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AstrAI/csrc/kernels/gqa_decode_attn_mma.cuh

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#pragma once
#include "gqa_common.cuh"
#include "gqa_mma_utils.cuh"
// Tensor-core decode via GQA head-packing with cp.async loads.
//
// Decode has q_len == 1, so S = q @ K^T is a GEMV per head — no tensor-core work
// on its own. But GQA gives us G = q_head / kv_head query heads that all share
// one kv_head. We pack those G heads into the M=16 rows of mma.sync.m16n8k16,
// turning G independent GEMVs into a single GEMM that reuses each loaded K/V tile
// across all G heads (K/V load is the decode bottleneck, so the reuse is the win,
// not the flops). Fragment layout is identical to the prefill mma kernel; the
// only differences are (1) the M rows come from different heads at position 0
// instead of different sequence positions of one head, and (2) causal masking is
// a single scalar bound shared by every row. One warp owns one (batch, kv_head);
// requires G <= 16.
//
// Optimizations:
// - cp.async global→shared for K/V (bypasses registers, cuts instruction count)
// - XOR swizzle (swiz_col): LD=HEAD_DIM, zero waste, no bank conflicts
// - pre-scaled Q: Q scaled during load, softmax skips per-tile multiply
// - single-buffer: keeps smem small for high occupancy
template <int HEAD_DIM, int BC>
__global__ void gqa_decode_attn_mma_kernel(GQAParams p) {
constexpr int BR = 16;
constexpr int KD = HEAD_DIM / 16; // Q/K k-tiles
constexpr int NC8 = BC / 8; // S n-tiles (N=8 each)
constexpr int KT2 = BC / 16; // P k-tiles (K=16 each)
constexpr int DN8 = HEAD_DIM / 8; // O n-tiles (N=8 each)
constexpr int LD = HEAD_DIM; // XOR swizzle handles bank conflicts, zero waste
constexpr int SWIZ_MASK = (HEAD_DIM >= 64) ? 7 : (HEAD_DIM / 8 - 1);
const int lane = threadIdx.x; // single warp
const int gid = lane >> 2; // 0..7 → rows gid, gid+8
const int tid4 = lane & 3;
const int kv_head = blockIdx.x;
const int batch = blockIdx.y;
const int G = p.q_head / p.kv_head;
const int q_head0 = kv_head * G;
extern __shared__ __align__(16) bf16 smem[];
bf16* sK = smem; // [BC][LD]
bf16* sV = sK + BC * LD; // [BC][LD]
bf16* sQ = sV + BC * LD; // [BR][LD]
// ---- stage Q into shared (pre-scaled, swizzled) ----
bf16 scale_bf16 = __float2bfloat16(p.scale);
for (int i = lane; i < BR * HEAD_DIM; i += 32) {
int r = i / HEAD_DIM, d = i % HEAD_DIM;
bf16 val = __float2bfloat16(0.0f);
if (r < G) {
int qh = q_head0 + r;
val = p.q[(batch * p.q_head + qh) * HEAD_DIM + d]; // q_len == 1
}
sQ[r * LD + swiz_col(d, r, SWIZ_MASK)] = __hmul(val, scale_bf16);
}
__syncwarp();
// Q resident A-fragments
unsigned Qa[KD][4];
