mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-05 18:32:14 +08:00
99 lines
4.1 KiB
Python
99 lines
4.1 KiB
Python
import torch
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from types import MethodType
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from typing import Any, Dict, Generator, List, Optional, Tuple
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from threading import Thread
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from transformers import PreTrainedModel, TextIteratorStreamer
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from llmtuner.extras.misc import dispatch_model, get_logits_processor, get_stopping_criteria
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from llmtuner.extras.template import get_template_and_fix_tokenizer
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from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer
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class ChatModel:
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def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
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model_args, data_args, finetuning_args, self.generating_args = get_infer_args(args)
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self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
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self.model = dispatch_model(self.model)
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self.model = self.model.eval() # change to eval mode
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self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
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self.source_prefix = data_args.source_prefix
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self.model.generate = MethodType(PreTrainedModel.generate, self.model) # disable custom method (for Qwen)
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def process_args(
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self,
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query: str,
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history: Optional[List[Tuple[str, str]]] = None,
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prefix: Optional[str] = None,
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**input_kwargs
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) -> Tuple[Dict[str, Any], int]:
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prefix = prefix or self.source_prefix
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prompt, _ = self.template.encode_oneturn(
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tokenizer=self.tokenizer, query=query, resp="", history=history, prefix=prefix
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)
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input_ids = torch.tensor([prompt], device=self.model.device)
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prompt_length = len(input_ids[0])
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do_sample = input_kwargs.pop("do_sample", None)
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temperature = input_kwargs.pop("temperature", None)
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top_p = input_kwargs.pop("top_p", None)
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top_k = input_kwargs.pop("top_k", None)
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repetition_penalty = input_kwargs.pop("repetition_penalty", None)
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max_length = input_kwargs.pop("max_length", None)
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max_new_tokens = input_kwargs.pop("max_new_tokens", None)
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gen_kwargs = self.generating_args.to_dict()
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gen_kwargs.update(dict(
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input_ids=input_ids,
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do_sample=do_sample if do_sample is not None else gen_kwargs["do_sample"],
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temperature=temperature or gen_kwargs["temperature"],
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top_p=top_p or gen_kwargs["top_p"],
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top_k=top_k or gen_kwargs["top_k"],
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repetition_penalty=repetition_penalty or gen_kwargs["repetition_penalty"],
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logits_processor=get_logits_processor(),
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stopping_criteria=get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
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))
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if max_length:
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gen_kwargs.pop("max_new_tokens", None)
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gen_kwargs["max_length"] = max_length
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if max_new_tokens:
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gen_kwargs.pop("max_length", None)
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gen_kwargs["max_new_tokens"] = max_new_tokens
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return gen_kwargs, prompt_length
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@torch.inference_mode()
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def chat(
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self,
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query: str,
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history: Optional[List[Tuple[str, str]]] = None,
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prefix: Optional[str] = None,
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**input_kwargs
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) -> Tuple[str, Tuple[int, int]]:
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gen_kwargs, prompt_length = self.process_args(query, history, prefix, **input_kwargs)
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generation_output = self.model.generate(**gen_kwargs)
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outputs = generation_output.tolist()[0][prompt_length:]
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response = self.tokenizer.decode(outputs, skip_special_tokens=True)
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response_length = len(outputs)
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return response, (prompt_length, response_length)
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@torch.inference_mode()
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def stream_chat(
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self,
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query: str,
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history: Optional[List[Tuple[str, str]]] = None,
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prefix: Optional[str] = None,
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**input_kwargs
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) -> Generator[str, None, None]:
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gen_kwargs, _ = self.process_args(query, history, prefix, **input_kwargs)
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streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
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gen_kwargs["streamer"] = streamer
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thread = Thread(target=self.model.generate, kwargs=gen_kwargs)
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thread.start()
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yield from streamer
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