56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
# Copyright The Lightning AI team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
from lightning.fabric import Fabric
|
|
|
|
from tests_fabric.helpers.runif import RunIf
|
|
|
|
|
|
class SimpleModel(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer = nn.Linear(2, 2)
|
|
self.tied_layer = nn.Linear(2, 2)
|
|
self.tied_layer.weight = self.layer.weight
|
|
self.register_buffer("buffer", torch.ones(3))
|
|
|
|
|
|
@pytest.mark.parametrize("strategy", ["ddp_spawn", pytest.param("ddp_fork", marks=RunIf(skip_windows=True))])
|
|
def test_memory_sharing_disabled(strategy):
|
|
"""Test that the multiprocessing launcher disables memory sharing on model parameters and buffers to avoid race
|
|
conditions on model updates."""
|
|
tensor = torch.rand(4)
|
|
model = SimpleModel()
|
|
assert not tensor.is_shared()
|
|
assert not model.layer.weight.is_shared()
|
|
assert model.layer.weight.data_ptr() == model.tied_layer.weight.data_ptr()
|
|
|
|
fabric = Fabric(accelerator="cpu", devices=2, strategy=strategy)
|
|
fabric.launch(_test_memory_sharing_disabled, tensor, model)
|
|
|
|
|
|
def _test_memory_sharing_disabled(fabric, tensor, model):
|
|
is_spawn = fabric.strategy.launcher._start_method == "spawn"
|
|
assert not is_spawn or tensor.is_shared()
|
|
assert not model.layer.weight.is_shared()
|
|
assert not model.tied_layer.weight.is_shared()
|
|
assert not model.buffer.is_shared()
|
|
|
|
# weights remain tied
|
|
assert model.layer.weight.data_ptr() == model.tied_layer.weight.data_ptr()
|
|
assert torch.equal(model.layer.weight.data, model.tied_layer.weight.data)
|
|
fabric.barrier()
|