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| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
| import time | |
| import numpy as np | |
| from torch.nn import functional as F | |
| import os | |
| from .base_model import BaseLLMModel | |
| from threading import Thread | |
| STABLELM_MODEL = None | |
| STABLELM_TOKENIZER = None | |
| class StopOnTokens(StoppingCriteria): | |
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| stop_ids = [50278, 50279, 50277, 1, 0] | |
| for stop_id in stop_ids: | |
| if input_ids[0][-1] == stop_id: | |
| return True | |
| return False | |
| class StableLM_Client(BaseLLMModel): | |
| def __init__(self, model_name, user_name="") -> None: | |
| super().__init__(model_name=model_name, user=user_name) | |
| global STABLELM_MODEL, STABLELM_TOKENIZER | |
| print(f"Starting to load StableLM to memory") | |
| if model_name == "StableLM": | |
| model_name = "stabilityai/stablelm-tuned-alpha-7b" | |
| else: | |
| model_name = f"models/{model_name}" | |
| if STABLELM_MODEL is None: | |
| STABLELM_MODEL = AutoModelForCausalLM.from_pretrained( | |
| model_name, torch_dtype=torch.float16).cuda() | |
| if STABLELM_TOKENIZER is None: | |
| STABLELM_TOKENIZER = AutoTokenizer.from_pretrained(model_name) | |
| self.generator = pipeline( | |
| 'text-generation', model=STABLELM_MODEL, tokenizer=STABLELM_TOKENIZER, device=0) | |
| print(f"Sucessfully loaded StableLM to the memory") | |
| self.system_prompt = """StableAssistant | |
| - StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI. | |
| - StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user. | |
| - StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes. | |
| - StableAssistant will refuse to participate in anything that could harm a human.""" | |
| self.max_generation_token = 1024 | |
| self.top_p = 0.95 | |
| self.temperature = 1.0 | |
| def _get_stablelm_style_input(self): | |
| history = self.history + [{"role": "assistant", "content": ""}] | |
| print(history) | |
| messages = self.system_prompt + \ | |
| "".join(["".join(["<|USER|>"+history[i]["content"], "<|ASSISTANT|>"+history[i + 1]["content"]]) | |
| for i in range(0, len(history), 2)]) | |
| return messages | |
| def _generate(self, text, bad_text=None): | |
| stop = StopOnTokens() | |
| result = self.generator(text, max_new_tokens=self.max_generation_token, num_return_sequences=1, num_beams=1, do_sample=True, | |
| temperature=self.temperature, top_p=self.top_p, top_k=1000, stopping_criteria=StoppingCriteriaList([stop])) | |
| return result[0]["generated_text"].replace(text, "") | |
| def get_answer_at_once(self): | |
| messages = self._get_stablelm_style_input() | |
| return self._generate(messages), len(messages) | |
| def get_answer_stream_iter(self): | |
| stop = StopOnTokens() | |
| messages = self._get_stablelm_style_input() | |
| # model_inputs = tok([messages], return_tensors="pt")['input_ids'].cuda()[:, :4096-1024] | |
| model_inputs = STABLELM_TOKENIZER( | |
| [messages], return_tensors="pt").to("cuda") | |
| streamer = TextIteratorStreamer( | |
| STABLELM_TOKENIZER, timeout=10., skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| model_inputs, | |
| streamer=streamer, | |
| max_new_tokens=self.max_generation_token, | |
| do_sample=True, | |
| top_p=self.top_p, | |
| top_k=1000, | |
| temperature=self.temperature, | |
| num_beams=1, | |
| stopping_criteria=StoppingCriteriaList([stop]) | |
| ) | |
| t = Thread(target=STABLELM_MODEL.generate, kwargs=generate_kwargs) | |
| t.start() | |
| partial_text = "" | |
| for new_text in streamer: | |
| partial_text += new_text | |
| yield partial_text | |