-
Notifications
You must be signed in to change notification settings - Fork 22.2k
/
test_fsdp_optim_state.py
1905 lines (1774 loc) · 74.3 KB
/
test_fsdp_optim_state.py
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
# Owner(s): ["oncall: distributed"]
import bisect
import sys
from copy import deepcopy
from enum import auto, Enum
from typing import Any, Callable, Dict, List, Optional, Tuple, Type
import torch
import torch.nn as nn
from torch import distributed as dist
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
_CHECKPOINT_WRAPPED_MODULE,
apply_activation_checkpointing,
)
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp._shard_utils import _gather_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import (
FullOptimStateDictConfig,
FullStateDictConfig,
OptimStateKeyType,
ShardedOptimStateDictConfig,
ShardedStateDictConfig,
StateDictSettings,
StateDictType,
)
from torch.distributed.optim import _NamedOptimizer
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import (
CUDAInitMode,
FSDPInitMode,
FSDPTest,
TransformerWithSharedParams,
)
from torch.testing._internal.common_utils import (
instantiate_parametrized_tests,
parametrize,
run_tests,
TEST_WITH_DEV_DBG_ASAN,
)
STATE_DICT_TYPES = [StateDictType.FULL_STATE_DICT, StateDictType.SHARDED_STATE_DICT]
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
class _OSDCommMethod(Enum):
"""Method for communicating the optimizer state dict for internal tests."""
BROADCAST_OBJECT_LIST = auto()
SCATTER_FULL_OSD = auto()
FLATTEN_SHARDED_OSD = auto()
OPTIM_STATE_DICT = auto()
class _ModelClass(Enum):
"""Different model type to test."""
NESTED = auto()
TRANSFORMER = auto()
class Bias(torch.nn.Module):
"""This module applies a 1D additive bias with dimension ``dim``."""
def __init__(self, dim: int) -> None:
super().__init__()
assert dim > 0
torch.manual_seed(0)
self.bias = torch.nn.Parameter(torch.randn((dim,)))
def forward(self, x):
return x + self.bias
class BlockA(torch.nn.Module):
"""
Used to define interesting nested structure for FSDP wrapping.
BlockA
Bias0
bias
weight
Bias1
bias
"""
def __init__(self, in_dim: int, out_dim: int) -> None:
super().__init__()
assert all(v > 0 for v in (in_dim, out_dim))
torch.manual_seed(0)
self.bias_module0 = Bias(out_dim)
self.weight = torch.nn.Parameter(torch.randn((in_dim, out_dim)))
self.bias_module1 = Bias(out_dim)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = x @ self.weight
x = self.bias_module0(x)
x = self.relu(x) # ensure biases have different gradients
x = self.bias_module1(x)
return x
class BlockB(torch.nn.Module):
"""
Used to define interesting nested structure for FSDP wrapping.
BlockB
weight
Bias
bias
Bias
bias
"""
def __init__(self, in_dim: int, out_dim: int) -> None:
super().__init__()
assert all(v > 0 for v in (in_dim, out_dim))
torch.manual_seed(0)
self.weight = torch.nn.Parameter(torch.randn((in_dim, out_dim)))
self.bias_module0 = Bias(out_dim)
self.bias_module1 = Bias(out_dim)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = x @ self.weight
x = self.bias_module0(x)
x = self.relu(x) # ensure biases have different gradients
x = self.bias_module1(x)
return x
class NestedModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.block0 = BlockB(5, 7)
self.block1 = BlockB(7, 7)
self.bias = torch.nn.Parameter(torch.randn((5,)))
self.block2 = torch.nn.Sequential(
BlockA(7, 9),
BlockA(9, 9),
BlockB(9, 5),
)
self.relu = torch.nn.ReLU()
def forward(self, x) -> torch.Tensor:
x = self.relu(self.block0(x))
x = self.relu(self.block1(x))
x = self.relu(self.block2(x))
x = x + self.bias
return x
def get_input(self, device):
BATCH_SIZE = 8
return (torch.randn((BATCH_SIZE, 5)).to(device),)
def get_loss(self, inp, output):
return output.sum()
def run_backward(self, loss):
loss.backward()
@staticmethod
def wrap(
model: torch.nn.Module,
group: Optional[dist.ProcessGroup] = None,
ignore_modules: bool = False,
fsdp_kwargs: Optional[Dict[str, Any]] = None,
) -> torch.nn.Module:
if fsdp_kwargs is None:
fsdp_kwargs = {}
# Flatten Bias0; then flatten weight and Bias1 together into `block1`
model.block1.bias_module0 = FSDP(
model.block1.bias_module0,
process_group=group,
**fsdp_kwargs,
)
model.block1 = FSDP(model.block1, process_group=group, **fsdp_kwargs)
# Flatten Bias0; flatten Bias1; then flatten weight into `block2[1]`
model.block2[1].bias_module0 = FSDP(
model.block2[1].bias_module0,
process_group=group,
**fsdp_kwargs,
)
model.block2[1].bias_module1 = FSDP(
model.block2[1].bias_module1,
process_group=group,
**fsdp_kwargs,
)
model.block2[1] = FSDP(model.block2[1], process_group=group, **fsdp_kwargs)
# Flatten weight, Bias, bias into `block2[2]`
ignored_modules = [model.block2[2].bias_module0] if ignore_modules else None
model.block2[2] = FSDP(
model.block2[2],
process_group=group,
ignored_modules=ignored_modules,
**fsdp_kwargs,
)
return model
@staticmethod
def wrap_alt(
model: torch.nn.Module,
group: Optional[dist.ProcessGroup] = None,
fsdp_kwargs: Optional[Dict[str, Any]] = None,
) -> torch.nn.Module:
if fsdp_kwargs is None:
fsdp_kwargs = {}
model.block0.bias_module0 = FSDP(
model.block0.bias_module0,
process_group=group,
**fsdp_kwargs,
)
model.block0 = FSDP(model.block0, process_group=group, **fsdp_kwargs)
return model
@staticmethod
def wrap_with_unmanaged_params(
model,
add_to_fsdp_module: bool,
group=None,
) -> Tuple[torch.nn.Module, List[torch.nn.Parameter]]:
"""Registers unmanaged parameters before wrapping with :meth:`wrap`."""
device = next(model.parameters()).device
unmanaged_param = torch.nn.Parameter(torch.randn(5, 5, device=device))
# Either register the parameter to a module to be wrapped with FSDP
# (`model.block2[2]`) or a module not to be wrapped with FSDP (`model`)
register_module = model.block2[2] if add_to_fsdp_module else model
register_module.register_parameter(
"unmanaged_param",
unmanaged_param,
)
# For simplicity, we only add a single unmanaged parameter, but should
# be easy to generalize if needed
return NestedModel.wrap(model, group), [unmanaged_param]
@staticmethod
def add_unmanaged_param_entry(osd, unmanaged_param, step) -> None:
"""Adds an entry for the unmanaged parameter ``unmanaged_param``
assuming Adam optimizer and a single parameter group."""
# The unmanaged parameters should be passed to this method in
# `model.parameters()` order since their parameter IDs will be assigned
# in order of the skipped IDs
# Assign a parameter ID to the unmanaged parameter
unmanaged_param_id = -1
param_ids = osd["param_groups"][0]["params"]
for i in range(1, len(param_ids)):
diff = param_ids[i] - param_ids[i - 1]
if diff != 1:
assert diff > 1, f"Invalid IDs: {param_ids[i - 1]} {param_ids[i]}"
unmanaged_param_id = param_ids[i - 1] + 1
break
if unmanaged_param_id == -1:
unmanaged_param_id = len(param_ids) # last ID skipped
assert unmanaged_param_id >= 0, "One parameter ID should be skipped"
# Add a state entry for the unmanaged parameter
state_device = next(iter(next(iter(osd["state"].values())).values())).device
osd["state"][unmanaged_param_id] = {
"step": torch.tensor(float(step), device=state_device),
"exp_avg": torch.randn(unmanaged_param.shape, device=state_device),
"exp_avg_sq": torch.randn(unmanaged_param.shape, device=state_device),
}
# Insert the ID into the parameter group in order
bisect.insort(osd["param_groups"][0]["params"], unmanaged_param_id)
# NOTE: We exclude `self.bias` from either parameter group to test the
# case where the optimizer input does not include all model parameters
def param_group0(self) -> List[torch.nn.Parameter]:
# Use `block1`'s parameters for the first parameter group to deviate
# from the `model.parameters()` order
return list(self.block1.parameters())
def param_group1(self) -> List[torch.nn.Parameter]:
# Deviate from the `model.parameters()` order further by rearranging
# `block2`'s parameters to be before `block0`'s parameters
return list(self.block2.parameters()) + list(self.block0.parameters())
