forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathgroup_offloading.py
More file actions
678 lines (577 loc) 路 30.6 KB
/
Copy pathgroup_offloading.py
File metadata and controls
678 lines (577 loc) 路 30.6 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
# Copyright 2024 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.
from contextlib import nullcontext
from typing import Dict, List, Optional, Set, Tuple
import torch
from ..utils import get_logger, is_accelerate_available
from .hooks import HookRegistry, ModelHook
if is_accelerate_available():
from accelerate.hooks import AlignDevicesHook, CpuOffload
from accelerate.utils import send_to_device
logger = get_logger(__name__) # pylint: disable=invalid-name
# fmt: off
_GROUP_OFFLOADING = "group_offloading"
_LAYER_EXECUTION_TRACKER = "layer_execution_tracker"
_LAZY_PREFETCH_GROUP_OFFLOADING = "lazy_prefetch_group_offloading"
_SUPPORTED_PYTORCH_LAYERS = (
torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d,
torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d,
torch.nn.Linear,
# TODO(aryan): look into torch.nn.LayerNorm, torch.nn.GroupNorm later, seems to be causing some issues with CogVideoX
# because of double invocation of the same norm layer in CogVideoXLayerNorm
)
# fmt: on
class ModuleGroup:
def __init__(
self,
modules: List[torch.nn.Module],
offload_device: torch.device,
onload_device: torch.device,
offload_leader: torch.nn.Module,
onload_leader: Optional[torch.nn.Module] = None,
parameters: Optional[List[torch.nn.Parameter]] = None,
buffers: Optional[List[torch.Tensor]] = None,
non_blocking: bool = False,
stream: Optional[torch.cuda.Stream] = None,
cpu_param_dict: Optional[Dict[torch.nn.Parameter, torch.Tensor]] = None,
onload_self: bool = True,
) -> None:
self.modules = modules
self.offload_device = offload_device
self.onload_device = onload_device
self.offload_leader = offload_leader
self.onload_leader = onload_leader
self.parameters = parameters
self.buffers = buffers
self.non_blocking = non_blocking or stream is not None
self.stream = stream
self.cpu_param_dict = cpu_param_dict
self.onload_self = onload_self
if self.stream is not None and self.cpu_param_dict is None:
raise ValueError("cpu_param_dict must be provided when using stream for data transfer.")
def onload_(self):
r"""Onloads the group of modules to the onload_device."""
context = nullcontext() if self.stream is None else torch.cuda.stream(self.stream)
if self.stream is not None:
# Wait for previous Host->Device transfer to complete
self.stream.synchronize()
with context:
for group_module in self.modules:
group_module.to(self.onload_device, non_blocking=self.non_blocking)
if self.parameters is not None:
for param in self.parameters:
param.data = param.data.to(self.onload_device, non_blocking=self.non_blocking)
if self.buffers is not None:
for buffer in self.buffers:
buffer.data = buffer.data.to(self.onload_device, non_blocking=self.non_blocking)
def offload_(self):
r"""Offloads the group of modules to the offload_device."""
if self.stream is not None:
torch.cuda.current_stream().synchronize()
for group_module in self.modules:
for param in group_module.parameters():
param.data = self.cpu_param_dict[param]
else:
for group_module in self.modules:
group_module.to(self.offload_device, non_blocking=self.non_blocking)
if self.parameters is not None:
for param in self.parameters:
param.data = param.data.to(self.offload_device, non_blocking=self.non_blocking)
if self.buffers is not None:
for buffer in self.buffers:
buffer.data = buffer.data.to(self.offload_device, non_blocking=self.non_blocking)
class GroupOffloadingHook(ModelHook):
r"""
A hook that offloads groups of torch.nn.Module to the CPU for storage and onloads to accelerator device for
computation. Each group has one "onload leader" module that is responsible for onloading, and an "offload leader"
module that is responsible for offloading. If prefetching is enabled, the onload leader of the previous module
group is responsible for onloading the current module group.
