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/******************************************************************************
* Copyright (c) 2024, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao.
******************************************************************************/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/barrier.h"
#include "cute/tensor.hpp"
#include "cutlass/gemm/collective/builders/sm90_common.inl"
#include "seqlen.h"
#include "named_barrier.hpp"
#include "utils.h"
namespace flash {
using namespace cute;
template <class TileShape_MNK_, class Element_, class ArchTag_,
int NumEpilogueThreads_, bool Varlen_, bool dKV_swapAB_, int AtomLayoutKdKV=1>
struct CollectiveEpilogueBwd {
using TileShape_MNK = TileShape_MNK_;
using Element = Element_;
using ArchTag = ArchTag_;
static constexpr int NumEpilogueThreads = NumEpilogueThreads_;
static constexpr bool Varlen = Varlen_;
static constexpr bool dKV_swapAB = dKV_swapAB_;
static constexpr bool Use_TMA = !Varlen && ArchTag::kMinComputeCapability >= 90;
static_assert(ArchTag::kMinComputeCapability >= 80);
using GmemTiledCopydKVTMA = cute::SM90_TMA_STORE;
// These are for storing the output tensor without TMA (e.g., for setting output to zero)
static constexpr int kGmemElemsPerLoad = sizeof(cute::uint128_t) / sizeof(Element);
static_assert(get<2>(TileShape_MNK{}) % kGmemElemsPerLoad == 0, "Headdim must be a multiple of kGmemElemsPerLoad");
static constexpr int kHeadDim = get<2>(TileShape_MNK{});
static constexpr int kGmemThreadsPerRow = cutlass::gcd(kHeadDim / kGmemElemsPerLoad, NumEpilogueThreads);
static_assert(NumEpilogueThreads % kGmemThreadsPerRow == 0, "NumEpilogueThreads must be a multiple of kGmemThreadsPerRow");
using GmemLayoutAtom = Layout<Shape <Int<NumEpilogueThreads / kGmemThreadsPerRow>, Int<kGmemThreadsPerRow>>,
Stride<Int<kGmemThreadsPerRow>, _1>>;
using GmemTiledCopydKV = decltype(
make_tiled_copy(Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, Element>{},
GmemLayoutAtom{},
Layout<Shape<_1, Int<kGmemElemsPerLoad>>>{})); // Val layout, 8 or 16 vals per store
using SmemLayoutAtomdKVTMA = decltype(cutlass::gemm::collective::detail::ss_smem_selector<GMMA::Major::K, Element,
// TODO: do we have to change this if dKV_swapAB is true?
decltype(cute::get<1>(TileShape_MNK{})), Int<CUTE_STATIC_V(cute::get<2>(TileShape_MNK{})) / AtomLayoutKdKV>>());
using SmemLayoutdKVTMA = decltype(tile_to_shape(SmemLayoutAtomdKVTMA{}, select<1, 2>(TileShape_MNK{})));
using SmemLayoutdKVtTMA =
decltype(cute::composition(SmemLayoutdKVTMA{},
make_layout(make_shape(get<2>(TileShape_MNK{}), get<1>(TileShape_MNK{})),
make_stride(decltype(get<1>(TileShape_MNK{})){}, _1{}))));
// If we don't use TMA
static constexpr int kBlockKSmem = kHeadDim % 64 == 0 ? 64 : (kHeadDim % 32 == 0 ? 32 : 16);
static constexpr int kSwizzle = kBlockKSmem == 64 ? 3 : (kBlockKSmem == 32 ? 2 : 1);
using SmemLayoutAtomdKVSTG =
decltype(composition(Swizzle<kSwizzle, 3, 3>{},
Layout<Shape<Int<8>, Int<kBlockKSmem>>,
Stride<Int<kBlockKSmem>, _1>>{}));
using SmemLayoutAtomdKV = std::conditional_t<Use_TMA, SmemLayoutAtomdKVTMA, SmemLayoutAtomdKVSTG>;
using SmemLayoutdKV = decltype(tile_to_shape(SmemLayoutAtomdKV{}, select<1, 2>(TileShape_MNK{})));
