| 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 = 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 |
|
|
