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- **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>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Utils for model executor."""
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from typing import Any, Dict, Optional
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import torch
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def set_random_seed(seed: int) -> None:
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from vllm.platforms import current_platform
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current_platform.seed_everything(seed)
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def set_weight_attrs(
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weight: torch.Tensor,
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weight_attrs: Optional[Dict[str, Any]],
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):
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"""Set attributes on a weight tensor.
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This method is used to set attributes on a weight tensor. This method
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will not overwrite existing attributes.
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Args:
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weight: The weight tensor.
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weight_attrs: A dictionary of attributes to set on the weight tensor.
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"""
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if weight_attrs is None:
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return
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for key, value in weight_attrs.items():
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assert not hasattr(
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weight, key), (f"Overwriting existing tensor attribute: {key}")
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# NOTE(woosuk): During weight loading, we often do something like:
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# narrowed_tensor = param.data.narrow(0, offset, len)
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# narrowed_tensor.copy_(real_weight)
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# expecting narrowed_tensor and param.data to share the same storage.
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# However, on TPUs, narrowed_tensor will lazily propagate to the base
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# tensor, which is param.data, leading to the redundant memory usage.
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# This sometimes causes OOM errors during model loading. To avoid this,
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# we sync the param tensor after its weight loader is called.
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# TODO(woosuk): Remove this hack once we have a better solution.
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from vllm.platforms import current_platform
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if current_platform.is_tpu() and key == "weight_loader":
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value = _make_synced_weight_loader(value)
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setattr(weight, key, value)
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def _make_synced_weight_loader(original_weight_loader):
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def _synced_weight_loader(param, *args, **kwargs):
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original_weight_loader(param, *args, **kwargs)
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torch._sync(param)
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return _synced_weight_loader
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