250 lines
11 KiB
Plaintext
250 lines
11 KiB
Plaintext
#pragma once
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#include "gqa_common.cuh"
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#include "gqa_mma_utils.cuh"
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// Tensor-core prefill, register-resident flash attention (raw mma.sync PTX).
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// One warp owns BR=16 query rows. S = Q@K^T and O = P@V run on bf16 tensor
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// cores via mma.sync.m16n8k16 (f32 accumulate). Q stays resident in registers;
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// S, O, and the online-softmax stats (m, l) live in registers too — nothing is
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// staged through shared memory except the cooperatively-loaded K/V tiles. The
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// mma fragment layout is used directly: the S accumulator (f32) maps element-
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// for-element onto the P matrix_a (bf16) operand, so softmax needs no shuffle
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// repack; row reductions fold across the 4-lane thread group. Templated on
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// <HEAD_DIM, WARPS, BC> with BC a multiple of 16.
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//
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// Optimizations vs v6 baseline: shared sQ staging (single area, serialized
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// per-warp load) → cuts smem from (2*BC + WARPS*BR)*LD to (2*BC + BR)*LD bf16,
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// raising occupancy; pre-scale Q by attention scale during Q load → removes
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// per-tile scale multiply in the softmax loop; cp.async global→shared for K/V
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// full-tile loads → eliminates shared-store bank conflicts and register staging,
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// scalar fallback only for the last partial tile; causal tile skipping (block-
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// level early break + warp-level skip); XOR swizzle (swiz_col) at 8-bf16 chunk
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// granularity → eliminates ldmatrix bank conflicts without LD padding, setting
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// LD=HEAD_DIM (zero waste).
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template <int HEAD_DIM, int WARPS, int BC>
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__global__ void gqa_prefill_attn_mma_kernel(GQAParams p) {
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constexpr int BR = 16;
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constexpr int KD = HEAD_DIM / 16; // Q/K k-tiles
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constexpr int NC8 = BC / 8; // S n-tiles (N=8 each)
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constexpr int KT2 = BC / 16; // P k-tiles (K=16 each)
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constexpr int DN8 = HEAD_DIM / 8; // O n-tiles (N=8 each)
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constexpr int LD = HEAD_DIM; // XOR swizzle (swiz_col) handles bank conflicts
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const int warp = threadIdx.x / 32;
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const int lane = threadIdx.x % 32;
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const int gid = lane >> 2; // 0..7 → rows gid, gid+8
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const int tid4 = lane & 3; // 0..3
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const int nthreads = WARPS * 32;
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const int q_head = blockIdx.y;
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const int batch = blockIdx.z;
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const int kv_head = q_head / (p.q_head / p.kv_head);
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const int qrow0 = (blockIdx.x * WARPS + warp) * BR;
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extern __shared__ __align__(16) bf16 smem[];
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bf16* sK = smem; // [BC][LD]
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bf16* sV = sK + BC * LD; // [BC][LD]
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bf16* sQ = sV + BC * LD; // shared staging [BR][LD]
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// Q resident A-fragments (loaded once per warp via shared staging).
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// Pre-scale by attention scale so softmax doesn't need to multiply later.
