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from expand import *
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast, BatchEncoding
from dataclasses import dataclass
from completions import prepare_inputs, find_next_tokens
type Tokenizer = PreTrainedTokenizer | PreTrainedTokenizerFast
@dataclass
class ExpanderOneBatchLLM:
model: PreTrainedModel
tokenizer: Tokenizer
def expand(self, batch: Batch) -> ExpansionOneResultBatch:
inputs = prepare_inputs([s.get_all_tokens() for s in batch.items], self.tokenizer, self.model.device)
next_tokens = find_next_tokens(self.model, inputs, self.tokenizer)
results = []
for s, next_tokens in zip(batch.items, next_tokens):
expansions = [Expansion(token=token, cost=cost) for token, cost in next_tokens]
results.append(ExpansionOneResult(series=s, expansions=expansions))
return ExpansionOneResultBatch(items=results)
def create_stopping_criterion_llm(tokenizer: Tokenizer) -> Callable[[Series, Expansion], bool]:
def stopping_criterion(series: Series, expansion: Expansion) -> bool:
d = default_completion_criterion(series, expansion)
if d:
return d
token_str = tokenizer.decode([expansion.token])
starts_with_space = token_str.startswith(" ")
print(f"-----{token_str}-----, {starts_with_space=}")
is_first_token = len(series.expansions) == 0
if is_first_token and not starts_with_space:
return True
if not is_first_token and starts_with_space:
return True
return False
return stopping_criterion
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