using SmemLayoutdKVt =
decltype(cute::composition(SmemLayoutdKV{},
make_layout(make_shape(get<2>(TileShape_MNK{}), get<1>(TileShape_MNK{})),
make_stride(decltype(get<1>(TileShape_MNK{})){}, _1{}))));
using SmemCopyAtomdKV = Copy_Atom<
std::conditional_t<
ArchTag::kMinComputeCapability >= 90,
std::conditional_t<!dKV_swapAB, cute::SM90_U32x4_STSM_N, cute::SM90_U16x8_STSM_T>,
AutoVectorizingCopyWithAssumedAlignment<128>
>,
Element>;
static constexpr size_t SmemAlignmentdKV = ArchTag::kMinComputeCapability >= 90 ? cutlass::detail::alignment_for_swizzle(SmemLayoutdKV{}) : 128;
static_assert(SmemAlignmentdKV >= 128, "Require at least 128B alignment");
struct TensorStorage : cute::aligned_struct<SmemAlignmentdKV> {
cute::array_aligned<Element, cute::cosize_v<SmemLayoutdKV>, SmemAlignmentdKV> smem_dk;
cute::array_aligned<Element, cute::cosize_v<SmemLayoutdKV>, SmemAlignmentdKV> smem_dv;
};
using ShapedKV = cute::Shape<int32_t, int32_t, int32_t, int32_t>; // (seqlen_k, d, head, batch)
using StridedKV = cute::Stride<int64_t, _1, int64_t, int64_t>;
using TMA_dKV = std::conditional_t<
Use_TMA,
decltype(make_tma_copy(
GmemTiledCopydKVTMA{},
make_tensor(make_gmem_ptr(static_cast<Element*>(nullptr)), ShapedKV{}, StridedKV{}),
SmemLayoutdKVTMA{},
select<1, 2>(TileShape_MNK{}),
_1{})), // no mcast for dKV
std::nullptr_t
>;
// Host side kernel arguments
struct Arguments {
Element* ptr_dK;
ShapedKV const shape_dK;
StridedKV const stride_dK;
Element* ptr_dV;
ShapedKV const shape_dV;
StridedKV const stride_dV;
int const num_heads_q;
int* dk_semaphore;
int* dv_semaphore;
int const* cu_seqlens;
int const* seqused;
};
// Device side kernel params
struct Params {
Element* ptr_dK;
ShapedKV const shape_dK;
StridedKV const stride_dK;
Element* ptr_dV;
ShapedKV const shape_dV;
StridedKV const stride_dV;
TMA_dKV tma_store_dK, tma_store_dV;
int const* cu_seqlens = nullptr;
int const* seqused = nullptr;
};
static Params
to_underlying_arguments(Arguments const& args) {
Tensor mdK = make_tensor(make_gmem_ptr(args.ptr_dK), args.shape_dK, args.stride_dK);
Tensor mdV = make_tensor(make_gmem_ptr(args.ptr_dV), args.shape_dV, args.stride_dV);
TMA_dKV tma_store_dK = [&] {
if constexpr (Use_TMA) {
return make_tma_copy(GmemTiledCopydKVTMA{}, mdK, SmemLayoutdKVTMA{}, select<1, 2>(TileShape_MNK{}), _1{}); // no mcast for dKV
} else {
return nullptr;
}
}();
TMA_dKV tma_store_dV = [&] {
if constexpr (Use_TMA) {
return make_tma_copy(GmemTiledCopydKVTMA{}, mdV, SmemLayoutdKVTMA{}, select<1, 2>(TileShape_MNK{}), _1{}); // no mcast for dKV
} else {
return nullptr;
}
}();
return {args.ptr_dK, args.shape_dK, args.stride_dK, args.ptr_dV, args.shape_dV, args.stride_dV,
tma_store_dK, tma_store_dV, args.cu_seqlens, args.seqused};
}
/// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance
CUTLASS_DEVICE
static void prefetch_tma_descriptors(Params const& params) {
if constexpr (Use_TMA) {
cute::prefetch_tma_descriptor(params.tma_store_dK.get_tma_descriptor());
cute::prefetch_tma_descriptor(params.tma_store_dV.get_tma_descriptor());
}
}
template <typename SharedStorage, typename FrgTensorO, typename TiledMma>
CUTLASS_DEVICE void
store(Params const& params,
FrgTensorO const& tdKrdK,
FrgTensorO const& tdVrdV,
SharedStorage& shared_storage,
