File size: 17,383 Bytes
a402b9b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 | import math
from typing import List, Optional, Tuple
import pytest
import torch
from einops import rearrange, repeat
from sgl_kernel.sparse_flash_attn import (
convert_vertical_slash_indexes,
convert_vertical_slash_indexes_mergehead,
sparse_attn_func,
)
from test_flash_attention import construct_local_mask, is_fa3_supported
def ref_attn(
q,
k,
v,
query_padding_mask=None,
key_padding_mask=None,
attn_bias=None,
dropout_p=0.0,
dropout_mask=None,
causal=False,
window_size=(-1, -1), # -1 means infinite window size
softcap=0.0,
upcast=True,
reorder_ops=False,
key_leftpad=None,
):
"""
Arguments:
q: (batch_size, seqlen_q, nheads, head_dim)
k: (batch_size, seqlen_k, nheads_k, head_dim)
v: (batch_size, seqlen_k, nheads_k, head_dim)
query_padding_mask: (batch_size, seqlen_q)
key_padding_mask: (batch_size, seqlen_k)
attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k)
dropout_p: float
dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
causal: whether to apply causal masking
window_size: (int, int), left and right window size
upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast
output back to fp16/bf16.
reorder_ops: whether to change the order of operations (scaling k instead of scaling q, etc.)
without changing the math. This is to estimate the numerical error from operation
reordering.
Output:
output: (batch_size, seqlen_q, nheads, head_dim)
lse: (batch_size, nheads, seqlen_q)
"""
if causal:
window_size = (window_size[0], 0)
dtype_og = q.dtype
if upcast:
q, k, v = q.float(), k.float(), v.float()
seqlen_q, seqlen_k = q.shape[1], k.shape[1]
k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2])
v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2])
d = q.shape[-1]
if not reorder_ops:
scores = torch.einsum("bthd,bshd->bhts", q / math.sqrt(d), k)
else:
scores = torch.einsum("bthd,bshd->bhts", q, k / math.sqrt(d))
lse_ref = scores.logsumexp(dim=-1)
if softcap > 0:
scores = scores / softcap
scores = scores.tanh()
scores = scores * softcap
if key_padding_mask is not None:
scores.masked_fill_(
rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")
)
if window_size[0] >= 0 or window_size[1] >= 0:
local_mask = construct_local_mask(
seqlen_q,
seqlen_k,
window_size,
query_padding_mask,
key_padding_mask,
q.device,
key_leftpad=key_leftpad,
)
scores.masked_fill_(local_mask, float("-inf"))
if attn_bias is not None:
scores = scores + attn_bias
attention = torch.softmax(scores, dim=-1).to(v.dtype)
# Some rows might be completely masked out so we fill them with zero instead of NaN
if window_size[0] >= 0 or window_size[1] >= 0:
attention = attention.masked_fill(
torch.all(local_mask, dim=-1, keepdim=True), 0.0
)
# We want to mask here so that the attention matrix doesn't have any NaNs
# Otherwise we'll get NaN in dV
if query_padding_mask is not None:
attention = attention.masked_fill(
rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0
)
dropout_scaling = 1.0 / (1 - dropout_p)
# attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling
# output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
if dropout_mask is not None:
attention_drop = attention.masked_fill(~dropout_mask, 0.0)
else:
attention_drop = attention
output = torch.einsum("bhts,bshd->bthd", attention_drop, v * dropout_scaling)
if query_padding_mask is not None:
output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0)
return output.to(dtype=dtype_og), lse_ref
def ref_paged_attn(
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
