294 lines
12 KiB
Plaintext
294 lines
12 KiB
Plaintext
#pragma once
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#include <cfloat>
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#include <cuda_fp16.h>
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#include <cuda_runtime.h>
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// Shared MMA utilities for tensor-core GQA kernels.
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// mma.sync.m16n8k16 PTX wrappers, ldmatrix helpers, and bf16 packing.
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// mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32
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__device__ __forceinline__ void mma16816(float* d, const unsigned* a,
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const unsigned* b, const float* c) {
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asm volatile(
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"mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 "
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"{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};"
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: "=f"(d[0]), "=f"(d[1]), "=f"(d[2]), "=f"(d[3])
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: "r"(a[0]), "r"(a[1]), "r"(a[2]), "r"(a[3]), "r"(b[0]), "r"(b[1]),
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"f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3]));
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}
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// read two adjacent bf16 from smem as one packed .b32 (elem0 low, elem1 high)
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__device__ __forceinline__ unsigned ld2(const bf16* p) {
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return *reinterpret_cast<const unsigned*>(p);
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}
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// pack two floats into one bf16x2 as .b32
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__device__ __forceinline__ unsigned pk2(float a, float b) {
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__nv_bfloat162 v = __floats2bfloat162_rn(a, b);
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return *reinterpret_cast<unsigned*>(&v);
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}
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// pack two (non-contiguous) bf16 into one .b32
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__device__ __forceinline__ unsigned pkb(bf16 a, bf16 b) {
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__nv_bfloat162 v;
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v.x = a;
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v.y = b;
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return *reinterpret_cast<unsigned*>(&v);
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}
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// ldmatrix: cooperatively load mma fragments from smem (one instruction per
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// 16x16 / 16x8 tile) with the exact register layout mma expects — replaces the
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// scalar per-thread fragment packing, cutting shared-load instructions and bank
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// conflicts. Each lane supplies the shared address of one 8-wide row.
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__device__ __forceinline__ void ldmatrix_x4(unsigned* r, const bf16* p) {
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unsigned a = __cvta_generic_to_shared(p);
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asm volatile("ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];"
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: "=r"(r[0]), "=r"(r[1]), "=r"(r[2]), "=r"(r[3])
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: "r"(a));
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}
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__device__ __forceinline__ void ldmatrix_x2(unsigned* r, const bf16* p) {
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unsigned a = __cvta_generic_to_shared(p);
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asm volatile("ldmatrix.sync.aligned.m8n8.x2.shared.b16 {%0,%1}, [%2];"
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: "=r"(r[0]), "=r"(r[1])
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: "r"(a));
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}
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__device__ __forceinline__ void ldmatrix_x2_trans(unsigned* r, const bf16* p) {
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unsigned a = __cvta_generic_to_shared(p);
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asm volatile("ldmatrix.sync.aligned.m8n8.x2.trans.shared.b16 {%0,%1}, [%2];"
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: "=r"(r[0]), "=r"(r[1])
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: "r"(a));
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}
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// XOR swizzle for shared-memory column at 8-bf16 chunk granularity.
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// Eliminates ldmatrix bank conflicts without LD padding: consecutive rows
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// land in distinct bank groups. swiz_col(d, r, mask) = ((d>>3)^(r&mask))<<3 | (d&7).
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// mask must cover log2(HEAD_DIM/8) chunk bits but stay within LD: use 7 for
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// HEAD_DIM>=64 (8+ chunks), 3 for HEAD_DIM=32 (4 chunks). Default 7 keeps
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// existing HEAD_DIM>=64 call sites working unchanged.
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__device__ __forceinline__ int swiz_col(int d, int r, int mask = 7) {
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return ((d >> 3) ^ (r & mask)) << 3 | (d & 7);
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}
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// cp.async: copy 16 bytes (8 bf16) from global to shared memory directly,
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// bypassing registers. Eliminates shared-store bank conflicts and cuts
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// load-loop instruction count in half (1 cp.async vs 1 LDG + 1 STS).
