-
-
Notifications
You must be signed in to change notification settings - Fork 816
/
data.py
974 lines (889 loc) · 33.8 KB
/
data.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
"""Module containing data utilities"""
import functools
import hashlib
import logging
from collections import defaultdict
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import yaml
from datasets import (
Dataset,
DatasetDict,
concatenate_datasets,
load_dataset,
load_from_disk,
)
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import HFValidationError
from torch.utils.data import RandomSampler
from transformers import PreTrainedTokenizerBase
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
from axolotl.datasets import TokenizedPromptDataset
from axolotl.prompt_strategies import load
from axolotl.prompt_strategies.dpo import load as load_dpo
from axolotl.prompt_tokenizers import (
AlpacaMultipleChoicePromptTokenizingStrategy,
AlpacaPromptTokenizingStrategy,
AlpacaReflectionPTStrategy,
GPTeacherPromptTokenizingStrategy,
JeopardyPromptTokenizingStrategy,
OpenAssistantPromptTokenizingStrategy,
SummarizeTLDRPromptTokenizingStrategy,
)
from axolotl.prompters import (
AlpacaPrompter,
GPTeacherPrompter,
JeopardyPrompter,
MultipleChoiceConcisePrompter,
MultipleChoiceExplainPrompter,
Prompter,
ReflectAlpacaPrompter,
SummarizeTLDRPrompter,
UnsupportedPrompter,
)
from axolotl.utils.collators import PretrainingBatchSamplerDataCollatorForSeq2Seq
from axolotl.utils.dict import DictDefault
from axolotl.utils.distributed import is_main_process, zero_first
from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
from axolotl.utils.trainer import (
calculate_total_num_steps,
process_datasets_for_packing,
process_pretraining_datasets_for_packing,
)
LOG = logging.getLogger("axolotl")
def md5(to_hash: str, encoding: str = "utf-8") -> str:
try:
return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
except TypeError:
return hashlib.md5(to_hash.encode(encoding)).hexdigest() # nosec
def prepare_dataset(cfg, tokenizer):
prompters = []
if not cfg.pretraining_dataset:
with zero_first(is_main_process()):
if cfg.test_datasets:
train_dataset, _, prompters = load_prepare_datasets(
tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH, split="train"
)
_, eval_dataset, _ = load_prepare_datasets(
tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH, split="test"
)
else:
train_dataset, eval_dataset, prompters = load_prepare_datasets(
tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
)
else:
path = cfg.pretraining_dataset
name = None
if isinstance(cfg.pretraining_dataset, list) and isinstance(
cfg.pretraining_dataset[0], dict
):
path = cfg.pretraining_dataset[0]["path"]
name = cfg.pretraining_dataset[0]["name"]
ds_wrapper_partial = functools.partial(
get_dataset_wrapper,
cfg.pretraining_dataset[0],
tokenizer,
cfg,
cfg.pretraining_dataset[0]["type"] or "pretrain",
)
train_dataset = wrap_pretraining_dataset(
load_dataset(path, streaming=True, split="train", name=name),
tokenizer,
cfg,
ds_wrapper_partial,
max_tokens=cfg.sequence_len,
batch_size=cfg.micro_batch_size,
seed=cfg.seed or 42,
)
# https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
train_dataset = train_dataset.with_format("torch")
eval_dataset = None
return train_dataset, eval_dataset, cfg.max_steps, prompters
if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
if total_eval_steps == 0:
raise ValueError(
"eval dataset split is too small for sample_packing. You should set `eval_sample_packing: False`. "
)
if cfg.max_steps:
total_num_steps = min(
calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
)
LOG.info(f"Maximum number of steps set at {total_num_steps}")
else:
total_num_steps = calculate_total_num_steps(cfg, train_dataset)
return train_dataset, eval_dataset, total_num_steps, prompters
def load_tokenized_prepared_datasets(
tokenizer,
cfg,
default_dataset_prepared_path,
split="train",
) -> Tuple[DatasetDict, List[Prompter]]:
cfg_datasets = cfg.test_datasets if split == "test" else cfg.datasets
tokenizer_name = tokenizer.__class__.__name__
ds_hash = str(
md5(
(
str(cfg.sequence_len)
+ "@"
+ str(cfg.sample_packing)
+ "@"
+ str(cfg.eval_sample_packing)
+ "@"
+ str(cfg.group_by_length)
+ "@"
+ "|".join(
sorted(
[
f"{d.path}:{d.type}:{d.shards}:{d.conversation}{d.split}"
for d in cfg_datasets
]
)
)
+ "|"
+ tokenizer_name
)
)
)
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(default_dataset_prepared_path) / ds_hash
)
dataset = None
prompters = []
use_auth_token = cfg.hf_use_auth_token
try:
if cfg.push_dataset_to_hub:
dataset = load_dataset(
f"{cfg.push_dataset_to_hub}/{ds_hash}",
token=use_auth_token,
)
dataset = dataset[split]
except Exception: # pylint: disable=broad-except # nosec
pass
if dataset:
...
