169 lines
6.4 KiB
Plaintext
169 lines
6.4 KiB
Plaintext
#pragma once
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#include "attn_common.cuh"
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#include "attn_mma_utils.cuh"
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// Split-K (FlashDecoding) tensor-core decode via GQA head-packing.
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//
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// Decode has q_len == 1, so S = q @ K^T is a GEMV per head — no tensor-core work
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// on its own. But GQA gives us G = q_head / kv_head query heads that all share
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// one kv_head. We pack those G heads into the M=16 rows of mma.sync.m16n8k16,
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// turning G independent GEMVs into a single GEMM that reuses each loaded K/V tile
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// across all G heads (K/V load is the decode bottleneck, so the reuse is the win,
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// not the flops). The KV sequence is partitioned across gridDim.z blocks so that
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// a decode with only batch*kv_head independent tasks can fill all SMs. Each
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// (batch, kv_head, split) block computes an UN-normalised partial (Oacc, m, l)
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// over its KV slice; the combine kernel below reduces across splits. Fixes the
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// "grid too small" bottleneck (0.04 waves/SM → many blocks) for long-context,
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// small-batch decode.
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//
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// Partial layout (float, contiguous):
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// o_part : [batch, q_head, num_splits, HEAD_DIM]
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// ml_part: [batch, q_head, num_splits, 2] (m, l)
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//
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// Optimizations:
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// - cp.async global→shared for K/V (bypasses registers, cuts instruction count)
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// - XOR swizzle (swiz_col): LD=HEAD_DIM, zero waste, no bank conflicts
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// - pre-scaled Q: Q scaled during load, softmax skips per-tile multiply
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// - single-buffer: keeps smem small for high occupancy
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template <int HEAD_DIM, int BC>
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__global__ void attn_decode_split_kv_mma_kernel(AttentionParams p) {
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constexpr int BR = 16;
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constexpr int KD = HEAD_DIM / 16;
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constexpr int NC8 = BC / 8;
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constexpr int KT2 = BC / 16;
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constexpr int DN8 = HEAD_DIM / 8;
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constexpr int LD = HEAD_DIM;
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constexpr int SWIZ_MASK = (HEAD_DIM >= 64) ? 7 : (HEAD_DIM / 8 - 1);
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const int lane = threadIdx.x;
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const int gid = lane >> 2;
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const int tid4 = lane & 3;
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const int kv_head = blockIdx.x;
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const int batch = blockIdx.y;
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const int split = blockIdx.z;
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const int G = p.q_head / p.kv_head;
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const int q_head0 = kv_head * G;
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__shared__ __align__(16) bf16 sK[BC * HEAD_DIM];
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__shared__ __align__(16) bf16 sV[BC * HEAD_DIM];
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__shared__ __align__(16) bf16 sQ[BR * HEAD_DIM];
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for (int i = lane; i < BR * HEAD_DIM; i += 32) {
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int r = i / HEAD_DIM, d = i % HEAD_DIM;
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bf16 val = __float2bfloat16(0.0f);
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if (r < G) {
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int qh = q_head0 + r;
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val = p.q[(batch * p.q_head + qh) * HEAD_DIM + d];
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}
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sQ[r * LD + swiz_col(d, r, SWIZ_MASK)] = val;
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}
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__syncwarp();
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unsigned Qa[KD][4];
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int qrow_l = (lane & 7) + (lane & 8);
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int qcol_l = (lane & 16) ? 8 : 0;
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#pragma unroll
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for (int kt = 0; kt < KD; kt++)
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ldmatrix_x4(Qa[kt], &sQ[qrow_l * LD + swiz_col(kt * 16 + qcol_l, qrow_l, SWIZ_MASK)]);
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float Oacc[DN8][4];
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#pragma unroll
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for (int j = 0; j < DN8; j++)
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Oacc[j][0] = Oacc[j][1] = Oacc[j][2] = Oacc[j][3] = 0.0f;
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float m0 = -FLT_MAX, m1 = -FLT_MAX, l0 = 0.0f, l1 = 0.0f;
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const int kv_base = (batch * p.kv_head + kv_head) * p.kv_len * HEAD_DIM;
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const int mask_base = batch * p.kv_len;
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const int tiles_total = (p.kv_len + BC - 1) / BC;