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// Requires sm_80+.
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__device__ __forceinline__ void cp_async_16(bf16* smem_ptr, const void* gmem_ptr) {
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unsigned smem_addr = __cvta_generic_to_shared(smem_ptr);
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asm volatile("cp.async.ca.shared.global [%0], [%1], 16;"
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:: "r"(smem_addr), "l"(gmem_ptr));
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}
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// Predicated cp.async: copy 16 bytes when `pred`, otherwise zero-fill the
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// destination (src-size operand = 0 → no bytes read from src, so an
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// out-of-bounds src address is never dereferenced). Lets full and partial
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// tiles share one uniform async load path — no scalar fallback branch.
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__device__ __forceinline__ void cp_async_16_pred(bf16* smem_ptr,
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const void* gmem_ptr,
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bool pred) {
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unsigned smem_addr = __cvta_generic_to_shared(smem_ptr);
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int src_size = pred ? 16 : 0;
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asm volatile("cp.async.ca.shared.global [%0], [%1], 16, %2;"
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:: "r"(smem_addr), "l"(gmem_ptr), "r"(src_size));
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}
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__device__ __forceinline__ void cp_async_commit() {
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asm volatile("cp.async.commit_group;");
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}
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__device__ __forceinline__ void cp_async_wait_all() {
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asm volatile("cp.async.wait_all;");
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}
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// Wait until at most N commit groups are still in flight. Used for
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// double-buffered pipelining: wait_group<1> lets the next tile's cp.async
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// continue while ensuring the current tile's data is ready.
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template <int N>
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__device__ __forceinline__ void cp_async_wait_group() {
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asm volatile("cp.async.wait_group %0;" :: "n"(N));
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}
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// ---------------------------------------------------------------------------
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// Q-load: load query rows directly from global memory into mma A-operand
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// register layout. One call replaces ~15 duplicated lines in each MMA kernel.
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// stride_row is p.q_stride_h for decode (q_len=1, G heads) or
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// p.q_stride_l for prefill (multi-q rows).
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// ---------------------------------------------------------------------------
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template <int KD>
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__device__ inline void load_q_mma_frags(
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const bf16* __restrict__ q,
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int stride_row,
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int stride_d,
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int qra, int qrb,
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bool va, bool vb,
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int tid4,
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unsigned Qa[KD][4])
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{
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#pragma unroll
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for (int kt = 0; kt < KD; kt++) {
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int c = kt * 16 + tid4 * 2;
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const unsigned* pau = reinterpret_cast<const unsigned*>(
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&q[qra * stride_row + c * stride_d]);
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const unsigned* pbu = reinterpret_cast<const unsigned*>(
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&q[qrb * stride_row + c * stride_d]);
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Qa[kt][0] = va ? pau[0] : 0u;
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Qa[kt][1] = vb ? pbu[0] : 0u;
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Qa[kt][2] = va ? pau[4] : 0u;
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Qa[kt][3] = vb ? pbu[4] : 0u;
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}
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}
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// ---------------------------------------------------------------------------
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// Shared MMA compute functions — used by both decode and prefill MMA kernels.
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// Extracted because S=Q@K^T, online softmax, and P@V are structurally identical
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// between the two kernels; only the per-row causal/mask bounds differ.
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// ---------------------------------------------------------------------------
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// S = Q @ K^T (Qa pre-loaded by the caller; scale applied post-mma in the
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// caller to avoid bf16 precision loss).
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// LD and SWIZ_MASK are constexpr in the calling kernel — passing them as
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// runtime ints lets the compiler fold them while keeping the signature clean.
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template <int KD, int NC8>
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__device__ inline void mma_compute_scores(
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const unsigned Qa[KD][4],
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const bf16* __restrict__ sK,
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int LD,
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int SWIZ_MASK,
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int lane,
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float Sacc[NC8][4])
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{
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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, SWIZ_MASK)]);
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mma16816(Sacc[n8], Qa[kt], b, Sacc[n8]);
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}
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}
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}
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// Online softmax + Oacc rescale for one K/V tile.