TiledMma tiled_mma,
int thread_idx,
cute::tuple<int32_t, int32_t, int32_t> const& block_coord
) {
auto [n_block, bidh, bidb] = block_coord;
Tensor sdK = cute::as_position_independent_swizzle_tensor(make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_dk.data()), SmemLayoutdKV{}));
Tensor sdV = cute::as_position_independent_swizzle_tensor(make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_dv.data()), SmemLayoutdKV{}));
Tensor sdKt = cute::as_position_independent_swizzle_tensor(make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_dk.data()), SmemLayoutdKVt{}));
Tensor sdVt = cute::as_position_independent_swizzle_tensor(make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_dv.data()), SmemLayoutdKVt{}));
auto smem_tiled_copy_dKV = make_tiled_copy_C(SmemCopyAtomdKV{}, tiled_mma);
auto smem_thr_copy_dKV = smem_tiled_copy_dKV.get_thread_slice(thread_idx);
Tensor tdVrdV_out = make_tensor_like<Element>(tdVrdV);
flash::convert_type_out(tdVrdV, tdVrdV_out);
Tensor tdKrdK_out = make_tensor_like<Element>(tdKrdK);
flash::convert_type_out(tdKrdK, tdKrdK_out);
Tensor taccdKrdK = smem_thr_copy_dKV.retile_S(tdKrdK_out); // ((Atom,AtomNum), MMA_M, MMA_N)
Tensor taccdVrdV = smem_thr_copy_dKV.retile_S(tdVrdV_out); // ((Atom,AtomNum), MMA_M, MMA_N)
// if (blockIdx.x == 0 && threadIdx.x == 128) { print(smem_thr_copy_dKV); print(sdK); printf("\n"); print(sdKt); printf("\n"); }
Tensor taccdKsdK = smem_thr_copy_dKV.partition_D(cute::conditional_return<!dKV_swapAB>(sdK, sdKt)); // ((Atom,AtomNum),PIPE_M,PIPE_N)
Tensor taccdVsdV = smem_thr_copy_dKV.partition_D(cute::conditional_return<!dKV_swapAB>(sdV, sdVt)); // ((Atom,AtomNum),PIPE_M,PIPE_N)
// Make sure all WGs have finished reading K and V
flash::named_barrier_sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
cute::copy(smem_tiled_copy_dKV, taccdVrdV, taccdVsdV);
cute::copy(smem_tiled_copy_dKV, taccdKrdK, taccdKsdK);
if constexpr (Use_TMA) {
cutlass::arch::fence_view_async_shared(); // ensure smem writes are visible to TMA
cutlass::arch::NamedBarrier::arrive(NumEpilogueThreads + cutlass::NumThreadsPerWarp,
cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
Tensor mdK = params.tma_store_dK.get_tma_tensor(params.shape_dK);
Tensor mdV = params.tma_store_dV.get_tma_tensor(params.shape_dV);
Tensor gdK = local_tile(mdK(_, _, bidh, bidb), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{})); // (M, K)
Tensor gdV = local_tile(mdV(_, _, bidh, bidb), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{})); // (M, K)
auto block_tma_dK = params.tma_store_dK.get_slice(_0{});
auto block_tma_dV = params.tma_store_dV.get_slice(_0{});
Tensor tdKgdK = block_tma_dK.partition_D(gdK); // (TMA, TMA_M, TMA_K)
Tensor tdKsdK = block_tma_dK.partition_S(sdK); // (TMA, TMA_M, TMA_K)
Tensor tdVgdV = block_tma_dV.partition_D(gdV); // (TMA, TMA_M, TMA_K)
Tensor tdVsdV = block_tma_dV.partition_S(sdV); // (TMA, TMA_M, TMA_K)
int warp_idx_sync = __shfl_sync(0xffffffff, thread_idx / cutlass::NumThreadsPerWarp, 0);
if (warp_idx_sync == NumEpilogueThreads / cutlass::NumThreadsPerWarp - 1) {
cutlass::arch::NamedBarrier::sync(NumEpilogueThreads + cutlass::NumThreadsPerWarp,
cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
if (cute::elect_one_sync()) {
