refactor: extract shared dispatcher header, unify MMA/scalar dispatch format
- Merge 3 duplicated dispatch blocks into single attn_dispatchers.cuh - Merge compute_num_splits from attn_utils.cuh into dispatcher header - All dim3 grid/block declarations and <<<>>> launches are single-line - Production .cu files (35-42 loc) only handle torch wrapping + pybind11 - Test files include dispatcher header directly, removing all #ifndef ASTRAI_NO_MMA duplication
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#pragma once
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// Shared attention dispatchers — used by both production .cu and test .cu.
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// No torch dependency; pure CUDA.
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#include <cuda_runtime.h>
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#include <algorithm>
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#include "attn_prefill_split_q.cuh"
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#include "attn_decode_split_kv.cuh"
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#include "attn_paged_decode_split_kv.cuh"
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#ifndef ASTRAI_NO_MMA
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#include "attn_prefill_split_q_mma.cuh"
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#include "attn_decode_split_kv_mma.cuh"
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#include "attn_paged_decode_split_kv_mma.cuh"
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#endif
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// Split-KV: compute number of splits to fill all SMs for small-batch decode.
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inline int compute_num_splits(int base_blocks, int tiles_total) {
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int sm_count = 0;
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cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, 0);
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int n = (2 * sm_count + base_blocks - 1) / base_blocks;
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return std::max(1, std::min(n, std::min(tiles_total, 32)));
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}
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// ======================================================================
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// Prefill
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// ======================================================================
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#ifndef ASTRAI_NO_MMA
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static inline void launch_prefill_mma(AttentionParams<bf16>& p) {
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constexpr int WARPS = 4;
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constexpr int BC = (HEAD_DIM <= 128) ? 32 : 16;
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using Traits = KernelTraits<HEAD_DIM, BC, WARPS, 2>;
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dim3 grid((p.q_len + Traits::BR * WARPS - 1) / (Traits::BR * WARPS), p.q_head, p.batch);
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dim3 block(Traits::NUM_THREADS);
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attn_prefill_split_q_mma_kernel<Traits, IsCausal, HasMask><<<grid, block>>>(p);
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}
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#endif
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static inline void launch_prefill_scalar(AttentionParams<bf16>& p) {
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constexpr int G = 8, ROWS = 32, P_BC = 32;
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dim3 grid((p.q_len + ROWS - 1) / ROWS, p.q_head, p.batch);
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dim3 block(G, ROWS);
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attn_prefill_split_q_kernel_t<HEAD_DIM, G, ROWS, P_BC, IsCausal, HasMask><<<grid, block>>>(p);
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}
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template <int HEAD_DIM>
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static inline void dispatch_prefill(AttentionParams<bf16>& p) {
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bool is_causal = (p.causal_offset >= 0);
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bool has_mask = (p.use_mask && p.mask);
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#ifndef ASTRAI_NO_MMA
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if (is_causal) {
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if (has_mask) launch_prefill_mma<HEAD_DIM, true, true>(p);
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else launch_prefill_mma<HEAD_DIM, true, false>(p);
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} else {
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if (has_mask) launch_prefill_mma<HEAD_DIM, false, true>(p);
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else launch_prefill_mma<HEAD_DIM, false, false>(p);
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}
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#else
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if (is_causal) {
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if (has_mask) launch_prefill_scalar<HEAD_DIM, true, true>(p);
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else launch_prefill_scalar<HEAD_DIM, true, false>(p);
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} else {
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if (has_mask) launch_prefill_scalar<HEAD_DIM, false, true>(p);
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else launch_prefill_scalar<HEAD_DIM, false, false>(p);
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}
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#endif
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}
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// ======================================================================
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// Decode
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// ======================================================================
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#ifndef ASTRAI_NO_MMA
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static inline void launch_decode_mma(AttentionParams<bf16>& p, int group_size) {
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int G = p.q_head / p.kv_head;
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if (G >= 1 && G <= 16) {
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int tiles_total = (p.kv_len + 32 - 1) / 32;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, tiles_total);
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constexpr int STAGES = (HEAD_DIM <= 128) ? 2 : 1;
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using Traits = KernelTraits<HEAD_DIM, 32, 1, STAGES>;
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dim3 grid(p.kv_head, p.batch, p.num_splits);
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attn_decode_split_kv_mma_kernel<Traits, IsCausal, HasMask><<<grid, 32>>>(p);
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} else {
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int chunks_total = (p.kv_len + DC_CHUNK - 1) / DC_CHUNK;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
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size_t smem = DC_CHUNK * p.head_dim * sizeof(bf16);
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dim3 grid(p.batch * p.kv_head, 1, p.num_splits);
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dim3 block(32, group_size);
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attn_decode_split_kv_kernel<HEAD_DIM, IsCausal, HasMask><<<grid, block, smem>>>(p);
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}
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}
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#endif
