schnell commited on
Commit
19a0761
1 Parent(s): 204d033

Model save

Browse files
last-checkpoint/config.json DELETED
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last-checkpoint/tokenizer.json DELETED
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last-checkpoint/tokenizer_config.json DELETED
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last-checkpoint/zero_to_fp32.py DELETED
@@ -1,482 +0,0 @@
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- #!/usr/bin/env python
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-
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- # This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
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- # copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
5
- # the future. Once extracted, the weights don't require DeepSpeed and can be used in any
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- # application.
7
- #
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- # example: python zero_to_fp32.py . pytorch_model.bin
9
-
10
- import argparse
11
- import torch
12
- import glob
13
- import math
14
- import os
15
- import re
16
- from collections import OrderedDict
17
-
18
- # while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
19
- # DeepSpeed data structures it has to be available in the current python environment.
20
- from deepspeed.utils import logger
21
- from deepspeed.checkpoint.constants import (DS_VERSION,
22
- OPTIMIZER_STATE_DICT,
23
- SINGLE_PARTITION_OF_FP32_GROUPS,
24
- FP32_FLAT_GROUPS,
25
- ZERO_STAGE,
26
- PARTITION_COUNT,
27
- PARAM_SHAPES,
28
- BUFFER_NAMES)
29
-
30
- debug = 0
31
-
32
- # load to cpu
33
- device = torch.device('cpu')
34
-
35
-
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- def atoi(text):
37
- return int(text) if text.isdigit() else text
38
-
39
-
40
- def natural_keys(text):
41
- '''
42
- alist.sort(key=natural_keys) sorts in human order
43
- http://nedbatchelder.com/blog/200712/human_sorting.html
44
- (See Toothy's implementation in the comments)
45
- '''
46
- return [atoi(c) for c in re.split(r'(\d+)', text)]
47
-
48
-
49
- def get_model_state_file(checkpoint_dir, zero_stage):
50
- if not os.path.isdir(checkpoint_dir):
51
- raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
52
-
53
- # there should be only one file
54
- if zero_stage == 2:
55
- file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
56
- elif zero_stage == 3:
57
- file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
58
-
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- if not os.path.exists(file):
60
- raise FileNotFoundError(f"can't find model states file at '{file}'")
61
-
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- return file
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-
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-
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- def get_optim_files(checkpoint_dir):
66
- # XXX: need to test that this simple glob rule works for multi-node setup too
67
- optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
68
- "*_optim_states.pt")),
69
- key=natural_keys)
70
-
71
- if len(optim_files) == 0:
72
- raise FileNotFoundError(
73
- f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")
74
-
75
- return optim_files
76
-
77
-
78
- def parse_model_state(file):
79
- state_dict = torch.load(file, map_location=device)
80
-
81
- if BUFFER_NAMES not in state_dict:
82
- raise ValueError(f"{file} is not a model state checkpoint")
83
- buffer_names = state_dict[BUFFER_NAMES]
84
- if debug:
85
- print("Found buffers:", buffer_names)
86
-
87
- # recover just the buffers while restoring them to fp32 if they were saved in fp16
88
- buffers = {
89
- k: v.float()
90
- for k,
91
- v in state_dict["module"].items() if k in buffer_names
92
- }
93
- param_shapes = state_dict[PARAM_SHAPES]
94
-
95
- ds_version = state_dict.get(DS_VERSION, None)
96
-
97
- return buffers, param_shapes, ds_version
98
-
99
-
100
- def parse_optim_states(files, ds_checkpoint_dir):
101
-
102
- total_files = len(files)
103
- state_dicts = []
104
- for f in files:
105
- state_dicts.append(torch.load(f, map_location=device))
106
-
107
- if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
108
- raise ValueError(f"{files[0]} is not a zero checkpoint")
109
- zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
110
- world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
111
-
112
- # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
113
- # parameters can be different from data parallelism for non-expert parameters. So we can just
114
- # use the max of the partition_count to get the dp world_size.
