5aa3e427a9
* Fix docker images * add-apt-repository requires gnupg, gpg-agent, software-properties-common * update * avoid importing ipex again
101 lines
4.4 KiB
Python
101 lines
4.4 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import argparse
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import sys
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# todo: support more model class
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from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, AutoConfig
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from transformers import TextIteratorStreamer
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from transformers.tools.agents import StopSequenceCriteria
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from transformers.generation.stopping_criteria import StoppingCriteriaList
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from colorama import Fore
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from ipex_llm import optimize_model
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SYSTEM_PROMPT = "A chat between a curious human <human> and an artificial intelligence assistant <bot>.\
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The assistant gives helpful, detailed, and polite answers to the human's questions."
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HUMAN_ID = "<human>"
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BOT_ID = "<bot>"
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# chat_history formated in [(iput_str, output_str)]
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def format_prompt(input_str,
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chat_history):
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prompt = [f"{SYSTEM_PROMPT}\n"]
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for history_input_str, history_output_str in chat_history:
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prompt.append(f"{HUMAN_ID} {history_input_str}\n{BOT_ID} {history_output_str}\n")
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prompt.append(f"{HUMAN_ID} {input_str}\n{BOT_ID} ")
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return "".join(prompt)
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def stream_chat(model,
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tokenizer,
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stopping_criteria,
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input_str,
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chat_history):
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prompt = format_prompt(input_str, chat_history)
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# print(prompt)
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input_ids = tokenizer([prompt], return_tensors="pt").to('xpu')
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(input_ids, streamer=streamer, max_new_tokens=512, stopping_criteria=stopping_criteria)
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from threading import Thread
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# to ensure non-blocking access to the generated text, generation process should be ran in a separate thread
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thread = Thread(target=model.generate, kwargs=generate_kwargs)
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thread.start()
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output_str = []
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print(Fore.BLUE+"BigDL-LLM: "+Fore.RESET, end="")
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for partial_output_str in streamer:
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output_str.append(partial_output_str)
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# remove the last HUMAN_ID if exists
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print(partial_output_str.replace(f"{HUMAN_ID}", ""), end="")
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chat_history.append((input_str, "".join(output_str).replace(f"{HUMAN_ID}", "").rstrip()))
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def auto_select_model(model_name):
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try:
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try:
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model = AutoModelForCausalLM.from_pretrained(model_path,
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low_cpu_mem_usage=True,
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torch_dtype="auto",
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trust_remote_code=True,
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use_cache=True)
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except:
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model = AutoModel.from_pretrained(model_path,
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low_cpu_mem_usage=True,
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torch_dtype="auto",
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trust_remote_code=True,
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use_cache=True)
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except:
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print("Sorry, the model you entered is not supported in installer.")
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sys.exit()
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return model
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-path", type=str, help="path to an llm")
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args = parser.parse_args()
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model_path = args.model_path
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model = auto_select_model(model_path)
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model = optimize_model(model)
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model = model.to('xpu')
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(HUMAN_ID, tokenizer)])
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chat_history = []
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while True:
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with torch.inference_mode():
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user_input = input(Fore.GREEN+"\nHuman: "+Fore.RESET)
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if user_input == "stop": # let's stop the conversation when user input "stop"
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break
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stream_chat(model=model,
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tokenizer=tokenizer,
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stopping_criteria=stopping_criteria,
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input_str=user_input,
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chat_history=chat_history)
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