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# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import argparse
import io
import os
import tempfile
from datetime import timedelta
import torch
import torch.serialization
from torch.distributed.checkpoint.format_utils import dcp_to_torch_save
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
import fla # noqa
from torchtitan.tools.logging import init_logger, logger
@torch.inference_mode()
def save_pretrained(
path: str,
step: int,
config: str,
tokenizer: str
):
logger.info(f"Loading the config from {config}")
config = AutoConfig.from_pretrained(config, trust_remote_code=True)
logger.info(f"Saving the config to {path}")
config.save_pretrained(path)
logger.info(f"Loading the tokenizer from {tokenizer}")
tokenizer = AutoTokenizer.from_pretrained(tokenizer, trust_remote_code=True)
logger.info(f"Saving the tokenizer to {path}")
tokenizer.save_pretrained(path)
with tempfile.TemporaryDirectory() as tmpdir:
# base_checkpoint_dir = os.path.dirname(path)
base_checkpoint_dir = path
checkpoint = os.path.join(base_checkpoint_dir, f'checkpoint/step-{step}')
checkpoint_path = os.path.join(tmpdir, 'checkpoint.pt')
logger.info(f"Saving the distributed checkpoint to {checkpoint_path}")
dcp_to_torch_save(checkpoint, checkpoint_path)
logger.info(f"Initializing the model from config\n{config}")
model = AutoModelForCausalLM.from_config(config)
logger.info(model)
logger.info("Loading state dict from the checkpoint")
# Add datetime.timedelta and io.BytesIO to safe globals
torch.serialization.add_safe_globals([timedelta, io.BytesIO])
# torch.load now with default weights_only=True will work
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu')['model'])
logger.info(f"Saving the model to {path}")
model.save_pretrained(path)
if __name__ == "__main__":
init_logger()
parser = argparse.ArgumentParser("Convert DCP format model weights to huggingface-style.")
parser.add_argument("--path", type=str, required=True)
parser.add_argument("--step", type=int, required=True)
parser.add_argument("--config", type=str, required=True)
parser.add_argument("--tokenizer", type=str, required=True)
args = parser.parse_args()
save_pretrained(args.path, args.step, args.config, args.tokenizer)
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