forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathattention_dispatch.py
More file actions
1951 lines (1691 loc) 路 66.7 KB
/
Copy pathattention_dispatch.py
File metadata and controls
1951 lines (1691 loc) 路 66.7 KB
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
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import functools
import inspect
import math
from enum import Enum
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple, Union
import torch
import torch.distributed._functional_collectives as funcol
from ..utils import (
get_logger,
is_flash_attn_3_available,
is_flash_attn_available,
is_flash_attn_version,
is_kernels_available,
is_sageattention_available,
is_sageattention_version,
is_torch_npu_available,
is_torch_version,
is_torch_xla_available,
is_torch_xla_version,
is_xformers_available,
is_xformers_version,
)
from ..utils.constants import DIFFUSERS_ATTN_BACKEND, DIFFUSERS_ATTN_CHECKS, DIFFUSERS_ENABLE_HUB_KERNELS
if TYPE_CHECKING:
from ._modeling_parallel import ParallelConfig
_REQUIRED_FLASH_VERSION = "2.6.3"
_REQUIRED_SAGE_VERSION = "2.1.1"
_REQUIRED_FLEX_VERSION = "2.5.0"
_REQUIRED_XLA_VERSION = "2.2"
_REQUIRED_XFORMERS_VERSION = "0.0.29"
_CAN_USE_FLASH_ATTN = is_flash_attn_available() and is_flash_attn_version(">=", _REQUIRED_FLASH_VERSION)
_CAN_USE_FLASH_ATTN_3 = is_flash_attn_3_available()
_CAN_USE_SAGE_ATTN = is_sageattention_available() and is_sageattention_version(">=", _REQUIRED_SAGE_VERSION)
_CAN_USE_FLEX_ATTN = is_torch_version(">=", _REQUIRED_FLEX_VERSION)
_CAN_USE_NPU_ATTN = is_torch_npu_available()
_CAN_USE_XLA_ATTN = is_torch_xla_available() and is_torch_xla_version(">=", _REQUIRED_XLA_VERSION)
_CAN_USE_XFORMERS_ATTN = is_xformers_available() and is_xformers_version(">=", _REQUIRED_XFORMERS_VERSION)
if _CAN_USE_FLASH_ATTN:
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.flash_attn_interface import _wrapped_flash_attn_backward, _wrapped_flash_attn_forward
else:
flash_attn_func = None
flash_attn_varlen_func = None
_wrapped_flash_attn_backward = None
_wrapped_flash_attn_forward = None
if _CAN_USE_FLASH_ATTN_3:
from flash_attn_interface import flash_attn_func as flash_attn_3_func
from flash_attn_interface import flash_attn_varlen_func as flash_attn_3_varlen_func
else:
flash_attn_3_func = None
flash_attn_3_varlen_func = None
if DIFFUSERS_ENABLE_HUB_KERNELS:
if not is_kernels_available():
raise ImportError(
"To use FA3 kernel for your hardware from the Hub, the `kernels` library must be installed. Install with `pip install kernels`."
)
from ..utils.kernels_utils import _get_fa3_from_hub
flash_attn_interface_hub = _get_fa3_from_hub()
flash_attn_3_func_hub = flash_attn_interface_hub.flash_attn_func
else:
flash_attn_3_func_hub = None
if _CAN_USE_SAGE_ATTN:
from sageattention import (
sageattn,
sageattn_qk_int8_pv_fp8_cuda,
sageattn_qk_int8_pv_fp8_cuda_sm90,
sageattn_qk_int8_pv_fp16_cuda,
sageattn_qk_int8_pv_fp16_triton,
sageattn_varlen,
)
else:
sageattn = None
sageattn_qk_int8_pv_fp16_cuda = None
sageattn_qk_int8_pv_fp16_triton = None
sageattn_qk_int8_pv_fp8_cuda = None
sageattn_qk_int8_pv_fp8_cuda_sm90 = None
sageattn_varlen = None
if _CAN_USE_FLEX_ATTN:
# We cannot import the flex_attention function from the package directly because it is expected (from the
# pytorch documentation) that the user may compile it. If we import directly, we will not have access to the
# compiled function.
import torch.nn.attention.flex_attention as flex_attention
if _CAN_USE_NPU_ATTN:
from torch_npu import npu_fusion_attention
else:
npu_fusion_attention = None
if _CAN_USE_XLA_ATTN:
from torch_xla.experimental.custom_kernel import flash_attention as xla_flash_attention
else:
xla_flash_attention = None
if _CAN_USE_XFORMERS_ATTN:
import xformers.ops as xops
else:
xops = None
# Version guard for PyTorch compatibility - custom_op was added in PyTorch 2.4
if torch.__version__ >= "2.4.0":
_custom_op = torch.library.custom_op
_register_fake = torch.library.register_fake
else:
def custom_op_no_op(name, fn=None, /, *, mutates_args, device_types=None, schema=None):
def wrap(func):
return func
return wrap if fn is None else fn
def register_fake_no_op(op, fn=None, /, *, lib=None, _stacklevel=1):
def wrap(func):
return func
return wrap if fn is None else fn
_custom_op = custom_op_no_op
_register_fake = register_fake_no_op
logger = get_logger(__name__) # pylint: disable=invalid-name
# TODO(aryan): Add support for the following:
# - Sage Attention++
# - block sparse, radial and other attention methods
# - CP with sage attention, flex, xformers, other missing backends
# - Add support for normal and CP training with backends that don't support it yet
_SAGE_ATTENTION_PV_ACCUM_DTYPE = Literal["fp32", "fp32+fp32"]
_SAGE_ATTENTION_QK_QUANT_GRAN = Literal["per_thread", "per_warp"]
_SAGE_ATTENTION_QUANTIZATION_BACKEND = Literal["cuda", "triton"]
class AttentionBackendName(str, Enum):
# EAGER = "eager"
# `flash-attn`
FLASH = "flash"
FLASH_VARLEN = "flash_varlen"
_FLASH_3 = "_flash_3"
_FLASH_VARLEN_3 = "_flash_varlen_3"
_FLASH_3_HUB = "_flash_3_hub"
# _FLASH_VARLEN_3_HUB = "_flash_varlen_3_hub" # not supported yet.
# PyTorch native
FLEX = "flex"
NATIVE = "native"
_NATIVE_CUDNN = "_native_cudnn"
_NATIVE_EFFICIENT = "_native_efficient"
_NATIVE_FLASH = "_native_flash"
_NATIVE_MATH = "_native_math"
_NATIVE_NPU = "_native_npu"
_NATIVE_XLA = "_native_xla"
# `sageattention`
SAGE = "sage"
SAGE_VARLEN = "sage_varlen"
_SAGE_QK_INT8_PV_FP8_CUDA = "_sage_qk_int8_pv_fp8_cuda"
_SAGE_QK_INT8_PV_FP8_CUDA_SM90 = "_sage_qk_int8_pv_fp8_cuda_sm90"
_SAGE_QK_INT8_PV_FP16_CUDA = "_sage_qk_int8_pv_fp16_cuda"
_SAGE_QK_INT8_PV_FP16_TRITON = "_sage_qk_int8_pv_fp16_triton"
# TODO: let's not add support for Sparge Attention now because it requires tuning per model
# We can look into supporting something "autotune"-ing in the future
# SPARGE = "sparge"
# `xformers`
XFORMERS = "xformers"
class _AttentionBackendRegistry:
_backends = {}
_constraints = {}
_supported_arg_names = {}
_supports_context_parallel = {}
_active_backend = AttentionBackendName(DIFFUSERS_ATTN_BACKEND)
_checks_enabled = DIFFUSERS_ATTN_CHECKS
@classmethod
def register(
cls,
backend: AttentionBackendName,
constraints: Optional[List[Callable]] = None,
supports_context_parallel: bool = False,
):
logger.debug(f"Registering attention backend: {backend} with constraints: {constraints}")
def decorator(func):
cls._backends[backend] = func
cls._constraints[backend] = constraints or []
cls._supported_arg_names[backend] = set(inspect.signature(func).parameters.keys())
cls._supports_context_parallel[backend] = supports_context_parallel
return func
return decorator
@classmethod
def get_active_backend(cls):
return cls._active_backend, cls._backends[cls._active_backend]
@classmethod
def list_backends(cls):
return list(cls._backends.keys())
@classmethod
def _is_context_parallel_enabled(
cls, backend: AttentionBackendName, parallel_config: Optional["ParallelConfig"]
) -> bool:
supports_context_parallel = backend in cls._supports_context_parallel
is_degree_greater_than_1 = parallel_config is not None and (
parallel_config.context_parallel_config.ring_degree > 1
or parallel_config.context_parallel_config.ulysses_degree > 1
)
return supports_context_parallel and is_degree_greater_than_1
@contextlib.contextmanager
def attention_backend(backend: Union[str, AttentionBackendName] = AttentionBackendName.NATIVE):
"""
Context manager to set the active attention backend.