query_lens: List[int],
kv_lens: List[int],
block_tables: torch.Tensor,
scale: float,
sliding_window: Optional[int] = None,
soft_cap: Optional[float] = None,
) -> torch.Tensor:
num_seqs = len(query_lens)
block_tables = block_tables.cpu().numpy()
_, block_size, num_kv_heads, head_size = key_cache.shape
outputs: List[torch.Tensor] = []
start_idx = 0
for i in range(num_seqs):
query_len = query_lens[i]
kv_len = kv_lens[i]
# clone to avoid clobbering the query tensor
q = query[start_idx : start_idx + query_len].clone()
q *= scale
num_kv_blocks = (kv_len + block_size - 1) // block_size
block_indices = block_tables[i, :num_kv_blocks]
k = key_cache[block_indices].view(-1, num_kv_heads, head_size)
k = k[:kv_len]
v = value_cache[block_indices].view(-1, num_kv_heads, head_size)
v = v[:kv_len]
if q.shape[1] != k.shape[1]:
k = torch.repeat_interleave(k, q.shape[1] // k.shape[1], dim=1)
v = torch.repeat_interleave(v, q.shape[1] // v.shape[1], dim=1)
attn = torch.einsum("qhd,khd->hqk", q, k).float()
empty_mask = torch.ones(query_len, kv_len)
mask = torch.triu(empty_mask, diagonal=kv_len - query_len + 1).bool()
if sliding_window is not None:
sliding_window_mask = (
torch.triu(
empty_mask, diagonal=kv_len - (query_len + sliding_window) + 1
)
.bool()
.logical_not()
)
mask |= sliding_window_mask
if soft_cap is not None:
attn = soft_cap * torch.tanh(attn / soft_cap)
attn.masked_fill_(mask, float("-inf"))
attn = torch.softmax(attn, dim=-1).to(v.dtype)
out = torch.einsum("hqk,khd->qhd", attn, v)
outputs.append(out)
start_idx += query_len
return torch.cat(outputs, dim=0)
@pytest.mark.skipif(
not is_fa3_supported(),
reason="flash_attn at sgl-kernel is only supported on sm90 or sm80",
)
@pytest.mark.parametrize("batch_size", [1, 2])
@pytest.mark.parametrize(
"seq_lens",
[
(1, 1),
(1, 1024),
(1, 2048),
(1023, 2049),
(1023, 1023),
(32, 32),
(65, 65),
(129, 129),
],
)
@pytest.mark.parametrize("num_heads", [1, 2, 4])
@pytest.mark.parametrize("head_size", [128])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("NNZ_S", [0, 1, 2, 3, 7, 15, 32])
@torch.inference_mode()
def test_sparse_attention(
batch_size,
seq_lens,
num_heads,
head_size,
dtype,
NNZ_S,
) -> None:
torch.set_default_device("cuda")
torch.cuda.manual_seed_all(0)
block_size_M = 64
block_size_N = 64
seqlen_q, seqlen_k = seq_lens
q = torch.randn(
batch_size, seqlen_q, num_heads, head_size, dtype=dtype, requires_grad=False
)
k = torch.randn(
batch_size, seqlen_k, num_heads, head_size, dtype=dtype, requires_grad=False
)
v = torch.randn(
batch_size, seqlen_k, num_heads, head_size, dtype=dtype, requires_grad=False
)
NUM_ROWS = (seqlen_q + block_size_M - 1) // block_size_M
if NNZ_S * block_size_N > seqlen_k:
return
NNZ_V = seqlen_k - NNZ_S * block_size_N
block_count = torch.tensor(
[NNZ_S] * batch_size * NUM_ROWS * num_heads, dtype=torch.int32
).reshape(batch_size, num_heads, NUM_ROWS)
column_count = torch.tensor(
[NNZ_V] * batch_size * NUM_ROWS * num_heads, dtype=torch.int32
).reshape(batch_size, num_heads, NUM_ROWS)
block_offset = torch.tensor(
[[i * block_size_N for i in range(NNZ_S)]] * batch_size * NUM_ROWS * num_heads,
dtype=torch.int32,
).reshape(batch_size, num_heads, NUM_ROWS, NNZ_S)
column_index = torch.tensor(
[[NNZ_S * block_size_N + i for i in range(NNZ_V)]]
* batch_size
* NUM_ROWS
* num_heads,
dtype=torch.int32,
).reshape(batch_size, num_heads, NUM_ROWS, NNZ_V)
out, lse = sparse_attn_func(
q,
k,
v,
block_count,
block_offset,
column_count,
column_index,
return_softmax_lse=True,
)
ref_out, ref_lse = ref_attn(q, k, v)