"""
_is_stateful = False
def __init__(
self,
group: ModuleGroup,
next_group: Optional[ModuleGroup] = None,
) -> None:
self.group = group
self.next_group = next_group
def initialize_hook(self, module: torch.nn.Module) -> torch.nn.Module:
if self.group.offload_leader == module:
self.group.offload_()
return module
def pre_forward(self, module: torch.nn.Module, *args, **kwargs):
# If there wasn't an onload_leader assigned, we assume that the submodule that first called its forward
# method is the onload_leader of the group.
if self.group.onload_leader is None:
self.group.onload_leader = module
# If the current module is the onload_leader of the group, we onload the group if it is supposed
# to onload itself. In the case of using prefetching with streams, we onload the next group if
# it is not supposed to onload itself.
if self.group.onload_leader == module:
if self.group.onload_self:
self.group.onload_()
if self.next_group is not None and not self.next_group.onload_self:
self.next_group.onload_()
args = send_to_device(args, self.group.onload_device, non_blocking=self.group.non_blocking)
kwargs = send_to_device(kwargs, self.group.onload_device, non_blocking=self.group.non_blocking)
return args, kwargs
def post_forward(self, module: torch.nn.Module, output):
if self.group.offload_leader == module:
self.group.offload_()
return output
class LazyPrefetchGroupOffloadingHook(ModelHook):
r"""
A hook, used in conjuction with GroupOffloadingHook, that applies lazy prefetching to groups of torch.nn.Module.
This hook is used to determine the order in which the layers are executed during the forward pass. Once the layer
invocation order is known, assignments of the next_group attribute for prefetching can be made, which allows
prefetching groups in the correct order.
"""
_is_stateful = False
def __init__(self):
self.execution_order: List[Tuple[str, torch.nn.Module]] = []
self._layer_execution_tracker_module_names = set()
def initialize_hook(self, module):
# To every submodule that contains a group offloading hook (at this point, no prefetching is enabled for any
# of the groups), we add a layer execution tracker hook that will be used to determine the order in which the
# layers are executed during the forward pass.
for name, submodule in module.named_modules():
if name == "" or not hasattr(submodule, "_diffusers_hook"):
continue
registry = HookRegistry.check_if_exists_or_initialize(submodule)
group_offloading_hook = registry.get_hook(_GROUP_OFFLOADING)
if group_offloading_hook is not None:
def make_execution_order_update_callback(current_name, current_submodule):
def callback():
logger.debug(f"Adding {current_name} to the execution order")
self.execution_order.append((current_name, current_submodule))
return callback
layer_tracker_hook = LayerExecutionTrackerHook(make_execution_order_update_callback(name, submodule))
registry.register_hook(layer_tracker_hook, _LAYER_EXECUTION_TRACKER)
self._layer_execution_tracker_module_names.add(name)
return module
def post_forward(self, module, output):
# At this point, for the current modules' submodules, we know the execution order of the layers. We can now
# remove the layer execution tracker hooks and apply prefetching by setting the next_group attribute for each
# group offloading hook.
num_executed = len(self.execution_order)
execution_order_module_names = {name for name, _ in self.execution_order}
# It may be possible that some layers were not executed during the forward pass. This can happen if the layer
# is not used in the forward pass, or if the layer is not executed due to some other reason. In such cases, we
# may not be able to apply prefetching in the correct order, which can lead to device-mismatch related errors
# if the missing layers end up being executed in the future.
if execution_order_module_names != self._layer_execution_tracker_module_names:
unexecuted_layers = list(self._layer_execution_tracker_module_names - execution_order_module_names)
logger.warning(
"It seems like some layers were not executed during the forward pass. This may lead to problems when "
"applying lazy prefetching with automatic tracing and lead to device-mismatch related errors. Please "
"make sure that all layers are executed during the forward pass. The following layers were not executed:\n"
f"{unexecuted_layers=}"
)
# Remove the layer execution tracker hooks from the submodules
base_module_registry = module._diffusers_hook
registries = [submodule._diffusers_hook for _, submodule in self.execution_order]
for i in range(num_executed):
registries[i].remove_hook(_LAYER_EXECUTION_TRACKER, recurse=False)
# Remove the current lazy prefetch group offloading hook so that it doesn't interfere with the next forward pass
base_module_registry.remove_hook(_LAZY_PREFETCH_GROUP_OFFLOADING, recurse=False)
# Apply lazy prefetching by setting required attributes
group_offloading_hooks = [registry.get_hook(_GROUP_OFFLOADING) for registry in registries]
if num_executed > 0:
base_module_group_offloading_hook = base_module_registry.get_hook(_GROUP_OFFLOADING)
base_module_group_offloading_hook.next_group = group_offloading_hooks[0].group
base_module_group_offloading_hook.next_group.onload_self = False
for i in range(num_executed - 1):
name1, _ = self.execution_order[i]
name2, _ = self.execution_order[i + 1]
logger.debug(f"Applying lazy prefetch group offloading from {name1} to {name2}")
group_offloading_hooks[i].next_group = group_offloading_hooks[i + 1].group
group_offloading_hooks[i].next_group.onload_self = False
return output
class LayerExecutionTrackerHook(ModelHook):
r"""
A hook that tracks the order in which the layers are executed during the forward pass by calling back to the
LazyPrefetchGroupOffloadingHook to update the execution order.
