109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
from threading import Thread
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from typing import Iterator, Optional
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, TextIteratorStreamer
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import torch
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from . import util
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from .llm_engine import LlmEngine
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class LocalLlmEngine(LlmEngine):
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def __init__(
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self,
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model_path: str,
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temperature: float,
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token_limit: int,
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):
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"""
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Initialize the LLM Engine with a model path.
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Args:
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model_path: Path to the model weights to be used.
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temperature: Temperature for sampling
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token_limit: Maximum number of tokens to generate
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"""
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self._temperature = temperature
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self._token_limit = token_limit
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self._tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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return_dict=True,
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low_cpu_mem_usage=True,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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if self._tokenizer.pad_token_id is None:
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self._tokenizer.pad_token_id = self._tokenizer.eos_token_id
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if model.config.pad_token_id is None:
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model.config.pad_token_id = model.config.eos_token_id
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self._pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=self._tokenizer,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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return_full_text=False,
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)
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def infer(self, system_prompt: str, main_context: str) -> Iterator[str]:
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"""
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Run inference using the system prompt and main context, while validating actions against the provided XML schema.
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Args:
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system_prompt: The system prompt string
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main_context: The main context string after templating
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Returns:
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Iterator[str]: An iterator that yields the generated text.
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": main_context}
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]
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prompt = self._tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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streamer = TextIteratorStreamer(
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self._tokenizer,
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skip_prompt=True
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)
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pipeline_kwargs = dict(
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text_inputs=prompt,
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do_sample=True,
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temperature=self._temperature,
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max_new_tokens=self._token_limit,
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streamer=streamer
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)
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thread = Thread(target=self._pipeline, kwargs=pipeline_kwargs)
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thread.start()
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return util.stop_before_value(streamer, '<|eot_id|>')
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def token_count(self, system_prompt: str, main_context: str) -> int:
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"""
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Count tokens for the given system prompt and main context.
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Args:
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system_prompt: The system prompt string
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main_context: The main context string
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Returns:
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int: Total number of tokens
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": main_context}
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]
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prompt = self._tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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return len(self._tokenizer.encode(prompt))
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def token_limit(self) -> int:
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"""
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Get the model's context window size.
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Returns:
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int: Maximum number of tokens the model can process
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"""
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return self._pipeline.model.config.max_position_embeddings |