cute::copy(params.tma_store_dV, tdVsdV, tdVgdV);
cute::copy(params.tma_store_dK, tdKsdK, tdKgdK);
tma_store_arrive();
}
}
tma_store_wait<0>();
// // Tell warp 0 that smem_k and smem_v are ready
// cutlass::arch::NamedBarrier::arrive(NumEpilogueThreads + cutlass::NumThreadsPerWarp, static_cast<uint32_t>(BwdNamedBarriers::KVEmpty) /*id*/);
} else {
flash::named_barrier_sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
static constexpr int kBlockN = get<1>(TileShape_MNK{});
flash::SeqlenInfo<Varlen, kBlockN> seqlen_info{bidb, size<0>(params.shape_dK), params.cu_seqlens, params.seqused};
bool const is_varlen = Varlen && params.cu_seqlens;
Tensor mdK = make_tensor(make_gmem_ptr(params.ptr_dK), params.shape_dK, params.stride_dK)(_, _, bidh, !is_varlen ? bidb : 0);
Tensor gdK = local_tile(cute::domain_offset(make_coord(seqlen_info.offset, _0{}), mdK), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{})); // (M, K)
Tensor mdV = make_tensor(make_gmem_ptr(params.ptr_dV), params.shape_dV, params.stride_dV)(_, _, bidh, !is_varlen ? bidb : 0);
Tensor gdV = local_tile(cute::domain_offset(make_coord(seqlen_info.offset, _0{}), mdV), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{})); // (M, K)
GmemTiledCopydKV gmem_tiled_copy_dKV;
auto gmem_thr_copy_dKV = gmem_tiled_copy_dKV.get_thread_slice(thread_idx);
Tensor tdKVgdV = gmem_thr_copy_dKV.partition_D(gdV);
Tensor tdKVsdV = gmem_thr_copy_dKV.partition_S(sdV); // (TMA, TMA_M, TMA_K)
Tensor tdKVgdK = gmem_thr_copy_dKV.partition_D(gdK);
Tensor tdKVsdK = gmem_thr_copy_dKV.partition_S(sdK); // (TMA, TMA_M, TMA_K)
Tensor tdKVrdV = make_fragment_like(tdKVgdV);
Tensor tdKVrdK = make_fragment_like(tdKVgdK);
Tensor cdKV = cute::make_identity_tensor(select<1, 2>(TileShape_MNK{})); // (BLK_N,BLK_K) -> (blk_n,blk_k)
// Repeat the partitioning with identity layouts
Tensor tdKVcdKV = gmem_thr_copy_dKV.partition_D(cdKV);
Tensor tdKVpdV = make_tensor<bool>(make_shape(size<2>(tdKVgdV)));
Tensor tdKVpdK = make_tensor<bool>(make_shape(size<2>(tdKVgdK)));
#pragma unroll
for (int k = 0; k < size(tdKVpdV); ++k) { tdKVpdV(k) = get<1>(tdKVcdKV(_0{}, _0{}, k)) < get<1>(params.shape_dV); }
#pragma unroll
for (int k = 0; k < size(tdKVpdK); ++k) { tdKVpdK(k) = get<1>(tdKVcdKV(_0{}, _0{}, k)) < get<1>(params.shape_dK); }
// Need to check OOB when reading from smem if kBlockN isn't evenly tiled
static constexpr bool EvenN = kBlockN % CUTE_STATIC_V(size<0>(GmemLayoutAtom{})) == 0;
flash::copy</*Is_even_MN=*/EvenN, /*Is_even_K=*/true, /*Clear_OOB_MN=*/false>(
gmem_tiled_copy_dKV, tdKVsdV, tdKVrdV, tdKVcdKV, tdKVpdV, kBlockN);
flash::copy</*Is_even_MN=*/EvenN, /*Is_even_K=*/true, /*Clear_OOB_MN=*/false>(
gmem_tiled_copy_dKV, tdKVsdK, tdKVrdK, tdKVcdKV, tdKVpdK, kBlockN);
// // Tell warp 0 that smem_k and smem_v are ready
// cutlass::arch::fence_view_async_shared(); // ensure smem reads are done before next TMA to smem_k/v
// flash::named_barrier_arrive(NumEpilogueThreads + cutlass::NumThreadsPerWarp, static_cast<uint32_t>(BwdNamedBarriers::KVEmpty) /*id*/);
// Construct identity layout for gdKV
// Clear_OOB_K must be false since we don't want to write zeros to gmem
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKVrdV, tdKVgdV, tdKVcdKV, tdKVpdV, std::min(seqlen_info.seqlen - n_block * kBlockN, kBlockN)
