forked from KoboldAI/KoboldAI-Client
-
Notifications
You must be signed in to change notification settings - Fork 132
/
prompt_tuner.py
1087 lines (950 loc) · 47.3 KB
/
prompt_tuner.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
import abc
import os
import sys
import math
import numpy as np
from logger import logger
import contextlib
import traceback
import random
import zipfile
import json
import uuid
import datetime
import base64
import pickle
import hashlib
import itertools
import functools
import bisect
import eventlet
import packaging
import gc
import time
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
from torch.nn import Embedding, CrossEntropyLoss
import transformers
from transformers import __version__ as transformers_version
from transformers import AutoTokenizer, GPT2Tokenizer, AutoConfig, AutoModelForCausalLM, GPTNeoForCausalLM, PreTrainedModel, modeling_utils
import accelerate
import accelerate.utils
from mkultra.tuning import GPTPromptTuningMixin, GPTNeoPromptTuningLM
from mkultra.soft_prompt import SoftPrompt
from typing import Dict, List, Optional, TextIO, Union
import logging
logging.getLogger("urllib3").setLevel(logging.ERROR)
import breakmodel
import modeling.lazy_loader as lazy_loader
import utils
use_breakmodel = True
class colors:
PURPLE = '\033[95m'
BLUE = '\033[94m'
CYAN = '\033[96m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
END = '\033[0m'
UNDERLINE = '\033[4m'
class Send_to_socketio(object):
def write(self, bar):
print(bar, end="")
time.sleep(0.01)
try:
if utils.emit is not None:
utils.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True)
except:
pass
def patch_transformers_download():
global transformers
import copy, requests, tqdm, time
class Send_to_socketio(object):
def write(self, bar):
bar = bar.replace("\r", "").replace("\n", "")
if bar != "" and [ord(num) for num in bar] != [27, 91, 65]: #No idea why we're getting the 27, 1, 65 character set, just killing to so we can move on
try:
print('\r' + bar, end='')
socketio.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True, room="UI_1")
eventlet.sleep(seconds=0)
except:
pass
def flush(self):
pass
def http_get(
url: str,
temp_file,
proxies=None,
resume_size=0,
headers=None,
file_name=None,
):
"""
Download remote file. Do not gobble up errors.
"""
headers = copy.deepcopy(headers)
if resume_size > 0:
headers["Range"] = f"bytes={resume_size}-"
r = requests.get(url, stream=True, proxies=proxies, headers=headers)
transformers.utils.hub._raise_for_status(r)
content_length = r.headers.get("Content-Length")
total = resume_size + int(content_length) if content_length is not None else None
# `tqdm` behavior is determined by `utils.logging.is_progress_bar_enabled()`
# and can be set using `utils.logging.enable/disable_progress_bar()`
if url[-11:] != 'config.json':
progress = tqdm.tqdm(
unit="B",
unit_scale=True,
unit_divisor=1024,
total=total,
initial=resume_size,
desc=f"Downloading {file_name}" if file_name is not None else "Downloading",
file=Send_to_socketio(),
)
koboldai_vars.status_message = "Download Model"
koboldai_vars.total_download_chunks = total
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
if url[-11:] != 'config.json':
progress.update(len(chunk))
koboldai_vars.downloaded_chunks += len(chunk)
temp_file.write(chunk)
if url[-11:] != 'config.json':
progress.close()
koboldai_vars.status_message = ""
transformers.utils.hub.http_get = http_get
def patch_transformers():
global transformers
patch_transformers_download()
old_from_pretrained = PreTrainedModel.from_pretrained.__func__
@classmethod
def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
utils.num_shards = None
utils.current_shard = 0
utils.from_pretrained_model_name = pretrained_model_name_or_path
utils.from_pretrained_index_filename = None
utils.from_pretrained_kwargs = kwargs
utils.bar = None
if utils.args is None or not utils.args.no_aria2:
utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
if(not hasattr(PreTrainedModel, "_kai_patched")):
PreTrainedModel.from_pretrained = new_from_pretrained
PreTrainedModel._kai_patched = True
if(hasattr(modeling_utils, "get_checkpoint_shard_files")):
old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files
def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs):
utils.num_shards = utils.get_num_shards(index_filename)
utils.from_pretrained_index_filename = index_filename
return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs)
modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files
# Some versions of transformers 4.17.0.dev0 are affected by
# https://github.com/huggingface/transformers/issues/15736
# This is a workaround for those versions of transformers.
if(transformers_version == "4.17.0.dev0"):
try:
from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding
except ImportError:
pass
else:
@torch.no_grad()
def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0):
bsz, seq_len = inputs_embeds.size()[:-1]
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
).unsqueeze(0).expand(input_shape).contiguous()
max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
if max_pos > self.weights.size(0):
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
XGLMSinusoidalPositionalEmbedding.forward = new_forward
# Fix a bug in OPTForCausalLM where self.lm_head is the wrong size
if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) < packaging.version.parse("4.20.0")):
try:
from transformers import OPTForCausalLM, OPTModel
except ImportError:
pass
else:
# This is the same as the original __init__ but with
# config.hidden_size
# replaced with
# config.word_embed_proj_dim
def new_init(self, config):
super(OPTForCausalLM, self).__init__(config)
self.model = OPTModel(config)
self.lm_head = torch.nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
self.post_init()
OPTForCausalLM.__init__ = new_init
def device_list(n_layers, primary=None, selected=None):
device_count = torch.cuda.device_count()
if(device_count < 2):
primary = None
gpu_blocks = breakmodel.gpu_blocks + (device_count - len(breakmodel.gpu_blocks))*[0]
logger.info(" DEVICE ID | LAYERS | DEVICE NAME{colors.END}")
for i in range(device_count):
name = torch.cuda.get_device_name(i)
if(len(name) > 47):
name = "..." + name[-44:]
row_color = colors.END
sep_color = colors.YELLOW
logger.info(f"{'(primary)' if i == primary else ' '*9} {i:3} | {gpu_blocks[i]:3} | {name}")
row_color = colors.END
sep_color = colors.YELLOW
logger.info(f" {' '*9} N/A | {breakmodel.disk_blocks:3} | (Disk cache)")
logger.info(f" {' '*9} N/A | {n_layers:3} | (CPU)")
def move_model_to_devices(model, usegpu, gpu_device):
global generator
if(not use_breakmodel):
if(usegpu):
model = model.half().to(gpu_device)
else:
model = model.to('cpu').float()
generator = model.generate
return
for key, value in model.state_dict().items():
target_dtype = torch.float32 if breakmodel.primary_device == "cpu" else torch.float16
if(value.dtype is not target_dtype):
accelerate.utils.set_module_tensor_to_device(model, key, target_dtype)
disk_blocks = breakmodel.disk_blocks
gpu_blocks = breakmodel.gpu_blocks
ram_blocks = len(utils.layers_module_names) - sum(gpu_blocks)
cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
device_map = {}
for name in utils.layers_module_names:
layer = int(name.rsplit(".", 1)[1])
device = ("disk" if layer < disk_blocks else "cpu") if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks)
device_map[name] = device
for name in utils.get_missing_module_names(model, list(device_map.keys())):
device_map[name] = breakmodel.primary_device
breakmodel.dispatch_model_ex(model, device_map, main_device=breakmodel.primary_device, offload_buffers=True, offload_dir="accelerate-disk-cache")
gc.collect()
generator = model.generate
return
_PromptTuningPreTrainedModel = Union["UniversalPromptTuningMixin", GPTPromptTuningMixin, transformers.PreTrainedModel]
class _WTEDummy:
def __init__(self, model: transformers.PreTrainedModel):
self.model = model
@property
def wte(self: "_WTEDummy"):
return self.model.get_input_embeddings()
@wte.setter
def wte(self: "_WTEDummy", v):
self.model.set_input_embeddings(v)
class _WTEMixin:
@property