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static inline void launch_decode_scalar(AttentionParams<bf16>& p, int group_size) {
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int chunks_total = (p.kv_len + DC_CHUNK - 1) / DC_CHUNK;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
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size_t smem = DC_CHUNK * p.head_dim * sizeof(bf16);
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dim3 grid(p.batch * p.kv_head, 1, p.num_splits);
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dim3 block(32, group_size);
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attn_decode_split_kv_kernel<HEAD_DIM, IsCausal, HasMask><<<grid, block, smem>>>(p);
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}
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template <int HEAD_DIM>
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static inline void dispatch_decode(AttentionParams<bf16>& p) {
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bool is_causal = (p.causal_offset >= 0);
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bool has_mask = (p.use_mask && p.mask);
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int group_size = p.q_head / p.kv_head;
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#ifndef ASTRAI_NO_MMA
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if (is_causal) {
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if (has_mask) launch_decode_mma<HEAD_DIM, true, true>(p, group_size);
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else launch_decode_mma<HEAD_DIM, true, false>(p, group_size);
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} else {
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if (has_mask) launch_decode_mma<HEAD_DIM, false, true>(p, group_size);
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else launch_decode_mma<HEAD_DIM, false, false>(p, group_size);
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}
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#else
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if (is_causal) {
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if (has_mask) launch_decode_scalar<HEAD_DIM, true, true>(p, group_size);
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else launch_decode_scalar<HEAD_DIM, true, false>(p, group_size);
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} else {
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if (has_mask) launch_decode_scalar<HEAD_DIM, false, true>(p, group_size);
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else launch_decode_scalar<HEAD_DIM, false, false>(p, group_size);
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}
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#endif
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attn_decode_combine_kernel<<<p.batch * p.q_head, p.head_dim>>>(p);
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}
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// ======================================================================
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// Paged Decode
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// ======================================================================
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#ifndef ASTRAI_NO_MMA
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static inline void launch_paged_decode_mma(PagedAttentionParams<bf16>& p, int group_size) {
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int G = p.q_head / p.kv_head;
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if (G >= 1 && G <= 16 && p.page_size >= 32) {
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int tiles_total = (p.kv_len + 32 - 1) / 32;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, tiles_total);
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constexpr int STAGES = (HEAD_DIM <= 128) ? 2 : 1;
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using Traits = KernelTraits<HEAD_DIM, 32, 1, STAGES>;
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dim3 grid(p.kv_head, p.batch, p.num_splits);
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paged_attn_decode_split_kv_mma_kernel<Traits, IsCausal, HasMask><<<grid, 32>>>(p);
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} else {
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int chunks_total = (p.kv_len + PDC_CHUNK - 1) / PDC_CHUNK;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
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size_t smem = PDC_CHUNK * p.head_dim * sizeof(bf16);
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dim3 grid(p.batch * p.kv_head, 1, p.num_splits);
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dim3 block(32, group_size);
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paged_attn_decode_split_kv_kernel<HEAD_DIM, IsCausal, HasMask><<<grid, block, smem>>>(p);
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}
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}
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#endif
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static inline void launch_paged_decode_scalar(PagedAttentionParams<bf16>& p, int group_size) {
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int chunks_total = (p.kv_len + PDC_CHUNK - 1) / PDC_CHUNK;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
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size_t smem = PDC_CHUNK * p.head_dim * sizeof(bf16);
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dim3 grid(p.batch * p.kv_head, 1, p.num_splits);
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dim3 block(32, group_size);
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paged_attn_decode_split_kv_kernel<HEAD_DIM, IsCausal, HasMask><<<grid, block, smem>>>(p);
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}
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template <int HEAD_DIM>
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static inline void dispatch_paged_decode(PagedAttentionParams<bf16>& p) {
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bool is_causal = (p.causal_offset >= 0);
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bool has_mask = (p.use_mask && p.mask);
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int group_size = p.q_head / p.kv_head;
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#ifndef ASTRAI_NO_MMA
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if (is_causal) {
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if (has_mask) launch_paged_decode_mma<HEAD_DIM, true, true>(p, group_size);
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else launch_paged_decode_mma<HEAD_DIM, true, false>(p, group_size);
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} else {
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if (has_mask) launch_paged_decode_mma<HEAD_DIM, false, true>(p, group_size);
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else launch_paged_decode_mma<HEAD_DIM, false, false>(p, group_size);
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}
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#else
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if (is_causal) {
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if (has_mask) launch_paged_decode_scalar<HEAD_DIM, true, true>(p, group_size);
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else launch_paged_decode_scalar<HEAD_DIM, true, false>(p, group_size);
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} else {
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if (has_mask) launch_paged_decode_scalar<HEAD_DIM, false, true>(p, group_size);
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else launch_paged_decode_scalar<HEAD_DIM, false, false>(p, group_size);
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}
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#endif
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paged_attn_decode_combine_kernel<<<p.batch * p.q_head, p.head_dim>>>(p);
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}
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