wehub-resource-sync
41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# DeepSpeed Team
|
|
|
|
from typing import Iterable, Tuple
|
|
import torch
|
|
|
|
from .base_engine import CheckpointEngineBase
|
|
|
|
|
|
class InMemoryModelEngine(CheckpointEngineBase):
|
|
"""
|
|
This "checkpoint" engine uses the existing interface to enable loading parameters into an
|
|
inference model from a model already instantiated in memory. In general, this is not the
|
|
recommended way to use the inference engine, and should only be used when absolutely necessary.
|
|
|
|
The primary limitation of this approach is that the model must be fully instantiated in memory.
|
|
In a tensor parallel scenario, this means that the model is either replicated many times in host
|
|
memory. Currently, it is also recommended to only use this approach for models held in host memory.
|
|
|
|
In order to free the memory held by this copy of the model, we delete the model in the first call
|
|
to `parameters`, so it is not safe to make this call twice.
|
|
"""
|
|
|
|
def __init__(self, model: torch.nn.Module) -> None:
|
|
"""
|
|
Create virtual checkpoint engine for the provided module.
|
|
|
|
Args:
|
|
model (torch.nn.Module): Model to load parameters from.
|
|
"""
|
|
super().__init__()
|
|
self.model = model
|
|
|
|
def parameters(self) -> Iterable[Tuple[str, torch.Tensor]]:
|
|
for name, parameter in self.model.named_parameters():
|
|
yield name, parameter
|
|
|
|
del self.model
|