"""
if backend not in _AttentionBackendRegistry._backends:
raise ValueError(f"Backend {backend} is not registered.")
backend = AttentionBackendName(backend)
_check_attention_backend_requirements(backend)
old_backend = _AttentionBackendRegistry._active_backend
_AttentionBackendRegistry._active_backend = backend
try:
yield
finally:
_AttentionBackendRegistry._active_backend = old_backend
def dispatch_attention_fn(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
attention_kwargs: Optional[Dict[str, Any]] = None,
*,
backend: Optional[AttentionBackendName] = None,
parallel_config: Optional["ParallelConfig"] = None,
) -> torch.Tensor:
attention_kwargs = attention_kwargs or {}
if backend is None:
# If no backend is specified, we either use the default backend (set via the DIFFUSERS_ATTN_BACKEND environment
# variable), or we use a custom backend based on whether user is using the `attention_backend` context manager
backend_name, backend_fn = _AttentionBackendRegistry.get_active_backend()
else:
backend_name = AttentionBackendName(backend)
backend_fn = _AttentionBackendRegistry._backends.get(backend_name)
if parallel_config is not None and not _AttentionBackendRegistry._is_context_parallel_enabled(
backend_name, parallel_config
):
raise ValueError(
f"Backend {backend_name} either does not support context parallelism or context parallelism "
f"was enabled with a world size of 1."
)
kwargs = {
"query": query,
"key": key,
"value": value,
"attn_mask": attn_mask,
"dropout_p": dropout_p,
"is_causal": is_causal,
"scale": scale,
**attention_kwargs,
"_parallel_config": parallel_config,
}
if is_torch_version(">=", "2.5.0"):
kwargs["enable_gqa"] = enable_gqa
if _AttentionBackendRegistry._checks_enabled:
removed_kwargs = set(kwargs) - set(_AttentionBackendRegistry._supported_arg_names[backend_name])
if removed_kwargs:
logger.warning(f"Removing unsupported arguments for attention backend {backend_name}: {removed_kwargs}.")
for check in _AttentionBackendRegistry._constraints.get(backend_name):
check(**kwargs)
kwargs = {k: v for k, v in kwargs.items() if k in _AttentionBackendRegistry._supported_arg_names[backend_name]}
return backend_fn(**kwargs)
# ===== Checks =====
# A list of very simple functions to catch common errors quickly when debugging.
def _check_attn_mask_or_causal(attn_mask: Optional[torch.Tensor], is_causal: bool, **kwargs) -> None:
if attn_mask is not None and is_causal:
raise ValueError("`is_causal` cannot be True when `attn_mask` is not None.")
def _check_device(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
if query.device != key.device or query.device != value.device:
raise ValueError("Query, key, and value must be on the same device.")
if query.dtype != key.dtype or query.dtype != value.dtype:
raise ValueError("Query, key, and value must have the same dtype.")
def _check_device_cuda(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
_check_device(query, key, value)
if query.device.type != "cuda":
raise ValueError("Query, key, and value must be on a CUDA device.")
def _check_device_cuda_atleast_smXY(major: int, minor: int) -> Callable:
def check_device_cuda(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
_check_device_cuda(query, key, value)
if torch.cuda.get_device_capability(query.device) < (major, minor):
raise ValueError(
f"Query, key, and value must be on a CUDA device with compute capability >= {major}.{minor}."
