tma_load_sfa;
TMA_SFB tma_load_sfb;
TMA_A tma_load_a_fallback;
TMA_B tma_load_b_fallback;
TMA_SFA tma_load_sfa_fallback;
TMA_SFB tma_load_sfb_fallback;
LayoutSFA layout_SFA;
LayoutSFB layout_SFB;
dim3 cluster_shape_fallback;
RuntimeDataTypeA runtime_data_type_a;
RuntimeDataTypeB runtime_data_type_b;
};
CUTLASS_DEVICE
CollectiveMma(Params const& params, ClusterShape cluster_shape, uint32_t block_rank_in_cluster)
: cluster_shape_(cluster_shape)
, block_rank_in_cluster_(block_rank_in_cluster)
, layout_SFA_(params.layout_SFA)
, layout_SFB_(params.layout_SFB)
, runtime_data_type_a_(params.runtime_data_type_a)
, runtime_data_type_b_(params.runtime_data_type_b) {
if constexpr (IsDynamicCluster) {
const bool is_fallback_cluster = (cute::size<0>(cluster_shape_) == params.cluster_shape_fallback.x &&
cute::size<1>(cluster_shape_) == params.cluster_shape_fallback.y);
observed_tma_load_a_ = is_fallback_cluster ? ¶ms.tma_load_a_fallback : ¶ms.tma_load_a;
observed_tma_load_b_ = is_fallback_cluster ? ¶ms.tma_load_b_fallback : ¶ms.tma_load_b;
observed_tma_load_sfa_ = is_fallback_cluster ? ¶ms.tma_load_sfa_fallback : ¶ms.tma_load_sfa;
observed_tma_load_sfb_ = is_fallback_cluster ? ¶ms.tma_load_sfb_fallback : ¶ms.tma_load_sfb;
}
else {
observed_tma_load_a_ = ¶ms.tma_load_a;
observed_tma_load_b_ = ¶ms.tma_load_b;
observed_tma_load_sfa_ = ¶ms.tma_load_sfa;
observed_tma_load_sfb_ = ¶ms.tma_load_sfb;
}
}
template <class ProblemShape>
static constexpr Params
to_underlying_arguments(
ProblemShape const& problem_shape,
Arguments const& args,
[[maybe_unused]] void* workspace,
cutlass::KernelHardwareInfo const& hw_info = cutlass::KernelHardwareInfo{}) {
// 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<TmaInternalElementA>(args.ptr_A);
auto ptr_B = recast_ptr<TmaInternalElementB>(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));
auto cluster_shape = cutlass::detail::select_cluster_shape(ClusterShape{}, hw_info.cluster_shape);
// Cluster layout for TMA construction
auto cluster_layout_vmnk = tiled_divide(make_layout(cluster_shape), make_tile(typename TiledMma::AtomThrID{}));
auto cluster_shape_fallback = cutlass::detail::select_cluster_shape(ClusterShape{}, hw_info.cluster_shape_fallback);
auto cluster_layout_vmnk_fallback = tiled_divide(make_layout(cluster_shape_fallback), make_tile(typename TiledMma::AtomThrID{}));
Tensor tensor_sfa = make_tensor(args.ptr_SFA, args.layout_SFA);
Tensor tensor_sfb = make_tensor(args.ptr_SFB, args.layout_SFB);
// Cluster layout for TMA construction of SFB
auto cluster_layout_sfb_vmnk = tiled_divide(make_layout(cluster_shape), make_tile(typename TiledMMA_SF::AtomThrID{}));
auto cluster_layout_sfb_vmnk_fallback = tiled_divide(make_layout(cluster_shape_fallback), make_tile(typename TiledMMA_SF::AtomThrID{}));
typename Params::TMA_A tma_load_a = make_tma_atom_A_sm100<TmaInternalElementA>(
GmemTiledCopyA{},
tensor_a,
SmemLayoutA{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
cluster_layout_vmnk);
typename Params::TMA_B tma_load_b = make_tma_atom_B_sm100<TmaInternalElementB>(
GmemTiledCopyB{},
tensor_b,
SmemLayoutB{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
cluster_layout_vmnk);
typename Params::TMA_A tma_load_a_fallback = make_tma_atom_A_sm100<TmaInternalElementA>(
GmemTiledCopyA{},
tensor_a,
SmemLayoutA{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
cluster_layout_vmnk_fallback);
typename Params::TMA_B tma_load_b_fallback = make_tma_atom_B_sm100<TmaInternalElementB>(
GmemTiledCopyB{},
tensor_b,
