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decrypt.py
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decrypt.py
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"""
Apply the delta weights on top of a base model.
Code is modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/model/apply_delta.py
"""
import argparse
import gc
import glob
import json
import os
import shutil
import tempfile
from huggingface_hub import snapshot_download
import torch
from torch import nn
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
GB = 1 << 30
def split_files(model_path, tmp_path, split_size):
if not os.path.exists(model_path):
model_path = snapshot_download(repo_id=model_path)
if not os.path.exists(tmp_path):
os.makedirs(tmp_path)
file_pattern = os.path.join(model_path, "pytorch_model-*.bin")
files = glob.glob(file_pattern)
part = 0
try:
for file_path in tqdm(files):
state_dict = torch.load(file_path)
new_state_dict = {}
current_size = 0
for name, param in state_dict.items():
param_size = param.numel() * param.element_size()
if current_size + param_size > split_size:
new_file_name = f"pytorch_model-{part}.bin"
new_file_path = os.path.join(tmp_path, new_file_name)
torch.save(new_state_dict, new_file_path)
current_size = 0
new_state_dict = None
gc.collect()
new_state_dict = {}
part += 1
new_state_dict[name] = param
current_size += param_size
new_file_name = f"pytorch_model-{part}.bin"
new_file_path = os.path.join(tmp_path, new_file_name)
torch.save(new_state_dict, new_file_path)
new_state_dict = None
gc.collect()
new_state_dict = {}
part += 1
except Exception as e:
print(f"An error occurred during split_files: {e}")
shutil.rmtree(tmp_path)
raise
def apply_delta_low_cpu_mem(base_model_path, target_model_path, delta_path):
base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
base_config = AutoConfig.from_pretrained(base_model_path)
if os.path.exists(target_model_path):
shutil.rmtree(target_model_path)
os.makedirs(target_model_path)
split_size = 4 * GB
with tempfile.TemporaryDirectory() as tmp_base_path, tempfile.TemporaryDirectory() as tmp_delta_path:
print(f"Split files for the base model to {tmp_base_path}")
split_files(base_model_path, tmp_base_path, split_size)
print(f"Split files for the delta weights to {tmp_delta_path}")
split_files(delta_path, tmp_delta_path, split_size)
base_pattern = os.path.join(tmp_base_path, "pytorch_model-*.bin")
base_files = glob.glob(base_pattern)
delta_pattern = os.path.join(tmp_delta_path, "pytorch_model-*.bin")
delta_files = glob.glob(delta_pattern)
delta_state_dict = torch.load(delta_files[0])
print("Applying the delta")
weight_map = {}
total_size = 0
for i, base_file in tqdm(enumerate(base_files)):
state_dict = torch.load(base_file)
file_name = f"pytorch_model-{i}.bin"
for name, param in state_dict.items():
if name not in delta_state_dict:
for delta_file in delta_files:
delta_state_dict = torch.load(delta_file)
gc.collect()
if name in delta_state_dict:
break
if name == 'model.embed_tokens.weight' or name == 'lm_head.weight':
param.data[:delta_state_dict[name].shape[0], : ] += delta_state_dict[name]
weight_map[name] = file_name
total_size += param.numel() * param.element_size()
gc.collect()
continue
state_dict[name] += delta_state_dict[name]
weight_map[name] = file_name
total_size += param.numel() * param.element_size()
gc.collect()
torch.save(state_dict, os.path.join(target_model_path, file_name))
with open(
os.path.join(target_model_path, "pytorch_model.bin.index.json"), "w"
) as f:
json.dump(
{"weight_map": weight_map, "metadata": {"total_size": total_size}}, f
)
print(f"Saving the target model to {target_model_path}")
base_tokenizer.save_pretrained(target_model_path)
base_config.save_pretrained(target_model_path)
def apply_delta(base_model_path, target_model_path, delta_path):
print(f"Loading the delta weights from {delta_path}")
delta = AutoModelForCausalLM.from_pretrained(
delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
)
print(f"Loading the base model from {base_model_path}")
base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
base = AutoModelForCausalLM.from_pretrained(
base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
)
print("Applying the delta")
for name, param in tqdm(base.state_dict().items(), desc="Applying delta"):
assert name in delta.state_dict()
if name == 'model.embed_tokens.weight' or name == 'lm_head.weight':
param.data[:delta.state_dict()[name].shape[0], : ] += delta.state_dict()[name]
continue
param.data += delta.state_dict()[name]
print(f"Saving the target model to {target_model_path}")
base.save_pretrained(target_model_path)
base_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)
parser.add_argument(
"--low-cpu-mem",
action="store_true",
help="Lower the cpu memory usage. This will split large files and use "
"disk as swap to reduce the memory usage below 10GB.",
)
args = parser.parse_args()
if args.low_cpu_mem:
apply_delta_low_cpu_mem(
args.delta_path, args.target_model_path, args.base_model_path
)
else:
apply_delta(args.delta_path, args.target_model_path, args.base_model_path)