49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
# Copyright The PyTorch Lightning 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 torch
|
|
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.plugins import DDPPlugin
|
|
from tests.helpers.boring_model import BoringModel
|
|
from tests.helpers.runif import RunIf
|
|
|
|
|
|
class BoringModelGPU(BoringModel):
|
|
|
|
def on_train_start(self) -> None:
|
|
# make sure that the model is on GPU when training
|
|
assert self.device == torch.device(f"cuda:{self.trainer.training_type_plugin.local_rank}")
|
|
self.start_cuda_memory = torch.cuda.memory_allocated()
|
|
|
|
|
|
@RunIf(skip_windows=True, min_gpus=2, special=True)
|
|
def test_ddp_with_2_gpus():
|
|
"""Tests if device is set correctely when training and after teardown for DDPPlugin."""
|
|
trainer = Trainer(gpus=2, accelerator="ddp", fast_dev_run=True)
|
|
# assert training type plugin attributes for device setting
|
|
assert isinstance(trainer.training_type_plugin, DDPPlugin)
|
|
assert trainer.training_type_plugin.on_gpu
|
|
assert not trainer.training_type_plugin.on_tpu
|
|
local_rank = trainer.training_type_plugin.local_rank
|
|
assert trainer.training_type_plugin.root_device == torch.device(f"cuda:{local_rank}")
|
|
|
|
model = BoringModelGPU()
|
|
|
|
trainer.fit(model)
|
|
|
|
# assert after training, model is moved to CPU and memory is deallocated
|
|
assert model.device == torch.device("cpu")
|
|
cuda_memory = torch.cuda.memory_allocated()
|
|
assert cuda_memory < model.start_cuda_memory
|