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b9e6920 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert a HuggingFace model to DCP checkpoint format.
Usage:
python scripts/hf2dcp.py convert --hf_path=/path/to/hf --dcp_path=/path/to/dcp
Arguments:
hf_path: Path to the HuggingFace model directory.
dcp_path: Output path (directory) for DCP checkpoint.
"""
import fire
import torch
import torch.distributed.checkpoint as dcp
from transformers import AutoModelForCausalLM
def convert(hf_path: str, dcp_path: str) -> None:
"""Convert HF model weights to DCP.
Args:
hf_path: HuggingFace model directory.
dcp_path: Output path (directory) for DCP checkpoint.
"""
if not hf_path or not dcp_path:
raise ValueError("Both 'hf_path' and 'dcp_path' are required.")
print(f"Loading HF model from {hf_path}...")
model = AutoModelForCausalLM.from_pretrained(hf_path, device_map="cpu", torch_dtype=torch.bfloat16)
print(f"Saving to DCP format at {dcp_path}...")
dcp.save(model.state_dict(), checkpoint_id=dcp_path)
print("Done!")
def help() -> None:
"""Show help message."""
print(__doc__)
if __name__ == "__main__":
fire.Fire({"convert": convert, "help": help, "--convert": convert})
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