inference_example.py

import argparse
import os

import torch
from colossalai.logging import get_dist_logger
from transformers import AutoTokenizer, AutoModelForCausalLM

logger = get_dist_logger()


def load_model(model_path, device="cuda", **kwargs):
    logger.info(
        "Please check whether the tokenizer and model weights are properly stored in the same folder."
    )
    model = AutoModelForCausalLM.from_pretrained(model_path, **kwargs)
    model.to(device)

    try:
        tokenizer = AutoTokenizer.from_pretrained(model_path)
    except OSError:
        raise ImportError("Tokenizer not found. Please check if the tokenizer exists or the model path is correct.")

    return model, tokenizer


@torch.inference_mode()
def generate(args):
    model, tokenizer = load_model(model_path=args.model_path, device=args.device)

    BASE_INFERENCE_SUFFIX = "\n\n->\n\n"
    input_txt = f"{args.input_txt}{BASE_INFERENCE_SUFFIX}"

    inputs = tokenizer(args.input_txt, return_tensors='pt').to(args.device)
    output = model.generate(**inputs,
                            max_new_tokens=args.max_new_tokens,
                            do_sample=args.do_sample,
                            temperature=args.temperature,
                            top_k=args.top_k,
                            top_p=args.top_p,
                            num_return_sequences=1)
    response = tokenizer.decode(output.cpu()[0], skip_special_tokens=True)[len(input_txt):]
    logger.info(f"Question: {input_txt} \n\n Answer: \n{response}")
    return response


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Colossal-LLaMA-2 inference Process.")
    parser.add_argument('--model_path', type=str, default="hpcai-tech/Colossal-LLaMA-2-7b-base", help="HF repo name or local path of the model")
    parser.add_argument('--device', type=str, default="cuda:0", help="Set the device")
    parser.add_argument('--max_new_tokens', type=int, default=512, help=" Set maximum numbers of tokens to generate, ignoring the number of tokens in the prompt")
    parser.add_argument('--do_sample', type=bool, default=True, help="Set whether or not to use sampling")
    parser.add_argument('--temperature', type=float, default=0.3, help="Set temperature value")
    parser.add_argument('--top_k', type=int, default=50, help="Set top_k value for top-k-filtering")
    parser.add_argument('--top_p', type=int, default=0.95, help="Set top_p value for generation")
    parser.add_argument('--input_txt', type=str, default="明月松间照，", help="The prompt input to the model")
    args = parser.parse_args()
    generate(args)