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* Copyright (c) 2024, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri
* Dao. Licensed under the BSD 3-Clause.
*
* Modified by the FlashInfer team.
*/
#ifndef FLASHINFER_ATTENTION_HOPPER_FP8_MAINLOOP_MMA_CUH_
#define FLASHINFER_ATTENTION_HOPPER_FP8_MAINLOOP_MMA_CUH_
#include <cutlass/array.h>
#include <cutlass/cutlass.h>
#include <cutlass/numeric_conversion.h>
#include <cutlass/numeric_types.h>
namespace flashinfer {
template <typename Ktraits, bool LEFT_SLIDING_WINDOW, bool CAUSAL, typename WarpScheduler,
typename AttentionVariant, typename Params, typename MainloopPipeline,
typename MainloopPipelineVt, typename PipelineState, typename SharedStorage,
typename FrgTensorO, typename AttentionUpdater>
CUTLASS_DEVICE void mma_fp8(const Params& mainloop_params, AttentionVariant& variant,
MainloopPipeline pipeline_k, MainloopPipelineVt pipeline_vt,
PipelineState& smem_pipe_read_k, PipelineState& smem_pipe_read_v,
FrgTensorO& tOrO, AttentionUpdater& attention_updater,
int kv_tile_idx_count, int swa_begin_kv_tile_idx,
int swa_end_kv_tile_idx, int thread_idx, int work_idx, int q_tile_idx,
SharedStorage& shared_storage, const int32_t qo_len,
const int32_t kv_len, const int32_t qo_head_idx,
const int32_t kv_head_idx, const int32_t batch_idx) {
using DTypeQ = typename Ktraits::DTypeQ;
using DTypeKV = typename Ktraits::DTypeKV;
using IdType = typename Ktraits::IdType;
using TileShape_QKD = typename Ktraits::TileShape_QKD;
static constexpr int NUM_MMA_THREADS = Ktraits::NUM_MMA_THREADS;
using SmemLayoutQ = typename Ktraits::SmemLayoutQ;
using SmemLayoutK = typename Ktraits::SmemLayoutK;
using SmemLayoutV = typename Ktraits::SmemLayoutV;
using SmemLayoutVt = typename Ktraits::SmemLayoutVt;
static_assert(is_rmem<FrgTensorO>::value, "O tensor must be rmem resident.");
static constexpr int CTA_Q = get<0>(TileShape_QKD{});
static constexpr int CTA_KV = get<1>(TileShape_QKD{});
Tensor sQ = make_tensor(make_smem_ptr(shared_storage.smem_q.data()), SmemLayoutQ{});
Tensor sK = make_tensor(make_smem_ptr(shared_storage.smem_k.data()), SmemLayoutK{});
Tensor sVt = make_tensor(make_smem_ptr(shared_storage.smem_vt.data()), SmemLayoutVt{});
typename Ktraits::TiledMmaQK tiled_mma_qk;
typename Ktraits::TiledMmaPV tiled_mma_pv;
auto threadMmaQK = tiled_mma_qk.get_thread_slice(thread_idx);
auto threadMmaPV = tiled_mma_pv.get_thread_slice(thread_idx);
Tensor tSrQ = threadMmaQK.partition_fragment_A(sQ);
Tensor tSrK = threadMmaQK.partition_fragment_B(sK);
Tensor tOrV = threadMmaPV.partition_fragment_B(sVt);
auto consumer_wait = [](auto& pipeline, auto& smem_pipe_read) {
auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
pipeline.consumer_wait(smem_pipe_read, barrier_token);
};
tiled_mma_pv.accumulate_ = GMMA::ScaleOut::Zero;
int kv_tile_idx = kv_tile_idx_count - 1;
cutlass::ConsumerToken barrier_token =
static_cast<cutlass::BarrierStatus>(shared_storage.barrier_Q.try_wait(work_idx % 2));
if (barrier_token == cutlass::BarrierStatus::WaitAgain) {