SmemLayoutB{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
cluster_layout_vmnk_fallback);
typename Params::TMA_SFA tma_load_sfa = make_tma_atom_A_sm100<uint16_t>(
GmemTiledCopySFA{},
tensor_sfa,
SmemLayoutSFA{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
cluster_layout_vmnk);
typename Params::TMA_SFB tma_load_sfb = make_tma_atom_B_sm100<uint16_t>(
GmemTiledCopySFB{},
tensor_sfb,
SmemLayoutSFB{}(_,_,_,cute::Int<0>{}),
TileShape_SF{},
TiledMMA_SF{},
cluster_layout_sfb_vmnk);
typename Params::TMA_SFA tma_load_sfa_fallback = make_tma_atom_A_sm100<uint16_t>(
GmemTiledCopySFA{},
tensor_sfa,
SmemLayoutSFA{}(_,_,_,cute::Int<0>{}),
TileShape{},
TiledMma{},
cluster_layout_vmnk_fallback);
typename Params::TMA_SFB tma_load_sfb_fallback = make_tma_atom_B_sm100<uint16_t>(
GmemTiledCopySFB{},
tensor_sfb,
SmemLayoutSFB{}(_,_,_,cute::Int<0>{}),
TileShape_SF{},
TiledMMA_SF{},
cluster_layout_sfb_vmnk_fallback);
return {
tma_load_a,
tma_load_b,
tma_load_sfa,
tma_load_sfb,
tma_load_a_fallback,
tma_load_b_fallback,
tma_load_sfa_fallback,
tma_load_sfb_fallback,
args.layout_SFA,
args.layout_SFB,
hw_info.cluster_shape_fallback,
args.runtime_data_type_a,
args.runtime_data_type_b
};
}
template <class ProblemShape>
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<ElementA, IsF8F6F4>();
constexpr int tma_alignment_bits_B = cutlass::detail::get_input_alignment_bits<ElementB, IsF8F6F4>();
bool implementable = true;
constexpr int min_tma_aligned_elements_A = tma_alignment_bits_A / cute::sizeof_bits<ElementA>::value;
implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), StrideA{});
constexpr int min_tma_aligned_elements_B = tma_alignment_bits_B / cute::sizeof_bits<ElementB>::value;
implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_B>(cute::make_shape(N,K,L), StrideB{});
// Check for SFA SFB layout requirement
const auto layout_sfa_ref = Sm100BlkScaledConfig::tile_atom_to_shape_SFA(problem_shape_MNKL);
const auto layout_sfb_ref = Sm100BlkScaledConfig::tile_atom_to_shape_SFB(problem_shape_MNKL);
implementable = implementable && (layout_sfa_ref == args.layout_SFA);
if (!implementable) {
CUTLASS_TRACE_HOST(" CAN IMPLEMENT: layout_SFA mismatch, layout_SFA needs to be K-major\n");
}
implementable = implementable && (layout_sfb_ref == args.layout_SFB);
if (!implementable) {
CUTLASS_TRACE_HOST(" CAN IMPLEMENT: layout_SFB mismatch, layout_SFB needs to be K-major\n");
}
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 void
prefetch_tma_descriptors() {
cute::prefetch_tma_descriptor(observed_tma_load_a_->get_tma_descriptor());
cute::prefetch_tma_descriptor(observed_tma_load_b_->get_tma_descriptor());
cute::prefetch_tma_descriptor(observed_tma_load_sfa_->get_tma_descriptor());
cute::prefetch_tma_descriptor(observed_tma_load_sfb_->get_tma_descriptor());
}
/// Construct A Single Stage's Accumulator Shape
CUTLASS_DEVICE static
auto
partition_accumulator_shape() {
auto acc_shape = partition_shape_C(TiledMma{}, take<0,2>(TileShape{})); // ((MMA_TILE_M,MMA_TILE_N),MMA_M,MMA_N)
return acc_shape;
}
template <class TmemStorage>
CUTLASS_DEVICE static
auto
slice_accumulator(TmemStorage tmem_storage, int stage) {
return cute::make_tuple(tmem_storage.accumulators(_,_,_,stage));
}
template<class EpilogueTile, bool IsOverlappingAccum = false>
CUTLASS_DEVICE static
auto
init_tmem_tensors(EpilogueTile epi_tile) {
TiledMma tiled_mma;
auto acc_shape = partition_accumulator_shape();
// ((MMA_TILE_M,MMA_TILE_N),MMA_M,MMA_N,ACC_PIPE) where ACC_PIPE=2 so we can double buffer our accumulators for mainloop and epilogue.
