80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
from inspect import BoundArguments
|
|
|
|
import torch
|
|
|
|
from .types import LayerReloadingInfo, LayerTensors
|
|
|
|
__all__ = [
|
|
"get_layer_tensors",
|
|
"get_layer_params_buffers",
|
|
"get_layer_size",
|
|
"has_device_tensors",
|
|
"get_info_size",
|
|
]
|
|
|
|
|
|
def get_layer_tensors(layer: torch.nn.Module) -> dict[str, torch.Tensor]:
|
|
"""Get all parameters and buffers from a module as a dict."""
|
|
params, buffers = get_layer_params_buffers(layer)
|
|
return params | buffers
|
|
|
|
|
|
def get_layer_params_buffers(layer: torch.nn.Module) -> LayerTensors:
|
|
"""Get all parameters and buffers of a module as a tuple of dicts."""
|
|
return (
|
|
{name: param for name, param in layer._parameters.items() if param is not None},
|
|
{name: buffer for name, buffer in layer._buffers.items() if buffer is not None},
|
|
)
|
|
|
|
|
|
def get_layer_size(layer: torch.nn.Module) -> int:
|
|
"""Calculate total number of elements across loadable tensors in a layer.
|
|
|
|
Excludes SKIP_TENSORS (e.g. _expert_map) which are never moved to meta
|
|
device and never loaded via weight_loader during layerwise reload.
|
|
"""
|
|
from .meta import SKIP_TENSORS
|
|
|
|
return sum(
|
|
tensor.numel()
|
|
for name, tensor in get_layer_tensors(layer).items()
|
|
if name not in SKIP_TENSORS
|
|
)
|
|
|
|
|
|
def has_device_tensors(bound_args: BoundArguments) -> bool:
|
|
"""
|
|
Return True if the loaded weights exist on an accelerator device
|
|
|
|
Args:
|
|
bound_args: args to load weights
|
|
|
|
Returns:
|
|
True if weights are on accelerator device
|
|
"""
|
|
return any(
|
|
isinstance(value, torch.Tensor) and value.device.type not in ("meta", "cpu")
|
|
for value in bound_args.arguments.values()
|
|
)
|
|
|
|
|
|
def get_info_size(info: LayerReloadingInfo) -> int:
|
|
"""
|
|
Calculate the number of bytes used by loaded weights for a given layer
|
|
|
|
Args:
|
|
info: layerwise info to get size of
|
|
|
|
Returns:
|
|
number of bytes used by loaded weights
|
|
"""
|
|
return sum(
|
|
value.nbytes
|
|
for _, args in info.loaded_weights
|
|
for value in args.arguments.values()
|
|
if isinstance(value, torch.Tensor) and value.device.type not in ("meta", "cpu")
|
|
)
|