80 lines
2.8 KiB
Python
80 lines
2.8 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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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.plugins import DDPSpawnPlugin
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from tests.helpers.boring_model import BoringDataModule, BoringModel
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from tests.helpers.runif import RunIf
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class BoringModelDDPCPU(BoringModel):
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def on_train_start(self) -> None:
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# make sure that the model is on CPU when training
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assert self.device == torch.device("cpu")
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class BoringCallbackDDPSpawnModel(BoringModel):
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def __init__(self, name: str, val: float):
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super().__init__()
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self.name = name
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self.val = val
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def validation_step(self, batch, batch_idx):
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self.log(self.name, self.val)
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return super().validation_step(batch, batch_idx)
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def add_to_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
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queue.put("test_val")
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return super().add_to_queue(queue)
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def get_from_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
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self.test_val = queue.get()
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return super().get_from_queue(queue)
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@RunIf(skip_windows=True)
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def test_ddp_cpu():
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"""Tests if device is set correctely when training for DDPSpawnPlugin."""
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trainer = Trainer(num_processes=2, 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, DDPSpawnPlugin)
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assert not trainer.training_type_plugin.on_gpu
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assert not trainer.training_type_plugin.on_tpu
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assert trainer.training_type_plugin.root_device == torch.device("cpu")
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model = BoringModelDDPCPU()
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trainer.fit(model)
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@RunIf(min_gpus=2)
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def test_ddp_spawn_extra_parameters(tmpdir):
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"""Tests if device is set correctely when training for DDPSpawnPlugin."""
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, accelerator="ddp_spawn")
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assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
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assert trainer.training_type_plugin.on_gpu
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assert trainer.training_type_plugin.root_device == torch.device("cuda:0")
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val: float = 1.0
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val_name: str = "val_acc"
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model = BoringCallbackDDPSpawnModel(val_name, val)
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dm = BoringDataModule()
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trainer.fit(model, datamodule=dm)
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assert trainer.callback_metrics[val_name] == torch.tensor(val)
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assert model.test_val == "test_val"
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