torch.testing.assert_close(
out, ref_out, atol=2e-2, rtol=1e-2
), f"{torch.max(torch.abs(out - ref_out))}"
torch.testing.assert_close(
lse, ref_lse, atol=2e-2, rtol=1e-2
), f"{torch.max(torch.abs(lse - ref_lse))}"
# sparse attention utils
# origin
@pytest.mark.skipif(
not is_fa3_supported(),
reason="flash_attn at sgl-kernel is only supported on sm90 or sm80",
)
@pytest.mark.parametrize("causal", [True, False])
def test_convert_vertical_slash_indexes(causal):
# Prepare small, hand-checkable inputs
q_seqlens = torch.tensor([4], dtype=torch.int32, device="cuda") # [BATCH]
kv_seqlens = torch.tensor([4], dtype=torch.int32, device="cuda")
vertical_indexes = torch.tensor(
[[[1, 3]]], dtype=torch.int32, device="cuda"
) # [BATCH, N_HEADS, NNZ_V]
slash_indexes = torch.tensor(
[[[2]]], dtype=torch.int32, device="cuda"
) # [BATCH, N_HEADS, NNZ_S]
context_size = 4
block_size_M = 2
block_size_N = 2
# Call your CUDA kernel wrapper
block_count, block_offset, column_count, column_index = (
convert_vertical_slash_indexes(
q_seqlens,
kv_seqlens,
vertical_indexes,
slash_indexes,
context_size,
block_size_M,
block_size_N,
causal=causal,
)
)
# Manually create expected outputs for this input
# There are 2 rows (blocks): row0 (tokens 0-1), row1 (tokens 2-3)
# Fill these expected tensors based on your CUDA kernel's logic
# For demonstration, we assume:
# - block_count: how many slash indices fall into each block
# - block_offset: the value of those indices
# - column_count: number of valid vertical indices per block
# - column_index: the actual vertical indices
expected_column_index = torch.tensor(
[[[[0, 0], [0, 0]]]], dtype=torch.int32, device="cuda"
)
# If causal=False, update these tensors according to expected behavior
if not causal:
# Update these values if your kernel produces different output in non-causal mode
expected_column_index = torch.tensor(
[[[[1, 0], [1, 3]]]], dtype=torch.int32, device="cuda"
)
# Assert that outputs match expectations
assert torch.equal(column_index, expected_column_index)
# mergehead
@pytest.mark.skipif(
not is_fa3_supported(),
reason="flash_attn at sgl-kernel is only supported on sm90 or sm80",
)
@pytest.mark.parametrize("causal", [True, False])
def test_convert_vertical_slash_indexes_mergehead(causal):
# Prepare small, hand-checkable inputs for mergehead version
q_seqlens = torch.tensor([4], dtype=torch.int32, device="cuda")
kv_seqlens = torch.tensor([4], dtype=torch.int32, device="cuda")
vertical_indexes = torch.tensor(
[
[
[1, 3], # head 0
[2, 0], # head 1
]
],
dtype=torch.int32,
device="cuda",
) # [BATCH, N_HEADS, NNZ_V]
slash_indexes = torch.tensor(
[
[
[2, 0], # head 0
[1, 3], # head 1
]
],
dtype=torch.int32,
device="cuda",
) # [BATCH, N_HEADS, NNZ_S]
vertical_indices_count = torch.tensor([2, 1], dtype=torch.int32, device="cuda")
slash_indices_count = torch.tensor([1, 2], dtype=torch.int32, device="cuda")
context_size = 4
block_size_M = 2
block_size_N = 2
# Call your CUDA kernel wrapper
block_count, block_offset, column_count, column_index = (
convert_vertical_slash_indexes_mergehead(
q_seqlens,
kv_seqlens,
vertical_indexes,
slash_indexes,
vertical_indices_count,
slash_indices_count,
context_size,
block_size_M,
block_size_N,
causal=causal,
)
)
# Manually create expected outputs for this input
# For demonstration, assume:
# - batch=1, head=2, num_rows=2, nnz_v=2, nnz_s=2
# Fill these expected tensors according to your kernel's behavior
expected_column_index = torch.tensor(
[[[[1, 0], [1, 3]], [[-1079459945, -1077788999], [-1080050043, -1104625879]]]],
