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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# code borrowed from: https://github.com/huggingface/peft/blob/v0.12.0/src/peft/utils/save_and_load.py#L420
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import os
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from typing import Optional
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import torch
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from huggingface_hub import file_exists, hf_hub_download
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from huggingface_hub.utils import EntryNotFoundError
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from safetensors.torch import load_file as safe_load_file
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from vllm.platforms import current_platform
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WEIGHTS_NAME = "adapter_model.bin"
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SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
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# Get current device name based on available devices
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def infer_device() -> str:
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if current_platform.is_cuda_alike():
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return "cuda"
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return "cpu"
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def load_peft_weights(model_id: str,
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device: Optional[str] = None,
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**hf_hub_download_kwargs) -> dict:
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r"""
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A helper method to load the PEFT weights from the HuggingFace Hub or locally
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Args:
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model_id (`str`):
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The local path to the adapter weights or the name of the adapter to
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load from the HuggingFace Hub.
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device (`str`):
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The device to load the weights onto.
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hf_hub_download_kwargs (`dict`):
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Additional arguments to pass to the `hf_hub_download` method when
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loading from the HuggingFace Hub.
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"""
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path = (os.path.join(model_id, hf_hub_download_kwargs["subfolder"]) if
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hf_hub_download_kwargs.get("subfolder") is not None else model_id)
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if device is None:
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device = infer_device()
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if os.path.exists(os.path.join(path, SAFETENSORS_WEIGHTS_NAME)):
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filename = os.path.join(path, SAFETENSORS_WEIGHTS_NAME)
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use_safetensors = True
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elif os.path.exists(os.path.join(path, WEIGHTS_NAME)):
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filename = os.path.join(path, WEIGHTS_NAME)
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use_safetensors = False
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else:
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token = hf_hub_download_kwargs.get("token")
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if token is None:
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token = hf_hub_download_kwargs.get("use_auth_token")
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hub_filename = (os.path.join(hf_hub_download_kwargs["subfolder"],
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SAFETENSORS_WEIGHTS_NAME)
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if hf_hub_download_kwargs.get("subfolder") is not None
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else SAFETENSORS_WEIGHTS_NAME)
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has_remote_safetensors_file = file_exists(
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repo_id=model_id,
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filename=hub_filename,
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revision=hf_hub_download_kwargs.get("revision"),
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repo_type=hf_hub_download_kwargs.get("repo_type"),
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token=token,
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)
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use_safetensors = has_remote_safetensors_file
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if has_remote_safetensors_file:
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# Priority 1: load safetensors weights
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filename = hf_hub_download(
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model_id,
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SAFETENSORS_WEIGHTS_NAME,
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**hf_hub_download_kwargs,
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)
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else:
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try:
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filename = hf_hub_download(model_id, WEIGHTS_NAME,
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**hf_hub_download_kwargs)
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except EntryNotFoundError:
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raise ValueError( # noqa: B904
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f"Can't find weights for {model_id} in {model_id} or \
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in the Hugging Face Hub. "
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f"Please check that the file {WEIGHTS_NAME} or \
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{SAFETENSORS_WEIGHTS_NAME} is present at {model_id}.")
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if use_safetensors:
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adapters_weights = safe_load_file(filename, device=device)
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else:
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adapters_weights = torch.load(filename,
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map_location=torch.device(device),
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weights_only=True)
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return adapters_weights
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