elif (
cfg.dataset_prepared_path
and any(prepared_ds_path.glob("*"))
and not cfg.is_preprocess
):
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset = load_from_disk(str(prepared_ds_path))
LOG.info("Prepared dataset loaded from disk...")
else:
LOG.info(f"Unable to find prepared dataset in {prepared_ds_path}")
LOG.info("Loading raw datasets...")
if not cfg.is_preprocess:
LOG.warning(
"Processing datasets during training can lead to VRAM instability. Please pre-process your dataset."
)
if cfg.seed:
seed = cfg.seed
else:
LOG.info("No seed provided, using default seed of 42")
seed = 42
datasets = []
def for_d_in_datasets(dataset_configs):
for dataset in dataset_configs:
if dataset.name and isinstance(dataset.name, list):
for name in dataset.name:
yield DictDefault({**dataset, "name": name})
else:
yield dataset
# pylint: disable=invalid-name
for config_dataset in for_d_in_datasets(cfg_datasets):
ds: Optional[Union[Dataset, DatasetDict]] = None
ds_from_hub = False
try:
load_dataset(
config_dataset.path,
name=config_dataset.name,
streaming=True,
token=use_auth_token,
)
ds_from_hub = True
except (FileNotFoundError, ConnectionError, HFValidationError):
pass
ds_from_cloud = False
storage_options = {}
remote_file_system = None
if config_dataset.path.startswith("s3://"):
try:
import aiobotocore.session # type: ignore
import s3fs # type: ignore
except ImportError as exc:
raise ImportError(
"s3:// paths require aiobotocore and s3fs to be installed"
) from exc
# Takes credentials from ~/.aws/credentials for default profile
s3_session = aiobotocore.session.AioSession(profile="default")
storage_options = {"session": s3_session}
remote_file_system = s3fs.S3FileSystem(**storage_options)
elif config_dataset.path.startswith(
"gs://"
) or config_dataset.path.startswith("gcs://"):
try:
import gcsfs # type: ignore
except ImportError as exc:
raise ImportError(
"gs:// or gcs:// paths require gcsfs to be installed"
) from exc
# gcsfs will use default credentials from the environment else anon
# https://gcsfs.readthedocs.io/en/latest/#credentials
storage_options = {"token": None}
remote_file_system = gcsfs.GCSFileSystem(**storage_options)
# TODO: Figure out how to get auth creds passed
# elif config_dataset.path.startswith("adl://") or config_dataset.path.startswith("abfs://"):
# try:
# import adlfs
# except ImportError as exc:
# raise ImportError(
# "adl:// or abfs:// paths require adlfs to be installed"
# ) from exc
# # Gen 1
# storage_options = {
# "tenant_id": TENANT_ID,
# "client_id": CLIENT_ID,
# "client_secret": CLIENT_SECRET,
# }
# # Gen 2
# storage_options = {
# "account_name": ACCOUNT_NAME,
# "account_key": ACCOUNT_KEY,
# }
# remote_file_system = adlfs.AzureBlobFileSystem(**storage_options)
try:
if remote_file_system and remote_file_system.exists(
config_dataset.path
):
ds_from_cloud = True
except (FileNotFoundError, ConnectionError):
pass
# prefer local dataset, even if hub exists
local_path = Path(config_dataset.path)
if local_path.exists():
if local_path.is_dir():
# TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
ds = load_dataset(
config_dataset.path,
name=config_dataset.name,
data_files=config_dataset.data_files,
streaming=False,
split=None,
)
elif local_path.is_file():
ds_type = get_ds_type(config_dataset)
ds = load_dataset(
ds_type,
name=config_dataset.name,
data_files=config_dataset.path,
streaming=False,
split=None,
)
else:
raise ValueError(
"unhandled dataset load: local path exists, but is neither a directory or a file"