Tensor accumulators = cutlass::detail::make_sm100_accumulator<AccumulatorPipelineStageCount, IsOverlappingAccum>(
tiled_mma, acc_shape, EpilogueTile{});
Tensor tCtSFA = make_tensor<typename TiledMma::FrgTypeSFA>(shape(SmemLayoutAtomSFA{}));
Tensor tCtSFB = make_tensor<typename TiledMma::FrgTypeSFB>(shape(SmemLayoutAtomSFB{}));
TmemStorage<decltype(accumulators), decltype(tCtSFA), decltype(tCtSFB)> tmem_storage;
tmem_storage.accumulators = accumulators;
tmem_storage.tCtSFA = tCtSFA;
tmem_storage.tCtSFB = tCtSFB;
return tmem_storage;
}
template<class AccTensor, class SfaTensor, class SfbTensor>
CUTLASS_DEVICE static
void
set_tmem_offsets(TmemStorage<AccTensor, SfaTensor, SfbTensor>& tmem_storage, uint32_t tmem_base_addr) {
tmem_storage.accumulators.data() = tmem_base_addr;
tmem_storage.tCtSFA.data() = tmem_storage.accumulators.data().get() + cutlass::detail::find_tmem_tensor_col_offset(tmem_storage.accumulators);
tmem_storage.tCtSFB.data() = tmem_storage.tCtSFA.data().get() + cutlass::detail::find_tmem_tensor_col_offset(tmem_storage.tCtSFA);
}
/// Set up the data needed by this collective for load.
/// Return tuple element contain
/// gA_mkl - The tiled tma tensor for input A
/// gB_nkl - The tiled tma tensor for input B
/// tAgA_mkl - partitioned gmem tensor for A
/// tBgB_nkl - partitioned gmem tensor for B
/// tAsA - partitioned smem tensor for A
/// tBsB - partitioned smem tensor for B
/// tAgSFA_mkl - partitioned gmem tensor for SFA
/// tBgSFB_nkl - partitioned gmem tensor for SFB
/// tAsSFA - partitioned tmem tensor for SFA
/// tAsSFB - partitioned tmem tensor for SFB
/// mcast_mask_a - tma multicast mask for A
/// mcast_mask_b - tma multicast mask for B
/// mcast_mask_sfa - tma multicast mask for SFA
/// mcast_mask_sfb - tma multicast mask for SFB
template <class ProblemShape_MNKL>
CUTLASS_DEVICE auto
load_init(
ProblemShape_MNKL const& problem_shape_MNKL,
TensorStorage& shared_tensors) const {
using X = Underscore;
// Separate out problem shape for convenience
auto [M,N,K,L] = problem_shape_MNKL;
// Represent the full tensors -- get these from TMA
Tensor mA_mkl = observed_tma_load_a_->get_tma_tensor(make_shape(M,K,L));
Tensor mB_nkl = observed_tma_load_b_->get_tma_tensor(make_shape(N,K,L));
// Tile the tensors and 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)
// Represent the full tensor of Scale factors
Tensor mSFA_mkl = observed_tma_load_sfa_->get_tma_tensor(shape(layout_SFA_));
auto mSFB_nkl = [=](){
if constexpr (IsCtaN192) {
Tensor mSFB_tmp = observed_tma_load_sfb_->get_tma_tensor(shape(layout_SFB_));
auto x = stride<0,1>(mSFB_tmp);
auto y = ceil_div(shape<0,1>(mSFB_tmp), 4);
auto new_shape = make_shape (make_shape( shape<0,0>(mSFB_tmp),
make_shape( make_shape(_2{}, _2{}), y)), shape<1>(mSFB_tmp), shape<2>(mSFB_tmp));
auto new_stride = make_stride(make_stride(stride<0,0>(mSFB_tmp),
make_stride(make_stride( x, x), x*3)), stride<1>(mSFB_tmp), stride<2>(mSFB_tmp));
return make_tensor(mSFB_tmp.data(), make_layout(new_shape, new_stride));
}
else {
return observed_tma_load_sfb_->get_tma_tensor(shape(layout_SFB_));
}
}();
Tensor gSFA_mkl = local_tile(mSFA_mkl, TileShape{}, make_coord(_,_,_), Step<_1, X,_1>{}); // (TILE_M,TILE_K,m,k,l)