)
return check_device_cuda
def _check_qkv_dtype_match(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
if query.dtype != key.dtype:
raise ValueError("Query and key must have the same dtype.")
if query.dtype != value.dtype:
raise ValueError("Query and value must have the same dtype.")
def _check_qkv_dtype_bf16_or_fp16(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
_check_qkv_dtype_match(query, key, value)
if query.dtype not in (torch.bfloat16, torch.float16):
raise ValueError("Query, key, and value must be either bfloat16 or float16.")
def _check_shape(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> None:
if query.shape[-1] != key.shape[-1]:
raise ValueError("Query and key must have the same last dimension.")
if query.shape[-2] != value.shape[-2]:
raise ValueError("Query and value must have the same second to last dimension.")
if attn_mask is not None and attn_mask.shape[-1] != key.shape[-2]:
raise ValueError("Attention mask must match the key's second to last dimension.")
# ===== Helper functions =====
def _check_attention_backend_requirements(backend: AttentionBackendName) -> None:
if backend in [AttentionBackendName.FLASH, AttentionBackendName.FLASH_VARLEN]:
if not _CAN_USE_FLASH_ATTN:
raise RuntimeError(
f"Flash Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `flash-attn>={_REQUIRED_FLASH_VERSION}`."
)
elif backend in [AttentionBackendName._FLASH_3, AttentionBackendName._FLASH_VARLEN_3]:
if not _CAN_USE_FLASH_ATTN_3:
raise RuntimeError(
f"Flash Attention 3 backend '{backend.value}' is not usable because of missing package or the version is too old. Please build FA3 beta release from source."
)
# TODO: add support Hub variant of FA3 varlen later
elif backend in [AttentionBackendName._FLASH_3_HUB]:
if not DIFFUSERS_ENABLE_HUB_KERNELS:
raise RuntimeError(
f"Flash Attention 3 Hub backend '{backend.value}' is not usable because the `DIFFUSERS_ENABLE_HUB_KERNELS` env var isn't set. Please set it like `export DIFFUSERS_ENABLE_HUB_KERNELS=yes`."
)
if not is_kernels_available():
raise RuntimeError(
f"Flash Attention 3 Hub backend '{backend.value}' is not usable because the `kernels` package isn't available. Please install it with `pip install kernels`."
)
elif backend in [
AttentionBackendName.SAGE,
AttentionBackendName.SAGE_VARLEN,
AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA,
AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA_SM90,
AttentionBackendName._SAGE_QK_INT8_PV_FP16_CUDA,
AttentionBackendName._SAGE_QK_INT8_PV_FP16_TRITON,
]:
if not _CAN_USE_SAGE_ATTN:
raise RuntimeError(
f"Sage Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `sageattention>={_REQUIRED_SAGE_VERSION}`."
)
elif backend == AttentionBackendName.FLEX:
if not _CAN_USE_FLEX_ATTN:
raise RuntimeError(
f"Flex Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `torch>=2.5.0`."
)
elif backend == AttentionBackendName._NATIVE_NPU:
if not _CAN_USE_NPU_ATTN:
raise RuntimeError(
f"NPU Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `torch_npu`."
)
elif backend == AttentionBackendName._NATIVE_XLA:
if not _CAN_USE_XLA_ATTN:
raise RuntimeError(
f"XLA Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `torch_xla>={_REQUIRED_XLA_VERSION}`."
)
elif backend == AttentionBackendName.XFORMERS:
if not _CAN_USE_XFORMERS_ATTN:
raise RuntimeError(
f"Xformers Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `xformers>={_REQUIRED_XFORMERS_VERSION}`."
