|
|
"""pretraining prompt strategies""" |
|
|
from typing import Generator |
|
|
|
|
|
from transformers import BatchEncoding |
|
|
|
|
|
from axolotl.prompt_tokenizers import PromptTokenizingStrategy |
|
|
|
|
|
|
|
|
class PretrainTokenizer: |
|
|
"""basic tokenization class for pretraining""" |
|
|
|
|
|
def build_prompt(self, prompt) -> Generator[str, None, None]: |
|
|
yield prompt |
|
|
|
|
|
|
|
|
class PretrainTokenizationStrategy(PromptTokenizingStrategy): |
|
|
"""handles tokenization for pretraining with strides""" |
|
|
|
|
|
@property |
|
|
def supports_batched(self): |
|
|
return True |
|
|
|
|
|
def __init__(self, *args, max_length=None, text_column="text", **kwargs): |
|
|
super().__init__(*args, **kwargs) |
|
|
if max_length: |
|
|
self.max_length = max_length |
|
|
self.text_column = text_column |
|
|
|
|
|
def _tokenize( |
|
|
self, prompt: str, add_eos_token: bool = True, strip_bos_token: bool = False |
|
|
) -> BatchEncoding: |
|
|
res = self.tokenizer( |
|
|
prompt, |
|
|
truncation=True, |
|
|
max_length=self.max_length - 1, |
|
|
add_special_tokens=True, |
|
|
return_overflowing_tokens=True, |
|
|
stride=256, |
|
|
) |
|
|
res["input_ids"] = [ |
|
|
seq + [self.tokenizer.eos_token_id] for seq in res["input_ids"] |
|
|
] |
|
|
res["attention_mask"] = [seq + [1] for seq in res["attention_mask"]] |
|
|
|
|
|
return res |
|
|
|
|
|
def tokenize_prompt(self, prompt): |
|
|
return self._tokenize(prompt[self.text_column]) |
|
|
|
|
|
|
|
|
def load(tokenizer, cfg): |
|
|
strat = PretrainTokenizationStrategy( |
|
|
PretrainTokenizer(), |
|
|
tokenizer, |
|
|
cfg.train_on_inputs, |
|
|
cfg.sequence_len, |
|
|
text_column=cfg.pretraining_dataset[0]["text_column"] or "text", |
|
|
max_length=cfg.sequence_len * 64, |
|
|
) |
|
|
return strat |
|
|
|
