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https://github.com/hiyouga/LLaMA-Factory.git
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c4bbac49b2
Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
77 lines
2.6 KiB
Python
77 lines
2.6 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 DCP checkpoint to HuggingFace model format.
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Usage:
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python scripts/dcp2hf.py convert --dcp_path=/path/to/dcp --hf_path=/path/to/hf --config_path=/path/to/config
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Arguments:
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dcp_path: Path to the DCP checkpoint directory.
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hf_path: Output path (directory) for HuggingFace model.
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config_path: Path to the HuggingFace model directory containing config.json.
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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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import transformers
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from transformers import AutoConfig
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def convert(dcp_path: str, hf_path: str, config_path: str) -> None:
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"""Convert DCP model weights to HF.
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Note: this script is used to convert a DCP checkpoint to HuggingFace model format,
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it will just convert the DCP checkpoint to a HuggingFace model format, for the tokenizer,
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you may need to copy from the original model.
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Args:
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dcp_path: DCP checkpoint directory.
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hf_path: Output path (directory) for HuggingFace model.
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config_path: Path to the HuggingFace model directory containing config.json.
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"""
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if not dcp_path or not hf_path or not config_path:
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raise ValueError("All 'dcp_path', 'hf_path', and 'config_path' are required.")
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print(f"Loading config from {config_path}...")
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config = AutoConfig.from_pretrained(config_path)
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architectures = getattr(config, "architectures", [])
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if architectures:
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model_cls = getattr(transformers, architectures[0], transformers.AutoModelForCausalLM)
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else:
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model_cls = transformers.AutoModelForCausalLM
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print("Initializing model on CPU...")
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model = model_cls(config).to(torch.bfloat16)
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print(f"Loading DCP from {dcp_path}...")
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state_dict = model.state_dict()
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dcp.load(state_dict, checkpoint_id=dcp_path)
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model.load_state_dict(state_dict)
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print(f"Saving to HF format at {hf_path}...")
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model.save_pretrained(hf_path)
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config.save_pretrained(hf_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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