File size: 5,948 Bytes
c0dd54c dc34aea 7874fb0 c0dd54c 779014d 8bac0a3 247118f 8bac0a3 54bfe84 8bac0a3 7874fb0 d00c49d 7874fb0 8bac0a3 d00c49d 7874fb0 d00c49d 7874fb0 dc34aea 774ae10 c0dd54c dc34aea 7874fb0 dc34aea 7874fb0 dc34aea c0dd54c 7874fb0 f21015a 8bac0a3 dc34aea 8bac0a3 54bfe84 dc34aea 20a108f ce8b3f6 7874fb0 |
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 |
import torch
from transformers.generation.utils import (
GenerationMixin,
validate_stopping_criteria,
StoppingCriteriaList,
)
from transformers import TextStreamer
def custom_generate(
self,
input_ids,
attention_mask=None,
max_length=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=None,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
streamer=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
decoder_start_token_id=None,
use_cache=None,
num_beam_groups=None,
diversity_penalty=None,
prefix_allowed_tokens_fn=None,
output_attentions=None,
output_hidden_states=None,
output_scores=None,
return_dict_in_generate=None,
forced_bos_token_id=None,
forced_eos_token_id=None,
remove_invalid_values=None,
synced_gpus=None,
**kwargs,
):
device = input_ids.device
with torch.no_grad():
finished_generating = torch.zeros(len(input_ids), dtype=torch.bool, device=device)
while not finished_generating.all() and input_ids.shape[1] < max_length:
# Sample the next token
new_ids = self(
input_ids[~finished_generating],
attention_mask=attention_mask[~finished_generating] if attention_mask is not None else None,
**kwargs
)['logits']
# Mask out the start and end thought tokens so we don't accidentally sample them
new_ids[:, :, self.tokenizer.vocab_size:] = -float("inf")
for list_idx, answer_idx in enumerate((~finished_generating).nonzero(as_tuple=True)[0]):
# Find the index of the last token that is not padding
base_answer_ids = input_ids[answer_idx]
new_answer_ids = new_ids[list_idx]
last_token_idx = (base_answer_ids != self.tokenizer.pad_token_id).nonzero(as_tuple=True)[0].max()
new_ids_sampled = torch.multinomial(
torch.nn.functional.softmax(new_answer_ids[last_token_idx] / temperature, dim=-1), 1)
# Assign the new id to the last token
if last_token_idx + 1 >= len(base_answer_ids):
# Add padding everywhere
new_padding = torch.full((len(input_ids), 1), self.tokenizer.pad_token_id, dtype=torch.long,
device=device)
input_ids = torch.cat([input_ids, new_padding], dim=-1)
if attention_mask is not None:
attention_mask = torch.cat([attention_mask, torch.zeros_like(new_padding)], dim=-1)
if attention_mask is not None:
attention_mask[answer_idx, last_token_idx + 1] = 1
input_ids[answer_idx, last_token_idx + 1] = new_ids_sampled
if new_ids_sampled == self.tokenizer.eos_token_id or new_ids_sampled == self.tokenizer.bos_token_id or new_ids_sampled == self.tokenizer.pad_token_id:
finished_generating[answer_idx] = 1
# Check if the end token is generated
if new_ids_sampled == self.tokenizer.convert_tokens_to_ids("</s>"):
finished_generating[answer_idx] = 1
if streamer is not None:
streamer.put(new_ids_sampled)
generated_token_ids = input_ids.tolist()
return generated_token_ids, attention_mask
def generate(
self,
input_ids,
attention_mask=None,
max_length=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=1.1,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
decoder_start_token_id=None,
use_cache=None,
num_beam_groups=None,
diversity_penalty=None,
prefix_allowed_tokens_fn=None,
output_attentions=None,
output_hidden_states=None,
output_scores=None,
return_dict_in_generate=None,
forced_bos_token_id=None,
forced_eos_token_id=None,
remove_invalid_values=None,
synced_gpus=None,
**model_kwargs,
):
streamer = TextStreamer(self.tokenizer, skip_prompt=False, skip_special_tokens=True)
generated_token_ids, attention_mask = custom_generate(
self,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
num_beams=num_beams,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
bad_words_ids=bad_words_ids,
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
length_penalty=length_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
num_return_sequences=num_return_sequences,
decoder_start_token_id=decoder_start_token_id,
use_cache=use_cache,
num_beam_groups=num_beam_groups,
diversity_penalty=diversity_penalty,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
remove_invalid_values=remove_invalid_values,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
)
return generated_token_ids, attention_mask |