)
@functools.lru_cache(maxsize=128)
def _prepare_for_flash_attn_or_sage_varlen_without_mask(
batch_size: int,
seq_len_q: int,
seq_len_kv: int,
device: Optional[torch.device] = None,
):
seqlens_q = torch.full((batch_size,), seq_len_q, dtype=torch.int32, device=device)
seqlens_k = torch.full((batch_size,), seq_len_kv, dtype=torch.int32, device=device)
cu_seqlens_q = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_k = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
cu_seqlens_k[1:] = torch.cumsum(seqlens_k, dim=0)
max_seqlen_q = seqlens_q.max().item()
max_seqlen_k = seqlens_k.max().item()
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)
def _prepare_for_flash_attn_or_sage_varlen_with_mask(
batch_size: int,
seq_len_q: int,
attn_mask: torch.Tensor,
device: Optional[torch.device] = None,
):
seqlens_q = torch.full((batch_size,), seq_len_q, dtype=torch.int32, device=device)
seqlens_k = attn_mask.sum(dim=1, dtype=torch.int32)
cu_seqlens_q = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_k = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
cu_seqlens_k[1:] = torch.cumsum(seqlens_k, dim=0)
max_seqlen_q = seqlens_q.max().item()
max_seqlen_k = seqlens_k.max().item()
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)
def _prepare_for_flash_attn_or_sage_varlen(
batch_size: int,
seq_len_q: int,
seq_len_kv: int,
attn_mask: Optional[torch.Tensor] = None,
device: Optional[torch.device] = None,
) -> None:
if attn_mask is None:
return _prepare_for_flash_attn_or_sage_varlen_without_mask(batch_size, seq_len_q, seq_len_kv, device)
return _prepare_for_flash_attn_or_sage_varlen_with_mask(batch_size, seq_len_q, attn_mask, device)
def _normalize_attn_mask(attn_mask: torch.Tensor, batch_size: int, seq_len_k: int) -> torch.Tensor:
"""
Normalize an attention mask to shape [batch_size, seq_len_k] (bool) suitable for inferring seqlens_[q|k] in
FlashAttention/Sage varlen.
Supports 1D to 4D shapes and common broadcasting patterns.
"""
if attn_mask.dtype != torch.bool:
raise ValueError(f"Attention mask must be of type bool, got {attn_mask.dtype}.")
if attn_mask.ndim == 1:
# [seq_len_k] -> broadcast across batch
attn_mask = attn_mask.unsqueeze(0).expand(batch_size, seq_len_k)
elif attn_mask.ndim == 2:
# [batch_size, seq_len_k]. Maybe broadcast across batch
if attn_mask.size(0) not in [1, batch_size]:
raise ValueError(
f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 2D attention mask."
)
attn_mask = attn_mask.expand(batch_size, seq_len_k)
elif attn_mask.ndim == 3:
# [batch_size, seq_len_q, seq_len_k] -> reduce over query dimension
# We do this reduction because we know that arbitrary QK masks is not supported in Flash/Sage varlen.
if attn_mask.size(0) not in [1, batch_size]:
raise ValueError(
f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 3D attention mask."
)
attn_mask = attn_mask.any(dim=1)
attn_mask = attn_mask.expand(batch_size, seq_len_k)
elif attn_mask.ndim == 4:
# [batch_size, num_heads, seq_len_q, seq_len_k] or broadcastable versions
if attn_mask.size(0) not in [1, batch_size]:
raise ValueError(
f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 4D attention mask."
)
attn_mask = attn_mask.expand(batch_size, -1, -1, seq_len_k) # [B, H, Q, K]
attn_mask = attn_mask.any(dim=(1, 2)) # [B, K]
else:
raise ValueError(f"Unsupported attention mask shape: {attn_mask.shape}")
if attn_mask.shape != (batch_size, seq_len_k):
raise ValueError(
f"Normalized attention mask shape mismatch: got {attn_mask.shape}, expected ({batch_size}, {seq_len_k})"
)
return attn_mask
def _flex_attention_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
return q_idx >= kv_idx
# ===== torch op registrations =====
# Registrations are required for fullgraph tracing compatibility
# TODO: this is only required because the beta release FA3 does not have it. There is a PR adding
# this but it was never merged: https://github.com/Dao-AILab/flash-attention/pull/1590
@_custom_op("_diffusers_flash_attn_3::_flash_attn_forward", mutates_args=(), device_types="cuda")
def _wrapped_flash_attn_3(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
softmax_scale: Optional[float] = None,
causal: bool = False,
qv: Optional[torch.Tensor] = None,
q_descale: Optional[torch.Tensor] = None,
k_descale: Optional[torch.Tensor] = None,
v_descale: Optional[torch.Tensor] = None,
attention_chunk: int = 0,
softcap: float = 0.0,
num_splits: int = 1,
pack_gqa: Optional[bool] = None,
deterministic: bool = False,
sm_margin: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Hardcoded for now because pytorch does not support tuple/int type hints
window_size = (-1, -1)
out, lse, *_ = flash_attn_3_func(
q=q,
k=k,
v=v,
softmax_scale=softmax_scale,
causal=causal,
qv=qv,
q_descale=q_descale,
k_descale=k_descale,
v_descale=v_descale,
window_size=window_size,
attention_chunk=attention_chunk,
softcap=softcap,
num_splits=num_splits,
pack_gqa=pack_gqa,
deterministic=deterministic,
sm_margin=sm_margin,
)
lse = lse.permute(0, 2, 1)
return out, lse
@_register_fake("_diffusers_flash_attn_3::_flash_attn_forward")
