103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
"""
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This script creates a CLI demo with transformers backend for the glm-4-9b model with Intel® Extension for Transformers
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"""
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import os
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MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/glm-4-9b-chat')
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import torch
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from threading import Thread
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from intel_extension_for_transformers.transformers import AutoModelForCausalLM
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from transformers import TextIteratorStreamer, StoppingCriteriaList, StoppingCriteria, AutoTokenizer
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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 = [151329, 151336, 151338]
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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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def initialize_model_and_tokenizer():
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_PATH,
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device_map="cpu", # Use Intel CPU for inference
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trust_remote_code=True,
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load_in_4bit=True
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)
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return tokenizer, model
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def get_user_input():
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return input("\nUser: ")
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def main():
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tokenizer, model = initialize_model_and_tokenizer()
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history = []
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max_length = 100
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top_p = 0.9
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temperature = 0.8
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stop = StopOnTokens()
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print("Welcome to the CLI chat. Type your messages below.")
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while True:
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user_input = get_user_input()
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if user_input.lower() in ["exit", "quit"]:
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break
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history.append([user_input, ""])
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messages = []
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for idx, (user_msg, model_msg) in enumerate(history):
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if idx == len(history) - 1 and not model_msg:
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messages.append({"role": "user", "content": user_msg})
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break
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if user_msg:
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messages.append({"role": "user", "content": user_msg})
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if model_msg:
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messages.append({"role": "assistant", "content": model_msg})
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model_inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt"
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)
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streamer = TextIteratorStreamer(
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tokenizer=tokenizer,
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timeout=60,
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skip_prompt=True,
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skip_special_tokens=True
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)
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generate_kwargs = {
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"input_ids": model_inputs,
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"streamer": streamer,
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"max_new_tokens": max_length,
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"do_sample": True,
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"top_p": top_p,
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"temperature": temperature,
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"stopping_criteria": StoppingCriteriaList([stop]),
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"repetition_penalty": 1.2,
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"eos_token_id": model.config.eos_token_id,
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}
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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print("Assistant:", end="", flush=True)
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for new_token in streamer:
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if new_token:
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print(new_token, end="", flush=True)
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history[-1][1] += new_token
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history[-1][1] = history[-1][1].strip()
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if __name__ == "__main__":
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main()
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