# Simple and boring model to test interface and some corner cases that do not
# require complicated wrapping strategy.
class TestDummyModel(torch.nn.Module):
def __init__(self):
super().__init__()
torch.manual_seed(0)
self.net1 = nn.Sequential(nn.Linear(8, 16), nn.ReLU())
self.net2 = nn.Sequential(nn.Linear(16, 32), nn.ReLU())
self.net3 = nn.Linear(32, 64)
self.net4 = nn.Sequential(nn.ReLU(), nn.Linear(64, 8))
def forward(self, x):
return self.net4(self.net3(self.net2(self.net1(x))))
def get_input(self):
return torch.rand(8, 8, device="cuda")
class TestFSDPOptimState(FSDPTest):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._model_class = {
_ModelClass.NESTED: self._init_nested_model,
_ModelClass.TRANSFORMER: self._init_transformer_model,
}
def _init_nested_model(
self,
wrap: bool,
wrap_alt: bool = False, # ignored if `wrap=False`
device: torch.device = torch.device("cuda"),
group=None,
optim_class: Type[torch.optim.Optimizer] = torch.optim.Adam,
use_multiple_param_groups: bool = False,
use_diff_optim_inputs: bool = False,
fsdp_kwargs: Optional[Dict[str, Any]] = None,
):
model = NestedModel().to(device)
if wrap:
model = (
NestedModel.wrap_alt(model, group, fsdp_kwargs)
if wrap_alt
else NestedModel.wrap(model, group, fsdp_kwargs=fsdp_kwargs)
)
if not use_multiple_param_groups:
optim_input = list(model.parameters())
else:
optim_input = [
{"params": model.param_group0()},
{"params": model.param_group1(), "weight_decay": 0.9},
]
# Use a reversed parameter order for the optimizer input on odd ranks
if use_diff_optim_inputs and self.rank % 2 == 1:
if isinstance(optim_input[0], dict):
for param_group in optim_input:
param_group["params"] = list(reversed(param_group["params"]))
else:
optim_input = list(reversed(optim_input))
optim = optim_class(optim_input, lr=0.01)
return model, optim, optim_input
def _init_transformer_model(
self,
wrap: bool,
device: torch.device = torch.device("cuda"),
group=None,
optim_class: Type[torch.optim.Optimizer] = torch.optim.Adam,
use_multiple_param_groups: bool = False,
use_diff_optim_inputs: bool = False,
):
if use_multiple_param_groups or use_diff_optim_inputs:
# Keep these as arguments for parity with `_init_nested_model()`;
# these settings are not implemented since the transformer is
# wrapped with FSDP at the top-level, which means that there is
# only a single flat parameter, making these booleans vacuous
raise NotImplementedError()
if group is None:
group = dist.distributed_c10d._get_default_group()
model = TransformerWithSharedParams.init(
group,
FSDPInitMode.RECURSIVE if wrap else FSDPInitMode.NO_FSDP,
CUDAInitMode.CUDA_BEFORE,
deterministic=True,
)
optim = optim_class(model.parameters(), lr=0.01)
return model, optim, None
def _step_model(
self,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
device: torch.device = torch.device("cuda"),
num_iters: int = 1,
) -> List[float]:
"""Performs a forward pass, backward pass, and optimizer step
``num_iters``-many times, and returns the per-iteration losses."""
torch.manual_seed(0) # set seed for determinism
losses = []
module = model.module if hasattr(model, "module") else model
for _ in range(num_iters):
optim.zero_grad()
inp = module.get_input(device)
output = model(*inp)
loss = module.get_loss(inp, output).to(device)
losses.append(loss.item())
module.run_backward(loss)
optim.step()
return losses
def _broadcast_full_osd(self, full_osd: Dict[str, Any], group=None):
"""Broadcasts the full optimizer state dict in place of using
``torch.save()`` and ``torch.load()`` so that all ranks can have it."""
obj_list = [full_osd]
dist.broadcast_object_list(
obj_list,
src=0,
group=group,
)
full_osd = obj_list[0]
return full_osd
def _are_equal_states(
self,
state1: Dict[str, Any],
state2: Dict[str, Any],
) -> bool:
"""Checks if ``state1`` and ``state2`` contain the same mappings."""