def wte(self: Union["_WTEMixin", transformers.PreTrainedModel]):
return self.get_input_embeddings()
@wte.setter
def wte(self: Union["_WTEMixin", transformers.PreTrainedModel], v):
self.set_input_embeddings(v)
class UniversalPromptTuningMixin:
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
model: _PromptTuningPreTrainedModel = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
if not hasattr(model, "transformer"):
model.transformer = _WTEDummy(model)
elif not hasattr(model.transformer, "wte"):
assert isinstance(model.transformer, type)
model.transformer.__class__ = type("_UniversalPromptTuning" + model.transformer.__class__.__name__, (_WTEMixin, model.transformer.__class__), {})
model.__class__ = type("_UniversalPromptTuning" + model.__class__.__name__, (UniversalPromptTuningMixin, model.__class__), {})
for param in model.parameters():
param.requires_grad = False
model.initialize_soft_prompt()
return model
def forward(
self: _PromptTuningPreTrainedModel,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
):
assert input_ids is not None
assert input_ids.ndim == 2
input_ids = F.pad(input_ids, (self.learned_embedding.size(0), 0, 0, 0), value=self.transformer.wte.weight.size(0) // 2)
if labels is not None:
labels = self._extend_labels(labels)
if attention_mask is not None:
attention_mask = self._extend_attention_mask(attention_mask)
old_embedding_call = Embedding.__call__
model = self
def new_embedding_call(self, input_ids, *args, **kwargs):
inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs)
if model.transformer.wte is self:
assert inputs_embeds.ndim == 3
inputs_embeds[:, :model.learned_embedding.size(0), :] = model.learned_embedding[None]
return inputs_embeds
Embedding.__call__ = new_embedding_call
try:
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
use_cache=use_cache,
return_dict=return_dict,
)
finally:
Embedding.__call__ = old_embedding_call
for k in dir(GPTPromptTuningMixin):
v = getattr(GPTPromptTuningMixin, k)
_v = getattr(UniversalPromptTuningMixin, k, None)
if _v is None or (_v is getattr(object, k, None) and callable(_v) and not isinstance(_v, type)):
setattr(UniversalPromptTuningMixin, k, v)
class AutoPromptTuningLM(UniversalPromptTuningMixin, transformers.AutoModelForCausalLM):
def __init__(self, config):
super().__init__(config)
default_quiet = False
def get_tokenizer(model_id, revision=None) -> transformers.PreTrainedTokenizerBase:
if(os.path.isdir(model_id)):
try:
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=revision, cache_dir="cache")
elif(os.path.isdir("models/{}".format(model_id.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=revision, cache_dir="cache")
else:
try:
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=revision, cache_dir="cache")
@contextlib.contextmanager
def _kai_no_prefix():
add_bos_token = getattr(tokenizer, "add_bos_token", False)
add_prefix_space = getattr(tokenizer, "add_prefix_space", False)
tokenizer.add_bos_token = False
tokenizer.add_prefix_space = False
try:
yield
finally:
tokenizer.add_bos_token = add_bos_token
tokenizer.add_prefix_space = add_prefix_space
tokenizer._kai_no_prefix = _kai_no_prefix
return tokenizer
class ConfigurationError(Exception):
def __init__(self, msg: str = "Unknown error", code: int = 1, quiet: Optional[bool] = None):
if quiet is None:
quiet = default_quiet
super().__init__(msg)
self.code = code
self.quiet = quiet
class TrainerBase(abc.ABC):
@abc.abstractmethod
def startup(self, step: int) -> None:
...
@abc.abstractmethod
def get_batch(self, step: int, size: int) -> np.ndarray:
...
@abc.abstractmethod
def get_num_sequences(self) -> int:
...
@abc.abstractmethod
def get_initial_soft_embeddings(self, model: transformers.PreTrainedModel) -> SoftPrompt:
...