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const int q_base = ((batch * p.q_head + q_head) * p.q_len) * HEAD_DIM;
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unsigned Qa[KD][4];
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bf16 scale_bf16 = __float2bfloat16(p.scale);
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int qrow_l = (lane & 7) + (lane & 8); // 0..15
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int qcol_l = (lane & 16) ? 8 : 0;
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for (int w = 0; w < WARPS; w++) {
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if (warp == w) {
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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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int qr = qrow0 + r;
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bf16 qv = (qr < p.q_len) ? p.q[q_base + qr * HEAD_DIM + d]
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: __float2bfloat16(0.0f);
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sQ[r * LD + swiz_col(d, r)] = __hmul(qv, scale_bf16);
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}
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__syncwarp();
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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)]);
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}
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__syncthreads(); // prevent next warp from overwriting sQ prematurely
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}
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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 tiles = (p.kv_len + BC - 1) / BC;
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const int qr0 = qrow0 + gid; // row for c0/c1
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const int qr1 = qrow0 + gid + 8; // row for c2/c3
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// Causal tile-skip bounds (no-op when is_causal == 0)
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const int use_skip = p.is_causal;
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const int max_kv = qrow0 + BR - 1 + p.causal_offset;
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const int block_max_kv =
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blockIdx.x * WARPS * BR + WARPS * BR - 1 + p.causal_offset;
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const int has_mask = p.use_mask && p.mask;
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const int mb = batch * p.kv_len;
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for (int ti = 0; ti < tiles; ti++) {
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int kv0 = ti * BC;
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// Block-level causal early break
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if (use_skip && kv0 > block_max_kv) break;
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// ---- load K/V tile to shared memory (cp.async on full tiles) ----
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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; // bf16 per cp.async unit (16 bytes)
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int total = BC * HEAD_DIM;
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#pragma unroll
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for (int i = threadIdx.x * VEC; i < total; i += nthreads * VEC) {
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int r = i / HEAD_DIM;
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int 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)], &p.k[kv_base + kc * HEAD_DIM + d]);
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cp_async_16(&sV[r * LD + swiz_col(d, r)], &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 = threadIdx.x; i < BC * HEAD_DIM; i += nthreads) {
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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)] = (kc < p.kv_len)
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? p.k[kv_base + kc * HEAD_DIM + d] : z;
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sV[r * LD + swiz_col(d, r)] = (kc < p.kv_len)
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? p.v[kv_base + kc * HEAD_DIM + d] : z;
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}
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}
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__syncthreads();
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// Warp-level causal skip
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if (!use_skip || kv0 <= max_kv) {
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// S = Q @ K^T → Sacc[n8][0..3] (n8: 8 kv cols each)
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float Sacc[NC8][4];
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#pragma unroll
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for (int n8 = 0; n8 < NC8; n8++) {
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Sacc[n8][0] = Sacc[n8][1] = Sacc[n8][2] = Sacc[n8][3] = 0.0f;
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int krow_l = n8 * 8 + (lane & 7);
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int kcol_h = (lane & 8) ? 8 : 0;
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#pragma unroll
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for (int kt = 0; kt < KD; kt++) {
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unsigned b[2];
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ldmatrix_x2(b, &sK[krow_l * LD + swiz_col(kt * 16 + kcol_h, krow_l)]);
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mma16816(Sacc[n8], Qa[kt], b, Sacc[n8]);
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}
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}
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// ---- online softmax (in registers) ----
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// Q is pre-scaled, so Sacc already includes the attention scale.
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int maxc0 = p.is_causal ? min(p.kv_len, qr0 + p.causal_offset + 1)
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: p.kv_len;
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int maxc1 = p.is_causal ? min(p.kv_len, qr1 + p.causal_offset + 1)
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: p.kv_len;
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float rmax0 = -FLT_MAX, rmax1 = -FLT_MAX;
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#pragma unroll
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for (int n8 = 0; n8 < NC8; n8++) {
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int cc = kv0 + n8 * 8 + 2 * tid4;
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int c1 = cc + 1;
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bool b0 = (cc >= maxc0) || (has_mask && !p.mask[mb + cc]);
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bool b1 = (c1 >= maxc0) || (has_mask && !p.mask[mb + c1]);
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bool b2 = (cc >= maxc1) || (has_mask && !p.mask[mb + cc]);
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bool b3 = (c1 >= maxc1) || (has_mask && !p.mask[mb + c1]);