shared_storage.barrier_Q.wait(work_idx % 2);
}
Tensor tSrS = partition_fragment_C(tiled_mma_qk, select<0, 1>(TileShape_QKD{}));
consumer_wait(pipeline_k, smem_pipe_read_k);
WarpScheduler::barrier_sync();
gemm</*init=*/true, /*wg_wait=*/-1>(tiled_mma_qk, tSrQ, tSrK(_, _, _, smem_pipe_read_k.index()),
tSrS);
WarpScheduler::barrier_arrive();
if (work_idx != 0) {
int lane_predicate = cute::elect_one_sync();
if (cutlass::canonical_warp_idx_sync() == Ktraits::NUM_WARPS - 1 && lane_predicate) {
#pragma unroll
for (uint32_t cta_id = 0; cta_id < 1; ++cta_id) {
shared_storage.barrier_O.arrive(cta_id, lane_predicate);
}
}
}
warpgroup_wait<0>();
pipeline_k.consumer_release(smem_pipe_read_k);
++smem_pipe_read_k;
auto col_limit_right = [&](int qo_idx) { return qo_idx + 1 + kv_len - qo_len; };
auto col_limit_left = [&](int qo_idx) {
return qo_idx + kv_len - qo_len - mainloop_params.window_left;
};
{
Tensor cS = cute::make_identity_tensor(select<0, 1>(TileShape_QKD{}));
Tensor tScS = threadMmaQK.partition_C(cS);
#pragma unroll
for (int i = 0; i < size(tSrS); ++i) {
int qo_idx = get<0>(tScS(i)) + q_tile_idx * CTA_Q;
int kv_idx = get<1>(tScS(i)) + kv_tile_idx * CTA_KV;
tSrS(i) = variant.LogitsTransform(mainloop_params, tSrS(i), /*batch_idx=*/batch_idx, qo_idx,
kv_idx, qo_head_idx, kv_head_idx);
if constexpr (!CAUSAL) { // Just masking based on col
if (kv_idx >= kv_len) {
tSrS(i) = AttentionUpdater::fill_value;
}
} else {
if (kv_idx >= std::min(kv_len, col_limit_right(qo_idx))) {
tSrS(i) = AttentionUpdater::fill_value;
}
}
if constexpr (LEFT_SLIDING_WINDOW) {
if (kv_idx < col_limit_left(qo_idx)) {
tSrS(i) = AttentionUpdater::fill_value;
}
}
}
}
attention_updater.update</*init=*/true>(tSrS);
// Re-quantize P after softmax
variant.PQuantize(tSrS);
// Cast back to FP8
Tensor tOrP =
make_tensor(convert_type<DTypeKV>(tSrS).data(), convert_layout_acc_Aregs_fp8(tSrS.layout()));
permute_regs_A_to_C(tOrP);
constexpr int n_masking_steps = CAUSAL ? cute::ceil_div(CTA_Q, CTA_KV) : 0;
// masking loops
#pragma unroll
for (int masking_step = 0; masking_step < n_masking_steps && kv_tile_idx > swa_begin_kv_tile_idx;
++masking_step, --kv_tile_idx) {
Tensor tSrS = partition_fragment_C(tiled_mma_qk, select<0, 1>(TileShape_QKD{}));
consumer_wait(pipeline_k, smem_pipe_read_k);
WarpScheduler::barrier_sync();
gemm</*init=*/true, /*wg_wait=*/-1>(tiled_mma_qk, tSrQ, tSrK(_, _, _, smem_pipe_read_k.index()),
tSrS);
if (masking_step > 0) {
attention_updater.rescale_o(tOrO);
}
consumer_wait(pipeline_vt, smem_pipe_read_v);
gemm</*init=*/false, /*wg_wait=*/-1>(tiled_mma_pv, tOrP,
tOrV(_, _, _, smem_pipe_read_v.index()), tOrO);
WarpScheduler::barrier_arrive();
warpgroup_wait<1>();
pipeline_k.consumer_release(smem_pipe_read_k); // release K
Tensor cS = cute::make_identity_tensor(select<0, 1>(TileShape_QKD{}));
Tensor tScS = threadMmaQK.partition_C(cS);
#pragma unroll
for (int i = 0; i < size(tSrS); ++i) {
int qo_idx = get<0>(tScS(i)) + q_tile_idx * CTA_Q;