@abc.abstractmethod
def tokenize_dataset_callback(self, tokenizer: transformers.PreTrainedTokenizerBase, text: str) -> List[int]:
...
class TrainerData:
def __init__(self):
self.__lazy_load_spec: Optional[dict] = None
self.model_spec: Optional[dict] = None
self.tokenizer_id: Optional[str] = None
self.newlinemode: Optional[str] = None
self.ckpt_path: Optional[str] = None
self.save_file: Optional[str] = None
self.params: Optional[dict] = None
self.stparams: Optional[dict] = None
self.gradient_accumulation_steps = -1
self.soft_in_dim = -1
self.prompt_method = "tokens"
self.prompt_seed = 42
@property
def lazy_load_spec(self):
logger.warning("WARNING: `TrainerData.lazy_load_spec` is currently unused")
return self.__lazy_load_spec
@lazy_load_spec.setter
def lazy_load_spec(self, value: Optional[dict]):
logger.warning("WARNING: `TrainerData.lazy_load_spec` is currently unused")
self.__lazy_load_spec = value
@property
def kaiming_size(self): # backwards compatibility
return self.soft_in_dim
@kaiming_size.setter
def kaiming_size(self, value: int): # backwards compatibility
self.prompt_method = "kaiming"
self.soft_in_dim = value
data: TrainerData
def __init__(self, universe: Optional[int] = None, quiet=False):
self.quiet = quiet
self.universe = universe
self.data = self.TrainerData()
self._spmodule: Optional[str] = None
if universe is not None:
logger.warning("WARNING: The `universe` argument of `TrainerBase.__init__` is currently unused")
def raise_configuration_error(self, msg, **kwargs):
if "quiet" not in kwargs:
kwargs["quiet"] = self.quiet
raise ConfigurationError(msg, **kwargs)
def _get_model_config(self) -> transformers.configuration_utils.PretrainedConfig:
REVISION = None
if(os.path.isdir(self.data.ckpt_path)):
model_config = AutoConfig.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
elif(os.path.isdir("models/{}".format(self.data.ckpt_path.replace('/', '_')))):
model_config = AutoConfig.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache")
else:
model_config = AutoConfig.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
return model_config
def get_hf_checkpoint_metadata(self) -> bool:
params = {}
model_config = self._get_model_config()
params["tokenizer_id"] = self.data.ckpt_path
tokenizer = get_tokenizer(self.data.ckpt_path)
params["newlinemode"] = params.get(
"newlinemode", "s" if model_config.model_type == "xglm" else "n"
)
params["max_batch_size"] = 2048
with tokenizer._kai_no_prefix():
params["eos_token"] = (
[50259, 50259] if model_config.model_type == "xglm" and model_config.eos_token_id == 50259 else [model_config.eos_token_id]
)
params["seq"] = 2048
self.data.params = params
return True
def get_tokenizer(self) -> transformers.PreTrainedTokenizerBase:
return get_tokenizer(self.data.ckpt_path)
def save_data(self):
pass
def export_to_kobold(self, output_file: str, name: str, author: str, supported: str, description: str):
try:
z = torch.load(self.data.save_file)
assert z["step"] > 0
assert z["tensor"].ndim == 2 and "opt_state" in z
assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
self.data.soft_in_dim = z["tensor"].shape[0]
except AssertionError:
self.raise_configuration_error("MKUSP file is corrupted.", code=14)
tensor = z["tensor"]
meta = {
"name": name,
"author": author,
"supported": supported,
"description": description,
}
if len(meta["author"].strip()) == 0:
meta.pop("author")
meta["supported"] = list(map(lambda m: m.strip(), supported.split(",")))
with zipfile.ZipFile(output_file, "w", compression=zipfile.ZIP_LZMA) as z:
with z.open("tensor.npy", "w") as f:
np.save(f, tensor.detach().cpu().numpy(), allow_pickle=False)
with zipfile.ZipFile(output_file, "a", compression=zipfile.ZIP_STORED) as z:
with z.open("meta.json", "w") as f:
f.write(json.dumps(meta, indent=2).encode("utf-8"))
def export_to_mkultra(self, output_file: str, soft_prompt_name: str, soft_prompt_description: str):
try:
z = torch.load(self.data.save_file)
assert z["step"] > 0
assert z["tensor"].ndim == 2 and "opt_state" in z
assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
self.data.soft_in_dim = z["tensor"].shape[0]
_step = z["step"]
except AssertionError:
self.raise_configuration_error("MKUSP file is corrupted.", code=14)
tensor = z["tensor"]
with open(output_file, "w") as f:
json.dump(
{
"metadata": {
"step": _step,
"loss": float(z["loss"]),
"uuid": str(uuid.uuid4()),
"name": soft_prompt_name,
"description": soft_prompt_description,
"epoch": datetime.datetime.now().timestamp(),
},
"tensor": base64.b64encode(
pickle.dumps(
tensor.detach().cpu(),
protocol=4,
),
).decode("ascii"),
},
f,
)
def tokenize_dataset(
self,
dataset_path: Union[str, TextIO],
output_file: Union[str, TextIO],
batch_size=2048,
epochs=1,
use_ftfy=True,
shuffle_seed: Optional[Union[int, float, str, bytes, bytearray]] = 1729,
):
dataset_path = dataset_path.replace("\\", "/")
output_file = output_file.replace("\\", "/")
if not isinstance(batch_size, int) or batch_size < 1:
self.raise_configuration_error(
"batch_size must be an integer greater than zero.", code=9
)
if (
not isinstance(epochs, int) and not isinstance(epochs, float)
) or epochs <= 0:
self.raise_configuration_error(
"epochs must be an int or float greater than zero.", code=10
)
if isinstance(output_file, str) and output_file.endswith("/"):
self.raise_configuration_error(
"output_file should be the path to a file, not a directory.", code=11
)
if isinstance(dataset_path, str) and not os.path.exists(dataset_path):
self.raise_configuration_error(
"dataset_path is not set to a valid file or directory.", code=12
)
if use_ftfy:
import ftfy
tokenizer = self.get_tokenizer()
batch_size = min(
batch_size,
self.data.params["max_batch_size"] - self.data.soft_in_dim,
)
assert batch_size >= 0
logger.info(
"\nIf you see a warning somewhere below about token indices, ignore it. That warning is normal.\n"
)
logger.info("Batch size: {}".format(batch_size))
logger.info("Tokenizing your dataset...\n")
if not isinstance(dataset_path, str):
files = [dataset_path]
elif os.path.isfile(dataset_path):
files = [dataset_path]
else:
files = sorted(
os.path.join(dataset_path, filename)
for filename in os.listdir(dataset_path)
)
if shuffle_seed is not None:
random.Random(shuffle_seed).shuffle(files)
tokens = []
eos = tokenizer.decode(self.data.params["eos_token"])
for path in files:
if isinstance(path, str):
f = open(path, 'r', encoding='utf-8')
else:
f = path
try:
text = f.read()
if use_ftfy:
text = ftfy.fix_text(text)
text = text.replace("<|endoftext|>", eos)
tokens.extend(self.tokenize_dataset_callback(tokenizer, text))
finally:
if isinstance(path, str):
f.close()
logger.info("Dataset size (in tokens): {}".format(len(tokens)))
if len(tokens) < batch_size + 1:
self.raise_configuration_error(
"Your dataset is too small! The number of tokens has to be greater than the batch size. Try increasing the epochs.",
code=13,
)
tail = len(tokens) % (batch_size + 1)
if tail:
logger.info(
f"We're removing the last {tail} tokens from your dataset to make the length a multiple of {batch_size+1}."