int qrow_l = (lane & 7) + (lane & 8);
int qcol_l = (lane & 16) ? 8 : 0;
#pragma unroll
for (int kt = 0; kt < KD; kt++)
ldmatrix_x4(Qa[kt], &sQ[qrow_l * LD + swiz_col(kt * 16 + qcol_l, qrow_l, SWIZ_MASK)]);
float Oacc[DN8][4];
#pragma unroll
for (int j = 0; j < DN8; j++)
Oacc[j][0] = Oacc[j][1] = Oacc[j][2] = Oacc[j][3] = 0.0f;
float m0 = -FLT_MAX, m1 = -FLT_MAX, l0 = 0.0f, l1 = 0.0f;
const int kv_base = (batch * p.kv_head + kv_head) * p.kv_len * HEAD_DIM;
const int mask_base = batch * p.kv_len;
const int tiles = (p.kv_len + BC - 1) / BC;
const int has_mask = p.use_mask && p.mask;
for (int ti = 0; ti < tiles; ti++) {
int kv0 = ti * BC;
// ---- load K/V tile to shared (cp.async on full tiles) ----
bool full_tile = (kv0 + BC <= p.kv_len);
if (full_tile) {
constexpr int VEC = 8; // 8 bf16 = 16 bytes per cp.async
int total = BC * HEAD_DIM;
#pragma unroll
for (int i = lane * VEC; i < total; i += 32 * VEC) {
int r = i / HEAD_DIM, d = i % HEAD_DIM;
int kc = kv0 + r;
cp_async_16(&sK[r * LD + swiz_col(d, r, SWIZ_MASK)],
&p.k[kv_base + kc * HEAD_DIM + d]);
cp_async_16(&sV[r * LD + swiz_col(d, r, SWIZ_MASK)],
&p.v[kv_base + kc * HEAD_DIM + d]);
}
cp_async_commit();
cp_async_wait_all();
} else {
for (int i = lane; i < BC * HEAD_DIM; i += 32) {
int r = i / HEAD_DIM, d = i % HEAD_DIM;
int kc = kv0 + r;
bf16 z = __float2bfloat16(0.0f);
sK[r * LD + swiz_col(d, r, SWIZ_MASK)] =
(kc < p.kv_len) ? p.k[kv_base + kc * HEAD_DIM + d] : z;
sV[r * LD + swiz_col(d, r, SWIZ_MASK)] =
(kc < p.kv_len) ? p.v[kv_base + kc * HEAD_DIM + d] : z;
}
}
__syncwarp();
// S = Q @ K^T (Q already pre-scaled, so Sacc includes scale)
float Sacc[NC8][4];
#pragma unroll
for (int n8 = 0; n8 < NC8; n8++) {
Sacc[n8][0] = Sacc[n8][1] = Sacc[n8][2] = Sacc[n8][3] = 0.0f;
int krow_l = n8 * 8 + (lane & 7);
int kcol_h = (lane & 8) ? 8 : 0;
#pragma unroll
for (int kt = 0; kt < KD; kt++) {
unsigned b[2];
ldmatrix_x2(b, &sK[krow_l * LD + swiz_col(kt * 16 + kcol_h, krow_l, SWIZ_MASK)]);
mma16816(Sacc[n8], Qa[kt], b, Sacc[n8]);
}
}
// ---- online softmax (Q pre-scaled → no per-tile scale multiply) ----
float rmax0 = -FLT_MAX, rmax1 = -FLT_MAX;
#pragma unroll
for (int n8 = 0; n8 < NC8; n8++) {
int cc = kv0 + n8 * 8 + 2 * tid4;
bool bc0 = (cc >= p.kv_len) ||
(has_mask && !p.mask[mask_base + cc]);
bool bc1 = (cc + 1 >= p.kv_len) ||
(has_mask && !p.mask[mask_base + cc + 1]);
bool cz = p.is_causal;
int off = p.causal_offset;
bool bad0 = bc0 || (cz && cc > off);
bool bad1 = bc1 || (cz && (cc + 1) > off);
float s0 = bad0 ? -FLT_MAX : Sacc[n8][0];
float s1 = bad1 ? -FLT_MAX : Sacc[n8][1];
float s2 = bad0 ? -FLT_MAX : Sacc[n8][2];
float s3 = bad1 ? -FLT_MAX : Sacc[n8][3];
