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import argparse |
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from typing import Optional |
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import torch |
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from peft import PeftConfig, PeftModel |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def resolve_dtype(name: str, device: str) -> Optional[torch.dtype]: |
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if name == "auto": |
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return torch.float32 if device == "cpu" else None |
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return {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32}[name] |
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def resolve_device_map(device: str): |
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return None if device == "cpu" else "auto" |
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def parse_args(): |
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p = argparse.ArgumentParser(description="Run a quick LoRA inference.") |
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p.add_argument("--lora_dir", default="outputs/tinyllama-lora", help="Path to LoRA adapter folder.") |
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p.add_argument("--prompt", default="### Instruction:\nExplain LoRA in one sentence.\n\n### Input:\nN/A\n\n### Response:\n") |
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p.add_argument("--max_new_tokens", type=int, default=128) |
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p.add_argument("--temperature", type=float, default=0.7) |
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p.add_argument("--top_p", type=float, default=0.9) |
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p.add_argument("--device", default="auto", choices=["auto", "cpu", "cuda", "mps"]) |
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p.add_argument("--torch_dtype", default="auto", choices=["auto", "float16", "bfloat16", "float32"]) |
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p.add_argument("--offload_dir", default=None, help="Optional offload directory when using device_map='auto'.") |
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return p.parse_args() |
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def main(): |
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args = parse_args() |
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cfg = PeftConfig.from_pretrained(args.lora_dir) |
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base_model = cfg.base_model_name_or_path |
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torch_dtype = resolve_dtype(args.torch_dtype, args.device) |
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device_map = resolve_device_map(args.device) if args.device != "auto" else "auto" |
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tokenizer = AutoTokenizer.from_pretrained(args.lora_dir, use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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base_model, |
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device_map=device_map, |
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torch_dtype=torch_dtype, |
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offload_folder=args.offload_dir, |
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) |
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model = PeftModel.from_pretrained(model, args.lora_dir) |
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prompt = args.prompt |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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with torch.inference_mode(): |
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out = model.generate( |
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**inputs, |
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max_new_tokens=args.max_new_tokens, |
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do_sample=True, |
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temperature=args.temperature, |
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top_p=args.top_p, |
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) |
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print(tokenizer.decode(out[0], skip_special_tokens=True)) |
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if __name__ == "__main__": |
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main() |
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