)
tokens = tokens[:-tail]
tokens = np.array(tokens, dtype=np.uint16).reshape((-1, batch_size + 1))
sequences_per_epoch = tokens.shape[0]
_epochs = math.ceil(epochs)
if _epochs > 1:
rng = np.random.Generator(np.random.PCG64(1729))
tokens = np.concatenate(
(
tokens,
*(rng.permutation(tokens, axis=0) for i in range(_epochs - 1)),
),
axis=0,
)
tokens = tokens[: math.ceil(epochs * sequences_per_epoch)]
logger.info(f"Total sequences in your dataset: {tokens.shape[0]}")
if isinstance(output_file, str):
f = open(output_file, "w")
else:
f = output_file
try:
np.save(output_file, tokens)
finally:
if isinstance(output_file, str):
f.close()
def train(
self,
breakmodel_primary_device: Optional[Union[str, int, torch.device]] = None,
breakmodel_gpulayers: Optional[List[int]] = None,
breakmodel_disklayers = 0,
):
if breakmodel_gpulayers is None:
breakmodel_gpulayers = []
if breakmodel_primary_device is None:
breakmodel_primary_device = 0 if sum(x if x >= 0 else 1 for x in breakmodel_gpulayers) else "cpu"
if self.data.params is not None and "max_batch_size" not in self.data.params:
self.data.params["max_batch_size"] = 2048
if not os.path.exists(self.data.save_file):
logger.info("We are starting a brand new soft-tuning session.\n")
self.startup(step=-1)
if self.data.soft_in_dim <= 0:
self.raise_configuration_error(
"You have not set a soft prompt size.", code=6
)
step = 0
else:
# If we're resuming a soft-tuning session, the soft prompt tensor is
# already in the save file and we just have to decode it.
try:
z = torch.load(self.data.save_file)
assert z["step"] > 0
assert z["tensor"].ndim == 2 and "opt_state" in z
assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
self.data.soft_in_dim = z["tensor"].shape[0]
step = z["step"]
opt_state = z["opt_state"]
except AssertionError:
self.raise_configuration_error("MKUSP file is corrupted.", code=14)
logger.info(f"We're resuming a previous soft-tuning session at step {step+1}.\n")
self.startup(step=step + 1)
soft_embeddings = z["tensor"]
REVISION = None
patch_transformers()
model: _PromptTuningPreTrainedModel
model_config = self._get_model_config()
n_layers = utils.num_layers(model_config)
breakmodel_gpulayers = [x if x >= 0 else n_layers for x in breakmodel_gpulayers]
convert_to_float16 = True
hascuda = torch.cuda.is_available()
usegpu = hascuda and not breakmodel_disklayers and len(breakmodel_gpulayers) == 1 and breakmodel_gpulayers[0] == n_layers
gpu_device = breakmodel_primary_device
use_breakmodel = bool(hascuda or breakmodel_disklayers or sum(breakmodel_gpulayers))
assert len(breakmodel_gpulayers) <= torch.cuda.device_count()
assert sum(breakmodel_gpulayers) + breakmodel_disklayers <= n_layers
breakmodel.gpu_blocks = breakmodel_gpulayers
breakmodel.disk_blocks = breakmodel_disklayers
disk_blocks = breakmodel.disk_blocks
gpu_blocks = breakmodel.gpu_blocks
ram_blocks = ram_blocks = n_layers - sum(gpu_blocks)
cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
device_list(ram_blocks, primary=breakmodel.primary_device)
def lazy_load_callback(model_dict: Dict[str, Union[lazy_loader.LazyTensor, torch.Tensor]], f, **_):
if lazy_load_callback.nested:
return
lazy_load_callback.nested = True
device_map: Dict[str, Union[str, int]] = {}
@functools.lru_cache(maxsize=None)
def get_original_key(key):
return max((original_key for original_key in utils.module_names if original_key.endswith(key)), key=len)
for key, value in model_dict.items():
original_key = get_original_key(key)
if isinstance(value, lazy_loader.LazyTensor) and not any(original_key.startswith(n) for n in utils.layers_module_names):
device_map[key] = gpu_device if hascuda and usegpu else "cpu" if not hascuda or not use_breakmodel else breakmodel.primary_device
else:
layer = int(max((n for n in utils.layers_module_names if original_key.startswith(n)), key=len).rsplit(".", 1)[1])
device = gpu_device if hascuda and usegpu else "disk" if layer < disk_blocks and layer < ram_blocks else "cpu" if not hascuda or not use_breakmodel else "shared" if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks)
device_map[key] = device