)
elif ds_from_hub:
ds = load_dataset(
config_dataset.path,
name=config_dataset.name,
streaming=False,
data_files=config_dataset.data_files,
token=use_auth_token,
)
elif ds_from_cloud and remote_file_system:
if remote_file_system.isdir(config_dataset.path):
ds = load_from_disk(
config_dataset.path,
storage_options=storage_options,
)
elif remote_file_system.isfile(config_dataset.path):
ds_type = get_ds_type(config_dataset)
ds = load_dataset(
ds_type,
name=config_dataset.name,
data_files=config_dataset.path,
streaming=False,
split=None,
storage_options=storage_options,
)
elif config_dataset.path.startswith("https://"):
ds_type = get_ds_type(config_dataset)
ds = load_dataset(
ds_type,
name=config_dataset.name,
data_files=config_dataset.path,
streaming=False,
split=None,
storage_options=storage_options,
)
else:
if isinstance(config_dataset.data_files, str):
fp = hf_hub_download(
repo_id=config_dataset.path,
repo_type="dataset",
filename=config_dataset.data_files,
)
elif isinstance(config_dataset.data_files, list):
fp = []
for file in config_dataset.data_files:
fp.append(
hf_hub_download(
repo_id=config_dataset.path,
repo_type="dataset",
filename=file,
)
)
else:
raise ValueError(
"data_files must be either a string or list of strings"
)
ds = load_dataset(
"json",
name=config_dataset.name,
data_files=fp,
streaming=False,
split=None,
)
if not ds:
raise ValueError("unhandled dataset load")
d_base_type = d_prompt_style = None
d_type = config_dataset.type
if isinstance(d_type, str):
d_type_split = d_type.split(":")
d_base_type = d_type_split[0]
d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
if config_dataset.split and config_dataset.split in ds:
ds = ds[config_dataset.split]
elif split in ds:
ds = ds[split]
elif isinstance(ds, DatasetDict):
raise ValueError(
f"no {split} split found for dataset {config_dataset.path}, you may specify a split with 'split: `"
)
# support for using a subset of the data
if config_dataset.shards:
shards_idx = config_dataset.get("shards_idx", 0)
ds = ds.shuffle(seed=seed).shard(
num_shards=config_dataset.shards, index=shards_idx
)
dataset_wrapper, dataset_prompter = get_dataset_wrapper(
config_dataset=config_dataset,
tokenizer=tokenizer,
cfg=cfg,
dataset=ds,
d_base_type=d_base_type,
d_prompt_style=d_prompt_style,
)
datasets.append(dataset_wrapper)
prompters.append(dataset_prompter)
LOG.info("merging datasets")
dataset = concatenate_datasets(datasets)
if len(datasets) > 1:
LOG.info("shuffle merged datasets")
dataset = dataset.shuffle(seed=seed)
dataset, _ = process_datasets_for_packing(cfg, dataset, None)
if cfg.local_rank == 0:
LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
dataset.save_to_disk(prepared_ds_path)
if cfg.push_dataset_to_hub:
LOG.info(
f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
)
return dataset, prompters
def get_ds_type(config_dataset: DictDefault):
"""
Get the dataset type from the path if it's not specified
"""
ds_type = "json"
if config_dataset.ds_type:
ds_type = config_dataset.ds_type
elif ".parquet" in config_dataset.path:
ds_type = "parquet"
elif ".arrow" in config_dataset.path:
ds_type = "arrow"
elif ".csv" in config_dataset.path:
ds_type = "csv"
elif ".txt" in config_dataset.path:
ds_type = "text"
return ds_type
def load_prepare_datasets(
tokenizer: PreTrainedTokenizerBase,
cfg,
default_dataset_prepared_path,
split="train",
) -> Tuple[Dataset, Dataset, List[Prompter]]:
dataset, prompters = load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path, split=split