if set(state1.keys()) != set(state2.keys()):
return False
for state_name, value1 in state1.items():
value2 = state2[state_name]
if type(value1) != type(value2):
return False
if torch.is_tensor(value1): # tensor state
assert torch.is_tensor(value2)
# Check the values on CPU to be device-agnostic
value1 = value1.cpu()
value2 = value2.cpu()
if value1.shape != value2.shape or not torch.all(
torch.isclose(value1, value2)
):
return False
else: # non-tensor state
if value1 != value2:
return False
return True
def _check_same_state(
self,
fsdp_osd,
ref_osd,
check_same_param_keys: bool,
):
"""Checks that ``full_osd`` and ``ref_osd`` have the same "state" part.
If ``check_same_param_keys=True``, then checks that the parameter keys
match (e.g. when both should be parameter names), and does not check
the parameter keys otherwise."""
assert "state" in ref_osd
self.assertTrue("state" in fsdp_osd)
ref_osd_state = ref_osd["state"]
fsdp_osd_state = {
k: _gather_state_dict(v) for k, v in fsdp_osd["state"].items()
}
if check_same_param_keys:
# Check parameter keys are the same first for earlier erroring
ref_osd_param_ids = set(ref_osd_state.keys())
fsdp_osd_param_ids = set(fsdp_osd_state.keys())
self.assertTrue(
ref_osd_param_ids == fsdp_osd_param_ids,
f"Rank {self.rank}: {(ref_osd_param_ids, fsdp_osd_param_ids)}",
)
# Check state values are the same
for param_id, param_state in fsdp_osd_state.items():
for state_name, value in param_state.items():
ref_value = ref_osd_state[param_id][state_name]
self.assertEqual(value, ref_value)
return
# Otherwise, only require the parameter keys to be isomorphic (e.g.
# between IDs and names)
ref_osd_states = list(ref_osd_state.values())
fsdp_osd_states = list(fsdp_osd_state.values())
self.assertEqual(len(ref_osd_states), len(fsdp_osd_states))
# Use brute-force quadratic-time comparison since it is hard to
# hash a tensor by value instead of by object
for fsdp_osd_state in fsdp_osd_states:
# Check for at least one match (may be > 1 in toy edge cases, e.g.
# multiple biases); nonetheless, each having >= 1 match and the two
# lists having equal length imply that the list contents are equal
self.assertTrue(
any(
self._are_equal_states(fsdp_osd_state, ref_osd_state)
for ref_osd_state in ref_osd_states
)
)
def _check_same_param_groups(
self,
full_osd,
ref_osd,
check_same_param_keys: bool,
):
"""Checks that ``full_osd`` and ``ref_osd`` have the same
"param_groups" part. If ``check_same_param_keys=True`, then checks that
the parameter keys match (e.g. when both should be parameter names),
and does not check the parameter keys otherwise."""
assert "param_groups" in ref_osd
self.assertTrue("param_groups" in full_osd)
ref_osd_param_groups = ref_osd["param_groups"]
full_osd_param_groups = full_osd["param_groups"]
self.assertTrue(len(full_osd_param_groups), len(ref_osd_param_groups))
for full_osd_pg, ref_osd_pg in zip(
full_osd_param_groups,
ref_osd_param_groups,
):
self.assertEqual(
set(full_osd_pg.keys()),
set(ref_osd_pg.keys()),
)
for name, full_osd_value in full_osd_pg.items():
if name == "params" and not check_same_param_keys:
continue
self.assertEqual(full_osd_value, ref_osd_pg[name])
def _check_state_device(self, osd: Dict[str, Any], on_gpu: bool):
"""Checks that all tensors in ``osd["state"]`` are on GPU if
``on_gpu=True`` and on CPU if ``on_gpu=False``."""
for param_state in osd["state"].values():
for value in param_state.values():
if torch.is_tensor(value) and value.dim() > 0:
if on_gpu:
self.assertTrue(value.is_cuda)
else:
self.assertFalse(value.is_cuda)
@skip_if_lt_x_gpu(2)
@parametrize("state_dict_type", STATE_DICT_TYPES)
@parametrize("use_multiple_param_groups", [False, True])
@parametrize("rank0_only", [False, True])
@parametrize("use_diff_optim_inputs", [False, True])
def test_optim_state_dict_nested(
self,
state_dict_type: StateDictType,
use_multiple_param_groups: bool,
rank0_only: bool,
use_diff_optim_inputs: bool,
) -> None:
"""
Tests :meth:`full_optim_state_dict` and meth:`sharded_optim_state_dict`
by comparing the returned dict for an FSDP-wrapped model with that of
an equivalent non-wrapped model.