if utils.num_shards is None or utils.current_shard == 0:
utils.offload_index = {}
if os.path.isdir("accelerate-disk-cache"):
# Delete all of the files in the disk cache folder without deleting the folder itself to allow people to create symbolic links for this folder
# (the folder doesn't contain any subfolders so os.remove will do just fine)
for filename in os.listdir("accelerate-disk-cache"):
try:
os.remove(os.path.join("accelerate-disk-cache", filename))
except OSError:
pass
os.makedirs("accelerate-disk-cache", exist_ok=True)
if utils.num_shards is not None:
num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs))
else:
num_tensors = len(device_map)
#print(flush=True)
utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio())
with zipfile.ZipFile(f, "r") as z:
try:
last_storage_key = None
f = None
current_offset = 0
able_to_pin_layers = True
if utils.num_shards is not None:
utils.current_shard += 1
for key in sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)):
storage_key = model_dict[key].key
if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset:
last_storage_key = storage_key
if isinstance(f, zipfile.ZipExtFile):
f.close()
f = z.open(f"archive/data/{storage_key}")
current_offset = 0
if current_offset != model_dict[key].seek_offset:
f.read(model_dict[key].seek_offset - current_offset)
current_offset = model_dict[key].seek_offset
device = device_map[key]
size = functools.reduce(lambda x, y: x * y, model_dict[key].shape, 1)
dtype = model_dict[key].dtype
nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
#print(f"Transferring <{key}> to {f'({device.upper()})' if isinstance(device, str) else '[device ' + str(device) + ']'} ... ", end="", flush=True)
model_dict[key] = model_dict[key].materialize(f, map_location="cpu")
# if model_dict[key].dtype is torch.float32:
# fp32_model = True
if convert_to_float16 and breakmodel.primary_device != "cpu" and hascuda and (use_breakmodel or usegpu) and model_dict[key].dtype is torch.float32:
model_dict[key] = model_dict[key].to(torch.float16)
if breakmodel.primary_device == "cpu" or (not usegpu and not use_breakmodel and model_dict[key].dtype is torch.float16):
model_dict[key] = model_dict[key].to(torch.float32)
if device == "shared":
model_dict[key] = model_dict[key].to("cpu").detach_()
if able_to_pin_layers:
try:
model_dict[key] = model_dict[key].pin_memory()
except:
able_to_pin_layers = False
elif device == "disk":
accelerate.utils.offload_weight(model_dict[key], get_original_key(key), "accelerate-disk-cache", index=utils.offload_index)
model_dict[key] = model_dict[key].to("meta")
else:
model_dict[key] = model_dict[key].to(device)
#print("OK", flush=True)
current_offset += nbytes
utils.bar.update(1)
finally:
if utils.num_shards is None or utils.current_shard >= utils.num_shards:
if utils.offload_index:
for name, tensor in utils.named_buffers:
if name not in utils.offload_index:
accelerate.utils.offload_weight(tensor, name, "accelerate-disk-cache", index=utils.offload_index)
accelerate.utils.save_offload_index(utils.offload_index, "accelerate-disk-cache")
utils.bar.close()
utils.bar = None
lazy_load_callback.nested = False
if isinstance(f, zipfile.ZipExtFile):
f.close()
lazy_load_callback.nested = False
# Since we're using lazy loader, we need to figure out what the model's hidden layers are called
with lazy_loader.use_lazy_load(dematerialized_modules=True):
try:
metamodel = AutoModelForCausalLM.from_config(model_config)
except Exception as e:
metamodel = GPTNeoForCausalLM.from_config(model_config)
utils.layers_module_names = utils.get_layers_module_names(metamodel)
utils.module_names = list(metamodel.state_dict().keys())
utils.named_buffers = list(metamodel.named_buffers(recurse=True))
with lazy_loader.use_lazy_load(callback=lazy_load_callback, dematerialized_modules=True):
if(os.path.isdir(self.data.ckpt_path)):
try:
model = AutoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
model = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
elif(os.path.isdir("models/{}".format(self.data.ckpt_path.replace('/', '_')))):
try:
model = AutoPromptTuningLM.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache")
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
model = GPTNeoPromptTuningLM.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache")
else:
try:
model = AutoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
model = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
if(hascuda):
if(usegpu):
model = model.half().to(gpu_device)
elif(use_breakmodel): # Use both RAM and VRAM (breakmodel)
move_model_to_devices(model, usegpu, gpu_device)
elif(__import__("breakmodel").disk_blocks > 0):
move_model_to_devices(model, usegpu, gpu_device)
else:
model = model.to('cpu').float()
elif(__import__("breakmodel").disk_blocks > 0):
move_model_to_devices(model, usegpu, gpu_device)
else:
model.to('cpu').float()
if step == 0:
soft_embeddings = self.get_initial_soft_embeddings(model)
else:
soft_embeddings = SoftPrompt.from_inputs_embeds(soft_embeddings)
model.set_soft_prompt(soft_embeddings)
steps = self.get_num_sequences() // self.data.gradient_accumulation_steps
warmup_steps = max(1, round(steps * self.data.stparams["warmup"]))
beta1: Optional[float] = self.data.stparams.get("beta1", 0.0)
if beta1 == 0.0:
beta1 = None
optimizer = transformers.Adafactor(
params=(model.get_soft_params(),),
scale_parameter=False,
relative_step=False,
warmup_init=False,
lr=self.data.stparams["lr"],
beta1=beta1,
decay_rate=self.data.stparams.get("decay_rate", -0.8),
weight_decay=self.data.stparams.get("weight_decay", 0.1),
)
if step != 0:
optimizer.load_state_dict(opt_state)
scheduler = transformers.get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=steps - warmup_steps,
num_cycles=(steps - warmup_steps) // self.data.stparams.get("training_steps_per_cycle", 56),
)
torch.cuda.empty_cache()
optimizer.state['step'] = step
cross_entropy_loss = CrossEntropyLoss()
def save_mkusp(
loss,
grad_norm,
):
with open(self.data.save_file, "wb") as f:
torch.save(
{
"tensor": soft_embeddings.get_inputs_embeds(),
"opt_state": optimizer.state_dict(),
"step": step,
"loss": loss,
"grad_norm": grad_norm,
},
f,
)
self.save_data()
bar1 = tqdm(initial=step + 1, total=steps, desc="CURRENT TRAINING STEP")
while step < steps:
step += 1
model.train()
total_loss = total_grad = total_grad_norm = 0
# Get the next sequences from the dataset
block = torch.tensor(np.int32(self.get_batch(step, self.data.gradient_accumulation_steps))).to(model.transformer.wte.weight.device)
for sequence in tqdm(block, desc="GRADIENT ACCUMULATION", leave=False):
# input_ids is the context to the model (without the soft prompt) and labels is what we expect the model to generate (the -100s represent soft prompt tokens for which loss is not calculated)
input_ids = sequence[:-1].unsqueeze(0).detach()
labels = torch.cat((torch.full((model.get_soft_params().size(0) - 1,), -100, device=sequence.device), sequence), dim=-1).unsqueeze(0).detach()
# Give the context to the model and compare the model's output logits with the labels to compute the loss
logits = model(input_ids=input_ids, labels=input_ids).logits
loss: torch.Tensor = cross_entropy_loss(logits.view(-1, model.transformer.wte.weight.size(0)), labels.view(-1))
total_loss += loss.detach()
# Compute the gradient of the loss function and add it to model.get_soft_params().grad (model.get_soft_params().grad += gradient)
loss.backward()
total_grad_norm += torch.linalg.norm(model.get_soft_params().grad.detach() - total_grad)
total_grad = model.get_soft_params().grad.detach()
del input_ids
del labels
del logits
torch.cuda.empty_cache()
mean_loss = (total_loss / self.data.gradient_accumulation_steps).item()
mean_grad_norm = (total_grad_norm / self.data.gradient_accumulation_steps).item()
# Apply the optimization algorithm using the accumulated gradients, which changes the contents of the soft prompt matrix very slightly to reduce the loss
optimizer.step()
lr = optimizer.param_groups[0]["lr"]
scheduler.step()
optimizer.zero_grad()
# Save checkpoint every few steps
if step == 1 or step % self.data.stparams["save_every"] == 0:
save_mkusp(mean_loss, mean_grad_norm)