Tensor gSFB_nkl = local_tile(mSFB_nkl, TileShape_SF{}, make_coord(_,_,_), Step< X,_1,_1>{}); // (TILE_N,TILE_K,n,k,l)
// Partition for this CTA
ThrMMA cta_mma = TiledMma{}.get_slice(blockIdx.x % size(typename TiledMma::AtomThrID{}));
Tensor tCgA_mkl = cta_mma.partition_A(gA_mkl); // (MMA, MMA_M, MMA_K, m, k, l)
Tensor tCgB_nkl = cta_mma.partition_B(gB_nkl); // (MMA, MMA_N, MMA_K, n, k, l)
Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.begin()), SmemLayoutA{}); // (MMA,MMA_M,MMA_K,PIPE)
Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.begin()), SmemLayoutB{}); // (MMA,MMA_N,MMA_K,PIPE)
ThrMMA cta_mma_sfb = TiledMMA_SF{}.get_slice(blockIdx.x % size(typename TiledMMA_SF::AtomThrID{}));
Tensor tCgSFA_mkl = cta_mma.partition_A(gSFA_mkl); // (MMA, MMA_M, MMA_K, m, k, l)
Tensor tCgSFB_nkl = cta_mma_sfb.partition_B(gSFB_nkl); // (MMA, MMA_N, MMA_K, n, k, l)
Tensor sSFA = make_tensor(make_smem_ptr(shared_tensors.smem_SFA.begin()), SmemLayoutSFA{});
Tensor sSFB = make_tensor(make_smem_ptr(shared_tensors.smem_SFB.begin()), SmemLayoutSFB{});
// Define the CTA-in-cluster Layout and Coord
Layout cta_layout_mnk = make_layout(cluster_shape_);
Layout cta_layout_vmnk = tiled_divide(cta_layout_mnk, make_tile(typename TiledMma::AtomThrID{}));
auto cta_coord_vmnk = cta_layout_vmnk.get_flat_coord(block_rank_in_cluster_);
Layout cta_layout_sfb_vmnk = tiled_divide(cta_layout_mnk, make_tile(typename TiledMMA_SF::AtomThrID{}));
auto cta_coord_sfb_vmnk = cta_layout_sfb_vmnk.get_flat_coord(block_rank_in_cluster_);
// Project the cta_layout for tma_a along the n-modes
auto [tAgA_mkl, tAsA] = tma_partition(*observed_tma_load_a_,
get<2>(cta_coord_vmnk), make_layout(size<2>(cta_layout_vmnk)),
group_modes<0,3>(sA), group_modes<0,3>(tCgA_mkl));
// Project the cta_layout for tma_b along the m-modes
auto [tBgB_nkl, tBsB] = tma_partition(*observed_tma_load_b_,
get<1>(cta_coord_vmnk), make_layout(size<1>(cta_layout_vmnk)),
group_modes<0,3>(sB), group_modes<0,3>(tCgB_nkl));
// Project the cta_layout for tma_a along the n-modes
auto [tAgSFA_mkl, tAsSFA] = tma_partition(*observed_tma_load_sfa_,
get<2>(cta_coord_vmnk), make_layout(size<2>(cta_layout_vmnk)),
group_modes<0,3>(sSFA), group_modes<0,3>(tCgSFA_mkl));
// Project the cta_layout for tma_b along the m-modes
auto [tBgSFB_nkl, tBsSFB] = tma_partition(*observed_tma_load_sfb_,
get<1>(cta_coord_sfb_vmnk), make_layout(size<1>(cta_layout_sfb_vmnk)),
group_modes<0,3>(sSFB), group_modes<0,3>(tCgSFB_nkl));
// TMA Multicast Masks
uint16_t mcast_mask_a = create_tma_multicast_mask<2>(cta_layout_vmnk, cta_coord_vmnk);
uint16_t mcast_mask_b = create_tma_multicast_mask<1>(cta_layout_vmnk, cta_coord_vmnk);
uint16_t mcast_mask_sfa = create_tma_multicast_mask<2>(cta_layout_vmnk, cta_coord_vmnk);
uint16_t mcast_mask_sfb = create_tma_multicast_mask<1>(cta_layout_sfb_vmnk, cta_coord_sfb_vmnk);
LoadParams load_params {
size<3>(gA_mkl), // for scheduler
tAgA_mkl, tBgB_nkl, tAsA, tBsB, // for input tensor values
tAgSFA_mkl, tBgSFB_nkl, tAsSFA, tBsSFB, // for input scale factor tensor values
mcast_mask_a, mcast_mask_b, mcast_mask_sfa, mcast_mask_sfb // multicast masks
};
return load_params;
}
/// Set up the data needed by this collective for mma compute.