The test checks the equivalence excluding the parameter keys since the
FSDP and normal optimizer state dicts key by names and IDs,
respectively. This means that the test can pass even if parameter keys
are incorrectly mapped to values. Their correct mapping is tested in
other tests that exercise the save/load workflow.
"""
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_optim_state_dict_nested,
state_dict_type=state_dict_type,
use_multiple_param_groups=use_multiple_param_groups,
rank0_only=rank0_only,
use_diff_optim_inputs=use_diff_optim_inputs,
)
def _test_optim_state_dict_nested(
self,
state_dict_type: StateDictType,
use_multiple_param_groups: bool,
rank0_only: bool,
use_diff_optim_inputs: bool,
use_optim_input: bool,
) -> None:
if rank0_only and state_dict_type == StateDictType.SHARDED_STATE_DICT:
return # not supported
NUM_ITERS = 3
model1, optim1, optim_input = self._init_nested_model(
wrap=True,
use_multiple_param_groups=use_multiple_param_groups,
use_diff_optim_inputs=use_diff_optim_inputs,
)
losses1 = self._step_model(model1, optim1, num_iters=NUM_ITERS)
if state_dict_type == StateDictType.FULL_STATE_DICT:
if use_optim_input:
fsdp_osd = FSDP.full_optim_state_dict(
model1,
optim1,
optim_input,
rank0_only=rank0_only,
)
else:
fsdp_osd = FSDP.full_optim_state_dict(
model1,
optim1,
rank0_only=rank0_only,
)
else:
fsdp_osd = FSDP.sharded_optim_state_dict(model1, optim1)
# Non-target ranks get an empty state dict
if rank0_only and self.rank != 0:
self.assertEqual(len(fsdp_osd), 0)
return
model2, optim2, _ = self._init_nested_model(
wrap=False,
use_multiple_param_groups=use_multiple_param_groups,
use_diff_optim_inputs=use_diff_optim_inputs,
)
losses2 = self._step_model(model2, optim2, num_iters=NUM_ITERS)
ref_osd = optim2.state_dict()
# Check the losses to eliminate model drift as a source of error
for i, (l1, l2) in enumerate(zip(losses1, losses2)):
assert l1 == l2, f"Losses differ on iter {i}: {l1:.5f} {l2:.5f}"
# Do not check the parameter keys since the full/sharded optimizer state
# dict uses parameter names, while the non-wrapped equivalent uses
# parameter IDs
check_same_param_keys = False
self._check_same_param_groups(
fsdp_osd,
ref_osd,
check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
fsdp_osd,
ref_osd,
check_same_param_keys=check_same_param_keys,
)
@skip_if_lt_x_gpu(2)
def test_full_optim_state_dict_keys(self):
"""Tests that the parameter keys returned by
:meth:`full_optim_state_dict` match those of :meth:`state_dict` with
full ``state_dict_type`` for a non-FSDP-root model with nested FSDP
instances and ignored modules."""
device = torch.device("cuda")
model = NestedModel().to(device)
wrapped_model = NestedModel.wrap(model, ignore_modules=True)
# Add checkpointing to ensure optim_state_dict and state_dict strip out
# checkpointing prefixes.
apply_activation_checkpointing(
model, check_fn=lambda module: isinstance(module, torch.nn.Sequential)
)
optim = torch.optim.Adam(wrapped_model.parameters(), lr=1e-3)
self._step_model(model, optim, device)
optim_state_dict = FSDP.full_optim_state_dict(
wrapped_model, optim, rank0_only=False
)
with FSDP.state_dict_type(wrapped_model, StateDictType.FULL_STATE_DICT):
state_dict = wrapped_model.state_dict()
self.assertEqual(optim_state_dict["state"].keys(), state_dict.keys())
# Check that checkpointing prefix was indeed stripped.
for key in optim_state_dict["state"]:
self.assertNotIn(_CHECKPOINT_WRAPPED_MODULE, key)
@skip_if_lt_x_gpu(2)
def test_full_optim_state_dict_nested_invalid(self):
"""Tests that :meth:`full_optim_state_dict` raises an error when
nonzero ranks are missing the optimizer state for parameters on rank
0."""
