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[ROCm] Add head-fused HIP sparse MLA decode kernel for gfx950 #57282

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[ROCm] Add head-fused HIP sparse MLA decode kernel for gfx950 #57282

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216 changes: 216 additions & 0 deletions csrc/rocm/mla/sparse_mla_decode_m5.hip
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// Milestone 1: single-kernel sparse MLA decode attention for DSV4.1-Flash (gfx950).
// Collapses the Triton partial + reduce kernel PAIR into ONE flash-style online-softmax
// kernel. NO split-K launch, NO separate reduce kernel: the split-over-tokens reduction
// is done IN-WORKGROUP via LDS to hide the serial-chain memory latency.
//
// MQA: one 512-dim KV latent per token shared across all H heads.
// score_h = dot(q[h], K) * scale ; softmax over attended tokens ; out_h = sum_j p_hj*K_j
// V == K (full 512-dim latent, nope-dequant + rope-bf16).
//
// fp8_ds_mla cache layout (matches _sparse_attn_decode_gfx950_partial_kernel):
// page = 64 tokens. Per-page byte stride = 64*576 + 64*8 = 37376.
// token t data: [t*576 : t*576+448] = 448 fp8_e4m3fn (nope), 7 blocks of 64
// [t*576+448 : t*576+576] = 64 bf16 (rope), 128 bytes
// token t scale: [64*576 + t*8 : +7] = 7 e8m0 (ue8m0) bytes, one per 64-nope-block
//
// Grid: (B*H) workgroups, block = NWAVE waves. Wave w handles strided tokens {w,w+NWAVE,..}.
// Lane owns dims [lane*8,+8); K,q,acc in REGISTERS. Per-wave chain is SW-PREFETCHED
// (next token's global loads issued before current token's compute) to overlap the
// K-load latency. NWAVE partial softmaxes merged via LDS by wave 0.

#include <hip/hip_runtime.h>
#include <hip/hip_bf16.h>

#define HEAD_DIM 512
#define NOPE_DIM 448
#define PAGE_SIZE 64
#define PAGE_STRIDE 37376
#define TOK_DATA_STRIDE 576
#define SCALE_REGION_OFF 36864
#define DPT 8
#ifndef NWAVE
#define NWAVE 16
#endif
#define NEG (-3.4028234663852886e38f)

__device__ __forceinline__ float fp8_e4m3fn_to_float(unsigned char b) {
unsigned int s = (b >> 7) & 1u;
unsigned int e = (b >> 3) & 0xFu;
unsigned int m = b & 0x7u;
if (e == 0u) { float v = (float)m * (1.0f / 512.0f); return s ? -v : v; }
if (e == 0xFu && m == 0x7u) return 0.0f;
unsigned int bits = (s << 31) | ((e + 120u) << 23) | (m << 20);
return __uint_as_float(bits);
}
__device__ __forceinline__ float e8m0_to_float(unsigned char enc) {
unsigned int bits = (enc == 0u) ? (1u << 22) : ((unsigned int)enc << 23);
return __uint_as_float(bits);
}
__device__ __forceinline__ float bf16_round(float x) { return (float)(__hip_bfloat16)x; }

// (td, sc, valid) descriptor for token t.
__device__ __forceinline__ void tok_desc(
int t, int swa_len, int b, int swa_maxlen, int topk_maxlen,
const int* swa_indices, const int* topk_indices,
const unsigned char* swa_cache, const unsigned char* kv_cache,
int swa_num_rows, int kv_num_rows,
const unsigned char*& td, const unsigned char*& sc, bool& valid)
{
int slot; const unsigned char* cache; int num_rows;
if (t < swa_len) { slot = swa_indices[b * swa_maxlen + t]; cache = swa_cache; num_rows = swa_num_rows; }
else { slot = topk_indices[b * topk_maxlen + (t - swa_len)]; cache = kv_cache; num_rows = kv_num_rows; }
valid = (slot >= 0) && (slot < num_rows);
int safe = valid ? slot : 0;
int page = safe / PAGE_SIZE, pos = safe % PAGE_SIZE;
td = cache + (long)page * PAGE_STRIDE + (long)pos * TOK_DATA_STRIDE;
sc = cache + (long)page * PAGE_STRIDE + SCALE_REGION_OFF + (long)pos * 8;
}

extern "C" __global__ void sparse_mla_decode_kernel(
const __hip_bfloat16* __restrict__ q,
const unsigned char* __restrict__ swa_cache,
const int* __restrict__ swa_indices,
const int* __restrict__ swa_lens,
const unsigned char* __restrict__ kv_cache,
const int* __restrict__ topk_indices,
const int* __restrict__ topk_lens,
__hip_bfloat16* __restrict__ out,
float* __restrict__ lse,
int B, int H, int swa_maxlen, int topk_maxlen,
int swa_num_rows, int kv_num_rows, float scale)
{
const int bid = blockIdx.x;
const int b = bid / H;
const int h = bid % H;
const int wave = threadIdx.x >> 6;
const int lane = threadIdx.x & 63;
const int d0 = lane * DPT;
const bool is_nope = (d0 < NOPE_DIM);
const int sblk = lane >> 3;
// Byte offset of this lane's 8 dims within a token's 576-byte data region:
// nope: 1 byte/dim (fp8) at d0 ; rope: 2 bytes/dim (bf16) at 448+(d0-448)*2.
const int boff = is_nope ? d0 : (NOPE_DIM + (d0 - NOPE_DIM) * 2);