"""
_is_stateful = False
def __init__(self, execution_order_update_callback):
self.execution_order_update_callback = execution_order_update_callback
def pre_forward(self, module, *args, **kwargs):
self.execution_order_update_callback()
return args, kwargs
def apply_group_offloading(
module: torch.nn.Module,
onload_device: torch.device,
offload_device: torch.device = torch.device("cpu"),
offload_type: str = "block_level",
num_blocks_per_group: Optional[int] = None,
non_blocking: bool = False,
use_stream: bool = False,
) -> None:
r"""
Applies group offloading to the internal layers of a torch.nn.Module. To understand what group offloading is, and
where it is beneficial, we need to first provide some context on how other supported offloading methods work.
Typically, offloading is done at two levels:
- Module-level: In Diffusers, this can be enabled using the `ModelMixin::enable_model_cpu_offload()` method. It
works by offloading each component of a pipeline to the CPU for storage, and onloading to the accelerator device
when needed for computation. This method is more memory-efficient than keeping all components on the accelerator,
but the memory requirements are still quite high. For this method to work, one needs memory equivalent to size of
the model in runtime dtype + size of largest intermediate activation tensors to be able to complete the forward
pass.
- Leaf-level: In Diffusers, this can be enabled using the `ModelMixin::enable_sequential_cpu_offload()` method. It
works by offloading the lowest leaf-level parameters of the computation graph to the CPU for storage, and
onloading only the leafs to the accelerator device for computation. This uses the lowest amount of accelerator
memory, but can be slower due to the excessive number of device synchronizations.
Group offloading is a middle ground between the two methods. It works by offloading groups of internal layers,
(either `torch.nn.ModuleList` or `torch.nn.Sequential`). This method uses lower memory than module-level
offloading. It is also faster than leaf-level/sequential offloading, as the number of device synchronizations is
reduced.
Another supported feature (for CUDA devices with support for asynchronous data transfer streams) is the ability to
overlap data transfer and computation to reduce the overall execution time compared to sequential offloading. This
is enabled using layer prefetching with streams, i.e., the layer that is to be executed next starts onloading to
the accelerator device while the current layer is being executed - this increases the memory requirements slightly.
Note that this implementation also supports leaf-level offloading but can be made much faster when using streams.
Args:
module (`torch.nn.Module`):
The module to which group offloading is applied.
onload_device (`torch.device`):
The device to which the group of modules are onloaded.
offload_device (`torch.device`, defaults to `torch.device("cpu")`):
The device to which the group of modules are offloaded. This should typically be the CPU. Default is CPU.
offload_type (`str`, defaults to "block_level"):
The type of offloading to be applied. Can be one of "block_level" or "leaf_level". Default is
"block_level".
num_blocks_per_group (`int`, *optional*):
The number of blocks per group when using offload_type="block_level". This is required when using
offload_type="block_level".
non_blocking (`bool`, defaults to `False`):
If True, offloading and onloading is done with non-blocking data transfer.
use_stream (`bool`, defaults to `False`):
If True, offloading and onloading is done asynchronously using a CUDA stream. This can be useful for
overlapping computation and data transfer.
