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Commit 9448264d authored by Tri Dao's avatar Tri Dao
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Remove seqq_parallel backward kernel that's not used

parent 1274ec3e
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Showing with 25 additions and 535 deletions
csrc/flash_attn/src/flash_bwd_kernel.h
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...@@ -1141,442 +1141,6 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in ...@@ -1141,442 +1141,6 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
//////////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_N, bool Is_even_K, typename Params>
inline __device__ void compute_dq_dk_dv_1rowblock(const Params &params, const int bidb, const int bidh, const int m_block) {
using Element = typename Kernel_traits::Element;
using ElementAccum = typename Kernel_traits::ElementAccum;
using index_t = typename Kernel_traits::index_t;
// Shared memory.
extern __shared__ char smem_[];
// The thread index.
const int tidx = threadIdx.x;
constexpr int kBlockM = Kernel_traits::kBlockM;
constexpr int kBlockN = Kernel_traits::kBlockN;
constexpr int kHeadDim = Kernel_traits::kHeadDim;
// constexpr int kNWarps = Kernel_traits::kNWarps;
constexpr int MMA_N_SdP = kBlockN / decltype(size<1>(typename Kernel_traits::TiledMmaSdP::TiledShape_MNK{}))::value;
constexpr int AtomLayoutMS = Kernel_traits::AtomLayoutMSdP;
const BlockInfo</*Varlen=*/!Is_even_N> binfo(params, bidb);
if (m_block * kBlockM >= binfo.actual_seqlen_q || binfo.actual_seqlen_k == 0) return;
int n_block_max = cute::ceil_div(binfo.actual_seqlen_k, kBlockN);
if (Is_causal) {
n_block_max = std::min(n_block_max, cute::ceil_div((m_block + 1) * kBlockM, kBlockN));
}
// We iterate over the blocks in reverse order. This is because the last block is the only one
// that needs masking when we read K and V from global memory. Moreover, iterating in reverse
// might save us 1 register (we just need n_block instead of both n_block and n_block_max).
const index_t row_offset_q = binfo.q_offset(params.q_batch_stride, params.q_row_stride, bidb)
+ m_block * kBlockM * params.q_row_stride + bidh * params.q_head_stride;
// We move K and V to the last block.
const index_t row_offset_k = binfo.k_offset(params.k_batch_stride, params.k_row_stride, bidb)
+ (n_block_max - 1) * kBlockN * params.k_row_stride + (bidh / params.h_h_k_ratio) * params.k_head_stride;
const index_t row_offset_v = binfo.k_offset(params.v_batch_stride, params.v_row_stride, bidb)
+ (n_block_max - 1) * kBlockN * params.v_row_stride + (bidh / params.h_h_k_ratio) * params.v_head_stride;
const index_t row_offset_do = binfo.q_offset(params.do_batch_stride, params.do_row_stride, bidb)
+ m_block * kBlockM * params.do_row_stride + bidh * params.do_head_stride;
const index_t row_offset_o = binfo.q_offset(params.o_batch_stride, params.o_row_stride, bidb)
+ m_block * kBlockM * params.o_row_stride + bidh * params.o_head_stride;
// We'll advance gdKaccum and gdVaccum before the first write.
const index_t row_offset_dkv_accum = ((bidb * params.h_k + (bidh / params.h_h_k_ratio)) * params.seqlen_k_rounded
+ n_block_max * kBlockN) * params.d_rounded;
const index_t row_offset_lse = (bidb * params.h + bidh) * params.seqlen_q + m_block * kBlockM;
// We assume that params.d == kHeadDim for now
Tensor gQ = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.q_ptr) + row_offset_q),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.q_row_stride, _1{}));
Tensor gK = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.k_ptr) + row_offset_k),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.k_row_stride, _1{}));
Tensor gV = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.v_ptr) + row_offset_v),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.v_row_stride, _1{}));
Tensor gdO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.do_ptr) + row_offset_do),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.do_row_stride, _1{}));