int kv_idx = get<1>(tScS(i)) + (kv_tile_idx - 1) * CTA_KV;
tSrS(i) = variant.LogitsTransform(mainloop_params, tSrS(i), /*batch_idx=*/batch_idx, qo_idx,
kv_idx, qo_head_idx, kv_head_idx);
if (kv_idx >= col_limit_right(qo_idx)) {
tSrS(i) = AttentionUpdater::fill_value;
}
if constexpr (LEFT_SLIDING_WINDOW) {
if (kv_idx < col_limit_left(qo_idx)) {
tSrS(i) = AttentionUpdater::fill_value;
}
}
}
attention_updater.update</*init=*/false>(tSrS);
// Re-quantize P after softmax
variant.PQuantize(tSrS);
warpgroup_wait<0>();
pipeline_vt.consumer_release(smem_pipe_read_v); // release V
++smem_pipe_read_k;
++smem_pipe_read_v;
cute::copy(make_tensor(convert_type<DTypeKV>(tSrS).data(),
convert_layout_acc_Aregs_fp8(tSrS.layout())),
tOrP);
permute_regs_A_to_C(tOrP);
}
#pragma unroll 1
for (; kv_tile_idx > swa_end_kv_tile_idx + 1; --kv_tile_idx) {
Tensor tSrS = partition_fragment_C(tiled_mma_qk, select<0, 1>(TileShape_QKD{}));
consumer_wait(pipeline_k, smem_pipe_read_k);
WarpScheduler::barrier_sync();
gemm</*init=*/true, /*wg_wait=*/-1>(tiled_mma_qk, tSrQ, tSrK(_, _, _, smem_pipe_read_k.index()),
tSrS);
attention_updater.rescale_o(tOrO);
consumer_wait(pipeline_vt, smem_pipe_read_v);
gemm</*init=*/false, /*wg_wait=*/-1>(tiled_mma_pv, tOrP,
tOrV(_, _, _, smem_pipe_read_v.index()), tOrO);
WarpScheduler::barrier_arrive();
warpgroup_wait<1>();
pipeline_k.consumer_release(smem_pipe_read_k); // release K
// #pragma unroll
Tensor cS = cute::make_identity_tensor(select<0, 1>(TileShape_QKD{}));
Tensor tScS = threadMmaQK.partition_C(cS);
#pragma unroll
for (int i = 0; i < size(tSrS); ++i) {
int qo_idx = get<0>(tScS(i)) + q_tile_idx * CTA_Q;
int kv_idx = get<1>(tScS(i)) + (kv_tile_idx - 1) * CTA_KV;
tSrS(i) = variant.LogitsTransform(mainloop_params, tSrS(i), /*batch_idx=*/batch_idx, qo_idx,
kv_idx, qo_head_idx, kv_head_idx);
}
attention_updater.update</*init=*/false>(tSrS);
// Re-quantize P after softmax
variant.PQuantize(tSrS);
warpgroup_wait<0>();
pipeline_vt.consumer_release(smem_pipe_read_v); // release V
++smem_pipe_read_k;
++smem_pipe_read_v;
cute::copy(make_tensor(convert_type<DTypeKV>(tSrS).data(),
convert_layout_acc_Aregs_fp8(tSrS.layout())),
tOrP);
permute_regs_A_to_C(tOrP);
}
// Tell warp 0 that smem_q is ready
cutlass::arch::NamedBarrier::arrive(NUM_MMA_THREADS + Ktraits::NUM_PRODUCER_THREADS,
/*id=*/static_cast<int>(NamedBarriers::kQueryEmpty));
attention_updater.rescale_o(tOrO);
consumer_wait(pipeline_vt, smem_pipe_read_v);
gemm</*init=*/false, /*wg_wait=*/-1>(tiled_mma_pv, tOrP, tOrV(_, _, _, smem_pipe_read_v.index()),
tOrO);
attention_updater.finalize(tSrS, variant.scale_pv);
warpgroup_wait<0>();
pipeline_vt.consumer_release(smem_pipe_read_v); // release V, otherwise producers will hang
++smem_pipe_read_v;
attention_updater.rescale_o(tOrO);
// Dequantize output o with P/V scale
variant.ODequantize(mainloop_params, tOrO, qo_head_idx, kv_head_idx);
return;
}
} // namespace flashinfer
#endif // FLASHINFER_ATTENTION_HOPPER_FP8_MAINLOOP_MMA_CUH_
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