Example:
```python
>>> from diffusers import CogVideoXTransformer3DModel
>>> from diffusers.hooks import apply_group_offloading
>>> transformer = CogVideoXTransformer3DModel.from_pretrained(
... "THUDM/CogVideoX-5b", subfolder="transformer", torch_dtype=torch.bfloat16
... )
>>> apply_group_offloading(
... transformer,
... onload_device=torch.device("cuda"),
... offload_device=torch.device("cpu"),
... offload_type="block_level",
... num_blocks_per_group=2,
... use_stream=True,
... )
```
"""
stream = None
if use_stream:
if torch.cuda.is_available():
stream = torch.cuda.Stream()
else:
raise ValueError("Using streams for data transfer requires a CUDA device.")
_raise_error_if_accelerate_model_or_sequential_hook_present(module)
if offload_type == "block_level":
if num_blocks_per_group is None:
raise ValueError("num_blocks_per_group must be provided when using offload_type='block_level'.")
_apply_group_offloading_block_level(
module, num_blocks_per_group, offload_device, onload_device, non_blocking, stream
)
elif offload_type == "leaf_level":
_apply_group_offloading_leaf_level(module, offload_device, onload_device, non_blocking, stream)
else:
raise ValueError(f"Unsupported offload_type: {offload_type}")
def _apply_group_offloading_block_level(
module: torch.nn.Module,
num_blocks_per_group: int,
offload_device: torch.device,
onload_device: torch.device,
non_blocking: bool,
stream: Optional[torch.cuda.Stream] = None,
) -> None:
r"""
This function applies offloading to groups of torch.nn.ModuleList or torch.nn.Sequential blocks. In comparison to
the "leaf_level" offloading, which is more fine-grained, this offloading is done at the top-level blocks.
Args:
module (`torch.nn.Module`):
The module to which group offloading is applied.
offload_device (`torch.device`):
The device to which the group of modules are offloaded. This should typically be the CPU.
onload_device (`torch.device`):
The device to which the group of modules are onloaded.
non_blocking (`bool`):
If True, offloading and onloading is done asynchronously. This can be useful for overlapping computation
and data transfer.
stream (`torch.cuda.Stream`, *optional*):
If provided, offloading and onloading is done asynchronously using the provided stream. This can be useful
for overlapping computation and data transfer.
"""
# Create a pinned CPU parameter dict for async data transfer if streams are to be used
cpu_param_dict = None
if stream is not None:
for param in module.parameters():
param.data = param.data.cpu().pin_memory()
cpu_param_dict = {param: param.data for param in module.parameters()}
# Create module groups for ModuleList and Sequential blocks
modules_with_group_offloading = set()
unmatched_modules = []
matched_module_groups = []
for name, submodule in module.named_children():
if not isinstance(submodule, (torch.nn.ModuleList, torch.nn.Sequential)):
unmatched_modules.append((name, submodule))
modules_with_group_offloading.add(name)
continue
for i in range(0, len(submodule), num_blocks_per_group):
current_modules = submodule[i : i + num_blocks_per_group]
group = ModuleGroup(
modules=current_modules,
offload_device=offload_device,
onload_device=onload_device,
offload_leader=current_modules[-1],
onload_leader=current_modules[0],
non_blocking=non_blocking,
stream=stream,
cpu_param_dict=cpu_param_dict,
onload_self=stream is None,
)
matched_module_groups.append(group)
for j in range(i, i + len(current_modules)):
modules_with_group_offloading.add(f"{name}.{j}")
# Apply group offloading hooks to the module groups
for i, group in enumerate(matched_module_groups):
next_group = (
matched_module_groups[i + 1] if i + 1 < len(matched_module_groups) and stream is not None else None
)
for group_module in group.modules:
_apply_group_offloading_hook(group_module, group, next_group)
# Parameters and Buffers of the top-level module need to be offloaded/onloaded separately
# when the forward pass of this module is called. This is because the top-level module is not
# part of any group (as doing so would lead to no VRAM savings).
parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading)
buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading)
parameters = [param for _, param in parameters]
buffers = [buffer for _, buffer in buffers]