Tensor gO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.o_ptr) + row_offset_o),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.o_row_stride, _1{}));
Tensor gdKaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dk_accum_ptr) + row_offset_dkv_accum),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
Stride<Int<kHeadDim>, _1>{});
Tensor gdVaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dv_accum_ptr) + row_offset_dkv_accum),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
Stride<Int<kHeadDim>, _1>{});
Tensor gLSE = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.softmax_lse_ptr) + row_offset_lse),
Shape<Int<kBlockM>>{}, Stride<_1>{});
Tensor sQ = make_tensor(make_smem_ptr(reinterpret_cast<Element *>(smem_)),
typename Kernel_traits::SmemLayoutQdO{});
Tensor sQt = make_tensor(sQ.data(), typename Kernel_traits::SmemLayoutQdOtransposed{});
Tensor sQtNoSwizzle = make_tensor(sQ.data(), typename Kernel_traits::SmemLayoutQdOtransposedNoSwizzle{});
Tensor sdO = make_tensor(sQ.data() + size(sQ), typename Kernel_traits::SmemLayoutQdO{});
Tensor sdOt = make_tensor(sdO.data(), typename Kernel_traits::SmemLayoutQdOtransposed{});
Tensor sdOtransposedNoSwizzle = make_tensor(sdO.data(),
typename Kernel_traits::SmemLayoutQdOtransposedNoSwizzle{});
Tensor sK = make_tensor(sdO.data() + size(sdO), typename Kernel_traits::SmemLayoutKV{});
// Double buffer for sK
Tensor sV = make_tensor(sK.data() + 2 * size(sK), typename Kernel_traits::SmemLayoutKV{});
Tensor sKt = make_tensor(sK.data(), typename Kernel_traits::SmemLayoutKtransposed{});
Tensor sKtNoSwizzle = make_tensor(sK.data(), typename Kernel_traits::SmemLayoutKtransposedNoSwizzle{});
Tensor sdS = make_tensor(sV.data() + size(sV), typename Kernel_traits::SmemLayoutPdS{});
Tensor sdSt = make_tensor(sdS.data(), typename Kernel_traits::SmemLayoutPdStransposed{});
Tensor sdStNoSwizzle = make_tensor(sdS.data(), typename Kernel_traits::SmemLayoutPdStransposedNoSwizzle{});
Tensor sP = make_tensor(sdS.data() + size(sdS), typename Kernel_traits::SmemLayoutPdS{});
Tensor sPt = make_tensor(sP.data(), typename Kernel_traits::SmemLayoutPdStransposed{});
Tensor sPtNoSwizzle = make_tensor(sP.data(), typename Kernel_traits::SmemLayoutPdStransposedNoSwizzle{});
Tensor sdPsum = make_tensor(make_smem_ptr(reinterpret_cast<ElementAccum *>(sdS.data().get())),
Shape<Int<kBlockM>>{});
typename Kernel_traits::GmemTiledCopyQKV gmem_tiled_copy_QKV;
auto gmem_thr_copy_QKV = gmem_tiled_copy_QKV.get_thread_slice(tidx);
typename Kernel_traits::GmemTiledCopydO gmem_tiled_copy_dO;
auto gmem_thr_copy_dO = gmem_tiled_copy_dO.get_thread_slice(tidx);
typename Kernel_traits::GmemTiledCopydQaccumAtomicAdd gmem_tiled_copy_dKVaccum;
auto gmem_thr_copy_dKVaccum = gmem_tiled_copy_dKVaccum.get_thread_slice(tidx);
Tensor tQgQ = gmem_thr_copy_QKV.partition_S(gQ);
Tensor tQsQ = gmem_thr_copy_QKV.partition_D(sQ);
Tensor tdOgdO = gmem_thr_copy_dO.partition_S(gdO);
Tensor tdOsdO = gmem_thr_copy_dO.partition_D(sdO);
Tensor tdOgO = gmem_thr_copy_dO.partition_S(gO);
Tensor tKgK = gmem_thr_copy_QKV.partition_S(gK); // (KCPY, KCPY_N, KCPY_K)
Tensor tKsK = gmem_thr_copy_QKV.partition_D(sK);
Tensor tVgV = gmem_thr_copy_QKV.partition_S(gV); // (VCPY, VCPY_N, VCPY_K)
Tensor tVsV = gmem_thr_copy_QKV.partition_D(sV);
Tensor tdKgdKaccum = gmem_thr_copy_dKVaccum.partition_D(gdKaccum);
Tensor tdVgdVaccum = gmem_thr_copy_dKVaccum.partition_D(gdVaccum);
typename Kernel_traits::TiledMmaSdP tiled_mma_sdp;
auto thr_mma_sdp = tiled_mma_sdp.get_thread_slice(tidx);
Tensor tSrQ = thr_mma_sdp.partition_fragment_A(sQ); // (MMA,MMA_N,MMA_K)
Tensor tSrK = thr_mma_sdp.partition_fragment_B(sK); // (MMA,MMA_N,MMA_K)
Tensor tdPrdO = thr_mma_sdp.partition_fragment_A(sdO); // (MMA,MMA_N,MMA_K)
Tensor tdPrV = thr_mma_sdp.partition_fragment_B(sV); // (MMA,MMA_N,MMA_K)
