File size: 10,460 Bytes
c623dea 4a05275 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 | import math
import torch
from kernels.benchmark import Benchmark
def _cdiv(a, b):
return (a + b - 1) // b
def _extract_output(result):
if isinstance(result, tuple):
return result[0]
return result
def _reference_mla_decode(q, blocked_k, block_table, cache_seqlens, head_dim_v, causal=False):
b, s_q, h_q, d = q.size()
block_size = blocked_k.size(1)
h_kv = blocked_k.size(2)
out = torch.empty(b, s_q, h_q, head_dim_v, dtype=torch.float32, device=q.device)
for i in range(b):
cur_len = int(cache_seqlens[i].item())
num_blocks = _cdiv(cur_len, block_size)
cur_blocks = block_table[i][:num_blocks]
kv = blocked_k[cur_blocks].reshape(-1, h_kv, d)[:cur_len]
query = q[i].transpose(0, 1).float() # [h_q, s_q, d]
key_val = kv.transpose(0, 1).float() # [h_kv, s_k, d]
if h_kv != h_q:
key_val = key_val.repeat_interleave(h_q // h_kv, dim=0)
attn = query @ key_val.transpose(-2, -1) / math.sqrt(d)
s_k = key_val.size(1)
if causal and s_q > 1:
mask = torch.ones(s_q, s_k, dtype=torch.bool, device=q.device).tril(
diagonal=s_k - s_q
)
attn.masked_fill_(~mask, float("-inf"))
attn = torch.softmax(attn, dim=-1)
output = attn @ key_val[..., :head_dim_v]
out[i] = output.transpose(0, 1)
return out.to(q.dtype)
def _varlen_reference_attention(q, k, v, cu_seqlens_q, cu_seqlens_k, causal=False):
batch_size = cu_seqlens_q.shape[0] - 1
total_tokens_q = q.shape[0]
num_heads = q.shape[1]
head_dim_v = v.shape[2]
scale = q.shape[-1] ** (-0.5)
out = torch.zeros(
(total_tokens_q, num_heads, head_dim_v), device=q.device, dtype=q.dtype
)
for b in range(batch_size):
start_q, end_q = cu_seqlens_q[b], cu_seqlens_q[b + 1]
start_k, end_k = cu_seqlens_k[b], cu_seqlens_k[b + 1]
q_b = q[start_q:end_q].transpose(0, 1).float() # [H, seq_q, D_qk]
k_b = k[start_k:end_k].transpose(0, 1).float() # [H, seq_k, D_qk]
v_b = v[start_k:end_k].transpose(0, 1).float() # [H, seq_k, D_v]
attn = q_b @ k_b.transpose(-2, -1) * scale
if causal:
seq_q, seq_k = q_b.size(1), k_b.size(1)
mask = torch.ones(seq_q, seq_k, dtype=torch.bool, device=q.device).tril(
diagonal=seq_k - seq_q
)
attn.masked_fill_(~mask, float("-inf"))
attn = torch.softmax(attn, dim=-1)
result = attn @ v_b # [H, seq_q, D_v]
out[start_q:end_q] = result.transpose(0, 1).to(q.dtype)
return out
# MLA decode constants (DeepSeek V3 architecture)
_HEAD_DIM = 576 # Q/K head dimension
_HEAD_DIM_V = 512 # V head dimension
_NUM_HEADS_K = 1 # MLA uses single KV head
_PAGE_BLOCK_SIZE = 64 # Page block size
def _setup_mla_decode(bench, batch_size, seq_k, num_heads_q):
max_num_blocks = _cdiv(seq_k, _PAGE_BLOCK_SIZE)
total_blocks = batch_size * max_num_blocks
bench.q = (
torch.randn(
batch_size, 1, num_heads_q, _HEAD_DIM, device="cuda", dtype=torch.bfloat16
)
/ 10
)
bench.blocked_k = (
torch.randn(
total_blocks,
_PAGE_BLOCK_SIZE,
_NUM_HEADS_K,
_HEAD_DIM,
device="cuda",
dtype=torch.bfloat16,
)
/ 10
)
bench.block_table = torch.arange(
total_blocks, device="cuda", dtype=torch.int32
).view(batch_size, max_num_blocks)
bench.cache_seqlens = torch.full(
(batch_size,), seq_k, device="cuda", dtype=torch.int32
)
bench.tile_scheduler_metadata, _ = bench.kernel.get_mla_metadata()
bench.out = torch.empty(
batch_size, 1, num_heads_q, _HEAD_DIM_V, device="cuda", dtype=torch.bfloat16
)
def _run_mla_decode(bench, causal=False):
out, lse = bench.kernel.flash_mla_with_kvcache(
q=bench.q,
k_cache=bench.blocked_k,
block_table=bench.block_table,
cache_seqlens=bench.cache_seqlens,
head_dim_v=_HEAD_DIM_V,
tile_scheduler_metadata=bench.tile_scheduler_metadata,
causal=causal,
)
bench.out = out
def _verify_mla_decode(bench, causal=False):
return _reference_mla_decode(
bench.q,
bench.blocked_k,
bench.block_table,
bench.cache_seqlens,
_HEAD_DIM_V,
causal=causal,
)
class FlashMLABenchmark(Benchmark):
seed: int = 42
# Workload: small (B=2, S_k=256, H_q=64)
def setup_small(self):
_setup_mla_decode(self, batch_size=2, seq_k=256, num_heads_q=64)
def benchmark_small(self):
_run_mla_decode(self, causal=False)
def verify_small(self) -> torch.Tensor:
return _verify_mla_decode(self, causal=False)
# Workload: medium (B=4, S_k=1024, H_q=64)
def setup_medium(self):
_setup_mla_decode(self, batch_size=4, seq_k=1024, num_heads_q=64)
