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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" |