def _(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
softmax_scale: Optional[float] = None,
causal: bool = False,
qv: Optional[torch.Tensor] = None,
q_descale: Optional[torch.Tensor] = None,
k_descale: Optional[torch.Tensor] = None,
v_descale: Optional[torch.Tensor] = None,
attention_chunk: int = 0,
softcap: float = 0.0,
num_splits: int = 1,
pack_gqa: Optional[bool] = None,
deterministic: bool = False,
sm_margin: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
window_size = (-1, -1) # noqa: F841
# A lot of the parameters here are not yet used in any way within diffusers.
# We can safely ignore for now and keep the fake op shape propagation simple.
batch_size, seq_len, num_heads, head_dim = q.shape
lse_shape = (batch_size, seq_len, num_heads)
return torch.empty_like(q), q.new_empty(lse_shape)
# ===== Helper functions to use attention backends with templated CP autograd functions =====
# https://github.com/pytorch/pytorch/blob/8904ba638726f8c9a5aff5977c4aa76c9d2edfa6/aten/src/ATen/native/native_functions.yaml#L14958
# forward declaration:
# aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0., bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
def _cudnn_attention_forward_op(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
return_lse: bool = False,
_save_ctx: bool = True,
_parallel_config: Optional["ParallelConfig"] = None,
):
if enable_gqa:
raise ValueError("`enable_gqa` is not yet supported for cuDNN attention.")
tensors_to_save = ()
# Contiguous is a must here! Calling cuDNN backend with aten ops produces incorrect results
# if the input tensors are not contiguous.
query = query.transpose(1, 2).contiguous()
key = key.transpose(1, 2).contiguous()
value = value.transpose(1, 2).contiguous()
tensors_to_save += (query, key, value)
out, lse, cum_seq_q, cum_seq_k, max_q, max_k, philox_seed, philox_offset, debug_attn_mask = (
torch.ops.aten._scaled_dot_product_cudnn_attention(
query=query,
key=key,
value=value,
attn_bias=attn_mask,
compute_log_sumexp=return_lse,
dropout_p=dropout_p,
is_causal=is_causal,
return_debug_mask=False,
scale=scale,
)
)
tensors_to_save += (out, lse, cum_seq_q, cum_seq_k, philox_seed, philox_offset)
if _save_ctx:
ctx.save_for_backward(*tensors_to_save)
ctx.dropout_p = dropout_p
ctx.is_causal = is_causal
ctx.scale = scale
ctx.attn_mask = attn_mask
ctx.max_q = max_q
ctx.max_k = max_k
out = out.transpose(1, 2).contiguous()
if lse is not None:
lse = lse.transpose(1, 2).contiguous()
return (out, lse) if return_lse else out
# backward declaration:
# aten::_scaled_dot_product_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor)
def _cudnn_attention_backward_op(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
**kwargs,
):
query, key, value, out, lse, cum_seq_q, cum_seq_k, philox_seed, philox_offset = ctx.saved_tensors
grad_out = grad_out.transpose(1, 2).contiguous()
key = key.transpose(1, 2).contiguous()
value = value.transpose(1, 2).contiguous()
# Cannot pass first 5 arguments as kwargs because: https://github.com/pytorch/pytorch/blob/d26ca5de058dbcf56ac52bb43e84dd98df2ace97/torch/_dynamo/variables/torch.py#L1341
grad_query, grad_key, grad_value = torch.ops.aten._scaled_dot_product_cudnn_attention_backward(
grad_out,
query,
key,
value,
out,
logsumexp=lse,
philox_seed=philox_seed,
philox_offset=philox_offset,
attn_bias=ctx.attn_mask,
cum_seq_q=cum_seq_q,
cum_seq_k=cum_seq_k,
max_q=ctx.max_q,
max_k=ctx.max_k,
dropout_p=ctx.dropout_p,
is_causal=ctx.is_causal,
scale=ctx.scale,
)
grad_query, grad_key, grad_value = (x.transpose(1, 2).contiguous() for x in (grad_query, grad_key, grad_value))
return grad_query, grad_key, grad_value
# Adapted from: https://github.com/Dao-AILab/flash-attention/blob/fd2fc9d85c8e54e5c20436465bca709bc1a6c5a1/flash_attn/flash_attn_interface.py#L807
def _flash_attention_forward_op(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
return_lse: bool = False,
_save_ctx: bool = True,
_parallel_config: Optional["ParallelConfig"] = None,
):
if attn_mask is not None:
raise ValueError("`attn_mask` is not yet supported for flash-attn 2.")