);
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKVrdK, tdKVgdK, tdKVcdKV, tdKVpdK, std::min(seqlen_info.seqlen - n_block * kBlockN, kBlockN)
);
}
}
CUTLASS_DEVICE void
store_tail() {
// if constexpr (Use_TMA) { tma_store_wait<0>(); }
}
// Write 0 to dK and dV
CUTLASS_DEVICE void
store_zero(
Params const& params,
int thread_idx,
cute::tuple<int32_t, int32_t, int32_t> const& block_coord
) {
static constexpr int kBlockN = get<1>(TileShape_MNK{});
auto [n_block, bidh, bidb] = block_coord;
flash::SeqlenInfo<Varlen, kBlockN> seqlen_info{bidb, size<0>(params.shape_dK), params.cu_seqlens, params.seqused};
bool const is_varlen = Varlen && params.cu_seqlens;
Tensor mdK = make_tensor(make_gmem_ptr(params.ptr_dK), params.shape_dK, params.stride_dK)(_, _, bidh, !is_varlen ? bidb : 0);
Tensor gdK = local_tile(cute::domain_offset(make_coord(seqlen_info.offset, _0{}), mdK), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{})); // (M, K)
Tensor mdV = make_tensor(make_gmem_ptr(params.ptr_dV), params.shape_dV, params.stride_dV)(_, _, bidh, !is_varlen ? bidb : 0);
Tensor gdV = local_tile(cute::domain_offset(make_coord(seqlen_info.offset, _0{}), mdV), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{})); // (M, K)
GmemTiledCopydKV gmem_tiled_copy_dKV;
auto gmem_thr_copy_dKV = gmem_tiled_copy_dKV.get_thread_slice(thread_idx);
Tensor tdKVgdK = gmem_thr_copy_dKV.partition_D(gdK);
Tensor tdKVgdV = gmem_thr_copy_dKV.partition_D(gdV);
Tensor tdKVrdKV = make_fragment_like(tdKVgdK);
clear(tdKVrdKV);
// Construct identity layout for gdKV
Tensor cdKV = cute::make_identity_tensor(select<1, 2>(TileShape_MNK{})); // (BLK_M,BLK_K) -> (blk_m,blk_k)
// Repeat the partitioning with identity layouts
Tensor tdKVcdKV = gmem_thr_copy_dKV.partition_D(cdKV);
Tensor tdKVpdK = make_tensor<bool>(make_shape(size<2>(tdKVgdK)));
Tensor tdKVpdV = make_tensor<bool>(make_shape(size<2>(tdKVgdV)));
#pragma unroll
for (int k = 0; k < size(tdKVpdK); ++k) { tdKVpdK(k) = get<1>(tdKVcdKV(_0{}, _0{}, k)) < get<1>(params.shape_dK); }
#pragma unroll
for (int k = 0; k < size(tdKVpdV); ++k) { tdKVpdV(k) = get<1>(tdKVcdKV(_0{}, _0{}, k)) < get<1>(params.shape_dV); }
// Clear_OOB_K must be false since we don't want to write zeros to gmem
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKVrdKV, tdKVgdK, tdKVcdKV, tdKVpdK, seqlen_info.seqlen - n_block * kBlockN
);
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKVrdKV, tdKVgdV, tdKVcdKV, tdKVpdV, seqlen_info.seqlen - n_block * kBlockN
);
}
};
template <class TileShape_MNK_, class ElementAccum, class ArchTag_,
int NumEpilogueThreads_, bool Varlen_, bool Deterministic>
struct CollectiveEpilogueBwdGQA {
using TileShape_MNK = TileShape_MNK_;
using Element = ElementAccum;
using ArchTag = ArchTag_;
static constexpr int NumEpilogueThreads = NumEpilogueThreads_;
static constexpr bool Varlen = Varlen_;
static constexpr bool Use_TMA = ArchTag::kMinComputeCapability >= 90;
static_assert(ArchTag::kMinComputeCapability >= 80);
static constexpr int kBlockN = get<1>(TileShape_MNK{});
static constexpr int kHeadDim = get<2>(TileShape_MNK{});
static_assert(NumEpilogueThreads % cutlass::NumThreadsPerWarp == 0, "NumEpilogueThreads must be a multiple of NumThreadsPerWarp");
