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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
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