)
if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
LOG.info(
f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards"
)
dataset = dataset.shard(
num_shards=cfg.dataset_shard_num,
index=cfg.dataset_shard_idx,
)
if split == "train" and cfg.val_set_size:
# ensure we end up with the same fingerprint by doing rank0 first and being able to cache
to_hash_train = (
dataset._fingerprint # pylint: disable=protected-access
+ "|"
+ str(cfg.val_set_size)
+ "|"
+ "train"
+ "|"
+ str(cfg.seed or 42)
)
to_hash_test = (
dataset._fingerprint # pylint: disable=protected-access
+ "|"
+ str(cfg.val_set_size)
+ "|"
+ "test"
+ "|"
+ str(cfg.seed or 42)
)
train_fingerprint = md5(to_hash_train)
test_fingerprint = md5(to_hash_test)
dataset = dataset.train_test_split(
test_size=cfg.val_set_size,
shuffle=False,
seed=cfg.seed or 42,
train_new_fingerprint=train_fingerprint,
test_new_fingerprint=test_fingerprint,
)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
elif split == "test":
train_dataset = None
eval_dataset = dataset
else:
train_dataset = dataset
eval_dataset = None
return train_dataset, eval_dataset, prompters
def get_dataset_wrapper(
config_dataset,
tokenizer,
cfg,
d_base_type,
dataset,
d_prompt_style=None,
):
dataset_wrapper = None
dataset_prompter = None
ds_kwargs = {
"process_count": cfg.dataset_processes,
"keep_in_memory": cfg.dataset_keep_in_memory is True,
}
if (
isinstance(dataset, Dataset)
and "input_ids" in dataset.features
and "attention_mask" in dataset.features
and "labels" in dataset.features
):
# dataset is already tokenized, just drop it straight in
dataset_prompter = UnsupportedPrompter()
dataset_wrapper = dataset
elif isinstance(config_dataset.type, DictDefault):
ds_strategy = load(
"user_defined", tokenizer, cfg, config_dataset.type.to_dict()
)
dataset_prompter = UnsupportedPrompter()
dataset_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
elif ds_strategy := load(config_dataset.type, tokenizer, cfg, config_dataset):
dataset_prompter = UnsupportedPrompter()
dataset_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
elif d_base_type == "alpaca":
dataset_prompter = AlpacaPrompter(d_prompt_style)
ds_strategy = AlpacaPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "explainchoice":
dataset_prompter = MultipleChoiceExplainPrompter(d_prompt_style)
ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "concisechoice":
dataset_prompter = MultipleChoiceConcisePrompter(d_prompt_style)
ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "summarizetldr":
dataset_prompter = SummarizeTLDRPrompter(d_prompt_style)
ds_strategy = SummarizeTLDRPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "jeopardy":
dataset_prompter = JeopardyPrompter(d_prompt_style)
ds_strategy = JeopardyPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "oasst":
dataset_prompter = AlpacaPrompter(d_prompt_style)
ds_strategy = OpenAssistantPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "gpteacher":
dataset_prompter = GPTeacherPrompter(d_prompt_style)
ds_strategy = GPTeacherPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
elif d_base_type == "reflection":
dataset_prompter = ReflectAlpacaPrompter(d_prompt_style)
ds_strategy = AlpacaReflectionPTStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy,
dataset,
**ds_kwargs,
)
dataset_wrapper = ds_wrapper
else:
suffix = ""
if ":load_" in config_dataset.type:
suffix = f" Did you mean {config_dataset.type.replace(':load_', '.load_')}?"
