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* refactor latest code * update api * add dummy dataset * update Readme * add setup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update files * add PP support * update arguments * update argument * reorg folder * update version * remove IB infor * update utils * update readme * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update save for zero * update save * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add apex * update --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
import argparse
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import torch
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from colossal_llama.dataset.conversation import default_conversation
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from colossalai.logging import get_dist_logger
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logger = get_dist_logger()
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def load_model(model_path, device="cuda", **kwargs):
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logger.info("Please check whether the tokenizer and model weights are properly stored in the same folder.")
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model = AutoModelForCausalLM.from_pretrained(model_path, **kwargs)
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model.to(device)
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side="left")
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except OSError:
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raise ImportError("Tokenizer not found. Please check if the tokenizer exists or the model path is correct.")
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return model, tokenizer
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@torch.inference_mode()
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def generate(args):
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model, tokenizer = load_model(model_path=args.model_path, device=args.device)
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if args.prompt_style == "sft":
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conversation = default_conversation.copy()
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conversation.append_message("Human", args.input_txt)
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conversation.append_message("Assistant", None)
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input_txt = conversation.get_prompt()
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else:
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BASE_INFERENCE_SUFFIX = "\n\n->\n\n"
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input_txt = f"{args.input_txt}{BASE_INFERENCE_SUFFIX}"
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inputs = tokenizer(input_txt, return_tensors="pt").to(args.device)
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num_input_tokens = inputs["input_ids"].shape[-1]
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output = model.generate(
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**inputs,
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max_new_tokens=args.max_new_tokens,
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do_sample=args.do_sample,
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temperature=args.temperature,
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top_k=args.top_k,
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top_p=args.top_p,
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num_return_sequences=1,
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)
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response = tokenizer.decode(output.cpu()[0, num_input_tokens:], skip_special_tokens=True)
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logger.info(f"\nHuman: {args.input_txt} \n\nAssistant: \n{response}")
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return response
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Colossal-LLaMA-2 inference Process.")
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parser.add_argument(
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"--model_path",
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type=str,
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default="hpcai-tech/Colossal-LLaMA-2-7b-base",
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help="HF repo name or local path of the model",
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)
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parser.add_argument("--device", type=str, default="cuda:0", help="Set the device")
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parser.add_argument(
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"--max_new_tokens",
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type=int,
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default=512,
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help=" Set maximum numbers of tokens to generate, ignoring the number of tokens in the prompt",
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)
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parser.add_argument("--do_sample", type=bool, default=True, help="Set whether or not to use sampling")
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parser.add_argument("--temperature", type=float, default=0.3, help="Set temperature value")
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parser.add_argument("--top_k", type=int, default=50, help="Set top_k value for top-k-filtering")
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parser.add_argument("--top_p", type=float, default=0.95, help="Set top_p value for generation")
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parser.add_argument("--input_txt", type=str, default="明月松间照，", help="The prompt input to the model")
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parser.add_argument("--prompt_style", choices=["sft", "pretrained"], default="sft", help="The style of the prompt")
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args = parser.parse_args()
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generate(args)
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