generated from fastai/nbdev_template
-
Notifications
You must be signed in to change notification settings - Fork 1k
/
sft_trainer.py
548 lines (485 loc) · 26 KB
/
sft_trainer.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
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import dataclasses
import inspect
import warnings
from functools import wraps
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from accelerate.state import PartialState
from datasets import Dataset
from datasets.arrow_writer import SchemaInferenceError
from datasets.builder import DatasetGenerationError
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollator,
DataCollatorForLanguageModeling,
PreTrainedModel,
PreTrainedTokenizerBase,
Trainer,
TrainingArguments,
)
from transformers.modeling_utils import unwrap_model
from transformers.trainer_callback import TrainerCallback
from transformers.trainer_utils import EvalPrediction
from ..extras.dataset_formatting import get_formatting_func_from_dataset
from ..import_utils import is_peft_available
from .utils import (
ConstantLengthDataset,
DataCollatorForCompletionOnlyLM,
RichProgressCallback,
neftune_post_forward_hook,
peft_module_casting_to_bf16,
trl_sanitze_kwargs_for_tagging,
)
if is_peft_available():
from peft import PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training
class SFTTrainer(Trainer):
r"""
Class definition of the Supervised Finetuning Trainer (SFT Trainer).
This class is a wrapper around the `transformers.Trainer` class and inherits all of its attributes and methods.
The trainer takes care of properly initializing the PeftModel in case a user passes a `PeftConfig` object.
Args:
model (Union[`transformers.PreTrainedModel`, `nn.Module`, `str`]):
The model to train, can be a `PreTrainedModel`, a `torch.nn.Module` or a string with the model name to
load from cache or download. The model can be also converted to a `PeftModel` if a `PeftConfig` object is
passed to the `peft_config` argument.
args (Optional[`transformers.TrainingArguments`]):
The arguments to tweak for training. Please refer to the official documentation of `transformers.TrainingArguments`
for more information.
data_collator (Optional[`transformers.DataCollator`]):
The data collator to use for training.
train_dataset (Optional[`datasets.Dataset`]):
The dataset to use for training. We recommend users to use `trl.trainer.ConstantLengthDataset` to create their dataset.
eval_dataset (Optional[Union[`datasets.Dataset`, Dict[`str`, `datasets.Dataset`]]]):
The dataset to use for evaluation. We recommend users to use `trl.trainer.ConstantLengthDataset` to create their dataset.
tokenizer (Optional[`transformers.PreTrainedTokenizer`]):
The tokenizer to use for training. If not specified, the tokenizer associated to the model will be used.
model_init (`Callable[[], transformers.PreTrainedModel]`):
The model initializer to use for training. If None is specified, the default model initializer will be used.
compute_metrics (`Callable[[transformers.EvalPrediction], Dict]`, *optional* defaults to None):
The function used to compute metrics during evaluation. It should return a dictionary mapping metric names to metric values.
If not specified, only the loss will be computed during evaluation.
callbacks (`List[transformers.TrainerCallback]`):
The callbacks to use for training.
optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
The optimizer and scheduler to use for training.
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
The function to use to preprocess the logits before computing the metrics.
peft_config (`Optional[PeftConfig]`):
The PeftConfig object to use to initialize the PeftModel.
dataset_text_field (`Optional[str]`):
The name of the text field of the dataset, in case this is passed by a user, the trainer will automatically create a
`ConstantLengthDataset` based on the `dataset_text_field` argument.
formatting_func (`Optional[Callable]`):
The formatting function to be used for creating the `ConstantLengthDataset`.
max_seq_length (`Optional[int]`):
The maximum sequence length to use for the `ConstantLengthDataset` and for automatically creating the Dataset. Defaults to `512`.
infinite (`Optional[bool]`):
Whether to use an infinite dataset or not. Defaults to `False`.
num_of_sequences (`Optional[int]`):
The number of sequences to use for the `ConstantLengthDataset`. Defaults to `1024`.
chars_per_token (`Optional[float]`):
The number of characters per token to use for the `ConstantLengthDataset`. Defaults to `3.6`. You can check how this is computed in the
stack-llama example: https://github.com/huggingface/trl/blob/08f550674c553c36c51d1027613c29f14f3676a5/examples/stack_llama/scripts/supervised_finetuning.py#L53.
packing (`Optional[bool]`):
Used only in case `dataset_text_field` is passed. This argument is used by the `ConstantLengthDataset` to pack the sequences
of the dataset.
dataset_num_proc (`Optional[int]`):
The number of workers to use to tokenize the data. Only used when `packing=False`. Defaults to None.
dataset_batch_size (`int`):
The number of examples to tokenize per batch. If batch_size <= 0 or batch_size == None,
tokenize the full dataset as a single batch. Defaults to 1000.
neftune_noise_alpha (`Optional[float]`):
If not `None`, this will activate NEFTune noise embeddings. This has been proven to drastically improve model performances for instruction
fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune
model_init_kwargs: (`Optional[Dict]`, *optional*):
Dict of Optional kwargs to pass when instantiating the model from a string
dataset_kwargs: (`Optional[Dict]`, *optional*):
Dict of Optional kwargs to pass when creating packed or non-packed datasets
eval_packing: (`Optional[bool]`, *optional*):
Whether to pack the eval dataset as well. Defaults to `packing` if `None` is passed.