# Create a group for the unmatched submodules of the top-level module so that they are on the correct
# device when the forward pass is called.
unmatched_modules = [unmatched_module for _, unmatched_module in unmatched_modules]
unmatched_group = ModuleGroup(
modules=unmatched_modules,
offload_device=offload_device,
onload_device=onload_device,
offload_leader=module,
onload_leader=module,
parameters=parameters,
buffers=buffers,
non_blocking=False,
stream=None,
cpu_param_dict=None,
onload_self=True,
)
next_group = matched_module_groups[0] if len(matched_module_groups) > 0 else None
_apply_group_offloading_hook(module, unmatched_group, next_group)
def _apply_group_offloading_leaf_level(
module: torch.nn.Module,
offload_device: torch.device,
onload_device: torch.device,
non_blocking: bool,
stream: Optional[torch.cuda.Stream] = None,
) -> None:
r"""
This function applies offloading to groups of leaf modules in a torch.nn.Module. This method has minimal memory
requirements. However, it can be slower compared to other offloading methods due to the excessive number of device
synchronizations. When using devices that support streams to overlap data transfer and computation, this method can
reduce memory usage without any performance degradation.
Args:
module (`torch.nn.Module`):
The module to which group offloading is applied.
offload_device (`torch.device`):
The device to which the group of modules are offloaded. This should typically be the CPU.
onload_device (`torch.device`):
The device to which the group of modules are onloaded.
non_blocking (`bool`):
If True, offloading and onloading is done asynchronously. This can be useful for overlapping computation
and data transfer.
stream (`torch.cuda.Stream`, *optional*):
If provided, offloading and onloading is done asynchronously using the provided stream. This can be useful
for overlapping computation and data transfer.
"""
# Create a pinned CPU parameter dict for async data transfer if streams are to be used
cpu_param_dict = None
if stream is not None:
for param in module.parameters():
param.data = param.data.cpu().pin_memory()
cpu_param_dict = {param: param.data for param in module.parameters()}
# Create module groups for leaf modules and apply group offloading hooks
modules_with_group_offloading = set()
for name, submodule in module.named_modules():
if not isinstance(submodule, _SUPPORTED_PYTORCH_LAYERS):
continue
group = ModuleGroup(
modules=[submodule],
offload_device=offload_device,
onload_device=onload_device,
offload_leader=submodule,
onload_leader=submodule,
non_blocking=non_blocking,
stream=stream,
cpu_param_dict=cpu_param_dict,
onload_self=True,
)
_apply_group_offloading_hook(submodule, group, None)
modules_with_group_offloading.add(name)
# Parameters and Buffers at all non-leaf levels need to be offloaded/onloaded separately when the forward pass
# of the module is called
module_dict = dict(module.named_modules())
parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading)
buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading)
# Find closest module parent for each parameter and buffer, and attach group hooks
parent_to_parameters = {}
for name, param in parameters:
parent_name = _find_parent_module_in_module_dict(name, module_dict)
if parent_name in parent_to_parameters:
parent_to_parameters[parent_name].append(param)
else:
parent_to_parameters[parent_name] = [param]
parent_to_buffers = {}
for name, buffer in buffers:
parent_name = _find_parent_module_in_module_dict(name, module_dict)
if parent_name in parent_to_buffers:
parent_to_buffers[parent_name].append(buffer)
else:
parent_to_buffers[parent_name] = [buffer]
parent_names = set(parent_to_parameters.keys()) | set(parent_to_buffers.keys())
for name in parent_names:
parameters = parent_to_parameters.get(name, [])
buffers = parent_to_buffers.get(name, [])
parent_module = module_dict[name]
assert getattr(parent_module, "_diffusers_hook", None) is None
group = ModuleGroup(
modules=[],
offload_device=offload_device,
onload_device=onload_device,
offload_leader=parent_module,
onload_leader=parent_module,
parameters=parameters,
buffers=buffers,
non_blocking=non_blocking,
stream=stream,
cpu_param_dict=cpu_param_dict,
onload_self=True,
)
_apply_group_offloading_hook(parent_module, group, None)
if stream is not None:
# When using streams, we need to know the layer execution order for applying prefetching (to overlap data transfer
# and computation). Since we don't know the order beforehand, we apply a lazy prefetching hook that will find the
# execution order and apply prefetching in the correct order.
unmatched_group = ModuleGroup(
modules=[],
offload_device=offload_device,
onload_device=onload_device,
offload_leader=module,
onload_leader=module,
parameters=None,
buffers=None,
non_blocking=False,
stream=None,
cpu_param_dict=None,
onload_self=True,
)
_apply_lazy_group_offloading_hook(module, unmatched_group, None)
def _apply_group_offloading_hook(
module: torch.nn.Module,
group: ModuleGroup,
next_group: Optional[ModuleGroup] = None,
) -> None:
registry = HookRegistry.check_if_exists_or_initialize(module)
# We may have already registered a group offloading hook if the module had a torch.nn.Parameter whose parent
# is the current module. In such cases, we don't want to overwrite the existing group offloading hook.
if registry.get_hook(_GROUP_OFFLOADING) is None:
hook = GroupOffloadingHook(group, next_group)
registry.register_hook(hook, _GROUP_OFFLOADING)
def _apply_lazy_group_offloading_hook(
module: torch.nn.Module,
group: ModuleGroup,
next_group: Optional[ModuleGroup] = None,
) -> None:
registry = HookRegistry.check_if_exists_or_initialize(module)
# We may have already registered a group offloading hook if the module had a torch.nn.Parameter whose parent
# is the current module. In such cases, we don't want to overwrite the existing group offloading hook.
if registry.get_hook(_GROUP_OFFLOADING) is None:
hook = GroupOffloadingHook(group, next_group)
registry.register_hook(hook, _GROUP_OFFLOADING)
lazy_prefetch_hook = LazyPrefetchGroupOffloadingHook()
registry.register_hook(lazy_prefetch_hook, _LAZY_PREFETCH_GROUP_OFFLOADING)
def _gather_parameters_with_no_group_offloading_parent(
module: torch.nn.Module, modules_with_group_offloading: Set[str]
) -> List[torch.nn.Parameter]:
parameters = []
for name, parameter in module.named_parameters():
has_parent_with_group_offloading = False
atoms = name.split(".")
while len(atoms) > 0:
parent_name = ".".join(atoms)
if parent_name in modules_with_group_offloading:
has_parent_with_group_offloading = True
break
atoms.pop()
if not has_parent_with_group_offloading:
parameters.append((name, parameter))
return parameters
def _gather_buffers_with_no_group_offloading_parent(
module: torch.nn.Module, modules_with_group_offloading: Set[str]
) -> List[torch.Tensor]:
buffers = []
for name, buffer in module.named_buffers():
has_parent_with_group_offloading = False
atoms = name.split(".")
while len(atoms) > 0:
parent_name = ".".join(atoms)
if parent_name in modules_with_group_offloading:
has_parent_with_group_offloading = True
break
atoms.pop()
if not has_parent_with_group_offloading:
buffers.append((name, buffer))
return buffers
def _find_parent_module_in_module_dict(name: str, module_dict: Dict[str, torch.nn.Module]) -> str:
atoms = name.split(".")
while len(atoms) > 0:
parent_name = ".".join(atoms)
if parent_name in module_dict:
return parent_name
atoms.pop()
return ""
def _raise_error_if_accelerate_model_or_sequential_hook_present(module: torch.nn.Module) -> None:
if not is_accelerate_available():
return
for name, submodule in module.named_modules():
if not hasattr(submodule, "_hf_hook"):
continue
if isinstance(submodule._hf_hook, (AlignDevicesHook, CpuOffload)):
raise ValueError(
f"Cannot apply group offloading to a module that is already applying an alternative "
f"offloading strategy from Accelerate. If you want to apply group offloading, please "
f"disable the existing offloading strategy first. Offending module: {name} ({type(submodule)})"
)
def _is_group_offload_enabled(module: torch.nn.Module) -> bool:
for submodule in module.modules():
if hasattr(submodule, "_diffusers_hook") and submodule._diffusers_hook.get_hook(_GROUP_OFFLOADING) is not None:
return True
return False
def _get_group_onload_device(module: torch.nn.Module) -> torch.device:
for submodule in module.modules():
if hasattr(submodule, "_diffusers_hook") and submodule._diffusers_hook.get_hook(_GROUP_OFFLOADING) is not None:
return submodule._diffusers_hook.get_hook(_GROUP_OFFLOADING).group.onload_device
raise ValueError("Group offloading is not enabled for the provided module.")