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float s0 = b0 ? -FLT_MAX : Sacc[n8][0];
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float s1 = b1 ? -FLT_MAX : Sacc[n8][1];
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float s2 = b2 ? -FLT_MAX : Sacc[n8][2];
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float s3 = b3 ? -FLT_MAX : Sacc[n8][3];
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Sacc[n8][0] = s0; Sacc[n8][1] = s1;
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Sacc[n8][2] = s2; Sacc[n8][3] = s3;
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rmax0 = fmaxf(rmax0, fmaxf(s0, s1));
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rmax1 = fmaxf(rmax1, fmaxf(s2, s3));
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}
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rmax0 = fmaxf(rmax0, __shfl_xor_sync(0xFFFFFFFF, rmax0, 1));
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rmax0 = fmaxf(rmax0, __shfl_xor_sync(0xFFFFFFFF, rmax0, 2));
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rmax1 = fmaxf(rmax1, __shfl_xor_sync(0xFFFFFFFF, rmax1, 1));
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rmax1 = fmaxf(rmax1, __shfl_xor_sync(0xFFFFFFFF, rmax1, 2));
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float nm0 = fmaxf(m0, rmax0), nm1 = fmaxf(m1, rmax1);
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float corr0 = (nm0 == -FLT_MAX) ? 1.0f : __expf(m0 - nm0);
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float corr1 = (nm1 == -FLT_MAX) ? 1.0f : __expf(m1 - nm1);
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float rsum0 = 0.0f, rsum1 = 0.0f;
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#pragma unroll
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for (int n8 = 0; n8 < NC8; n8++) {
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float p0 = (Sacc[n8][0] == -FLT_MAX) ? 0.0f
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: __expf(Sacc[n8][0] - nm0);
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float p1 = (Sacc[n8][1] == -FLT_MAX) ? 0.0f
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: __expf(Sacc[n8][1] - nm0);
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float p2 = (Sacc[n8][2] == -FLT_MAX) ? 0.0f
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: __expf(Sacc[n8][2] - nm1);
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float p3 = (Sacc[n8][3] == -FLT_MAX) ? 0.0f
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: __expf(Sacc[n8][3] - nm1);
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Sacc[n8][0] = p0; Sacc[n8][1] = p1;
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Sacc[n8][2] = p2; Sacc[n8][3] = p3;
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rsum0 += p0 + p1;
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rsum1 += p2 + p3;
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}
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rsum0 += __shfl_xor_sync(0xFFFFFFFF, rsum0, 1);
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rsum0 += __shfl_xor_sync(0xFFFFFFFF, rsum0, 2);
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rsum1 += __shfl_xor_sync(0xFFFFFFFF, rsum1, 1);
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rsum1 += __shfl_xor_sync(0xFFFFFFFF, rsum1, 2);
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l0 = l0 * corr0 + rsum0;
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l1 = l1 * corr1 + rsum1;
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m0 = nm0; m1 = nm1;
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// rescale O accumulator by per-row correction
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#pragma unroll
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for (int j = 0; j < DN8; j++) {
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Oacc[j][0] *= corr0; Oacc[j][1] *= corr0;
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Oacc[j][2] *= corr1; Oacc[j][3] *= corr1;
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}
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// O += P @ V
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#pragma unroll
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for (int kt2 = 0; kt2 < KT2; kt2++) {
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unsigned Pa[4];
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Pa[0] = pk2(Sacc[kt2 * 2][0], Sacc[kt2 * 2][1]);
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Pa[1] = pk2(Sacc[kt2 * 2][2], Sacc[kt2 * 2][3]);
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Pa[2] = pk2(Sacc[kt2 * 2 + 1][0], Sacc[kt2 * 2 + 1][1]);
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Pa[3] = pk2(Sacc[kt2 * 2 + 1][2], Sacc[kt2 * 2 + 1][3]);
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int vrow_l = kt2 * 16 + (lane & 15);
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#pragma unroll
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for (int dn8 = 0; dn8 < DN8; dn8++) {
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unsigned b[2];
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ldmatrix_x2_trans(b, &sV[vrow_l * LD + swiz_col(dn8 * 8, vrow_l)]);
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mma16816(Oacc[dn8], Pa, b, Oacc[dn8]);
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}
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}
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} // if active (warp-level causal skip)
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__syncthreads();
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}
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// ---- write output ----
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float rl0 = (l0 > 1e-20f) ? (1.0f / l0) : 0.0f;
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float rl1 = (l1 > 1e-20f) ? (1.0f / l1) : 0.0f;
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const int o_base = ((batch * p.q_head + q_head) * p.q_len) * HEAD_DIM;
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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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if (qr0 < p.q_len) {
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p.o[o_base + qr0 * HEAD_DIM + d] =
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__float2bfloat16(Oacc[dn8][0] * rl0);
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p.o[o_base + qr0 * HEAD_DIM + d + 1] =
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__float2bfloat16(Oacc[dn8][1] * rl0);
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}
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if (qr1 < p.q_len) {
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p.o[o_base + qr1 * HEAD_DIM + d] =
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__float2bfloat16(Oacc[dn8][2] * rl1);
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p.o[o_base + qr1 * HEAD_DIM + d + 1] =
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__float2bfloat16(Oacc[dn8][3] * rl1);
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}
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}
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}
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