template <class TmemStorage>
CUTLASS_DEVICE auto
mma_init(
TmemStorage tmem_storage,
TensorStorage& shared_tensors) const {
// Allocate "fragments/descriptors" for A and B matrices
Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.begin()), SmemLayoutA{}); // (BLK_M,BLK_K,PIPE)
Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.begin()), SmemLayoutB{}); // (BLK_N,BLK_K,PIPE)
// Allocate "fragments/descriptors" for A and B matrices
Tensor tCrA = TiledMma::make_fragment_A(sA); // (MMA,MMA_M,MMA_K,PIPE)
Tensor tCrB = TiledMma::make_fragment_B(sB); // (MMA,MMA_N,MMA_K,PIPE)
CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<3>(sA)); // PIPE
CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<3>(sB)); // PIPE
//
// Scale Factor
//
Tensor tCtSFA = tmem_storage.tCtSFA;
Tensor tCtSFB = tmem_storage.tCtSFB;
// Setup smem descriptors for UTCCP
Tensor tCsSFA = make_tensor(make_smem_ptr(shared_tensors.smem_SFA.begin()), SmemLayoutSFA{});
Tensor tCsSFB = make_tensor(make_smem_ptr(shared_tensors.smem_SFB.begin()), SmemLayoutSFB{});
// Make SMEM and TMEM tensors compact removing the zero strides to eliminate unnecessary copy instructions.
auto tCsSFA_compact = make_tensor(tCsSFA.data(), filter_zeros(tCsSFA.layout()));
auto tCtSFA_compact = make_tensor(tCtSFA.data(), filter_zeros(tCtSFA.layout()));
auto tCsSFB_compact = make_tensor(tCsSFB.data(), filter_zeros(tCsSFB.layout()));
auto tCtSFB_compact = make_tensor(tCtSFB.data(), filter_zeros(tCtSFB.layout()));
// Create the SMEM to TMEM copy operations based on the MMA atom used (1CTA vs 2CTA)
using AtomThrID = typename TiledMma::AtomThrID;
using UtccpOp = cute::conditional_t<(decltype(cute::size(AtomThrID{}) == Int<2>{})::value),
SM100_UTCCP_4x32dp128bit_2cta, SM100_UTCCP_4x32dp128bit_1cta>;
auto tiled_copy_s2t_SFA = make_utccp_copy(UtccpOp{}, tCtSFA_compact);
auto tiled_copy_s2t_SFB = make_utccp_copy(UtccpOp{}, tCtSFB_compact);
auto thr_copy_s2t_SFA = tiled_copy_s2t_SFA.get_slice(0);
auto thr_tCsSFA_compact_s2t_ = thr_copy_s2t_SFA.partition_S(tCsSFA_compact);
// SMEM to TMEM copy operation requires source SMEM operand to be an SMEM descriptor
auto thr_tCsSFA_compact_s2t = get_utccp_smem_desc_tensor<UtccpOp>(thr_tCsSFA_compact_s2t_);
auto thr_tCtSFA_compact_s2t = thr_copy_s2t_SFA.partition_D(tCtSFA_compact);
auto thr_copy_s2t_SFB = tiled_copy_s2t_SFB.get_slice(0);
auto thr_tCsSFB_compact_s2t_ = thr_copy_s2t_SFB.partition_S(tCsSFB_compact);
// SMEM to TMEM copy operation requires source SMEM operand to be an SMEM descriptor
auto thr_tCsSFB_compact_s2t = get_utccp_smem_desc_tensor<UtccpOp>(thr_tCsSFB_compact_s2t_);
auto thr_tCtSFB_compact_s2t = thr_copy_s2t_SFB.partition_D(tCtSFB_compact);
TiledMma tiled_mma;
if constexpr (IsRuntimeDataType) {
// Update instruction descriptor according to runtime argument.