"""
_tag_names = ["trl", "sft"]
def __init__(
self,
model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
args: Optional[TrainingArguments] = None,
data_collator: Optional[DataCollator] = None, # type: ignore
train_dataset: Optional[Dataset] = None,
eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
model_init: Optional[Callable[[], PreTrainedModel]] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
peft_config: Optional["PeftConfig"] = None,
dataset_text_field: Optional[str] = None,
packing: Optional[bool] = False,
formatting_func: Optional[Callable] = None,
max_seq_length: Optional[int] = None,
infinite: Optional[bool] = None,
num_of_sequences: Optional[int] = 1024,
chars_per_token: Optional[float] = 3.6,
dataset_num_proc: Optional[int] = None,
dataset_batch_size: int = 1000,
neftune_noise_alpha: Optional[float] = None,
model_init_kwargs: Optional[Dict] = None,
dataset_kwargs: Optional[Dict] = None,
eval_packing: Optional[bool] = None,
):
if model_init_kwargs is None:
model_init_kwargs = {}
elif not isinstance(model, str):
raise ValueError("You passed model_kwargs to the SFTTrainer. But your model is already instantiated.")
if infinite is not None:
warnings.warn(
"The `infinite` argument is deprecated and will be removed in a future version of TRL. Use `TrainingArguments.max_steps` or `TrainingArguments.num_train_epochs` instead to control training length."
)
if isinstance(model, str):
warnings.warn(
"You passed a model_id to the SFTTrainer. This will automatically create an "
"`AutoModelForCausalLM` or a `PeftModel` (if you passed a `peft_config`) for you."
)
model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
if packing and data_collator is not None and isinstance(data_collator, DataCollatorForCompletionOnlyLM):
raise ValueError(
"You passed a `DataCollatorForCompletionOnlyLM` to the SFTTrainer. This is not compatible with the `packing` argument."
)
if is_peft_available() and peft_config is not None:
if not isinstance(peft_config, PeftConfig):
raise ValueError(
"If you want to use the PeftModel, you need to pass a PeftConfig object to the SFTTrainer."
f" and you passed a {type(peft_config)}."
)
if not isinstance(model, PeftModel):
_support_gc_kwargs = hasattr(
args, "gradient_checkpointing_kwargs"
) and "gradient_checkpointing_kwargs" in list(
inspect.signature(prepare_model_for_kbit_training).parameters
)
gradient_checkpointing_kwargs = getattr(args, "gradient_checkpointing_kwargs", None) or {}
is_sharded_qlora = False
# Below is to support QLoRA + FSDP / DS-Zero3 - one should never call
# peft_module_casting_to_bf16 or prepare_model_for_kbit_training when doing
# QLoRA + FSDP / DS-Zero3
if getattr(model, "is_loaded_in_4bit", False):
for _, param in model.named_parameters():
if param.__class__.__name__ == "Params4bit":
is_sharded_qlora = param.data.device.type == "cpu"
break
if getattr(model, "is_loaded_in_8bit", False) or (
getattr(model, "is_loaded_in_4bit", False) and not is_sharded_qlora
):
prepare_model_kwargs = {
"use_gradient_checkpointing": getattr(args, "gradient_checkpointing", False)
}
if _support_gc_kwargs:
prepare_model_kwargs["gradient_checkpointing_kwargs"] = gradient_checkpointing_kwargs
model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
if args is not None:
args = dataclasses.replace(args, gradient_checkpointing=False)
elif getattr(args, "gradient_checkpointing", False) and (
"use_reentrant" not in gradient_checkpointing_kwargs
or gradient_checkpointing_kwargs["use_reentrant"]
):
# For backward compatibility with older versions of transformers
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
else:
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
model = get_peft_model(model, peft_config)
if (
args is not None
and args.bf16
and getattr(model, "is_loaded_in_4bit", False)
and not is_sharded_qlora
):
peft_module_casting_to_bf16(model)
if tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained(model.config._name_or_path)
if getattr(tokenizer, "pad_token", None) is None:
tokenizer.pad_token = tokenizer.eos_token
if max_seq_length is None:
# to overcome some issues with broken tokenizers
max_seq_length = min(tokenizer.model_max_length, 1024)
warnings.warn(
f"You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to {max_seq_length}"
)
self.dataset_num_proc = dataset_num_proc
self.dataset_batch_size = dataset_batch_size
self._trainer_supports_neftune = hasattr(args, "neftune_noise_alpha")
if neftune_noise_alpha is not None and self._trainer_supports_neftune:
args.neftune_noise_alpha = neftune_noise_alpha
warnings.warn(
"You passed a `neftune_noise_alpha` argument to the SFTTrainer, the value you passed will override the one in the `TrainingArguments`."