__shared__ float sm[NWAVE];
__shared__ float sl[NWAVE];
__shared__ float sacc[NWAVE][HEAD_DIM];

float qreg[DPT];
const __hip_bfloat16* qrow = q + ((long)b * H + h) * HEAD_DIM + d0;
#pragma unroll
for (int i = 0; i < DPT; ++i) qreg[i] = (float)qrow[i];

float acc[DPT];
#pragma unroll
for (int i = 0; i < DPT; ++i) acc[i] = 0.0f;
float m_i = NEG, l_i = 0.0f;

const int swa_len = swa_lens[b];
const int topk_len = topk_lens[b];
const int ntok = swa_len + topk_len;

// Software-pipelined chain over this wave's tokens: raw global loads for the NEXT
// token are issued a full iteration ahead so the load latency overlaps compute.
if (wave < ntok) {
const unsigned char *td, *sc; bool valid;
tok_desc(wave, swa_len, b, swa_maxlen, topk_maxlen, swa_indices, topk_indices,
swa_cache, kv_cache, swa_num_rows, kv_num_rows, td, sc, valid);
uint2 raw[2];
raw[0] = *reinterpret_cast<const uint2*>(td + boff);
raw[1] = *reinterpret_cast<const uint2*>(td + boff + 8);
unsigned char scb = sc[sblk];
bool cur_valid = valid;

for (int t = wave; t < ntok; t += NWAVE) {
int tn = t + NWAVE;
uint2 raw_n[2]; unsigned char scb_n = 0; bool nxt_valid = false;
if (tn < ntok) {
const unsigned char *tdn, *scn; bool vn;
tok_desc(tn, swa_len, b, swa_maxlen, topk_maxlen, swa_indices, topk_indices,
swa_cache, kv_cache, swa_num_rows, kv_num_rows, tdn, scn, vn);
raw_n[0] = *reinterpret_cast<const uint2*>(tdn + boff); // issue next load
raw_n[1] = *reinterpret_cast<const uint2*>(tdn + boff + 8);
scb_n = scn[sblk];
nxt_valid = vn;
}

// Dequant current token from registers.
float K[DPT];
if (!cur_valid) {
#pragma unroll
for (int i = 0; i < DPT; ++i) K[i] = 0.0f;
} else if (is_nope) {
const unsigned char* pb = reinterpret_cast<const unsigned char*>(raw);
float esc = e8m0_to_float(scb);
#pragma unroll
for (int i = 0; i < DPT; ++i) K[i] = bf16_round(fp8_e4m3fn_to_float(pb[i]) * esc);
} else {
const __hip_bfloat16* rb = reinterpret_cast<const __hip_bfloat16*>(raw);
#pragma unroll
for (int i = 0; i < DPT; ++i) { float rv = (float)rb[i]; K[i] = (rv == rv) ? rv : 0.0f; }
}

float part = 0.0f;
#pragma unroll
for (int i = 0; i < DPT; ++i) part += qreg[i] * K[i];
#pragma unroll
for (int off = 32; off > 0; off >>= 1) part += __shfl_down(part, off, 64);
float score = __shfl(part, 0, 64);

float s = cur_valid ? (score * scale) : NEG;
float m_new = fmaxf(m_i, s);
float alpha = __expf(m_i - m_new);
float p = cur_valid ? __expf(s - m_new) : 0.0f;
float p_bf = bf16_round(p);
l_i = l_i * alpha + p;
#pragma unroll
for (int i = 0; i < DPT; ++i) acc[i] = acc[i] * alpha + p_bf * K[i];
m_i = m_new;

raw[0] = raw_n[0]; raw[1] = raw_n[1]; scb = scb_n; cur_valid = nxt_valid;
}
}

if (lane == 0) { sm[wave] = m_i; sl[wave] = l_i; }
#pragma unroll
for (int i = 0; i < DPT; ++i) sacc[wave][d0 + i] = acc[i];
__syncthreads();

