72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from unittest import mock
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import torch
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from torch.nn.parallel import DistributedDataParallel
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from pytorch_lightning import Trainer
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from pytorch_lightning.plugins import DDPPlugin
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.runif import RunIf
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class BoringModelGPU(BoringModel):
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def on_train_start(self) -> None:
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# make sure that the model is on GPU when training
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assert self.device == torch.device(f"cuda:{self.trainer.training_type_plugin.local_rank}")
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self.start_cuda_memory = torch.cuda.memory_allocated()
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@RunIf(skip_windows=True, min_gpus=2, special=True)
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def test_ddp_with_2_gpus():
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"""Tests if device is set correctely when training and after teardown for DDPPlugin."""
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trainer = Trainer(gpus=2, accelerator="ddp", fast_dev_run=True)
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# assert training type plugin attributes for device setting
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assert isinstance(trainer.training_type_plugin, DDPPlugin)
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assert trainer.training_type_plugin.on_gpu
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assert not trainer.training_type_plugin.on_tpu
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local_rank = trainer.training_type_plugin.local_rank
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assert trainer.training_type_plugin.root_device == torch.device(f"cuda:{local_rank}")
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model = BoringModelGPU()
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trainer.fit(model)
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# assert after training, model is moved to CPU and memory is deallocated
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assert model.device == torch.device("cpu")
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cuda_memory = torch.cuda.memory_allocated()
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assert cuda_memory < model.start_cuda_memory
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class BarrierModel(BoringModel):
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def setup(self, stage=None):
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assert not isinstance(self.trainer.accelerator.model, DistributedDataParallel)
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self.trainer.accelerator.barrier("barrier before model is wrapped")
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def on_train_start(self):
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assert isinstance(self.trainer.accelerator.model, DistributedDataParallel)
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self.trainer.accelerator.barrier("barrier after model is wrapped")
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@RunIf(min_gpus=4, special=True)
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@mock.patch("torch.distributed.barrier")
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def test_ddp_barrier_non_consecutive_device_ids(barrier_mock, tmpdir):
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"""Test correct usage of barriers when device ids do not start at 0 or are not consecutive."""
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model = BoringModel()
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gpus = [1, 3]
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, gpus=gpus, accelerator="ddp")
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trainer.fit(model)
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barrier_mock.assert_any_call(device_ids=[gpus[trainer.local_rank]])
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