)
# self.neftune_noise_alpha is done at Trainer level
elif not self._trainer_supports_neftune:
self.neftune_noise_alpha = neftune_noise_alpha
if formatting_func is None and dataset_text_field is None:
# check if dataset has ChatML format or instruction format and is supported
# if not stays #None
formatting_func = get_formatting_func_from_dataset(train_dataset, tokenizer)
if not packing:
if dataset_text_field is None and formatting_func is None:
raise ValueError(
"You passed `packing=False` to the SFTTrainer, but you didn't pass a `dataset_text_field` or `formatting_func` argument."
)
if data_collator is None:
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# Pre-process the datasets only once per node. The remaining processes will use the cache.
with PartialState().local_main_process_first():
if dataset_kwargs is None:
dataset_kwargs = {}
if train_dataset is not None:
train_dataset = self._prepare_dataset(
train_dataset,
tokenizer,
packing,
dataset_text_field,
max_seq_length,
formatting_func,
num_of_sequences,
chars_per_token,
remove_unused_columns=args.remove_unused_columns if args is not None else True,
**dataset_kwargs,
)
if eval_dataset is not None:
_multiple = isinstance(eval_dataset, dict)
_eval_datasets = eval_dataset if _multiple else {"singleton": eval_dataset}
eval_packing = packing if eval_packing is None else eval_packing
for _eval_dataset_name, _eval_dataset in _eval_datasets.items():
_eval_datasets[_eval_dataset_name] = self._prepare_dataset(
_eval_dataset,
tokenizer,
eval_packing,
dataset_text_field,
max_seq_length,
formatting_func,
num_of_sequences,
chars_per_token,
remove_unused_columns=args.remove_unused_columns if args is not None else True,
**dataset_kwargs,
)
if not _multiple:
eval_dataset = _eval_datasets["singleton"]
if tokenizer.padding_side is not None and tokenizer.padding_side != "right":
warnings.warn(
"You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to "
"overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code."
)
super().__init__(
model=model,
args=args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
model_init=model_init,
compute_metrics=compute_metrics,
callbacks=callbacks,
optimizers=optimizers,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
# Add tags for models that have been loaded with the correct transformers version
if hasattr(self.model, "add_model_tags"):
self.model.add_model_tags(self._tag_names)
if self.args.max_steps > 0 and packing:
warnings.warn(
"You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached."
)
self.train_dataset.infinite = True
elif self.args.max_steps == -1 and packing:
self.train_dataset.infinite = False
if any(isinstance(callback, RichProgressCallback) for callback in self.callback_handler.callbacks):
for callback in self.callback_handler.callbacks:
# Remove the PrinterCallback to avoid duplicated prints in case we passed a `RichProgressCallback`
if callback.__class__.__name__ == "PrinterCallback":
self.callback_handler.pop_callback(callback)
@wraps(Trainer.train)
def train(self, *args, **kwargs):
# Activate neftune right before training.
if self.neftune_noise_alpha is not None and not self._trainer_supports_neftune:
self.model = self._trl_activate_neftune(self.model)
output = super().train(*args, **kwargs)