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const int tiles_per_split = (tiles_total + p.num_splits - 1) / p.num_splits;
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const int ti_begin = split * tiles_per_split;
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const int ti_end = min(tiles_total, ti_begin + tiles_per_split);
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const int has_mask = p.use_mask && p.mask;
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for (int ti = ti_begin; ti < ti_end; ti++) {
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int kv0 = ti * BC;
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bool full_tile = (kv0 + BC <= p.kv_len);
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if (full_tile) {
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constexpr int VEC = 8;
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int total = BC * HEAD_DIM;
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#pragma unroll
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for (int i = lane * VEC; i < total; i += 32 * VEC) {
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int r = i / HEAD_DIM, d = i % HEAD_DIM;
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int kc = kv0 + r;
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cp_async_16(&sK[r * LD + swiz_col(d, r, SWIZ_MASK)],
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&p.k[kv_base + kc * HEAD_DIM + d]);
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cp_async_16(&sV[r * LD + swiz_col(d, r, SWIZ_MASK)],
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&p.v[kv_base + kc * HEAD_DIM + d]);
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}
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cp_async_commit();
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cp_async_wait_all();
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} else {
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for (int i = lane; i < BC * HEAD_DIM; i += 32) {
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int r = i / HEAD_DIM, d = i % HEAD_DIM;
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int kc = kv0 + r;
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bf16 z = __float2bfloat16(0.0f);
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sK[r * LD + swiz_col(d, r, SWIZ_MASK)] =
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(kc < p.kv_len) ? p.k[kv_base + kc * HEAD_DIM + d] : z;
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sV[r * LD + swiz_col(d, r, SWIZ_MASK)] =
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(kc < p.kv_len) ? p.v[kv_base + kc * HEAD_DIM + d] : z;
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}
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}
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__syncwarp();
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float Sacc[NC8][4];
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mma_compute_scores<KD, NC8>(Qa, sK, LD, SWIZ_MASK, lane, Sacc);
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#pragma unroll
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for (int n8 = 0; n8 < NC8; n8++)
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Sacc[n8][0] *= p.scale, Sacc[n8][1] *= p.scale,
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Sacc[n8][2] *= p.scale, Sacc[n8][3] *= p.scale;
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int maxc = p.is_causal ? min(p.kv_len, p.causal_offset + 1) : p.kv_len;
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mma_softmax_tile<NC8, DN8>(kv0, maxc, maxc,
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mask_base, p.mask, has_mask,
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Sacc, Oacc, m0, m1, l0, l1, lane);
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mma_pv_accumulate<DN8, KT2>(Sacc, sV, LD, SWIZ_MASK, lane, Oacc);
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__syncwarp();
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}
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// ---- write UN-normalised partials for this split ----
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auto split_slot = [&](int h) -> size_t {
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size_t bh = (size_t)batch * p.q_head + h;
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return bh * p.num_splits + split;
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};
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#pragma unroll
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for (int dn8 = 0; dn8 < DN8; dn8++) {
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int d = dn8 * 8 + 2 * tid4;
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int r0 = gid, r1 = gid + 8;
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if (r0 < G) {
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int h = q_head0 + r0;
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float* op = p.o_part + split_slot(h) * HEAD_DIM;
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op[d] = Oacc[dn8][0];
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op[d + 1] = Oacc[dn8][1];
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}
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if (r1 < G) {
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int h = q_head0 + r1;
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float* op = p.o_part + split_slot(h) * HEAD_DIM;
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op[d] = Oacc[dn8][2];
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op[d + 1] = Oacc[dn8][3];
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}
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}
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if (tid4 == 0) {
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int r0 = gid, r1 = gid + 8;
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if (r0 < G) {
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int h = q_head0 + r0;
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float* mp = p.ml_part + split_slot(h) * 2;
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mp[0] = m0; mp[1] = l0;
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}
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if (r1 < G) {
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int h = q_head0 + r1;
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float* mp = p.ml_part + split_slot(h) * 2;
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mp[0] = m1; mp[1] = l1;
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}
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}
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}
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