// Applying bitmask (0b111) to help compiler deduce that the conversion and assignment are safe.
tiled_mma.idesc_.a_format_ = uint8_t(runtime_data_type_a_) & 0b111;
tiled_mma.idesc_.b_format_ = uint8_t(runtime_data_type_b_) & 0b111;
}
MmaParams<
decltype(tCrA), decltype(tCrB), decltype(tCtSFA), decltype(tCtSFB),
decltype(tiled_copy_s2t_SFA), decltype(thr_tCsSFA_compact_s2t), decltype(thr_tCtSFA_compact_s2t),
decltype(tiled_copy_s2t_SFB), decltype(thr_tCsSFB_compact_s2t), decltype(thr_tCtSFB_compact_s2t)
> mma_params {
tiled_mma,
tCrA, tCrB, tCtSFA, tCtSFB,
tiled_copy_s2t_SFA, thr_tCsSFA_compact_s2t, thr_tCtSFA_compact_s2t,
tiled_copy_s2t_SFB, thr_tCsSFB_compact_s2t, thr_tCtSFB_compact_s2t
};
return mma_params;
}
/// Perform a collective-scoped matrix multiply-accumulate
/// Producer Perspective
template <
class LoadParams,
class TileCoordMNKL,
class KTileIterator
>
CUTLASS_DEVICE auto
load(
MainloopPipeline mainloop_pipeline,
MainloopPipelineState mainloop_pipe_producer_state,
LoadParams const& load_inputs,
TileCoordMNKL const& cta_coord_mnkl,
KTileIterator k_tile_iter, int k_tile_count) {
auto [unused_k_tiles,
tAgA_mkl, tBgB_nkl, tAsA, tBsB,
tAgSFA_mkl, tBgSFB_nkl, tAsSFA, tBsSFB,
mcast_mask_a, mcast_mask_b, mcast_mask_sfa, mcast_mask_sfb] = load_inputs;
// slice out the work coord from partitioned tensors
Tensor tAgA = tAgA_mkl(_, get<0>(cta_coord_mnkl) / size(typename TiledMma::AtomThrID{}), _, get<3>(cta_coord_mnkl));
Tensor tBgB = tBgB_nkl(_, get<1>(cta_coord_mnkl), _, get<3>(cta_coord_mnkl));
Tensor tAgSFA = tAgSFA_mkl(_, get<0>(cta_coord_mnkl) / size(typename TiledMma::AtomThrID{}), _, get<3>(cta_coord_mnkl));
Tensor tBgSFB = tBgSFB_nkl(_, get<1>(cta_coord_mnkl), _, get<3>(cta_coord_mnkl));
auto barrier_token = mainloop_pipeline.producer_try_acquire(mainloop_pipe_producer_state);
// Issue the Mainloop loads
CUTLASS_PRAGMA_NO_UNROLL
while (k_tile_count > 0) {
// LOCK mainloop_pipe_producer_state for _writing_
mainloop_pipeline.producer_acquire(mainloop_pipe_producer_state, barrier_token);
// Note: We don't synchronize the sf_pipeline for "Buffer_Empty". We use mainloop pipeline
// to do the synchronization at once.