typename Kernel_traits::TiledMmadKV tiled_mma_dkv;
auto thr_mma_dkv = tiled_mma_dkv.get_thread_slice(tidx);
Tensor tdKrdSt = thr_mma_dkv.partition_fragment_A(sdStNoSwizzle); // (MMA, MMA_N, MMA_N)
Tensor tdKrQt = thr_mma_dkv.partition_fragment_B(sQtNoSwizzle); // (MMA, MMA_K, MMA_N)
Tensor tdVrPt = thr_mma_dkv.partition_fragment_A(sPtNoSwizzle); // (MMA, MMA_N, MMA_N)
Tensor tdVrdO = thr_mma_dkv.partition_fragment_B(sdOtransposedNoSwizzle); // (MMA, MMA_K, MMA_N)
typename Kernel_traits::TiledMmadQ tiled_mma_dq;
auto thr_mma_dq = tiled_mma_dq.get_thread_slice(tidx);
Tensor tdQrdS = thr_mma_dq.partition_fragment_A(sdS); // (MMA, MMA_N, MMA_N)
Tensor tdQrKt = thr_mma_dq.partition_fragment_B(sKtNoSwizzle); // (MMA, MMA_K, MMA_N)
Tensor acc_dq = partition_fragment_C(tiled_mma_dq, Shape<Int<kBlockM>, Int<kHeadDim>>{}); // MMA, MMA_M_SdP, MMA_K
//
// Copy Atom retiling
//
auto smem_tiled_copy_QdO = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_sdp);
auto smem_thr_copy_QdO = smem_tiled_copy_QdO.get_thread_slice(tidx);
Tensor tSsQ = smem_thr_copy_QdO.partition_S(sQ);
Tensor tdPsdO = smem_thr_copy_QdO.partition_S(sdO);
auto smem_tiled_copy_KV = make_tiled_copy_B_warpcontiguousN<MMA_N_SdP>(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_sdp);
auto smem_thr_copy_KV = smem_tiled_copy_KV.get_thread_slice(tidx);
Tensor tSsK = smem_thr_copy_KV.partition_S(sK);
Tensor tdPsV = smem_thr_copy_KV.partition_S(sV);
// Partition sP and sdS to match the accumulator partitioning
// This has to be tiled_mma_sdp, not tiled_mma_dkv
auto smem_tiled_copy_PdS = make_tiled_copy_C_warpcontiguousN<MMA_N_SdP>(typename Kernel_traits::SmemCopyAtomPdS{}, tiled_mma_sdp);
auto smem_thr_copy_PdS = smem_tiled_copy_PdS.get_thread_slice(tidx);
Tensor tPsP = smem_thr_copy_PdS.partition_D(sP); // ((Atom,AtomNum),PIPE_M,PIPE_N)
Tensor tdSsdS = smem_thr_copy_PdS.partition_D(sdS); // ((Atom,AtomNum),PIPE_M,PIPE_N)
auto smem_tiled_copy_PdSt = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma_dkv);
auto smem_thr_copy_PdSt = smem_tiled_copy_PdSt.get_thread_slice(tidx);
Tensor tdVsPt = smem_thr_copy_PdSt.partition_S(sPt);
Tensor tdKsdSt = smem_thr_copy_PdSt.partition_S(sdSt);
auto smem_tiled_copy_QdOt = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma_dkv);
auto smem_thr_copy_QdOt = smem_tiled_copy_QdOt.get_thread_slice(tidx);
Tensor tdVsdOt = smem_thr_copy_QdOt.partition_S(sdOt);
Tensor tdKsQt = smem_thr_copy_QdOt.partition_S(sQt);
auto smem_tiled_copy_dS = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_dq);
auto smem_thr_copy_dS = smem_tiled_copy_dS.get_thread_slice(tidx);
Tensor tdQsdS = smem_thr_copy_dS.partition_S(sdS);
auto smem_tiled_copy_Kt = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma_dq);
auto smem_thr_copy_Kt = smem_tiled_copy_Kt.get_thread_slice(tidx);
Tensor tdQsKt = smem_thr_copy_Kt.partition_S(sKt);
//
// PREDICATES
//
// Construct identity layout for sQ and sK
Tensor cQ = make_identity_tensor(make_shape(size<0>(sQ), size<1>(sQ))); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor cKV = make_identity_tensor(make_shape(size<0>(sK), size<1>(sK))); // (BLK_N,BLK_K) -> (blk_n,blk_k)
// Repeat the partitioning with identity layouts
Tensor tQcQ = gmem_thr_copy_QKV.partition_S(cQ); // (ACPY,ACPY_M,ACPY_K) -> (blk_m,blk_k)
Tensor tKVcKV = gmem_thr_copy_QKV.partition_S(cKV); // (BCPY,BCPY_N,BCPY_K) -> (blk_n,blk_k)
// Allocate predicate tensors for k
Tensor tQpQ = make_tensor<bool>(make_shape(size<2>(tQsQ)));
Tensor tKVpKV = make_tensor<bool>(make_shape(size<2>(tKsK)));
// Set predicates for k bounds
if (!Is_even_K) {
#pragma unroll
for (int k = 0; k < size(tQpQ); ++k) { tQpQ(k) = get<1>(tQcQ(0, 0, k)) < params.d; }
#pragma unroll
for (int k = 0; k < size(tKVpKV); ++k) { tKVpKV(k) = get<1>(tKVcKV(0, 0, k)) < params.d; }
}
// Prologue
Tensor tdOrdO = make_fragment_like(tdOgdO);
Tensor tdOrO = make_fragment_like(tdOgO);
// TODO: Might need to exit early and write 0 to gdQ.