def benchmark_medium(self):
_run_mla_decode(self, causal=False)
def verify_medium(self) -> torch.Tensor:
return _verify_mla_decode(self, causal=False)
# Workload: large (B=8, S_k=4096, H_q=128)
def setup_large(self):
_setup_mla_decode(self, batch_size=8, seq_k=4096, num_heads_q=128)
def benchmark_large(self):
_run_mla_decode(self, causal=False)
def verify_large(self) -> torch.Tensor:
return _verify_mla_decode(self, causal=False)
class FlashMLACausalBenchmark(Benchmark):
seed: int = 42
# Workload: small (B=2, S_k=256, H_q=64)
def setup_small(self):
_setup_mla_decode(self, batch_size=2, seq_k=256, num_heads_q=64)
def benchmark_small(self):
_run_mla_decode(self, causal=True)
def verify_small(self) -> torch.Tensor:
return _verify_mla_decode(self, causal=True)
# Workload: medium (B=4, S_k=1024, H_q=64)
def setup_medium(self):
_setup_mla_decode(self, batch_size=4, seq_k=1024, num_heads_q=64)
def benchmark_medium(self):
_run_mla_decode(self, causal=True)
def verify_medium(self) -> torch.Tensor:
return _verify_mla_decode(self, causal=True)
# Workload: large (B=8, S_k=4096, H_q=128)
def setup_large(self):
_setup_mla_decode(self, batch_size=8, seq_k=4096, num_heads_q=128)
def benchmark_large(self):
_run_mla_decode(self, causal=True)
def verify_large(self) -> torch.Tensor:
return _verify_mla_decode(self, causal=True)
# class FlashMLAVarlenBenchmark(Benchmark):
# seed: int = 42
# # Workload: small (3 sequences, max_seqlen=64)
# def setup_small(self):
# H, D = 8, 64
# seqlens = [32, 48, 64]
# total = sum(seqlens)
# self.q = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.k = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.v = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.cu_seqlens = torch.tensor(
# [0] + list(torch.cumsum(torch.tensor(seqlens), 0)),
# device="cuda",
# dtype=torch.int32,
# )
# self.max_seqlen = max(seqlens)
# self.out = torch.empty(total, H, D, device="cuda", dtype=torch.bfloat16)
# def benchmark_small(self):
# self.out = _extract_output(
# self.kernel.flash_attn_varlen_func(
# self.q,
# self.k,
# self.v,
# self.cu_seqlens,
# self.cu_seqlens,
# self.max_seqlen,
# self.max_seqlen,
# )
# )
# def verify_small(self) -> torch.Tensor:
# return _varlen_reference_attention(
# self.q, self.k, self.v, self.cu_seqlens, self.cu_seqlens, causal=False
# )
# # Workload: medium (5 sequences, max_seqlen=256)
# def setup_medium(self):
# H, D = 16, 64
# seqlens = [128, 192, 256, 200, 150]
# total = sum(seqlens)
# self.q = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.k = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.v = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.cu_seqlens = torch.tensor(
# [0] + list(torch.cumsum(torch.tensor(seqlens), 0)),
# device="cuda",
# dtype=torch.int32,
# )
# self.max_seqlen = max(seqlens)
# self.out = torch.empty(total, H, D, device="cuda", dtype=torch.bfloat16)
# def benchmark_medium(self):
# self.out = _extract_output(
# self.kernel.flash_attn_varlen_func(
# self.q,
# self.k,
# self.v,
# self.cu_seqlens,
# self.cu_seqlens,
# self.max_seqlen,
# self.max_seqlen,
# )
# )
# def verify_medium(self) -> torch.Tensor:
# return _varlen_reference_attention(
# self.q, self.k, self.v, self.cu_seqlens, self.cu_seqlens, causal=False
# )
# # Workload: large (8 sequences, max_seqlen=512)
# def setup_large(self):
# H, D = 32, 128
# seqlens = [256, 384, 512, 448, 320, 480, 400, 512]
# total = sum(seqlens)
# self.q = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.k = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.v = torch.randn(total, H, D, device="cuda", dtype=torch.bfloat16)
# self.cu_seqlens = torch.tensor(
# [0] + list(torch.cumsum(torch.tensor(seqlens), 0)),
# device="cuda",
# dtype=torch.int32,
# )
# self.max_seqlen = max(seqlens)
# self.out = torch.empty(total, H, D, device="cuda", dtype=torch.bfloat16)
# def benchmark_large(self):
# self.out = _extract_output(
# self.kernel.flash_attn_varlen_func(
# self.q,
# self.k,
# self.v,
# self.cu_seqlens,
# self.cu_seqlens,
# self.max_seqlen,
# self.max_seqlen,
# )
# )
# def verify_large(self) -> torch.Tensor:
# return _varlen_reference_attention(
# self.q, self.k, self.v, self.cu_seqlens, self.cu_seqlens, causal=False
# )
|