static constexpr int NumWarpGroups = NumEpilogueThreads / cutlass::NumThreadsPerWarpGroup;
// Thread layout, 256 or 384 threads per row
// We split into NumWarpGroups so that we can use the same postprocessing kernel as dQ
using R2SLayoutAtomdKVaccum = Layout<Shape<Int<cutlass::NumThreadsPerWarpGroup>, Int<NumWarpGroups>>>;
using R2STiledCopydKVaccum = decltype(make_tiled_copy(Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, ElementAccum>{}, R2SLayoutAtomdKVaccum{},
Layout<Shape < _4>>{})); // Val layout, 4 vals per store
// For Sm80
using R2GLayoutAtomdKVaccum = Layout<Shape<Int<NumEpilogueThreads>>>;
using R2GTiledCopydKVaccum = decltype(make_tiled_copy(Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, ElementAccum>{}, R2GLayoutAtomdKVaccum{},
Layout<Shape < _1>>{})); // Val layout, 1 vals per store
using SmemLayoutdKVaccum = Layout<Shape<Int<kBlockN * kHeadDim / NumWarpGroups>, Int<NumWarpGroups>>>;
using SmemLayoutdKVaccumFlat = Layout<Shape<Int<kBlockN * kHeadDim>>>;
// Strangely without this SmemAlignment, the total smem for hdim 128 (80 x 128) is 228KB even though we
// only need 227KB. We use the same alignment as the non-GQA epilogue to avoid this issue.
static constexpr int SmemAlignment = kHeadDim % 64 == 0 ? 1024 : (kHeadDim % 32 == 0 ? 512 : 256);
struct TensorStorageTMA : cute::aligned_struct<SmemAlignment> {
cute::array_aligned<ElementAccum, cute::cosize_v<SmemLayoutdKVaccum>, SmemAlignment> smem_dkv;
};
struct TensorStorageSTG {
cute::array<ElementAccum, 0> smem_dkv;
};
using TensorStorage = std::conditional_t<Use_TMA, TensorStorageTMA, TensorStorageSTG>;
using ShapedKV = cute::Shape<int32_t, int32_t, int32_t>; // (seqlen_k_rounded * d, head, batch)
using StridedKV = cute::Stride<_1, int64_t, int64_t>;
// Host side kernel arguments
struct Arguments {
ElementAccum* ptr_dKaccum;
ShapedKV const shape_dKaccum;
StridedKV const stride_dKaccum;
ElementAccum* ptr_dVaccum;
ShapedKV const shape_dVaccum;
StridedKV const stride_dVaccum;
int num_heads_q;
int* dk_semaphore;
int* dv_semaphore;
int const* cu_seqlens;
int const* seqused;
};
// Device side kernel params
struct Params {
ElementAccum* ptr_dKaccum;
ShapedKV const shape_dKaccum;
StridedKV const stride_dKaccum;
ElementAccum* ptr_dVaccum;
ShapedKV const shape_dVaccum;
StridedKV const stride_dVaccum;
cutlass::FastDivmod qhead_per_khead_divmod;
int* dk_semaphore;
int* dv_semaphore;
int const* cu_seqlens = nullptr;
int const* seqused = nullptr;
};
static Params
to_underlying_arguments(Arguments const& args) {
if constexpr (Deterministic) {
assert(args.dk_semaphore != nullptr);
assert(args.dv_semaphore != nullptr);
}
return {args.ptr_dKaccum, args.shape_dKaccum, args.stride_dKaccum, args.ptr_dVaccum, args.shape_dVaccum, args.stride_dVaccum,
cutlass::FastDivmod(cute::ceil_div(args.num_heads_q, get<1>(args.shape_dKaccum))),
args.dk_semaphore, args.dv_semaphore,
args.cu_seqlens, args.seqused};
}
/// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance
CUTLASS_DEVICE
static void prefetch_tma_descriptors(Params const& params) {
}
template <typename SharedStorage, typename FrgTensorO, typename TiledMma>
CUTLASS_DEVICE void
store(Params const& params,
FrgTensorO const& tdKrdK,
FrgTensorO const& tdVrdV,
SharedStorage& shared_storage,
TiledMma tiled_mma,
int thread_idx,
cute::tuple<int32_t, int32_t, int32_t> const& block_coord
) {
auto [n_block, bidh, bidb] = block_coord;
int bidh_idx_in_group;
int bidh_kv = params.qhead_per_khead_divmod.divmod(bidh_idx_in_group, bidh);
Tensor sdKV = make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_dkv.data()), SmemLayoutdKVaccum{});
Tensor sdKV_flat = make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_dkv.data()), SmemLayoutdKVaccumFlat{});
static constexpr int dKV_TMA_num_bytes = CUTE_STATIC_V(size(sdKV_flat)) * sizeof(ElementAccum);
flash::SeqlenInfo<Varlen, kBlockN> seqlen_info{bidb, size<0>(params.shape_dKaccum), params.cu_seqlens, params.seqused};
bool const is_varlen = Varlen && params.cu_seqlens;
Tensor mdKaccum = make_tensor(make_gmem_ptr(params.ptr_dKaccum), params.shape_dKaccum, params.stride_dKaccum)(_, bidh_kv, !is_varlen ? bidb : 0);
Tensor mdVaccum = make_tensor(make_gmem_ptr(params.ptr_dVaccum), params.shape_dVaccum, params.stride_dVaccum)(_, bidh_kv, !is_varlen ? bidb : 0);
Tensor gdKaccum = local_tile(domain_offset(make_coord(seqlen_info.offset_padded * kHeadDim), mdKaccum), Shape<Int<kBlockN * kHeadDim>>{}, make_coord(n_block)); // (M * K)
Tensor gdVaccum = local_tile(domain_offset(make_coord(seqlen_info.offset_padded * kHeadDim), mdVaccum), Shape<Int<kBlockN * kHeadDim>>{}, make_coord(n_block)); // (M * K)
R2STiledCopydKVaccum r2s_tiled_copy_dKVaccum;
auto r2s_thr_copy_dKVaccum = r2s_tiled_copy_dKVaccum.get_thread_slice(thread_idx);
Tensor tdKVsdKVaccum = r2s_thr_copy_dKVaccum.partition_D(sdKV);
// Only used if !Use_TMA
R2GTiledCopydKVaccum r2g_tiled_copy_dKVaccum;
auto r2g_thr_copy_dKVaccum = r2g_tiled_copy_dKVaccum.get_thread_slice(thread_idx);
// Make sure all WGs have finished reading K and V, otherwise we get racy dQ
// because smem_q could be changed.
flash::named_barrier_sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
if constexpr (Use_TMA) {
Tensor taccdKVrdV = r2s_thr_copy_dKVaccum.retile_S(tdVrdV); // ((Atom,AtomNum), MMA_M, MMA_N)
cute::copy(r2s_tiled_copy_dKVaccum, taccdKVrdV, tdKVsdKVaccum);
}
// int const num_batch = params.num_batch;
int const num_batch = get<2>(params.shape_dKaccum);
int const num_head_kv = get<1>(params.shape_dKaccum);
int *lock_ptr = !Deterministic ? nullptr : params.dv_semaphore + bidb * num_head_kv + bidh_kv;
using Barrier = cutlass::GenericBarrier<cutlass::detail::SyncwarpSync>;
// if (thread_idx == 0) { printf("blockIdx.x = %d, blockIdx.y = %d, blockIdx.z = %d, bidb = %d, bidh_kv = %d, lock_ptr = %p, dv_semaphore = %p, num_batch = %d, num_head_kv = %d, n_block = %d, bihd_idx_in_group = %d\n", blockIdx.x, blockIdx.y, blockIdx.z, bidb, bidh_kv, lock_ptr, params.dv_semaphore, num_batch, num_head_kv, n_block, bidh_idx_in_group);}
if constexpr (Deterministic) {
Barrier::wait_eq(lock_ptr, thread_idx, n_block * num_batch * num_head_kv, bidh_idx_in_group);
}
// if (thread_idx == 0) { printf("After barrier blockIdx.x = %d, blockIdx.y = %d, blockIdx.z = %d, bidb = %d, bidh_kv = %d, lock_ptr = %p, dv_semaphore = %p\n", blockIdx.x, blockIdx.y, blockIdx.z, bidb, bidh_kv, lock_ptr, params.dv_semaphore);}
if constexpr (Use_TMA) {
cutlass::arch::fence_view_async_shared();