115
-
116
- if type(world_size) is list:
117
- world_size = max(world_size)
118
-
119
- if world_size != total_files:
120
- raise ValueError(
121
- f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
122
- "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
123
- )
124
-
125
- # the groups are named differently in each stage
126
- if zero_stage == 2:
127
- fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
128
- elif zero_stage == 3:
129
- fp32_groups_key = FP32_FLAT_GROUPS
130
- else:
131
- raise ValueError(f"unknown zero stage {zero_stage}")
132
-
133
- if zero_stage == 2:
134
- fp32_flat_groups = [
135
- state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key]
136
- for i in range(len(state_dicts))
137
- ]
138
- elif zero_stage == 3:
139
- # if there is more than one param group, there will be multiple flattened tensors - one
140
- # flattened tensor per group - for simplicity merge them into a single tensor
141
- #
142
- # XXX: could make the script more memory efficient for when there are multiple groups - it
143
- # will require matching the sub-lists of param_shapes for each param group flattened tensor
144
-
145
- fp32_flat_groups = [
146
- torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key],
147
- 0) for i in range(len(state_dicts))
148
- ]
149
-
150
- return zero_stage, world_size, fp32_flat_groups
151
-
152
-
153
- def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
154
- """
155
- Returns fp32 state_dict reconstructed from ds checkpoint
156
-
157
- Args:
158
- - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
159
-
160
- """
161
- print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
162
-
163
- optim_files = get_optim_files(ds_checkpoint_dir)
164
- zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
165
- print(
166
- f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
167
-
168
- model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
169
- buffers, param_shapes, ds_version = parse_model_state(model_file)
170
- print(f'Parsing checkpoint created by deepspeed=={ds_version}')
171
-
172
- if zero_stage == 2:
173
- return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
174
- param_shapes,
175
- fp32_flat_groups,
176
- buffers)
177
- elif zero_stage == 3:
178
- return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
179
- param_shapes,
180
- fp32_flat_groups,
181
- buffers)
182
-
183
-
184
- def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
185
- param_shapes,
186
- fp32_flat_groups,
187
- buffers):
188
-
189
- # Reconstruction protocol:
190
- #
191
- # XXX: document this
192
-
193
- if debug:
194
- for i in range(world_size):
195
- for j in range(len(fp32_flat_groups[0])):
196
- print(
197
- f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
198
-
199
- # XXX: memory usage doubles here (zero2)
200
- num_param_groups = len(fp32_flat_groups[0])
201
- merged_single_partition_of_fp32_groups = []
202
- for i in range(num_param_groups):
203
- merged_partitions = [sd[i] for sd in fp32_flat_groups]
204
- full_single_fp32_vector = torch.cat(merged_partitions, 0)
205
- merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
206
- avail_numel = sum([
207
- full_single_fp32_vector.numel()
208
- for full_single_fp32_vector in merged_single_partition_of_fp32_groups
209
- ])
210
-
211
- if debug:
212
- wanted_params = sum([len(shapes) for shapes in param_shapes])
213
- wanted_numel = sum(
214
- [sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
215
- # not asserting if there is a mismatch due to possible padding
216
- print(f"Have {avail_numel} numels to process.")
217
- print(f"Need {wanted_numel} numels in {wanted_params} params.")
218
-
219
- state_dict = OrderedDict()
220
-
221
- # buffers
222
- state_dict.update(buffers)
223
- if debug:
224
- print(f"added {len(buffers)} buffers")
225
-
226
- # params
227
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
228
- # out-of-core computing solution
229
- total_numel = 0
230
- total_params = 0
231
- for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
232
- offset = 0
233
- avail_numel = full_single_fp32_vector.numel()
234
- for name, shape in shapes.items():
235
-
236
- unpartitioned_numel = shape.numel()
237
- total_numel += unpartitioned_numel
238
- total_params += 1
239
-
240
- if debug:
241
- print(
242
- f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} "
243
- )
244
- state_dict[name] = full_single_fp32_vector.narrow(
245
- 0,
246
- offset,
247
- unpartitioned_numel).view(shape)
248
- offset += unpartitioned_numel
249
-
250
- # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
251
- # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
252
- # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
253
- # live optimizer object, so we are checking that the numbers are within the right range
254
- align_to = 2 * world_size
255
-
256
- def zero2_align(x):
257
- return align_to * math.ceil(x / align_to)
258
-
259
- if debug:
260
- print(f"original offset={offset}, avail_numel={avail_numel}")
261
-
262
- offset = zero2_align(offset)
263
- avail_numel = zero2_align(avail_numel)
264
-
265
- if debug:
266
- print(f"aligned offset={offset}, avail_numel={avail_numel}")
267
-
268
- # Sanity check
269
- if offset != avail_numel:
270
- raise ValueError(
271
- f"consumed {offset} numels out of {avail_numel} - something is wrong")
272
-
273
- print(
274
- f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
275
- )
276
-
277
- return state_dict
278
-
279
-
280
- def zero3_partitioned_param_info(unpartitioned_numel, world_size):
281
- remainder = unpartitioned_numel % world_size
282
- padding_numel = (world_size - remainder) if remainder else 0
283
- partitioned_numel = math.ceil(unpartitioned_numel / world_size)
284
- return partitioned_numel, padding_numel
285
-
286
-
287
- def _get_fp32_state_dict_from_zero3_checkpoint(world_size,
288
- param_shapes,
289
- fp32_flat_groups,
290
- buffers):
291
-
292
- # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
293
- # param, re-consolidating each param, while dealing with padding if any
294
-
295
- avail_numel = fp32_flat_groups[0].numel() * world_size
296
- # merge list of dicts, preserving order
297
- param_shapes = {k: v for d in param_shapes for k, v in d.items()}
298
-
299
- if debug:
300
- for i in range(world_size):
301
- print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
302
-
303
- wanted_params = len(param_shapes)
304
- wanted_numel = sum(shape.numel() for shape in param_shapes.values())
305
- # not asserting if there is a mismatch due to possible padding
306
- print(f"Have {avail_numel} numels to process.")
