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| import argparse | |
| import torch | |
| from tqdm import tqdm | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from iGPT.models.husky_src.husky_chat import Blip2LlaMAForConditionalGeneration | |
| def apply_delta(base_model_path, target_model_path, delta_path): | |
| print("Loading base model") | |
| base = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) | |
| print("Loading delta") | |
| delta_tokenizer = AutoTokenizer.from_pretrained(delta_path, use_fast=False) | |
| delta = Blip2LlaMAForConditionalGeneration.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) | |
| print("Applying delta") | |
| for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"): | |
| if name.startswith('language_model'): | |
| name = name[len('language_model.'):] | |
| if param.data.shape == base.state_dict()[name].shape: | |
| param.data += base.state_dict()[name] | |
| else: | |
| bparam = base.state_dict()[name] | |
| param.data[:bparam.shape[0], :bparam.shape[1]] += bparam | |
| else: | |
| pass | |
| print("Saving target model") | |
| delta.save_pretrained(target_model_path) | |
| delta_tokenizer.save_pretrained(target_model_path) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--base-model-path", type=str, required=True) | |
| parser.add_argument("--target-model-path", type=str, required=True) | |
| parser.add_argument("--delta-path", type=str, required=True) | |
| args = parser.parse_args() | |
| apply_delta(args.base_model_path, args.target_model_path, args.delta_path) | |
| # srun -p INTERN2 --gres=gpu:0 python apply_delta.py --base-model-path "/mnt/petrelfs/share_data/wangweiyun/share_hf/llama-7b-hf" --target-model-path "/mnt/petrelfs/share_data/wangweiyun/share_hf/husky-7b-demo-v0_01" --delta-path "/mnt/petrelfs/share_data/wangweiyun/share_hf/husky-7b-delta-v0_01" |