if enable_gqa:
raise ValueError("`enable_gqa` is not yet supported for flash-attn 2.")
# Hardcoded for now
window_size = (-1, -1)
softcap = 0.0
alibi_slopes = None
deterministic = False
grad_enabled = any(x.requires_grad for x in (query, key, value))
if scale is None:
scale = query.shape[-1] ** (-0.5)
# flash-attn only returns LSE if dropout_p > 0. So, we need to workaround.
if grad_enabled or (_parallel_config is not None and _parallel_config.context_parallel_config._world_size > 1):
dropout_p = dropout_p if dropout_p > 0 else 1e-30
with torch.set_grad_enabled(grad_enabled):
out, lse, S_dmask, rng_state = _wrapped_flash_attn_forward(
query,
key,
value,
dropout_p,
scale,
is_causal,
window_size[0],
window_size[1],
softcap,
alibi_slopes,
return_lse,
)
lse = lse.permute(0, 2, 1)
if _save_ctx:
ctx.save_for_backward(query, key, value, out, lse, rng_state)
ctx.dropout_p = dropout_p
ctx.scale = scale
ctx.is_causal = is_causal
ctx.window_size = window_size
ctx.softcap = softcap
ctx.alibi_slopes = alibi_slopes
ctx.deterministic = deterministic
return (out, lse) if return_lse else out
def _flash_attention_backward_op(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
**kwargs,
):
query, key, value, out, lse, rng_state = ctx.saved_tensors
grad_query, grad_key, grad_value = torch.empty_like(query), torch.empty_like(key), torch.empty_like(value)
lse_d = _wrapped_flash_attn_backward( # noqa: F841
grad_out,
query,
key,
value,
out,
lse,
grad_query,
grad_key,
grad_value,
ctx.dropout_p,
ctx.scale,
ctx.is_causal,
ctx.window_size[0],
ctx.window_size[1],
ctx.softcap,
ctx.alibi_slopes,
ctx.deterministic,
rng_state,
)
# Head dimension may have been padded
grad_query = grad_query[..., : grad_out.shape[-1]]
grad_key = grad_key[..., : grad_out.shape[-1]]
grad_value = grad_value[..., : grad_out.shape[-1]]
return grad_query, grad_key, grad_value
def _sage_attention_forward_op(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
return_lse: bool = False,
_save_ctx: bool = True,
_parallel_config: Optional["ParallelConfig"] = None,
):
if attn_mask is not None:
raise ValueError("`attn_mask` is not yet supported for Sage attention.")
if dropout_p > 0.0:
raise ValueError("`dropout_p` is not yet supported for Sage attention.")
if enable_gqa:
raise ValueError("`enable_gqa` is not yet supported for Sage attention.")
out = sageattn(
q=query,
k=key,
v=value,
tensor_layout="NHD",
is_causal=is_causal,
sm_scale=scale,
return_lse=return_lse,
)
lse = None
if return_lse:
out, lse, *_ = out
lse = lse.permute(0, 2, 1)
return (out, lse) if return_lse else out
def _sage_attention_backward_op(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
):
raise NotImplementedError("Backward pass is not implemented for Sage attention.")