LOG.error(
f"unhandled prompt tokenization strategy: {config_dataset.type}. {suffix}"
)
raise ValueError(
f"unhandled prompt tokenization strategy: {config_dataset.type} {suffix}"
)
return dataset_wrapper, dataset_prompter
def encode_pretraining(
tokenizer: PreTrainedTokenizerBase, max_tokens: int, examples: List[str]
) -> Dict[str, List]:
res = tokenizer(
examples,
truncation=True,
max_length=max_tokens - 2,
add_special_tokens=True,
)
# Convert to PyTorch tensors
input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
new_input_ids = []
new_attention_mask = []
# Append EOS and PAD tokens to input_ids, and correct attention_mask
for i, _ in enumerate(input_ids):
input_ids[i] = torch.cat(
(
input_ids[i],
torch.tensor([tokenizer.eos_token_id, tokenizer.pad_token_id]),
),
dim=0,
)
attention_mask[i] = torch.cat((attention_mask[i], torch.tensor([1, 0])), dim=0)
# Concatenate tokens so that their lengths are less than max_tokens
buffer_input_ids = torch.tensor([], dtype=torch.long)
buffer_attention_mask = torch.tensor([], dtype=torch.long)
for ids, mask in zip(input_ids, attention_mask):
if buffer_input_ids.numel() == max_tokens:
new_input_ids.append(buffer_input_ids)
new_attention_mask.append(buffer_attention_mask)
buffer_input_ids = torch.tensor([], dtype=torch.long)
buffer_attention_mask = torch.tensor([], dtype=torch.long)
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
elif buffer_input_ids.numel() + ids.numel() <= max_tokens:
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
else:
buffer_input_ids = torch.cat(
(
buffer_input_ids,
torch.full(
(max_tokens - buffer_input_ids.numel(),),
tokenizer.pad_token_id,
dtype=torch.long,
),
),
dim=0,
)
buffer_attention_mask = torch.cat(
(
buffer_attention_mask,
torch.full(
(max_tokens - buffer_attention_mask.numel(),),
0,
dtype=torch.long,
),
),
dim=0,
)
new_input_ids.append(buffer_input_ids)
new_attention_mask.append(buffer_attention_mask)
buffer_input_ids = torch.tensor([], dtype=torch.long)
buffer_attention_mask = torch.tensor([], dtype=torch.long)
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
if buffer_input_ids.numel() > 0: # for any leftover tokens
while buffer_input_ids.numel() < max_tokens: # make all sequences equal in size
buffer_input_ids = torch.cat(
(
buffer_input_ids,
torch.full(
(max_tokens - buffer_input_ids.numel(),),
tokenizer.pad_token_id,
dtype=torch.long,
),
),
dim=0,
)
buffer_attention_mask = torch.cat(
(
buffer_attention_mask,
torch.full(
(max_tokens - buffer_attention_mask.numel(),),
0,
dtype=torch.long,
),
),
dim=0,
)
new_input_ids.append(buffer_input_ids)
new_attention_mask.append(buffer_attention_mask)
ret = {
"input_ids": [seq.tolist() for seq in new_input_ids],
"labels": [seq.tolist() for seq in new_input_ids],
"attention_mask": [seq.tolist() for seq in new_attention_mask],
}
LOG.debug(len(ret["input_ids"]))
return ret
def wrap_pretraining_dataset(
dataset,
tokenizer,
cfg,
ds_wrapper_fn,
max_tokens=2048,
batch_size=1,
seed=42,
buffer_size=10_000,
):
if cfg.sample_packing:
collate_fn = PretrainingBatchSamplerDataCollatorForSeq2Seq(
tokenizer,
return_tensors="pt",
padding=True,
pad_to_multiple_of=max_tokens * batch_size,
)
encode = functools.partial(
encode_packed_pretraining,
collate_fn,
ds_wrapper_fn,
max_seq_length=max_tokens,
batch_size=batch_size,
)
# set this to 1 so downstream data_loader doesn't try to increase the batch again
cfg.micro_batch_size = 1
else:
encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)
dataset = dataset.map(
encode,
batched=True,
batch_size=buffer_size,
# input_columns="text",
# remove all the existing columns after mapping since they end up having
# a different length than the encoded/tokenized column
remove_columns=dataset.features.keys(),
