epilogue_fwd.hpp

/******************************************************************************
 * Copyright (c) 2024, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao.
 ******************************************************************************/

#pragma once

#include <cutlass/cutlass.h>
#include <cutlass/fast_math.h>  // For FastDivMod
#include "cute/tensor.hpp"

#include "cutlass/gemm/collective/builders/sm90_common.inl"
#include "cutlass/epilogue/collective/builders/sm90_common.inl"

#include "seqlen.h"
#include "named_barrier.hpp"
#include "pack_gqa.h"
#include "utils.h"

namespace flash {

using namespace cute;

template <class TileShape_MNK_, class ClusterShape_, class Element_, class ArchTag_,
          int NumEpilogueThreads_, bool Varlen_, bool PackGQA_, bool FP8PermuteCol=false>
struct CollectiveEpilogueFwd {

    using TileShape_MNK = TileShape_MNK_;
    using ClusterShape = ClusterShape_;
    using Element = Element_;
    using ArchTag = ArchTag_;
    static constexpr int NumEpilogueThreads = NumEpilogueThreads_;
    static constexpr bool Varlen = Varlen_;
    static constexpr bool PackGQA = PackGQA_;
    static constexpr bool Use_smem = sizeof(Element) <= 2;
    static constexpr bool Use_TMA_O = ArchTag::kMinComputeCapability >= 90 && !Varlen && Use_smem && !PackGQA;

    static_assert(ArchTag::kMinComputeCapability >= 80);
    static_assert(ArchTag::kMinComputeCapability >= 90 || CUTE_STATIC_V(size(ClusterShape{})) == 1);

    static constexpr int kBlockM = get<0>(TileShape_MNK{});
    static constexpr int kHeadDim = get<2>(TileShape_MNK{});

    using GmemTiledCopyOTMA = cute::SM90_TMA_STORE;

    // These are for storing the output tensor without TMA (e.g., for setting output to zero)
    static constexpr int kGmemElemsPerStore = sizeof(cute::uint128_t) / sizeof(Element);
    static_assert(kHeadDim % kGmemElemsPerStore == 0, "Headdim must be a multiple of kGmemElemsPerStore");
    // We want each "row" to have 64 elements (128 bytes, i.e. 1 cache line). We want each thread to have 4 elements
    // in the M direction and 2 elements in the K direction. In the case of PackGQA, this reduces the number of times
    // we need to call divmod.
    static constexpr int kBytePerRow = kHeadDim * sizeof(Element);
    static constexpr int kBlockKGmem = (kBytePerRow % 128 == 0 ? 128 : (kBytePerRow % 64 == 0 ? 64 : 32)) / sizeof(Element);
    // static constexpr int kBlockKGmem = kHeadDim % 128 == 0 ? 128 : (kHeadDim % 64 == 0 ? 64 : 32);
    // static constexpr int kGmemThreadsPerRow = cutlass::gcd(kHeadDim / kGmemElemsPerStore, NumEpilogueThreads);
    static constexpr int kGmemThreadsPerRow = kBlockKGmem / kGmemElemsPerStore;
    // If PackGQA, we split the work of compute O_ptr among threads in the same row, so we need this to within a warp
    static_assert(cutlass::NumThreadsPerWarp % kGmemThreadsPerRow == 0);
    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>>;
    static_assert(kBlockM % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0, "kBlockM must be a multiple of NumEpilogueThreads / kGmemThreadsPerRow");
    using GmemTiledCopyO = decltype(
        make_tiled_copy(Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, Element>{},
                        GmemLayoutAtom{},
                        Layout<Shape<_1, Int<kGmemElemsPerStore>>>{}));  // Val layout, 8 or 16 vals per store

    using SmemLayoutAtomOTMA = decltype(cutlass::gemm::collective::detail::ss_smem_selector<GMMA::Major::K, Element,
        decltype(cute::get<0>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
    using SmemLayoutOTMA = decltype(tile_to_shape(SmemLayoutAtomOTMA{}, select<0, 2>(TileShape_MNK{})));
    static constexpr int kSwizzle = kBlockKGmem == 128 ? 4 : (kBlockKGmem == 64 ? 3 : (kBlockKGmem == 32 ? 2 : 1));
    static constexpr int kSwizzleBase = sizeof(Element) == 4 ? 2 : (sizeof(Element) == 2 ? 3 : 4);
    using SmemLayoutAtomO = decltype(
        composition(Swizzle<kSwizzle, kSwizzleBase, kSwizzleBase>{},
                    Layout<Shape<_8, Int<kBlockKGmem>>,
                           Stride<Int<kBlockKGmem>, _1>>{}));
    using SmemLayoutOSTS = decltype(tile_to_shape(SmemLayoutAtomO{}, select<0, 2>(TileShape_MNK{})));
    using SmemLayoutO = std::conditional_t<ArchTag::kMinComputeCapability >= 90, SmemLayoutOTMA, SmemLayoutOSTS>;