307
- print(f"Need {wanted_numel} numels in {wanted_params} params.")
308
-
309
- state_dict = OrderedDict()
310
-
311
- # buffers
312
- state_dict.update(buffers)
313
- if debug:
314
- print(f"added {len(buffers)} buffers")
315
-
316
- # params
317
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
318
- # out-of-core computing solution
319
- offset = 0
320
- total_numel = 0
321
- total_params = 0
322
- for name, shape in param_shapes.items():
323
-
324
- unpartitioned_numel = shape.numel()
325
- total_numel += unpartitioned_numel
326
- total_params += 1
327
-
328
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
329
-
330
- if debug:
331
- print(
332
- f"{total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
333
- )
334
-
335
- # XXX: memory usage doubles here
336
- state_dict[name] = torch.cat(
337
- tuple(fp32_flat_groups[i].narrow(0,
338
- offset,
339
- partitioned_numel)
340
- for i in range(world_size)),
341
- 0).narrow(0,
342
- 0,
343
- unpartitioned_numel).view(shape)
344
- offset += partitioned_numel
345
-
346
- offset *= world_size
347
-
348
- # Sanity check
349
- if offset != avail_numel:
350
- raise ValueError(
351
- f"consumed {offset} numels out of {avail_numel} - something is wrong")
352
-
353
- print(
354
- f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
355
- )
356
-
357
- return state_dict
358
-
359
-
360
- def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
361
- """
362
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
363
- ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
364
- via a model hub.
365
-
366
- Args:
367
- - ``checkpoint_dir``: path to the desired checkpoint folder
368
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
369
-
370
- Returns:
371
- - pytorch ``state_dict``
372
-
373
- Note: this approach may not work if your application doesn't have sufficient free CPU memory and
374
- you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
375
- the checkpoint.
376
-
377
- A typical usage might be ::
378
-
379
- from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
380
- # do the training and checkpoint saving
381
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
382
- model = model.cpu() # move to cpu
383
- model.load_state_dict(state_dict)
384
- # submit to model hub or save the model to share with others
385
-
386
- In this example the ``model`` will no longer be usable in the deepspeed context of the same
387
- application. i.e. you will need to re-initialize the deepspeed engine, since
388
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
389
-
390
- If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
391
-
392
- """
393
- if tag is None:
394
- latest_path = os.path.join(checkpoint_dir, 'latest')
395
- if os.path.isfile(latest_path):
396
- with open(latest_path, 'r') as fd:
397
- tag = fd.read().strip()
398
- else:
399
- raise ValueError(f"Unable to find 'latest' file at {latest_path}")
400
-
401
- ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
402
-
403
- if not os.path.isdir(ds_checkpoint_dir):
404
- raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
405
-
406
- return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
407
-
408
-
409
- def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
410
- """
411
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
412
- loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
413
-
414
- Args:
415
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
416
- - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
417
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
418
- """
419
-
420
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
421
- print(f"Saving fp32 state dict to {output_file}")
422
- torch.save(state_dict, output_file)
423
-
424
-
425
- def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
426
- """
427
- 1. Put the provided model to cpu
428
- 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
429
- 3. Load it into the provided model
430
-
431
- Args:
432
- - ``model``: the model object to update
433
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
434
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
435
-
436
- Returns:
437
- - ``model`: modified model
438
-
439
- Make sure you have plenty of CPU memory available before you call this function. If you don't
440
- have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
441
- conveniently placed for you in the checkpoint folder.
442
-
443
- A typical usage might be ::
444
-
445
- from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
446
- model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
447
- # submit to model hub or save the model to share with others
448
-
449
- Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
450
- of the same application. i.e. you will need to re-initialize the deepspeed engine, since
451
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
452
-
453
- """
454
- logger.info(f"Extracting fp32 weights")
455
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
456
-
457
- logger.info(f"Overwriting model with fp32 weights")
458
- model = model.cpu()
459
- model.load_state_dict(state_dict, strict=False)
460
-
461
- return model
462
-
463
-
464
- if __name__ == "__main__":
465
-
466
- parser = argparse.ArgumentParser()
467
- parser.add_argument(
468
- "checkpoint_dir",
469
- type=str,
470
- help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
471
- parser.add_argument(
472
- "output_file",
473
- type=str,
474
- help=
475
- "path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)"
476
- )
477
- parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
478
- args = parser.parse_args()
479
-
480
- debug = args.debug
481
-
482
- convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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