# After training we make sure to retrieve back the original forward pass method
# for the embedding layer by removing the forward post hook.
if self.neftune_noise_alpha is not None and not self._trainer_supports_neftune:
unwrapped_model = unwrap_model(self.model)
if is_peft_available() and isinstance(unwrapped_model, PeftModel):
embeddings = unwrapped_model.base_model.model.get_input_embeddings()
else:
embeddings = unwrapped_model.get_input_embeddings()
self.neftune_hook_handle.remove()
del embeddings.neftune_noise_alpha
return output
@wraps(Trainer.push_to_hub)
def push_to_hub(self, commit_message: Optional[str] = "End of training", blocking: bool = True, **kwargs) -> str:
"""
Overwrite the `push_to_hub` method in order to force-add the tag "sft" when pushing the
model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
"""
kwargs = trl_sanitze_kwargs_for_tagging(model=self.model, tag_names=self._tag_names, kwargs=kwargs)
return super().push_to_hub(commit_message=commit_message, blocking=blocking, **kwargs)
def _prepare_dataset(
self,
dataset,
tokenizer,
packing,
dataset_text_field,
max_seq_length,
formatting_func,
num_of_sequences,
chars_per_token,
remove_unused_columns=True,
append_concat_token=True,
add_special_tokens=True,
):
if dataset is None:
raise ValueError("The dataset should not be None")
# check if torch dataset / dataloader and do nothing
if isinstance(dataset, (torch.utils.data.IterableDataset, torch.utils.data.Dataset, ConstantLengthDataset)):
return dataset
if not packing:
return self._prepare_non_packed_dataloader(
tokenizer,
dataset,
dataset_text_field,
max_seq_length,
formatting_func,
add_special_tokens,
remove_unused_columns,
)
else:
return self._prepare_packed_dataloader(
tokenizer,
dataset,
dataset_text_field,
max_seq_length,
num_of_sequences,
chars_per_token,
formatting_func,
append_concat_token,
add_special_tokens,
)
def _prepare_non_packed_dataloader(
self,
tokenizer,
dataset,
dataset_text_field,
max_seq_length,
formatting_func=None,
add_special_tokens=True,
remove_unused_columns=True,
):
use_formatting_func = formatting_func is not None and dataset_text_field is None
self._dataset_sanity_checked = False
# Inspired from: https://huggingface.co/learn/nlp-course/chapter7/6?fw=pt
def tokenize(element):
outputs = tokenizer(
element[dataset_text_field] if not use_formatting_func else formatting_func(element),
add_special_tokens=add_special_tokens,
truncation=True,
padding=False,
max_length=max_seq_length,
return_overflowing_tokens=False,
return_length=False,
)
if use_formatting_func and not self._dataset_sanity_checked:
if not isinstance(formatting_func(element), list):
raise ValueError(
"The `formatting_func` should return a list of processed strings since it can lead to silent bugs."
)
else:
self._dataset_sanity_checked = True
return {"input_ids": outputs["input_ids"], "attention_mask": outputs["attention_mask"]}
signature_columns = ["input_ids", "labels", "attention_mask"]
extra_columns = list(set(dataset.column_names) - set(signature_columns))
if not remove_unused_columns and len(extra_columns) > 0:
warnings.warn(
"You passed `remove_unused_columns=False` on a non-packed dataset. This might create some issues with the default collator and yield to errors. If you want to "
f"inspect dataset other columns (in this case {extra_columns}), you can subclass `DataCollatorForLanguageModeling` in case you used the default collator and create your own data collator in order to inspect the unused dataset columns."
)
tokenized_dataset = dataset.map(
tokenize,
batched=True,
remove_columns=dataset.column_names if remove_unused_columns else None,
num_proc=self.dataset_num_proc,
batch_size=self.dataset_batch_size,
)
return tokenized_dataset
def _prepare_packed_dataloader(
self,
tokenizer,
dataset,
dataset_text_field,
max_seq_length,
num_of_sequences,
chars_per_token,
formatting_func=None,
append_concat_token=True,
add_special_tokens=True,
):
if dataset_text_field is not None or formatting_func is not None:
if tokenizer is None:
raise ValueError("You need to pass a tokenizer when using `dataset_text_field` with `SFTTrainer`.")
constant_length_iterator = ConstantLengthDataset(
tokenizer,
dataset,
dataset_text_field=dataset_text_field,
formatting_func=formatting_func,
seq_length=max_seq_length,
infinite=False,
num_of_sequences=num_of_sequences,
chars_per_token=chars_per_token,
eos_token_id=tokenizer.eos_token_id,
append_concat_token=append_concat_token,
add_special_tokens=add_special_tokens,
)
def data_generator(constant_length_iterator):
yield from constant_length_iterator
try:
packed_dataset = Dataset.from_generator(
data_generator, gen_kwargs={"constant_length_iterator": constant_length_iterator}
)
except (DatasetGenerationError, SchemaInferenceError) as exc:
raise ValueError(
"Error occurred while packing the dataset. "
"Make sure that your dataset has enough samples to at least yield one packed sequence."
) from exc
return packed_dataset
else:
raise ValueError(
"You need to pass a `dataset_text_field` or `formatting_func` argument to the SFTTrainer if you want to use the `ConstantLengthDataset`."
)
def _trl_activate_neftune(self, model):
r"""
Activates the neftune as presented in this code: https://github.com/neelsjain/NEFTune and paper: https://arxiv.org/abs/2310.05914
Since in transformers Trainer we do have an `_activate_neftune` method, we need to rename this method to avoid conflicts.
"""
unwrapped_model = unwrap_model(model)
if is_peft_available() and isinstance(unwrapped_model, PeftModel):
embeddings = unwrapped_model.base_model.model.get_input_embeddings()
else:
embeddings = unwrapped_model.get_input_embeddings()
embeddings.neftune_noise_alpha = self.neftune_noise_alpha
hook_handle = embeddings.register_forward_hook(neftune_post_forward_hook)
self.neftune_hook_handle = hook_handle
return model