    using ShapeO = cute::Shape<int32_t, int32_t, int32_t, int32_t, int32_t>;  // (seqlen_q, d, head, batch, num_splits)
    using StrideO = cute::Stride<int64_t, _1, int64_t, int64_t, int64_t>;
    using StrideLSE = cute::Stride<_1, int64_t, int64_t, int64_t>;            // (seqlen_q, head, batch, num_splits)
    // ((qhead_per_khead, seqlen_q), d, nheads_kv, batch, num_splits)
    using ShapeOPacked = std::conditional_t<!PackGQA, ShapeO, cute::Shape<cute::Shape<int32_t, int32_t>, int32_t, int32_t, int32_t, int32_t>>;
    using StrideOPacked = std::conditional_t<!PackGQA, StrideO, cute::Stride<cute::Stride<int64_t, int64_t>, _1, int64_t, int64_t, int64_t>>;
    // ((qhead_per_khead, seqlen_q), nheads_kv, batch, num_splits)
    using ShapeLSEPacked = std::conditional_t<!PackGQA, cute::Shape<int32_t, int32_t, int32_t, int32_t>, cute::Shape<cute::Shape<int32_t, int32_t>, int32_t, int32_t, int32_t>>;
    using StrideLSEPacked = std::conditional_t<!PackGQA, StrideLSE, cute::Stride<cute::Stride<int64_t, _1>, int64_t, int64_t, int64_t>>;

    using CopyOpR2S = std::conditional_t<
        ArchTag::kMinComputeCapability >= 90,
        // cute::SM90_U32x4_STSM_N if Element size is 2 bytes (fp16, bf16)
        decltype(cutlass::epilogue::collective::detail::sm90_get_smem_store_op_for_accumulator<StrideO, Element>()),
        AutoVectorizingCopyWithAssumedAlignment<128>
    >;
    using SmemCopyAtomO = Copy_Atom<CopyOpR2S, Element>;

    // static constexpr size_t SmemAlignmentO = cutlass::detail::alignment_for_swizzle(SmemLayoutO{});
    // static_assert(SmemAlignmentO >= 128, "Require at least 128B alignment");
    // struct TensorStorage : cute::aligned_struct<SmemAlignmentO> {
    //     cute::array_aligned<Element, Use_smem ? cute::cosize_v<SmemLayoutO> : 0, SmemAlignmentO> smem_o;
    // };
    struct TensorStorage : cute::aligned_struct<128> {
        cute::array_aligned<Element, Use_smem ? cute::cosize_v<SmemLayoutO> : 0> smem_o;
    };

    using TMA_O = std::conditional_t<
        Use_TMA_O,
        decltype(make_tma_copy(
            GmemTiledCopyOTMA{},
            make_tensor(make_gmem_ptr(static_cast<Element*>(nullptr)), ShapeO{}, StrideO{}),
            SmemLayoutOTMA{},
            select<0, 2>(TileShape_MNK{}),
            _1{})),  // no mcast for O
        std::nullptr_t
    >;

    // Host side kernel arguments
    struct Arguments {
        Element* ptr_O;
        ShapeO const shape_O;
        StrideO const stride_O;
        float* ptr_LSE;
        StrideLSE const stride_LSE;
        int32_t const nheads_kv;
        int const* cu_seqlens = nullptr;
        int const* seqused = nullptr;
    };

    // Device side kernel params
    struct Params {
        Element* ptr_O;
        ShapeO const shape_O;
        StrideO const stride_O;
        ShapeOPacked const shape_O_packed;
        StrideOPacked const stride_O_packed;
        float* ptr_LSE;
        StrideLSE const stride_LSE;
        ShapeLSEPacked const shape_LSE_packed;
        StrideLSEPacked const stride_LSE_packed;
        cutlass::FastDivmod qhead_per_khead_divmod;
        TMA_O tma_store_O;
        int const* cu_seqlens = nullptr;
        int const* seqused = nullptr;
    };

    static Params
    to_underlying_arguments(Arguments const& args) {
        Tensor mO = make_tensor(make_gmem_ptr(args.ptr_O), args.shape_O, args.stride_O);
        TMA_O tma_store_O = [&]{
            if constexpr (Use_TMA_O) {
                return make_tma_copy(GmemTiledCopyOTMA{}, mO, SmemLayoutO{}, select<0, 2>(TileShape_MNK{}), _1{}); // no mcast
            } else {
                return nullptr;
            }
        }();
        // If PackGQA, reshape O to be ((qhead_per_khead, seqlen_q), head_size, nhead_k, batch_size, num_splits)
        int const qhead_per_khead = !PackGQA ? 1 : cute::ceil_div(get<2>(args.shape_O), args.nheads_kv);
        auto const shape_O_packed = cute::conditional_return<!PackGQA>(
            args.shape_O,
            make_shape(make_shape(qhead_per_khead, get<0>(args.shape_O)), get<1>(args.shape_O), args.nheads_kv, get<3>(args.shape_O), get<4>(args.shape_O))
        );
        auto const stride_O_packed = cute::conditional_return<!PackGQA>(
            args.stride_O,
            make_stride(make_stride(get<2>(args.stride_O), get<0>(args.stride_O)), get<1>(args.stride_O), get<2>(args.stride_O) * qhead_per_khead, get<3>(args.stride_O), get<4>(args.stride_O))
        );
        // If PackGQA, Reshape LSE to be ((qhead_per_khead, seqlen_q), nhead_k, batch_size, num_splits)
        auto const shape_LSE_packed = cute::conditional_return<!PackGQA>(
            select<0, 2, 3, 4>(args.shape_O),
            make_shape(make_shape(qhead_per_khead, get<0>(args.shape_O)), args.nheads_kv, get<3>(args.shape_O), get<4>(args.shape_O))
        );
        auto const stride_LSE_packed = cute::conditional_return<!PackGQA>(
            args.stride_LSE,
            make_stride(make_stride(get<1>(args.stride_LSE), get<0>(args.stride_LSE)), get<1>(args.stride_LSE) * qhead_per_khead, get<2>(args.stride_LSE), get<3>(args.stride_LSE))
        );
        return {args.ptr_O, args.shape_O, args.stride_O, shape_O_packed, stride_O_packed,
                args.ptr_LSE, args.stride_LSE, shape_LSE_packed, stride_LSE_packed,
                cutlass::FastDivmod(qhead_per_khead),
                tma_store_O, 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_O) {
            cute::prefetch_tma_descriptor(params.tma_store_O.get_tma_descriptor());
        }
    }

    template <typename SharedStorage, typename FrgTensorO, typename FrgTensorLSE, typename TiledMma>
    CUTLASS_DEVICE void
    store(Params const& params,
          FrgTensorO const& tOrO,
          FrgTensorLSE const& lse,
          SharedStorage& shared_storage,
          TiledMma tiled_mma,
          int thread_idx,
          cute::tuple<int32_t, int32_t, int32_t, int32_t> const& block_coord
          ) {

        auto [m_block, bidh, bidb, split_idx] = block_coord;
        Tensor sO = make_tensor(make_smem_ptr(shared_storage.tensors.epilogue.smem_o.data()), SmemLayoutO{});
        // Tensor sO_pi = cute::as_position_independent_swizzle_tensor(sO);

        Tensor tOrO_out = make_tensor_like<Element>(tOrO);
        flash::convert_type_out(tOrO, tOrO_out);
        if constexpr (FP8PermuteCol && (sizeof(Element) == 2 || sizeof(Element) == 4)) { flash::permute_output_fp8_Vcolmajor(tOrO_out); }

        // Make sure all WGs have finished reading V
        // Technically we don't need this if we're not using smem, but the mainloop makes the assumption that
        // all epilogue threads sync at least once during the epilogue (so that we can start loading Q with
        // cp.async if we need).
        flash::named_barrier_sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);

        // Step 1: Write O from rmem -> smem
        if constexpr (Use_smem) {
            auto smem_tiled_copy_O = make_tiled_copy_C(SmemCopyAtomO{}, tiled_mma);
            auto smem_thr_copy_O = smem_tiled_copy_O.get_thread_slice(thread_idx);
            Tensor taccOrO = smem_thr_copy_O.retile_S(tOrO_out);        // ((Atom,AtomNum), MMA_M, MMA_N)
            Tensor taccOsO = smem_thr_copy_O.partition_D(sO);     // ((Atom,AtomNum),PIPE_M,PIPE_N)
            // Tensor taccOsO = smem_thr_copy_O.partition_D(sO_pi);     // ((Atom,AtomNum),PIPE_M,PIPE_N)
            cute::copy(smem_tiled_copy_O, taccOrO, taccOsO);
            if constexpr (Use_TMA_O) {
                cutlass::arch::fence_view_async_shared(); // ensure smem writes are visible to TMA
                cutlass::arch::NamedBarrier::arrive(NumEpilogueThreads + cutlass::NumThreadsPerWarp,
                                                    cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
            } else {
                flash::named_barrier_sync(NumEpilogueThreads, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier);
            }
        } else {
            if constexpr (ArchTag::kMinComputeCapability >= 90) {
                #pragma unroll
                for (uint32_t cta_id = 0; cta_id < size(ClusterShape{}); ++cta_id) {
                    shared_storage.pipelines.barrier_O.arrive(cta_id);
                }
            }
        }

        flash::SeqlenInfo<Varlen, kBlockM> seqlen_info{bidb, size<0>(params.shape_O), params.cu_seqlens, params.seqused};
        bool is_varlen = Varlen && params.cu_seqlens;
        int offset_o = seqlen_info.offset;
        int seqlen_o = seqlen_info.seqlen;

        // Step 2: Write LSE from rmem -> gmem
        auto thread_mma = tiled_mma.get_thread_slice(thread_idx);
        // (MMA,MMA_M,MMA_K)
        Tensor taccOcO = thread_mma.partition_C(cute::make_identity_tensor(select<0, 2>(TileShape_MNK{})));
        static_assert(decltype(size<0, 0>(taccOcO))::value == 2);
        static_assert(decltype(size<0, 1>(taccOcO))::value == 2);
        Tensor taccOcO_rowcol = make_tensor(taccOcO.data(), flash::convert_layout_acc_rowcol(taccOcO.layout()));
        Tensor taccOcO_row = taccOcO_rowcol(_, _0{});
        CUTE_STATIC_ASSERT_V(size(lse) == size(taccOcO_row));                     // MMA_M

        using PackGQAt = flash::PackGQAManager<get<0>(TileShape_MNK{}), get<2>(TileShape_MNK{}), NumEpilogueThreads, Element>;

        Tensor mLSE = make_tensor(make_gmem_ptr(params.ptr_LSE + offset_o * get<0>(params.stride_LSE)), params.shape_LSE_packed, params.stride_LSE_packed)(_, bidh, !is_varlen ? bidb : 0, split_idx);
        // if (thread_idx == 0) { printf("Before LSE write, m_block: %d, bidh: %d, bidb: %d, split_idx: %d, offset_o: %d, seqlen_o: %d\n", m_block, bidh, bidb, split_idx, offset_o, seqlen_o); print(mLSE); printf("\n"); }
        if constexpr (!PackGQA) {
            #pragma unroll
            for (int mi = 0; mi < size(lse); ++mi) {
                int const row = m_block * kBlockM + get<0>(taccOcO_row(mi));
                if (get<1>(taccOcO_row(_0{})) == 0 && row < seqlen_o) { mLSE(row) = lse(mi); }
            }
        } else {
            PackGQAt::store_LSE(mLSE, lse, tiled_mma, params.qhead_per_khead_divmod, thread_idx, seqlen_o, m_block);
        }

        // Step 3: Write O from smem -> gmem
        if constexpr (Use_TMA_O) {
            Tensor mO = params.tma_store_O.get_tma_tensor(params.shape_O)(_, _, bidh, bidb, split_idx);
            Tensor gO = local_tile(mO, select<0, 2>(TileShape_MNK{}), make_coord(m_block, _0{}));  // (M, K)
            auto block_tma_O = params.tma_store_O.get_slice(_0{});
            Tensor tOgO = block_tma_O.partition_D(gO);  // (TMA, TMA_M, TMA_K)
            Tensor tOsO = block_tma_O.partition_S(sO); // (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_O, tOsO, tOgO);
                    tma_store_arrive();
                    tma_store_wait<0>();
                    #pragma unroll
                    for (uint32_t cta_id = 0; cta_id < size(ClusterShape{}); ++cta_id) {
                        shared_storage.pipelines.barrier_O.arrive(cta_id);
                    }
                }
            }
        } else {  // Don't use TMA in Varlen case since we don't want to overwrite the output of another sequence
            Tensor mO = make_tensor(make_gmem_ptr(params.ptr_O + offset_o * get<0>(params.stride_O)), params.shape_O_packed, params.stride_O_packed)(_, _, bidh, !is_varlen ? bidb : 0, split_idx);
            Tensor gO = local_tile(mO, select<0, 2>(TileShape_MNK{}), make_coord(m_block, _0{}));  // (M, K)
            // if (thread_idx == 0) { printf("Before O write, m_block: %d, bidh: %d, bidb: %d, split_idx: %d, offset_o: %d, seqlen_o: %d, mO_addr = %p, addr diff = %d\n", m_block, bidh, bidb, split_idx, offset_o, seqlen_o, mO.data(), reinterpret_cast<int>(&mO(0)) - reinterpret_cast<int>(params.ptr_O)); }
            if constexpr (Use_smem) {
                GmemTiledCopyO gmem_tiled_copy_O;
                auto gmem_thr_copy_O = gmem_tiled_copy_O.get_thread_slice(thread_idx);
                Tensor tOsO = gmem_thr_copy_O.partition_S(sO);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
                // Tensor tOsO = gmem_thr_copy_O.partition_S(sO_pi);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
                Tensor tOrO = make_fragment_like(tOsO);
                cute::copy(gmem_tiled_copy_O, tOsO, tOrO);
                if constexpr (ArchTag::kMinComputeCapability >= 90) {
                    cutlass::arch::fence_view_async_shared(); // ensure smem reads are done before next TMA to smem_v
                    #pragma unroll
                    for (uint32_t cta_id = 0; cta_id < size(ClusterShape{}); ++cta_id) {
                        shared_storage.pipelines.barrier_O.arrive(cta_id);
                    }
                }
                if constexpr (!PackGQA) {
                    // (BLK_M,BLK_K) -> (blk_m,blk_k)
                    Tensor tOcO = gmem_thr_copy_O.partition_D(cute::make_identity_tensor(select<0, 2>(TileShape_MNK{})));
                    Tensor tOpO = make_tensor<bool>(make_shape(size<2>(tOsO)));
                    #pragma unroll
                    for (int k = 0; k < size(tOpO); ++k) { tOpO(k) = get<1>(tOcO(_0{}, _0{}, k)) < get<1>(params.shape_O); }
                    Tensor tOgO = gmem_thr_copy_O.partition_D(gO);
                    // 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_O, tOrO, tOgO, tOcO, tOpO, seqlen_o - m_block * kBlockM
                    );
                } else {
                    // If PackGQA, we split the work of compute O_ptr among threads in the same row
                    PackGQAt::store_O(mO, tOrO, params.qhead_per_khead_divmod, thread_idx, seqlen_o, m_block);
                }
            } else {
                // We already arrived on barrier_O earlier
                if constexpr (!PackGQA) {
                    static constexpr int kGmemElemsPerStoreDirect = 2;
                    cute::Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, Element> gmem_copy_direct;
                    // Reshape acc from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
                    Tensor tOrO_rowcol = make_tensor(tOrO_out.data(), flash::convert_layout_acc_rowcol(tOrO.layout()));
                    Tensor tOrO_copy = cute::tiled_divide(tOrO_rowcol, Shape<_1, Int<kGmemElemsPerStoreDirect>>{});
                    Tensor tOgO = thread_mma.partition_C(gO);
                    Tensor tOgO_rowcol = make_tensor(tOgO.data(), flash::convert_layout_acc_rowcol(tOgO.layout()));
                    Tensor tOgO_copy = cute::tiled_divide(tOgO_rowcol, Shape<_1, Int<kGmemElemsPerStoreDirect>>{});
                    Tensor taccOcO_col = taccOcO_rowcol(_0{}, _);
                    #pragma unroll
                    for (int m = 0; m < size(taccOcO_row); ++m) {
                        if (get<0>(taccOcO_row(m)) < seqlen_o - m_block * kBlockM) {
                            #pragma unroll
                            for (int k = 0; k < size(taccOcO_col) / kGmemElemsPerStoreDirect; ++k) {
                                if (get<1>(taccOcO_col(k * kGmemElemsPerStoreDirect)) < get<1>(params.shape_O)) {
                                    cute::copy(gmem_copy_direct, tOrO_copy(_, m, k), tOgO_copy(_, m, k));
                                }
                            }
                        }
                    }
                } else {
                    PackGQAt::store_O_direct(mO, tOrO_out, tiled_mma, params.qhead_per_khead_divmod, thread_idx, seqlen_o, m_block);
                }
            }
        }
    }

    CUTLASS_DEVICE void
    store_tail() {
        // Don't need to do tma_store_wait<0>() here since we already did in @store
    }

    // Write 0 to output and -inf to LSE
    template <bool Clear_O=true>
    CUTLASS_DEVICE void
    store_zero(
         Params const& params,
         int thread_idx,
         cute::tuple<int32_t, int32_t, int32_t, int32_t> const& block_coord
         ) {
        static constexpr int kBlockM = get<0>(TileShape_MNK{});
        auto [m_block, bidh, bidb, split_idx] = block_coord;
        flash::SeqlenInfo<Varlen, kBlockM> seqlen_info{bidb, size<0>(params.shape_O), params.cu_seqlens, params.seqused};
        bool const is_varlen = Varlen && params.cu_seqlens;
        int offset_o = seqlen_info.offset;
        int seqlen_o = seqlen_info.seqlen;
        int qhead_per_khead = !PackGQA ? 1 : params.qhead_per_khead_divmod.divisor;
        Tensor mO = make_tensor(make_gmem_ptr(params.ptr_O + offset_o * get<0>(params.stride_O)), params.shape_O_packed, params.stride_O_packed)(_, _, bidh, !is_varlen ? bidb : 0, split_idx);
        Tensor mLSE = make_tensor(make_gmem_ptr(params.ptr_LSE + offset_o * get<0>(params.stride_LSE)), params.shape_LSE_packed, params.stride_LSE_packed)(_, bidh, !is_varlen ? bidb : 0, split_idx);
        Tensor gLSE = local_tile(mLSE, Shape<Int<kBlockM>>{}, make_coord(m_block));

        static_assert(kBlockM <= NumEpilogueThreads);
        if (thread_idx < kBlockM) {
            const int row = m_block * kBlockM + thread_idx;
            if constexpr (!PackGQA) {
                if (row < seqlen_o) { mLSE(row) = -INFINITY; }
            } else {
                if (row < seqlen_o * qhead_per_khead) {
                    int m_idx, h_idx;
                    m_idx = params.qhead_per_khead_divmod.divmod(h_idx, row);
                    // mLSE shape shape ((qhead_per_khead, seqlen_q)) and it's unhappy with just 1 "make_coord"
                    mLSE(make_coord(make_coord(h_idx, m_idx))) = -INFINITY;
                }
            }
        }

        if constexpr (!Clear_O) { return; }

        GmemTiledCopyO gmem_tiled_copy_O;
        auto gmem_thr_copy_O = gmem_tiled_copy_O.get_thread_slice(thread_idx);
        Tensor tOcO = gmem_thr_copy_O.partition_D(cute::make_identity_tensor(select<0, 2>(TileShape_MNK{})));
        if constexpr (!PackGQA) {
            Tensor tOpO = make_tensor<bool>(make_shape(size<2>(tOcO)));
            #pragma unroll
            for (int k = 0; k < size(tOpO); ++k) { tOpO(k) = get<1>(tOcO(_0{}, _0{}, k)) < get<1>(params.shape_O); }
            Tensor gO = local_tile(mO, select<0, 2>(TileShape_MNK{}), make_coord(m_block, _0{}));  // (M, K)
            Tensor tOgO = gmem_thr_copy_O.partition_D(gO);
            Tensor tOrO = make_fragment_like(tOgO);
            cute::clear(tOrO);
            // 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_O, tOrO, tOgO, tOcO, tOpO, seqlen_o - m_block * kBlockM
            );
        } else {
            // If PackGQA, we split the work of compute O_ptr among threads in the same row
            using PackGQAt = flash::PackGQAManager<get<0>(TileShape_MNK{}), get<2>(TileShape_MNK{}), NumEpilogueThreads, Element>;
            Tensor tOrO = make_tensor<Element>(make_shape(Shape<_1, Int<kGmemElemsPerStore>>{}, size<1>(tOcO), size<2>(tOcO)));
            cute::clear(tOrO);
            PackGQAt::store_O(mO, tOrO, params.qhead_per_khead_divmod, thread_idx, seqlen_o, m_block);
        }

    }

};

} // namespace flash