device = torch.device("cuda")
model = NestedModel.wrap(NestedModel().to(device), None)
optim_input = list(model.parameters())
if self.rank != 0:
# Exclude a parameter so that nonzero ranks are missing state
optim_input = optim_input[:-1]
optim = torch.optim.Adam(optim_input, lr=1e-3)
self._step_model(model, optim, num_iters=3)
error_regex = (
"FSDP currently requires each rank to have at least the "
"optimizer states needed by rank 0's optimizer but some ranks "
"are missing some of those states"
)
with self.assertRaisesRegex(RuntimeError, error_regex):
FSDP.full_optim_state_dict(model, optim)
@skip_if_lt_x_gpu(2)
@parametrize("use_multiple_param_groups", [False, True])
@parametrize("wrap_alt", [False, True])
@parametrize("use_diff_optim_inputs", [False, True])
def test_shard_full_optim_state_dict_nested(
self,
use_multiple_param_groups: bool,
wrap_alt: bool,
use_diff_optim_inputs: bool,
):
"""Tests :meth:`shard_full_optim_state_dict` for a non-FSDP-root model
with nested FSDP instances."""
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_load_optim_state,
model_class=_ModelClass.NESTED,
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=False,
osd_comm_method=_OSDCommMethod.BROADCAST_OBJECT_LIST,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(),
),
use_multiple_param_groups=False,
halve_world_size=False,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_shard_full_optim_state_dict_nested_halve_world_size(self):
"""Tests :meth:`shard_full_optim_state_dict` for a non-FSDP-root model
with nested FSDP instances when loading into a new process group with
halved world size."""
# To save CI costs, we test with the "harder" settings:
use_multiple_param_groups = True
use_diff_optim_inputs = True
wrap_alt = True
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_load_optim_state,
model_class=_ModelClass.NESTED,
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=True,
osd_comm_method=_OSDCommMethod.BROADCAST_OBJECT_LIST,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(),
),
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=True,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_shard_full_optim_state_dict_transformer(self) -> None:
"""Tests :meth:`shard_full_optim_state_dict` for an FSDP-root
transformer model with shared parameters."""
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_load_optim_state,
model_class=_ModelClass.TRANSFORMER,
use_multiple_param_groups=False,
halve_world_size=True,
osd_comm_method=_OSDCommMethod.BROADCAST_OBJECT_LIST,
use_diff_optim_inputs=False,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.TRANSFORMER,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(),
),
use_multiple_param_groups=False,
halve_world_size=True,
use_diff_optim_inputs=False,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
@parametrize("use_multiple_param_groups", [False, True])
@parametrize("wrap_alt", [False, True])
@parametrize("use_diff_optim_inputs", [False, True])
def test_scatter_full_optim_state_dict_nested(
self,
use_multiple_param_groups: bool,
wrap_alt: bool,
use_diff_optim_inputs: bool,
):
"""Tests :meth:`scatter_full_optim_state_dict` for a non-FSDP-root
model with nested FSDP instances."""
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_load_optim_state,
model_class=_ModelClass.NESTED,
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=False,
osd_comm_method=_OSDCommMethod.SCATTER_FULL_OSD,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(rank0_only=True),
),
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=False,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_scatter_full_optim_state_dict_nested_halve_world_size(self):
"""Tests :meth:`scatter_full_optim_state_dict` for a non-FSDP-root
model with nested FSDP instances when loading into a new process group
with halved world size."""
# To save CI costs, we test with the "harder" settings:
use_multiple_param_groups = True
use_diff_optim_inputs = True
wrap_alt = True
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_load_optim_state,
model_class=_ModelClass.NESTED,
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=True,
osd_comm_method=_OSDCommMethod.SCATTER_FULL_OSD,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(rank0_only=True),
),
use_multiple_param_groups=use_multiple_param_groups,
halve_world_size=True,
use_diff_optim_inputs=use_diff_optim_inputs,
wrap_alt=wrap_alt,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_scatter_full_optim_state_dict_transformer(self) -> None:
"""Tests :meth:`scatter_full_optim_state_dict` for an FSDP-root
transformer model with shared parameters."""
