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| """ | |
| https://github.com/lm-sys/FastChat/blob/main/fastchat/model/apply_delta.py | |
| Apply the delta weights on top of a base model. | |
| Usage: | |
| python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta-v1.1 | |
| """ | |
| 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): | |
| delta_tokenizer = AutoTokenizer.from_pretrained(delta_path, use_fast=False) | |
| delta_config = AutoConfig.from_pretrained(delta_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 | |
| 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}") | |
| delta_tokenizer.save_pretrained(target_model_path) | |
| delta_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_tokenizer = AutoTokenizer.from_pretrained(delta_path, use_fast=False) | |
| 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 = 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() | |
| 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) | |
| 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.base_model_path, args.target_model_path, args.delta_path) | |
| else: | |
| apply_delta(args.base_model_path, args.target_model_path, args.delta_path) | |