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