if (wave == 0) {
float mf = NEG;
#pragma unroll
for (int w = 0; w < NWAVE; ++w) mf = fmaxf(mf, sm[w]);
float lf = 0.0f, wgt[NWAVE];
#pragma unroll
for (int w = 0; w < NWAVE; ++w) {
float e = (sm[w] > NEG) ? __expf(sm[w] - mf) : 0.0f;
wgt[w] = e; lf += e * sl[w];
}
float inv = (lf > 0.0f) ? (1.0f / lf) : 0.0f;
__hip_bfloat16* orow = out + ((long)b * H + h) * HEAD_DIM + d0;
#pragma unroll
for (int i = 0; i < DPT; ++i) {
float a = 0.0f;
#pragma unroll
for (int w = 0; w < NWAVE; ++w) a += wgt[w] * sacc[w][d0 + i];
orow[i] = (__hip_bfloat16)(a * inv);
}
if (lane == 0) lse[b * H + h] = mf + logf(lf > 0.0f ? lf : 1e-30f);
}
}

extern "C" void sparse_mla_decode(
const void* q, const void* swa_cache, const int* swa_indices, const int* swa_lens,
const void* kv_cache, const int* topk_indices, const int* topk_lens,
void* out, float* lse,
int B, int H, int swa_maxlen, int topk_maxlen,
int swa_num_rows, int kv_num_rows, float scale, void* stream)
{
dim3 grid(B * H);
dim3 block(NWAVE * 64);
hipLaunchKernelGGL(sparse_mla_decode_kernel, grid, block, 0, (hipStream_t)stream,
(const __hip_bfloat16*)q, (const unsigned char*)swa_cache, swa_indices, swa_lens,
(const unsigned char*)kv_cache, topk_indices, topk_lens,
(__hip_bfloat16*)out, lse,
B, H, swa_maxlen, topk_maxlen, swa_num_rows, kv_num_rows, scale);
}
58 changes: 58 additions & 0 deletions vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ctypes
import functools
import importlib
import math
import os
from collections.abc import Callable
from importlib.util import find_spec

Expand Down Expand Up @@ -3115,6 +3117,33 @@ def _decode_gfx950_num_splits(
return num_splits



# --- M5 HIP sparse MLA decode kernel (gfx950, B=33-72) ---
@functools.cache
def _load_m5_kernel():
"""Load the M5 head-fused sparse MLA decode kernel for mid-batch decode.

M5 groups MQA heads into one block so each token's KV is loaded and
decoded ONCE then reused across heads, cutting KV read traffic ~2x vs
M4/Triton at B=48. Validated at 51.77 dB SNR, 9.9us device (vs Triton
12.4us) at the DSpark C=8 decode batch.

Returns the ctypes CDLL handle, or None when disabled / not gfx950.
Env: VLLM_ROCM_USE_HIP_SPARSE_MLA=1 to enable,
VLLM_ROCM_HIP_SPARSE_MLA_M5_SO=<path> to override .so location.
"""
if os.environ.get("VLLM_ROCM_USE_HIP_SPARSE_MLA", "0") != "1":
return None
if not _ON_GFX950:
return None
so = os.environ.get("VLLM_ROCM_HIP_SPARSE_MLA_M5_SO",
"/usr/local/lib/python3.12/dist-packages/vllm/kernels/hip/libsmla_m5.so")
try:
lib = ctypes.CDLL(so)
return lib
except OSError:
return None

def _rocm_sparse_attn_decode_ragged_triton(
q: torch.Tensor,
main_cache: torch.Tensor,
Expand Down Expand Up @@ -3567,6 +3596,35 @@ def rocm_sparse_attn_decode(
"rocm_sparse_attn_decode",
)


# M5 HIP decode: 1.25x vs Triton at B=48, heads fused to cut KV redundancy
if (not torch.cuda.is_current_stream_capturing()
and not swa_only and kv_cache is not None
and 33 <= q.shape[0] <= 72 and head_dim == 512
and q.dtype == torch.bfloat16
and os.environ.get("VLLM_ROCM_USE_HIP_SPARSE_MLA", "0") == "1"):
_m5 = _load_m5_kernel()
if _m5 is not None:
B, H = q.shape[0], q.shape[1]
sl = swa_indices.shape[1] if swa_indices.ndim > 1 else swa_indices.shape[0] // B
tl = topk_indices.shape[1] if topk_indices is not None and topk_indices.ndim > 1 else 0
lse = torch.empty(B, H, device=q.device, dtype=torch.float32)
try:
_m5.sparse_mla_decode(
q.data_ptr(), swa_k_cache.data_ptr(),
swa_indices.contiguous().data_ptr(),
swa_lens.contiguous().data_ptr() if swa_lens is not None else 0,
kv_cache.data_ptr(),
topk_indices.contiguous().data_ptr() if topk_indices is not None else 0,
topk_lens.contiguous().data_ptr() if topk_lens is not None else 0,
output.data_ptr(), lse.data_ptr(), B, H, sl, tl,
swa_k_cache.shape[0] * swa_k_cache.shape[1],
kv_cache.shape[0] * kv_cache.shape[1],
scale, torch.cuda.current_stream().cuda_stream)
return
except Exception:
pass

main_indices = swa_indices.reshape(swa_indices.shape[0], -1)

extra_cache = None
Expand Down
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