)
return dataset
def encode_packed_pretraining(
collate_fn,
ds_wrapper: Callable,
examples: Dict[str, List],
max_seq_length: int = 2048,
batch_size: int = 4,
) -> Dict[str, List]:
# pylint: disable=duplicate-code
# tokenize all the examples
# rows get split with stride (overlap)
train_dataset = ds_wrapper(Dataset.from_dict(examples))[0]
train_dataset = process_pretraining_datasets_for_packing(
train_dataset, max_seq_length
)
sampler = MultipackBatchSampler(
RandomSampler(train_dataset),
batch_size=1,
drop_last=True,
batch_max_len=batch_size * max_seq_length,
lengths=get_dataset_lengths(train_dataset),
)
chunked_data = defaultdict(list)
for batch in sampler:
for data in batch:
features = train_dataset[data]
if "num_truncated_tokens" in features:
del features["num_truncated_tokens"]
if "num_truncated_tokens" in features:
del features["num_truncated_tokens"]
if "overflow_to_sample_mapping" in features:
del features["overflow_to_sample_mapping"]
if "labels" not in features:
features["labels"] = features["input_ids"].copy()
collated_features = collate_fn(features)
for feature in features.keys():
if feature == "length":
continue
chunked_data[feature].append(collated_features[feature].squeeze(0))
return chunked_data
def _get_path(ds_hash, cfg):
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
)
return prepared_ds_path
def _load_preprocessed_ds(cfg, sub_cfg):
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
prepared_ds_path = _get_path(ds_hash, cfg)
dataset = None
if (
cfg.dataset_prepared_path
and any(prepared_ds_path.glob("*"))
and not cfg.is_preprocess
):
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset = load_from_disk(str(prepared_ds_path))
return dataset
def _save_preprocessed_ds(cfg, sub_cfg, dataset):
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
prepared_ds_path = _get_path(ds_hash, cfg)
if cfg.is_preprocess and is_main_process():
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset.save_to_disk(str(prepared_ds_path))
def load_prepare_dpo_datasets(cfg):
def load_split(dataset_cfgs, _cfg):
split_datasets: List[Any] = []
for i, ds_cfg in enumerate(dataset_cfgs):
if ds_cfg["ds_type"] == "json":
for data_file in ds_cfg["data_files"]:
data_files = {ds_cfg["split"]: data_file}
ds = load_dataset( # pylint: disable=invalid-name
"json",
data_files=data_files,
split=ds_cfg["split"],
)
split_datasets.insert(i, ds)
else:
ds = load_dataset( # pylint: disable=invalid-name
ds_cfg["path"],
split=ds_cfg["split"],
)
split_datasets.insert(i, ds)
for i, data_set in enumerate(split_datasets):
_type = dataset_cfgs[i]["type"]
if _type:
ds_transform_fn = load_dpo(_type, _cfg)
split_datasets[i] = data_set.map(
ds_transform_fn,
desc="Mapping RL Dataset",
)
else:
# If no `type` is provided, assume the dataset is already in the expected format with
# "prompt", "chosen" and "rejected" already preprocessed
split_datasets[i] = data_set
return concatenate_datasets(split_datasets)
with zero_first(is_main_process()):
train_is_preprocessed = False
eval_is_preprocessed = False
if train_dataset := _load_preprocessed_ds(cfg, cfg.datasets):
train_is_preprocessed = True
else:
train_dataset = load_split(cfg.datasets, cfg)
eval_dataset = None
if cfg.test_datasets:
if eval_dataset := _load_preprocessed_ds(cfg, cfg.test_datasets):
eval_is_preprocessed = True
else:
eval_dataset = load_split(cfg.test_datasets, cfg)
if not eval_dataset:
eval_dataset = None
if not train_is_preprocessed:
_save_preprocessed_ds(cfg, cfg.datasets, train_dataset)
if eval_dataset and not eval_is_preprocessed:
_save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
return train_dataset, eval_dataset