flash::copy</*Is_even_MN=*/false, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgdO, tdOrdO, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
flash::copy</*Is_even_MN=*/false, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgO, tdOrO, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
Tensor tQrQ = make_fragment_like(tQgQ);
flash::copy</*Is_even_MN=*/false, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tQgQ, tQsQ, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
int n_block = n_block_max - 1;
if (n_block % 2 == 1) {
tKsK.data() = tKsK.data() + size(sK);
tSsK.data() = tSsK.data() + size(sK);
tdQsKt.data() = tdQsKt.data() + size(sK);
}
flash::copy<Is_even_N, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV, binfo.actual_seqlen_k - n_block * kBlockN
);
flash::copy<Is_even_N, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV, binfo.actual_seqlen_k - n_block * kBlockN
);
Tensor caccS = make_identity_tensor(Shape<Int<kBlockM>, Int<kBlockN>>{}); // (BLK_M,BLK_N) -> (blk_m,blk_n)
Tensor taccScS = thr_mma_sdp.partition_C(caccS); // (MMA,MMA_N,MMA_N)
static_assert(decltype(size<0>(taccScS))::value == 4);
// Convert to ((2, 2), MMA_N, MMA_N) then take only the row indices.
Tensor taccScS_row = logical_divide(taccScS, Shape<_2>{})(make_coord(0, _), _, 0);
Tensor lse = make_tensor<ElementAccum>(Shape<Int<decltype(size(taccScS_row))::value>>{});
#pragma unroll
for (int mi = 0; mi < size(lse); ++mi) {
const int row = get<0>(taccScS_row(mi));
lse(mi) = row < binfo.actual_seqlen_q - m_block * kBlockM ? gLSE(row) : 0;
}
cute::cp_async_fence();
Tensor dP_sum = make_fragment_like(lse);
cute::copy(tdOrdO, tdOsdO);
dot_do_o<Kernel_traits::kGmemThreadsPerRow>(
tdOrdO, tdOrO, sdPsum,
Kernel_traits::kNThreads / (Kernel_traits::kGmemThreadsPerRow), params.p_dropout
);
__syncthreads();
#pragma unroll
for (int mi = 0; mi < size(dP_sum); ++mi) { dP_sum(mi) = sdPsum(get<0>(taccScS_row(mi))); }
flash::Dropout dropout(params.rng_state[0], params.rng_state[1], params.p_dropout_in_uint8_t,
bidb, bidh, tidx, params.h);
clear(acc_dq);
float alibi_slope = !Has_alibi ? 0.0f : reinterpret_cast<float *>(params.alibi_slopes_ptr)[bidb * params.alibi_slopes_batch_stride + bidh] / params.scale_softmax;
for (; n_block >= 0; --n_block) {
Tensor acc_s = partition_fragment_C(tiled_mma_sdp, Shape<Int<kBlockM>, Int<kBlockN>>{}); // (MMA=4, MMA_M_SdP, MMA_N)
clear(acc_s);
flash::cp_async_wait<0>();
__syncthreads();
flash::gemm(acc_s, tSrQ, tSrK, tSsQ, tSsK, tiled_mma_sdp,
smem_tiled_copy_QdO, smem_tiled_copy_KV, smem_thr_copy_QdO, smem_thr_copy_KV);
// Reshape acc_s from (MMA=4, MMA_N, MMA_N) to (col=(2, MMA_N), row=(2, MMA_N))
Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
if (Has_alibi) {
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
binfo.actual_seqlen_k,
m_block * kBlockM + get<0>(taccScS_row(0)),
binfo.actual_seqlen_q,
AtomLayoutMS * 16,
alibi_slope
);
}
// We don't need to mask out the elements beyond actual_seqlen_k, because acc_s would
// be some finite value for those indices. In the end when we multiply with K to get dQ,
// the corresponding values of K would be 0, so the result would still be correct.
if (Is_causal && m_block * kBlockM < (n_block + 1) * kBlockN) {
flash::apply_mask_causal(scores, n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
binfo.actual_seqlen_k, m_block * kBlockM + get<0>(taccScS_row(0)),
// binfo.actual_seqlen_k, m_block * kBlockM + (tidx / 32) % AtomLayoutMS * 16 + (tidx % 32) / 4,
binfo.actual_seqlen_q,
AtomLayoutMS * 16);
}
// Compute the exponential value.
flash::scale_apply_exp2</*scale_max=*/false>(scores, lse, params.scale_softmax_log2);
if (Is_dropout) {
int warp_id = tidx / 32;
int block_row_idx = m_block * (kBlockM / 16) + warp_id % AtomLayoutMS;
// Need col to be multiples of 32, since we're doing dropout with block of 16 x 32
static_assert(MMA_N_SdP % 2 == 0);
int block_col_idx = n_block * (kBlockN / 32) + (warp_id / AtomLayoutMS) * (MMA_N_SdP / 2);
dropout.template apply_dropout</*encode_dropout_in_sign_bit=*/true>(
scores, block_row_idx, block_col_idx, AtomLayoutMS
);
}
// Convert scores from fp32 to fp16/bf16
Tensor rP = !Is_dropout
? flash::convert_type<Element>(scores)
: flash::convert_type_relu<Element>(scores);
// Reshape rP from (nrow=(2, MMA_N), ncol=(2, MMA_N)) to ((2, 2, 2), MMA_N, MMA_N / 2)
// if using m16n8k16 or ((2, 2, 1), MMA_N, MMA_N) if using m16n8k8.
Tensor tPrP = make_tensor(rP.data(), flash::convert_layout_rowcol_Aregs<Kernel_traits::TiledMmaSdP>(rP.layout()));
Tensor tPaP = smem_thr_copy_PdS.retile_S(tPrP); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_PdS, tPaP, tPsP);
Tensor acc_dp = partition_fragment_C(tiled_mma_sdp, Shape<Int<kBlockM>, Int<kBlockN>>{}); // (MMA=4, MMA_N, MMA_N)
CUTE_STATIC_ASSERT_V(size<0>(acc_dp) == size<0>(acc_s)); // MMA
CUTE_STATIC_ASSERT_V(size<1>(acc_dp) == size<1>(acc_s)); // MMA
CUTE_STATIC_ASSERT_V(size<2>(acc_dp) == size<2>(acc_s)); // MMA
clear(acc_dp);
flash::gemm(acc_dp, tdPrdO, tdPrV, tdPsdO, tdPsV, tiled_mma_sdp,
smem_tiled_copy_QdO, smem_tiled_copy_KV, smem_thr_copy_QdO, smem_thr_copy_KV);
// Reshape acc_dp from (MMA=4, MMA_N, MMA_N) to (col=(2, MMA_N), row=(2, MMA_N))
Tensor dS = make_tensor(acc_dp.data(), scores.layout());
auto pointwise_mult = [](float p, float dp, float d) {
return p * (!Is_dropout || p >= 0 ? dp - d : d);
};
#pragma unroll
for (int mi = 0; mi < size<0>(dS); ++mi) {
#pragma unroll
for (int ni = 0; ni < size<1>(dS); ++ni) {
dS(mi, ni) = pointwise_mult(scores(mi, ni), dS(mi, ni), dP_sum(mi));
}
}
Tensor dS_reshaped = make_tensor(dS.data(), acc_dp.layout());
// Convert dS from fp32 to fp16
Tensor tdSrdS = flash::convert_type<Element>(dS_reshaped);
Tensor tdSadS = smem_thr_copy_PdS.retile_S(tdSrdS); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_PdS, tdSadS, tdSsdS);
__syncthreads();
if (n_block > 0) {
// Double buffer for sK
const int sK_offset = n_block % 2 == 0 ? size(sK) : -size(sK);
tKsK.data() = tKsK.data() + sK_offset;
tSsK.data() = tSsK.data() + sK_offset;
// Advance gK, gV
tKgK.data() = tKgK.data() + (-int(kBlockN * params.k_row_stride));
tVgV.data() = tVgV.data() + (-int(kBlockN * params.v_row_stride));
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV);
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV);
// This cp_async_fence needs to be in the if block, otherwise the synchronization
// isn't right and we get race conditions.
cute::cp_async_fence();
}
Tensor acc_dv = partition_fragment_C(tiled_mma_dkv, Shape<Int<kBlockN>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
clear(acc_dv);
flash::gemm(acc_dv, tdVrPt, tdVrdO, tdVsPt, tdVsdOt, tiled_mma_dkv,
smem_tiled_copy_PdSt, smem_tiled_copy_QdOt, smem_thr_copy_PdSt, smem_thr_copy_QdOt);
// if (threadIdx.x == 0 && blockIdx.y == 0 && blockIdx.z == 0) { print(acc_dv); }
tdVgdVaccum.data() = tdVgdVaccum.data() + (-int(kBlockN * params.d_rounded));
#pragma unroll
for (int i = 0; i < size(acc_dv); ++i) { atomicAdd(&tdVgdVaccum(i), acc_dv(i)); }
__syncthreads();
Tensor acc_dk = partition_fragment_C(tiled_mma_dkv, Shape<Int<kBlockN>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
clear(acc_dk);
flash::gemm(acc_dk, tdKrdSt, tdKrQt, tdKsdSt, tdKsQt, tiled_mma_dkv,
smem_tiled_copy_PdSt, smem_tiled_copy_QdOt, smem_thr_copy_PdSt, smem_thr_copy_QdOt);
tdKgdKaccum.data() = tdKgdKaccum.data() + (-int(kBlockN * params.d_rounded));
#pragma unroll
for (int i = 0; i < size(acc_dk); ++i) { atomicAdd(&tdKgdKaccum(i), acc_dk(i)); }
flash::gemm(acc_dq, tdQrdS, tdQrKt, tdQsdS, tdQsKt, tiled_mma_dq,
smem_tiled_copy_dS, smem_tiled_copy_Kt, smem_thr_copy_dS, smem_thr_copy_Kt);
// Double buffer for sK
tdQsKt.data() = tdQsKt.data() + (n_block % 2 == 0 ? size(sK) : -size(sK));
}
// Epilogue
#pragma unroll
for (int i = 0; i < size(acc_dq); ++i) { acc_dq(i) *= params.scale_softmax_rp_dropout; }
// Convert acc_dq from fp32 to fp16
Tensor rdQ = flash::convert_type<Element>(acc_dq);
Tensor sdQ = make_tensor(sQ.data(), typename Kernel_traits::SmemLayoutdQ{});
// Partition sdV and sdK to match the accumulator partitioning
auto smem_tiled_copy_dQ = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomdQ{}, tiled_mma_dq);
auto smem_thr_copy_dQ = smem_tiled_copy_dQ.get_thread_slice(tidx);
Tensor taccdQrdQ = smem_thr_copy_dQ.retile_S(rdQ); // ((Atom,AtomNum), MMA_N, MMA_N)
Tensor taccdQsdQ = smem_thr_copy_dQ.partition_D(sdQ); // ((Atom,AtomNum),PIPE_M,PIPE_N)
__syncthreads();
cute::copy(smem_tiled_copy_dQ, taccdQrdQ, taccdQsdQ);
const index_t row_offset_dq = binfo.q_offset(params.dq_batch_stride, params.dq_row_stride, bidb)
+ m_block * kBlockM * params.dq_row_stride + bidh * params.dq_head_stride;
Tensor gdQ = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dq_ptr) + row_offset_dq),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.dq_row_stride, _1{}));
typename Kernel_traits::GmemTiledCopydQ gmem_tiled_copy_dQ;
auto gmem_thr_copy_dQ = gmem_tiled_copy_dQ.get_thread_slice(tidx);
Tensor tdQsdQ = gmem_thr_copy_dQ.partition_S(sdQ); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdQgdQ = gmem_thr_copy_dQ.partition_D(gdQ);
__syncthreads();
Tensor tdQrdQ = make_tensor<Element>(shape(tdQgdQ));
cute::copy(gmem_tiled_copy_dQ, tdQsdQ, tdQrdQ);
Tensor cdQ = make_identity_tensor(Shape<Int<kBlockM>, Int<kHeadDim>>{}); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor tdQcdQ = gmem_thr_copy_dQ.partition_D(cdQ);
Tensor tdQpdQ = make_tensor<bool>(make_shape(size<2>(tdQgdQ)));
if (!Is_even_K) {
#pragma unroll
for (int k = 0; k < size(tdQpdQ); ++k) { tdQpdQ(k) = get<1>(tdQcdQ(0, 0, k)) < params.d; }
}
// 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, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dQ, tdQrdQ, tdQgdQ, tdQcdQ, tdQpdQ, binfo.actual_seqlen_q - m_block * kBlockM
);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_M, bool Is_even_K, typename Params> template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_M, bool Is_even_K, typename Params>
inline __device__ void compute_dq_dk_dv(const Params &params) { inline __device__ void compute_dq_dk_dv(const Params &params) {
...@@ -1618,19 +1182,5 @@ inline __device__ void compute_dq_dk_dv_seqk_parallel(const Params &params) { ...@@ -1618,19 +1182,5 @@ inline __device__ void compute_dq_dk_dv_seqk_parallel(const Params &params) {
} }
} }
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_N, bool Is_even_K, typename Params>
inline __device__ void compute_dq_dk_dv_seqq_parallel(const Params &params) {
const int m_block = blockIdx.x;
// The block index for the batch.
const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.z;
compute_dq_dk_dv_1rowblock<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_N, Is_even_K>(params, bidb, bidh, m_block);
}
//////////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace flash } // namespace flash
csrc/flash_attn/src/flash_bwd_launch_template.h
View file @ 9448264d
...@@ -29,11 +29,6 @@ __global__ void flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel(Flash_bwd_params pa ...@@ -29,11 +29,6 @@ __global__ void flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel(Flash_bwd_params pa
flash::compute_dq_dk_dv_seqk_parallel<Kernel_traits, Is_dropout, Is_causal, Is_local, Has_alibi, Is_even_MN, Is_even_K>(params); flash::compute_dq_dk_dv_seqk_parallel<Kernel_traits, Is_dropout, Is_causal, Is_local, Has_alibi, Is_even_MN, Is_even_K>(params);
} }
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_N, bool Is_even_K>
__global__ void flash_bwd_dq_dk_dv_loop_seqq_parallel_kernel(Flash_bwd_params params) {
flash::compute_dq_dk_dv_seqq_parallel<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_N, Is_even_K>(params);
}
template<typename Kernel_traits> template<typename Kernel_traits>
__global__ void flash_bwd_convert_dq_kernel(Flash_bwd_params params, const int nsplits) { __global__ void flash_bwd_convert_dq_kernel(Flash_bwd_params params, const int nsplits) {
flash::convert_dQ<Kernel_traits>(params, nsplits); flash::convert_dQ<Kernel_traits>(params, nsplits);
...@@ -100,48 +95,6 @@ void run_flash_bwd_seqk_parallel(Flash_bwd_params &params, cudaStream_t stream, ...@@ -100,48 +95,6 @@ void run_flash_bwd_seqk_parallel(Flash_bwd_params &params, cudaStream_t stream,
C10_CUDA_KERNEL_LAUNCH_CHECK(); C10_CUDA_KERNEL_LAUNCH_CHECK();
} }
template<typename Kernel_traits, bool Is_dropout>
void run_flash_bwd_seqq_parallel(Flash_bwd_params &params, cudaStream_t stream, const bool configure) {
const int num_n_block = (params.seqlen_k + Kernel_traits::kBlockN - 1) / Kernel_traits::kBlockN;
dim3 grid_n(num_n_block, params.b, params.h_k);
flash_bwd_clear_dkvaccum_kernel<Kernel_traits><<<grid_n, Kernel_traits::kNThreads, 0, stream>>>(params);
C10_CUDA_KERNEL_LAUNCH_CHECK();
const int num_m_block = (params.seqlen_q + Kernel_traits::kBlockM - 1) / Kernel_traits::kBlockM;
dim3 grid_m(num_m_block, params.b, params.h);
// We also use is_even_N to set Unpadded in the BlockInfo constructor, so we need to check
// for cu_seqlens_k as well.
const bool is_even_N = params.cu_seqlens_q == nullptr && params.cu_seqlens_k == nullptr && params.seqlen_k % Kernel_traits::kBlockN == 0;
const bool is_even_K = params.d == Kernel_traits::kHeadDim;
constexpr int smem_size_dq_dk_dv = Kernel_traits::kSmemSize1rowblock;
// printf("smem_size_dq_dk_dv = %d\n", smem_size_dq_dk_dv);
BOOL_SWITCH(params.is_causal, Is_causal, [&] {
BOOL_SWITCH(is_even_N, IsEvenNConst, [&] {
BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
BOOL_SWITCH(params.alibi_slopes_ptr != nullptr, Has_alibi, [&] {
// If not IsEvenKConst, we also set IsEvenMNConst to false to reduce number of templates.
auto kernel = &flash_bwd_dq_dk_dv_loop_seqq_parallel_kernel<Kernel_traits, Is_dropout, Is_causal, Has_alibi, IsEvenNConst && IsEvenKConst, IsEvenKConst>;
// auto kernel = &flash_bwd_dq_dk_dv_loop_seqq_parallel_kernel<Kernel_traits, false, false, IsEvenNConst, IsEvenKConst>;
if (smem_size_dq_dk_dv >= 48 * 1024) {
C10_CUDA_CHECK(cudaFuncSetAttribute(
kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size_dq_dk_dv));
}
kernel<<<grid_m, Kernel_traits::kNThreads, smem_size_dq_dk_dv, stream>>>(params);
C10_CUDA_KERNEL_LAUNCH_CHECK();
});
});
});
});
auto kernel_dkv = &flash_bwd_convert_dkv_kernel<Kernel_traits>;
if (Kernel_traits::kSmemKVSize >= 48 * 1024) {
C10_CUDA_CHECK(cudaFuncSetAttribute(
kernel_dkv, cudaFuncAttributeMaxDynamicSharedMemorySize, Kernel_traits::kSmemKVSize));
}
kernel_dkv<<<grid_n, Kernel_traits::kNThreads, Kernel_traits::kSmemKVSize, stream>>>(params);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
template<typename Kernel_traits, bool Is_dropout> template<typename Kernel_traits, bool Is_dropout>
void run_flash_bwd(Flash_bwd_params &params, cudaStream_t stream, const bool configure) { void run_flash_bwd(Flash_bwd_params &params, cudaStream_t stream, const bool configure) {
if (configure) return; if (configure) return;
...@@ -202,7 +155,6 @@ void run_mha_bwd_hdim64(Flash_bwd_params &params, cudaStream_t stream, const boo ...@@ -202,7 +155,6 @@ void run_mha_bwd_hdim64(Flash_bwd_params &params, cudaStream_t stream, const boo
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, false, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, false, false, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, true, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, true, false, T>, Is_dropout>(params, stream, configure);
// } else { // } else {
// run_flash_bwd_seqq_parallel<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, false, false, T>, Is_dropout>(params, stream, configure);
// } // }
} }
}); });
...@@ -231,20 +183,16 @@ void run_mha_bwd_hdim96(Flash_bwd_params &params, cudaStream_t stream, const boo ...@@ -231,20 +183,16 @@ void run_mha_bwd_hdim96(Flash_bwd_params &params, cudaStream_t stream, const boo
} }
// printf("max_smem_per_block = %d\n", max_smem_per_block); // printf("max_smem_per_block = %d\n", max_smem_per_block);
BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] { BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
// if (params.h == params.h_k) { if (max_smem_per_block >= 116 * 1024) {
if (max_smem_per_block >= 116 * 1024) { if constexpr(!Is_dropout) { // 92KB
if constexpr(!Is_dropout) { // 92KB
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream, configure);
} else { // 116 KB
// This is faster for dropout since we don't have many registers to spare
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>, Is_dropout>(params, stream, configure);
}
} else {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream, configure); run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream, configure);
} else { // 116 KB
// This is faster for dropout since we don't have many registers to spare
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>, Is_dropout>(params, stream, configure);
} }
// } else { } else {
// run_flash_bwd_seqq_parallel<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 4, 4, false, false, T>>(params, stream, configure); run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream, configure);
// } }
}); });
} }
...@@ -261,29 +209,24 @@ void run_mha_bwd_hdim128(Flash_bwd_params &params, cudaStream_t stream, const bo ...@@ -261,29 +209,24 @@ void run_mha_bwd_hdim128(Flash_bwd_params &params, cudaStream_t stream, const bo
} }
// printf("max_smem_per_block = %d\n", max_smem_per_block); // printf("max_smem_per_block = %d\n", max_smem_per_block);
BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] { BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
// if (params.h == params.h_k) { // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 32, 128, 8, 2, 2, 2, false, false, T>>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 32, 128, 8, 2, 2, 2, false, false, T>>(params, stream, configure); // This is faster, in the case of sequence-parallel bwd (where we need fewer registers).
// This is faster, in the case of sequence-parallel bwd (where we need fewer registers). // Out of these three, the 2nd one is slightly faster (2% faster than the first). Idk why.
// Out of these three, the 2nd one is slightly faster (2% faster than the first). Idk why. // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 2, 2, false, false, T>>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 2, 2, false, false, T>>(params, stream, configure); if (max_smem_per_block >= 144 * 1024) {
if (max_smem_per_block >= 144 * 1024) { run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, false, false, T>, Is_dropout>(params, stream, configure);
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, false, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd_seqk_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd_seqk_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd_seqk_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, true, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd_seqk_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, true, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, true, false, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd_seqq_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, true, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 2, true, false, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream, configure); } else {
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 2, true, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream, configure);
} else { run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, true, false, T>, Is_dropout>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream, configure); }
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, true, false, T>, Is_dropout>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>>(params, stream, configure);
}
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>>(params, stream, configure);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 4, 4, false, false, T>>(params, stream, configure); // run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 4, 4, false, false, T>>(params, stream, configure);
// } else {
// run_flash_bwd_seqq_parallel<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 4, 4, false, false, T>>(params, stream, configure);
// }
}); });
} }
......
csrc/flash_attn/src/kernel_traits.h
View file @ 9448264d
...@@ -288,9 +288,6 @@ struct Flash_bwd_kernel_traits : public Base { ...@@ -288,9 +288,6 @@ struct Flash_bwd_kernel_traits : public Base {
+ (!Is_V_in_regs + (!Is_V_in_regs
? kSmemKVSize + kSmemdSSize + kSmemPSize ? kSmemKVSize + kSmemdSSize + kSmemPSize
: std::max(kSmemKVSize, kSmemKVSize / 2 + kSmemdSSize + kSmemPSize)); : std::max(kSmemKVSize, kSmemKVSize / 2 + kSmemdSSize + kSmemPSize));
static constexpr int kSmemSize1rowblock = kSmemQdOSize / 3 * 2 + kSmemKVSize / 2 * 3
+ kSmemdSSize + kSmemPSize;
static constexpr int kGmemElemsPerLoad = sizeof(cute::uint128_t) / sizeof(Element); static constexpr int kGmemElemsPerLoad = sizeof(cute::uint128_t) / sizeof(Element);
static_assert(kHeadDim % kGmemElemsPerLoad == 0, "kHeadDim must be a multiple of kGmemElemsPerLoad"); static_assert(kHeadDim % kGmemElemsPerLoad == 0, "kHeadDim must be a multiple of kGmemElemsPerLoad");
......
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