self.run_subtests(
{"use_optim_input": [False, True]},
self._test_load_optim_state,
model_class=_ModelClass.TRANSFORMER,
use_multiple_param_groups=False,
halve_world_size=True,
osd_comm_method=_OSDCommMethod.SCATTER_FULL_OSD,
use_diff_optim_inputs=False,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.TRANSFORMER,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(rank0_only=True),
),
use_multiple_param_groups=False,
halve_world_size=True,
use_diff_optim_inputs=False,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_flatten_sharded_optim_state_dict_nested(self) -> None:
"""Tests :meth:`flatten_sharded_optim_state_dict` for an FSDP-root
nested model."""
self._test_load_optim_state(
_ModelClass.NESTED,
use_multiple_param_groups=False,
halve_world_size=False,
osd_comm_method=_OSDCommMethod.FLATTEN_SHARDED_OSD,
use_diff_optim_inputs=False,
use_optim_input=False,
wrap_alt=True,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.SHARDED_STATE_DICT,
ShardedStateDictConfig(),
ShardedOptimStateDictConfig(),
),
use_multiple_param_groups=False,
halve_world_size=False,
use_diff_optim_inputs=False,
wrap_alt=True,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_flatten_sharded_optim_state_dict_transformer(self) -> None:
"""Tests :meth:`flatten_sharded_optim_state_dict` for an FSDP-root
transformer model."""
self._test_load_optim_state(
_ModelClass.TRANSFORMER,
use_multiple_param_groups=False,
halve_world_size=False,
osd_comm_method=_OSDCommMethod.FLATTEN_SHARDED_OSD,
use_diff_optim_inputs=False,
use_optim_input=False,
num_iters=3,
)
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.TRANSFORMER,
state_dict_settings=StateDictSettings(
StateDictType.SHARDED_STATE_DICT,
ShardedStateDictConfig(),
ShardedOptimStateDictConfig(),
),
use_multiple_param_groups=False,
halve_world_size=False,
use_diff_optim_inputs=False,
num_iters=3,
)
@skip_if_lt_x_gpu(2)
def test_use_orig_params(self) -> None:
"""Tests :meth:`optim_state_dict` for an FSDP-root nested model."""
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(),
),
use_multiple_param_groups=False,
halve_world_size=False,
use_diff_optim_inputs=False,
wrap_alt=True,
num_iters=3,
fsdp_kwargs={"use_orig_params": True},
)
# Enable this once use_orig_params supports rank0_only=Treu
"""
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(),
FullOptimStateDictConfig(rank0_only=True),
),
use_multiple_param_groups=False,
halve_world_size=False,
use_diff_optim_inputs=False,
wrap_alt=True,
num_iters=3,
fsdp_kwargs={"use_orig_params": True},
)
"""
self._test_load_optim_state_with_optim_state_dict(
_ModelClass.NESTED,
state_dict_settings=StateDictSettings(
StateDictType.SHARDED_STATE_DICT,
ShardedStateDictConfig(),
ShardedOptimStateDictConfig(),
),
use_multiple_param_groups=False,
halve_world_size=False,
use_diff_optim_inputs=False,
wrap_alt=True,
num_iters=3,
fsdp_kwargs={"use_orig_params": True},
)
def _test_load_optim_state(
self,
model_class: _ModelClass,
use_multiple_param_groups: bool,
halve_world_size: bool,
osd_comm_method: _OSDCommMethod,
use_diff_optim_inputs: bool,
use_optim_input: bool,
num_iters: int,
**new_model_kwargs,
):
"""
(1) Runs a model with full world size for K iterations to generate a
full/sharded optimizer state dict;
(2) initializes a model with halved world size and possibly different
FSDP wrapping scheme (based on ``new_model_kwargs``);
(3) loads the full/sharded optimizer state dict from (1) according to the
halved-world-size model;
(4) runs the halved-world-size model for K iterations; and
(5) checks that the sharded optimizer state dict from (3) matches the
halved-world-size model's local optimizer state dict, meaning that the
former could have equivalently been loaded into the local optimizer.
"""
initializer = self._model_class[model_class]
if osd_comm_method == _OSDCommMethod.OPTIM_STATE_DICT:
osd_method = FSDP.optim_state_dict
elif osd_comm_method == _OSDCommMethod.FLATTEN_SHARDED_OSD:
osd_method = FSDP.sharded_optim_state_dict
else: