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"""
Apply the delta weights on top of a base model.
Adapted from: https://github.com/lm-sys/FastChat/blob/main/fastchat/model/apply_delta.py.
"""
import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
def apply_delta(base_model_path, target_model_path, delta_path):
print(f"Loading the base model from {base_model_path}")
base = AutoModelForCausalLM.from_pretrained(
base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print(f"Loading the delta from {delta_path}")
delta = AutoModelForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
delta_tokenizer = AutoTokenizer.from_pretrained(delta_path, use_fast=False)
DEFAULT_PAD_TOKEN = "[PAD]"
base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
num_new_tokens = base_tokenizer.add_special_tokens(dict(pad_token=DEFAULT_PAD_TOKEN))
base.resize_token_embeddings(len(base_tokenizer))
input_embeddings = base.get_input_embeddings().weight.data
output_embeddings = base.get_output_embeddings().weight.data
input_embeddings[-num_new_tokens:] = 0
output_embeddings[-num_new_tokens:] = 0
print("Applying the delta")
for name, param in tqdm(base.state_dict().items(), desc="Applying delta"):
assert name in delta.state_dict()
param.data += delta.state_dict()[name]
print(f"Saving the target model to {target_model_path}")
base.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)
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