using BarrierType = typename MainloopPipeline::ProducerBarrierType;
BarrierType* tma_barrier = mainloop_pipeline.producer_get_barrier(mainloop_pipe_producer_state);
int write_stage = mainloop_pipe_producer_state.index();
++mainloop_pipe_producer_state;
barrier_token = mainloop_pipeline.producer_try_acquire(mainloop_pipe_producer_state);
if (cute::elect_one_sync()) {
copy(observed_tma_load_a_->with(*tma_barrier, mcast_mask_a), tAgA(_,*k_tile_iter), tAsA(_,write_stage));
copy(observed_tma_load_b_->with(*tma_barrier, mcast_mask_b), tBgB(_,*k_tile_iter), tBsB(_,write_stage));
copy(observed_tma_load_sfa_->with(*tma_barrier, mcast_mask_sfa), tAgSFA(_,*k_tile_iter), tAsSFA(_,write_stage));
copy(observed_tma_load_sfb_->with(*tma_barrier, mcast_mask_sfb), tBgSFB(_,*k_tile_iter), tBsSFB(_,write_stage));
}
--k_tile_count;
++k_tile_iter;
}
return cute::make_tuple(mainloop_pipe_producer_state, k_tile_iter);
}
/// Perform a Producer Epilogue to prevent early exit of ctas in a Cluster
CUTLASS_DEVICE void
load_tail(MainloopPipeline mainloop_pipeline, MainloopPipelineState mainloop_pipe_producer_state) {
// Issue the epilogue waits
// This helps avoid early exit of ctas 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
mainloop_pipeline.producer_tail(mainloop_pipe_producer_state);
}
/// Perform a collective-scoped matrix multiply-accumulate
/// Consumer Perspective
template <
class AccumulatorPipeline,
class FrgEngine, class FrgLayout,
class MmaParams,
class CtaTileCoord
>
CUTLASS_DEVICE auto
mma(cute::tuple<MainloopPipeline,
AccumulatorPipeline> pipelines,
cute::tuple<MainloopPipelineState,
typename AccumulatorPipeline::PipelineState> pipeline_states,
cute::tuple<cute::Tensor<FrgEngine, FrgLayout>> const& accumulators_pair,
MmaParams const& mma_inputs,
CtaTileCoord cta_tile_coord,
int k_tile_count
) {
static_assert(is_tmem<FrgEngine>::value, "Accumulator must be tmem resident.");
static_assert(rank(FrgLayout{}) == 3, "Accumulator must be MMA-partitioned: (MMA, MMA_M, MMA_N)");
auto accumulators = get<0>(accumulators_pair);
auto [tiled_mma,
tCrA, tCrB, tCtSFA, tCtSFB,
tiled_copy_s2t_SFA, thr_tCsSFA_s2t,
thr_tCtSFA_s2t, tiled_copy_s2t_SFB,
thr_tCsSFB_s2t, thr_tCtSFB_s2t] = mma_inputs;
auto [mainloop_pipeline, accumulator_pipeline] = pipelines;
auto [mainloop_pipe_consumer_state, accumulator_pipe_producer_state] = pipeline_states;
auto tCtSFB_mma = [tCtSFB = tCtSFB, cta_tile_coord]() {
if constexpr (IsCtaN192) {
// If this is an ODD tile, shift the TMEM start address for N=192 case by two words (ignores first 64 columns of SFB)
auto tCtSFB_tmp = tCtSFB;
if (get<1>(cta_tile_coord) % 2 == 1) {
tCtSFB_tmp.data() = tCtSFB_tmp.data().get() + 2;
}
return tCtSFB_tmp;
}
else {
return tCtSFB;
}
}();
uint32_t skip_wait = k_tile_count <= 0;
auto barrier_token = mainloop_pipeline.consumer_try_wait(mainloop_pipe_consumer_state, skip_wait);
//
// PIPELINED MAIN LOOP
//
tiled_mma.accumulate_ = UMMA::ScaleOut::Zero;
if (k_tile_count > 0) { // first iteraion
// WAIT on mainloop_pipe_consumer_state until its data are available
// (phase bit flips from mainloop_pipe_consumer_state.phase() value)
mainloop_pipeline.consumer_wait(mainloop_pipe_consumer_state, barrier_token);
// Compute on k_tile
int read_stage = mainloop_pipe_consumer_state.index();
// Save current mainlop pipeline read state
auto curr_mainloop_pipe_consumer_state = mainloop_pipe_consumer_state;