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f9f11dcb97
Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Convert a HuggingFace model to DCP checkpoint format.
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Usage:
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python scripts/hf2dcp.py convert --hf_path=/path/to/hf --dcp_path=/path/to/dcp
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Arguments:
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hf_path: Path to the HuggingFace model directory.
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dcp_path: Output path (directory) for DCP checkpoint.
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"""
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import fire
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import torch
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import torch.distributed.checkpoint as dcp
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from transformers import AutoModelForCausalLM
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def convert(hf_path: str, dcp_path: str) -> None:
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"""Convert HF model weights to DCP.
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Args:
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hf_path: HuggingFace model directory.
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dcp_path: Output path (directory) for DCP checkpoint.
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"""
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if not hf_path or not dcp_path:
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raise ValueError("Both 'hf_path' and 'dcp_path' are required.")
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print(f"Loading HF model from {hf_path}...")
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model = AutoModelForCausalLM.from_pretrained(hf_path, device_map="cpu", torch_dtype=torch.bfloat16)
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print(f"Saving to DCP format at {dcp_path}...")
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dcp.save(model.state_dict(), checkpoint_id=dcp_path)
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print("Done!")
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def help() -> None:
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"""Show help message."""
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print(__doc__)
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if __name__ == "__main__":
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fire.Fire({"convert": convert, "help": help, "--convert": convert})
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