Sacc[n8][0] = s0; Sacc[n8][1] = s1; Sacc[n8][2] = s2; Sacc[n8][3] = s3;
rmax0 = fmaxf(rmax0, fmaxf(s0, s1));
rmax1 = fmaxf(rmax1, fmaxf(s2, s3));
}
rmax0 = fmaxf(rmax0, __shfl_xor_sync(0xFFFFFFFF, rmax0, 1));
rmax0 = fmaxf(rmax0, __shfl_xor_sync(0xFFFFFFFF, rmax0, 2));
rmax1 = fmaxf(rmax1, __shfl_xor_sync(0xFFFFFFFF, rmax1, 1));
rmax1 = fmaxf(rmax1, __shfl_xor_sync(0xFFFFFFFF, rmax1, 2));
float nm0 = fmaxf(m0, rmax0), nm1 = fmaxf(m1, rmax1);
float corr0 = (nm0 == -FLT_MAX) ? 1.0f : __expf(m0 - nm0);
float corr1 = (nm1 == -FLT_MAX) ? 1.0f : __expf(m1 - nm1);
float rsum0 = 0.0f, rsum1 = 0.0f;
#pragma unroll
for (int n8 = 0; n8 < NC8; n8++) {
float p0 = (Sacc[n8][0] == -FLT_MAX) ? 0.0f : __expf(Sacc[n8][0] - nm0);
float p1 = (Sacc[n8][1] == -FLT_MAX) ? 0.0f : __expf(Sacc[n8][1] - nm0);
float p2 = (Sacc[n8][2] == -FLT_MAX) ? 0.0f : __expf(Sacc[n8][2] - nm1);
float p3 = (Sacc[n8][3] == -FLT_MAX) ? 0.0f : __expf(Sacc[n8][3] - nm1);
Sacc[n8][0] = p0; Sacc[n8][1] = p1; Sacc[n8][2] = p2; Sacc[n8][3] = p3;
rsum0 += p0 + p1;
rsum1 += p2 + p3;
}
rsum0 += __shfl_xor_sync(0xFFFFFFFF, rsum0, 1);
rsum0 += __shfl_xor_sync(0xFFFFFFFF, rsum0, 2);
rsum1 += __shfl_xor_sync(0xFFFFFFFF, rsum1, 1);
rsum1 += __shfl_xor_sync(0xFFFFFFFF, rsum1, 2);
l0 = l0 * corr0 + rsum0;
l1 = l1 * corr1 + rsum1;
m0 = nm0; m1 = nm1;
#pragma unroll
for (int j = 0; j < DN8; j++) {
Oacc[j][0] *= corr0; Oacc[j][1] *= corr0;
Oacc[j][2] *= corr1; Oacc[j][3] *= corr1;
}
// O += P @ V
#pragma unroll
for (int kt2 = 0; kt2 < KT2; kt2++) {
unsigned Pa[4];
Pa[0] = pk2(Sacc[kt2 * 2][0], Sacc[kt2 * 2][1]);
Pa[1] = pk2(Sacc[kt2 * 2][2], Sacc[kt2 * 2][3]);
Pa[2] = pk2(Sacc[kt2 * 2 + 1][0], Sacc[kt2 * 2 + 1][1]);
Pa[3] = pk2(Sacc[kt2 * 2 + 1][2], Sacc[kt2 * 2 + 1][3]);
int vrow_l = kt2 * 16 + (lane & 15);
#pragma unroll
for (int dn8 = 0; dn8 < DN8; dn8++) {
unsigned b[2];
ldmatrix_x2_trans(b, &sV[vrow_l * LD + swiz_col(dn8 * 8, vrow_l, SWIZ_MASK)]);
mma16816(Oacc[dn8], Pa, b, Oacc[dn8]);
}
}
__syncwarp(); // sK/sV reused next tile
}
// ---- write output ----
float rl0 = (l0 > 1e-20f) ? (1.0f / l0) : 0.0f;
float rl1 = (l1 > 1e-20f) ? (1.0f / l1) : 0.0f;
#pragma unroll
for (int dn8 = 0; dn8 < DN8; dn8++) {
int d = dn8 * 8 + 2 * tid4;
int r0 = gid, r1 = gid + 8;
if (r0 < G) {
int o_off = (batch * p.q_head + q_head0 + r0) * HEAD_DIM + d;
p.o[o_off] = __float2bfloat16(Oacc[dn8][0] * rl0);
p.o[o_off + 1] = __float2bfloat16(Oacc[dn8][1] * rl0);
}
if (r1 < G) {
int o_off = (batch * p.q_head + q_head0 + r1) * HEAD_DIM + d;
p.o[o_off] = __float2bfloat16(Oacc[dn8][2] * rl1);
p.o[o_off + 1] = __float2bfloat16(Oacc[dn8][3] * rl1);
}
}
}
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