42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from .types import LayerTensors
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__all__ = [
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"get_layer_tensors",
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"get_layer_params_buffers",
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"get_layer_size",
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]
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def get_layer_tensors(layer: torch.nn.Module) -> dict[str, torch.Tensor]:
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"""Get all parameters and buffers from a module as a dict."""
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params, buffers = get_layer_params_buffers(layer)
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return params | buffers
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def get_layer_params_buffers(layer: torch.nn.Module) -> LayerTensors:
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"""Get all parameters and buffers of a module as a tuple of dicts."""
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return (
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{name: param for name, param in layer._parameters.items() if param is not None},
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{name: buffer for name, buffer in layer._buffers.items() if buffer is not None},
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)
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def get_layer_size(layer: torch.nn.Module) -> int:
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"""Calculate total number of elements across loadable tensors in a layer.
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Excludes SKIP_TENSORS (e.g. _expert_map) which are never moved to meta
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device and never loaded via weight_loader during layerwise reload.
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"""
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from .meta import SKIP_TENSORS
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return sum(
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tensor.numel()
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for name, tensor in get_layer_tensors(layer).items()
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if name not in SKIP_TENSORS
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)
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