cutlass::arch::NamedBarrier::sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
if (thread_idx == 0) {
SM90_BULK_REDUCE_ADD::copy(raw_pointer_cast(sdKV_flat.data()), raw_pointer_cast(gdVaccum.data()), dKV_TMA_num_bytes, static_cast<uint64_t>(TMA::CacheHintSm90::EVICT_LAST));
tma_store_arrive();
tma_store_wait<0>();
}
} else {
Tensor tdVrdV_atomic = r2g_thr_copy_dKVaccum.retile_S(tdVrdV);
Tensor tdVgdV_atomic = r2g_thr_copy_dKVaccum.partition_D(gdVaccum);
static_assert(CUTE_STATIC_V(size(tdVrdV_atomic)) == CUTE_STATIC_V(size(tdVgdV_atomic)));
#pragma unroll
for (int i = 0; i < size(tdVrdV_atomic); ++i) { atomicAdd(&tdVgdV_atomic(i), tdVrdV_atomic(i)); }
}
if constexpr (Deterministic) {
Barrier::arrive_inc(lock_ptr, thread_idx, n_block * num_batch * num_head_kv);
}
if constexpr (Use_TMA) {
cutlass::arch::NamedBarrier::sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
Tensor taccdKVrdK = r2s_thr_copy_dKVaccum.retile_S(tdKrdK); // ((Atom,AtomNum), MMA_M, MMA_N)
cute::copy(r2s_tiled_copy_dKVaccum, taccdKVrdK, tdKVsdKVaccum);
}
lock_ptr = !Deterministic ? nullptr : params.dk_semaphore + bidb * num_head_kv + bidh_kv;
// if (thread_idx == 0) { printf("blockIdx.x = %d, blockIdx.y = %d, blockIdx.z = %d, bidb = %d, bidh_kv = %d, lock_ptr = %p, dk_semaphore = %p, num_batch = %d, num_head_kv = %d, n_block = %d, bihd_idx_in_group = %d\n", blockIdx.x, blockIdx.y, blockIdx.z, bidb, bidh_kv, lock_ptr, params.dk_semaphore, num_batch, num_head_kv, n_block, bidh_idx_in_group);}
if constexpr (Deterministic) {
Barrier::wait_eq(lock_ptr, thread_idx, n_block * num_batch * num_head_kv, bidh_idx_in_group);
}
// if (thread_idx == 0) { printf("After barrier blockIdx.x = %d, blockIdx.y = %d, blockIdx.z = %d, bidb = %d, bidh_kv = %d, lock_ptr = %p, dk_semaphore = %p\n", blockIdx.x, blockIdx.y, blockIdx.z, bidb, bidh_kv, lock_ptr, params.dk_semaphore);}
if constexpr (Use_TMA) {
cutlass::arch::fence_view_async_shared();
cutlass::arch::NamedBarrier::sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
if (thread_idx == 0) {
SM90_BULK_REDUCE_ADD::copy(raw_pointer_cast(sdKV_flat.data()), raw_pointer_cast(gdKaccum.data()), dKV_TMA_num_bytes, static_cast<uint64_t>(TMA::CacheHintSm90::EVICT_LAST));
tma_store_arrive();
tma_store_wait<0>();
}
} else {
Tensor tdKrdK_atomic = r2g_thr_copy_dKVaccum.retile_S(tdKrdK);
Tensor tdKgdK_atomic = r2g_thr_copy_dKVaccum.partition_D(gdKaccum);
static_assert(CUTE_STATIC_V(size(tdKrdK_atomic)) == CUTE_STATIC_V(size(tdKgdK_atomic)));
#pragma unroll
for (int i = 0; i < size(tdKrdK_atomic); ++i) { atomicAdd(&tdKgdK_atomic(i), tdKrdK_atomic(i)); }
}
if constexpr (Deterministic) {
Barrier::arrive_inc(lock_ptr, thread_idx, n_block * num_batch * num_head_kv);
}
// // Tell warp 0 that smem_k and smem_v are ready
// flash::named_barrier_arrive(NumEpilogueThreads + cutlass::NumThreadsPerWarp, static_cast<uint32_t>(BwdNamedBarriers::KVEmpty) /*id*/);
}
CUTLASS_DEVICE void
store_tail() {
}
// Write 0 to dK and dV
CUTLASS_DEVICE void
store_zero(
Params const& params,
int thread_idx,
cute::tuple<int32_t, int32_t, int32_t> const& block_coord
) {
// Don't need to do anything since dKaccum and dVaccum are already zero-initialized
}
};
} // namespace flash
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