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// maxc0/maxc1: per-row KV column bounds (prefill: per-query-row causal limits;
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// decode: same value for both rows since q_len==1).
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// qrow0/qrow1: query row indices (for 3D mask indexing; decode passes 0).
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// mask_b_stride/mask_q_stride: mask layout (2D: mask_q_stride=0; 3D: =kv_len).
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// Reads Sacc (Q@K^T scores), applies causal/mask, computes P = exp(S - nm),
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// rescales Oacc by exp(m_old - nm), and updates m/l — all in place.
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template <int NC8, int DN8>
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__device__ inline void mma_softmax_tile(
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int kv0,
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int maxc0,
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int maxc1,
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int qrow0,
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int qrow1,
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int mask_b_stride,
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int mask_q_stride,
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int mask_batch,
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const bool* __restrict__ mask,
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bool has_mask,
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float Sacc[NC8][4],
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float Oacc[DN8][4],
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float& m0, float& m1,
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float& l0, float& l1,
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int lane)
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{
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int tid4 = lane & 3;
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// Mask out-of-bounds / masked columns: set -FLT_MAX so expf → 0 downstream
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// without per-element sentinel checks. Compute tile-local row maxima.
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float rmax0 = -FLT_MAX, rmax1 = -FLT_MAX;
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int mask_base0 = mask_batch * mask_b_stride + qrow0 * mask_q_stride;
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int mask_base1 = mask_batch * mask_b_stride + qrow1 * mask_q_stride;
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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 && !mask[mask_base0 + cc]);
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bool b1 = (c1 >= maxc0) || (has_mask && !mask[mask_base0 + c1]);
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bool b2 = (cc >= maxc1) || (has_mask && !mask[mask_base1 + cc]);
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bool b3 = (c1 >= maxc1) || (has_mask && !mask[mask_base1 + 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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// Warp-reduce row maxima across the 4-lane thread group (xor 1, xor 2).
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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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// nm = max(running max m, tile-local max rmax) — updated running maximum.
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float nm0 = fmaxf(m0, rmax0), nm1 = fmaxf(m1, rmax1);
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// corr rescales Oacc and l by exp(m_old - nm). When all-masked (m == nm ==
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// -FLT_MAX), exp(0) = 1 — correct, no guard needed.
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float corr0 = __expf(m0 - nm0);
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float corr1 = __expf(m1 - nm1);
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// pn guards only the all-masked-row edge: if nm == -FLT_MAX, exp(S - nm)
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// gives 1 not 0 for masked entries. Two scalar masks replace 4*NC8
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// per-element comparisons.
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float pn0 = (nm0 == -FLT_MAX) ? 0.0f : 1.0f;
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float pn1 = (nm1 == -FLT_MAX) ? 0.0f : 1.0f;
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// P = exp(S - nm) for each element. Masked entries (Sacc = -FLT_MAX) give
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// exp(-inf) ≈ 0 naturally; pn zero-fills the all-masked-row edge.
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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 = pn0 * __expf(Sacc[n8][0] - nm0);
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float p1 = pn0 * __expf(Sacc[n8][1] - nm0);
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float p2 = pn1 * __expf(Sacc[n8][2] - nm1);
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float p3 = pn1 * __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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#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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}
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// O += P @ V (Sacc must contain P = attention weights after softmax).
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template <int DN8, int KT2>
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__device__ inline void mma_pv_accumulate(
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float Sacc[][4],
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const bf16* __restrict__ sV,
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int LD, int SWIZ_MASK, int lane,
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float Oacc[DN8][4])
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{
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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, SWIZ_MASK)]);
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mma16816(Oacc[dn8], Pa, b, Oacc[dn8]);
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}
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}
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}
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