# ===== Context parallel =====
# Reference:
# - https://github.com/pytorch/pytorch/blob/f58a680d09e13658a52c6ba05c63c15759846bcc/torch/distributed/_functional_collectives.py#L827
# - https://github.com/pytorch/pytorch/blob/f58a680d09e13658a52c6ba05c63c15759846bcc/torch/distributed/_functional_collectives.py#L246
# For fullgraph=True tracing compatibility (since FakeTensor does not have a `wait` method):
def _wait_tensor(tensor):
if isinstance(tensor, funcol.AsyncCollectiveTensor):
tensor = tensor.wait()
return tensor
def _all_to_all_single(x: torch.Tensor, group) -> torch.Tensor:
shape = x.shape
# HACK: We need to flatten because despite making tensors contiguous, torch single-file-ization
# to benchmark triton codegen fails somewhere:
# buf25 = torch.ops._c10d_functional.all_to_all_single.default(buf24, [1, 1], [1, 1], '3')
# ValueError: Tensors must be contiguous
x = x.flatten()
x = funcol.all_to_all_single(x, None, None, group)
x = x.reshape(shape)
x = _wait_tensor(x)
return x
class TemplatedRingAttention(torch.autograd.Function):
@staticmethod
def forward(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor],
dropout_p: float,
is_causal: bool,
scale: Optional[float],
enable_gqa: bool,
return_lse: bool,
forward_op,
backward_op,
_parallel_config: Optional["ParallelConfig"] = None,
):
ring_mesh = _parallel_config.context_parallel_config._ring_mesh
rank = _parallel_config.context_parallel_config._ring_local_rank
world_size = _parallel_config.context_parallel_config.ring_degree
next_rank = (rank + 1) % world_size
prev_out = prev_lse = None
ctx.forward_op = forward_op
ctx.backward_op = backward_op
ctx.q_shape = query.shape
ctx.kv_shape = key.shape
ctx._parallel_config = _parallel_config
kv_buffer = torch.cat([key.flatten(), value.flatten()]).contiguous()
kv_buffer = funcol.all_gather_tensor(kv_buffer, gather_dim=0, group=ring_mesh.get_group())
kv_buffer = kv_buffer.chunk(world_size)
for i in range(world_size):
if i > 0:
kv = kv_buffer[next_rank]
key_numel = key.numel()
key = kv[:key_numel].reshape_as(key)
value = kv[key_numel:].reshape_as(value)
next_rank = (next_rank + 1) % world_size
out, lse = forward_op(
ctx,
query,
key,
value,
attn_mask,
dropout_p,
is_causal,
scale,
enable_gqa,
True,
_save_ctx=i == 0,
_parallel_config=_parallel_config,
)
if _parallel_config.context_parallel_config.convert_to_fp32:
out = out.to(torch.float32)
lse = lse.to(torch.float32)
lse = lse.unsqueeze(-1)
if prev_out is not None:
out = prev_out - torch.nn.functional.sigmoid(lse - prev_lse) * (prev_out - out)
lse = prev_lse - torch.nn.functional.logsigmoid(prev_lse - lse)
prev_out = out
prev_lse = lse
out = out.to(query.dtype)
lse = lse.squeeze(-1)
return (out, lse) if return_lse else out
@staticmethod
def backward(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
):
ring_mesh = ctx._parallel_config.context_parallel_config._ring_mesh
rank = ctx._parallel_config.context_parallel_config._ring_local_rank
world_size = ctx._parallel_config.context_parallel_config.ring_degree
next_rank = (rank + 1) % world_size
next_ranks = list(range(1, world_size)) + [0]
accum_dtype = torch.float32 if ctx._parallel_config.context_parallel_config.convert_to_fp32 else grad_out.dtype
grad_query = torch.zeros(ctx.q_shape, dtype=accum_dtype, device=grad_out.device)
grad_key = torch.zeros(ctx.kv_shape, dtype=accum_dtype, device=grad_out.device)
grad_value = torch.zeros(ctx.kv_shape, dtype=accum_dtype, device=grad_out.device)
next_grad_kv = None
query, key, value, *_ = ctx.saved_tensors
kv_buffer = torch.cat([key.flatten(), value.flatten()]).contiguous()
kv_buffer = funcol.all_gather_tensor(kv_buffer, gather_dim=0, group=ring_mesh.get_group())
kv_buffer = kv_buffer.chunk(world_size)
for i in range(world_size):
if i > 0:
kv = kv_buffer[next_rank]
key_numel = key.numel()
key = kv[:key_numel].reshape_as(key)
value = kv[key_numel:].reshape_as(value)