dtype=torch.int32,
device="cuda",
)
if not causal:
# If non-causal mode output is different, update these values
expected_column_index = torch.tensor(
[[[[1, 0], [1, 3]], [[2, -1077788999], [2, -1104625879]]]],
dtype=torch.int32,
device="cuda",
)
# Assert that outputs match expectations
assert torch.equal(column_index, expected_column_index)
# skip cause use fa2 for test
# @pytest.mark.parametrize("seq_lens", [[(1024, 1328)],
# [(1024, 1328), (1, 2048)],
# [(1025, 1328), (2, 2048)],
# [(1025, 2049), (2, 1281)],
# ])
# @pytest.mark.parametrize("head_size", [128])
# @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
# @torch.inference_mode()
# def test_sparse_attention_varlen(
# seq_lens,
# head_size,
# dtype,
# ) -> None:
# torch.set_default_device("cuda")
# torch.cuda.manual_seed_all(0)
# block_size_M = 64
# block_size_N = 64
# num_seqs = len(seq_lens)
# query_lens = [x[0] for x in seq_lens]
# kv_lens = [x[1] for x in seq_lens]
# num_heads = 1
# query = torch.randn(sum(query_lens),
# num_heads,
# head_size,
# dtype=dtype)
# key = torch.randn(sum(kv_lens),
# num_heads,
# head_size,
# dtype=dtype)
# value = torch.randn_like(key)
# cu_query_lens = torch.tensor([0] + query_lens,
# dtype=torch.int32).cumsum(dim=0,
# dtype=torch.int32)
# cu_kv_lens = torch.tensor([0] + kv_lens,
# dtype=torch.int32).cumsum(dim=0,
# dtype=torch.int32)
# max_query_len = max(query_lens)
# max_kv_len = max(kv_lens)
# NUM_ROWS = (max_query_len + block_size_M - 1) // block_size_M
# NNZ_S = 20
# NNZ_V = 2048
# batch_size = len(query_lens)
# block_counts = []
# column_counts = []
# block_offsets = []
# column_indices = []
# for b in range(batch_size):
# block_counts.append(torch.tensor([NNZ_S] * NUM_ROWS * num_heads, dtype=torch.int32).reshape(num_heads, NUM_ROWS))
# columns = kv_lens[b] - NNZ_S * block_size_N
# column_counts.append(torch.tensor([columns] * NUM_ROWS * num_heads, dtype=torch.int32).reshape(num_heads, NUM_ROWS))
# block_offsets.append(torch.tensor([[i * block_size_N for i in range(NNZ_S)]] * NUM_ROWS * num_heads, dtype=torch.int32).reshape(num_heads, NUM_ROWS, NNZ_S))
# column_indices.append(torch.tensor([[NNZ_S * block_size_N + i for i in range(NNZ_V)]] * NUM_ROWS * num_heads, dtype=torch.int32).reshape(num_heads, NUM_ROWS, NNZ_V))
# block_count = torch.concat(block_counts).reshape(batch_size, num_heads, NUM_ROWS)
# column_count = torch.concat(column_counts).reshape(batch_size, num_heads, NUM_ROWS)
# block_offset = torch.concat(block_offsets).reshape(batch_size, num_heads, NUM_ROWS, NNZ_S)
# column_index = torch.concat(column_indices).reshape(batch_size, num_heads, NUM_ROWS, NNZ_V)
# out, lse = sparse_attn_varlen_func(
# query,
# key,
# value,
# block_count,
# block_offset,
# column_count,
# column_index,
# cu_seqlens_q=cu_query_lens,
# cu_seqlens_k=cu_kv_lens,
# max_seqlen_q=max_query_len,
# max_seqlen_k=max_kv_len,
# return_softmax_lse=True,
# )
# max_num_blocks_per_seq = (max_kv_len + 2048 - 1) // 2048
# block_tables = torch.randint(0,
# 2048,
# (len(query_lens), max_num_blocks_per_seq),
# dtype=torch.int32)
# scale = head_size**-0.5
# ref_out, ref_lse, _ = ref_paged_attn(
# query,
# key,
# value,
# query_lens=query_lens,
# kv_lens=kv_lens,
# block_tables=block_tables,
# scale=scale
# )
# torch.testing.assert_close(out, ref_out, atol=2e-2, rtol=1e-2), \
# f"{torch.max(torch.abs(out - ref_out))}"
# torch.testing.assert_close(lse, ref_lse, atol=2e-2, rtol=1e-2), \
# f"{torch.max(torch.abs(lse - ref_lse))}"
if __name__ == "__main__":
pytest.main([__file__])
|