34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
import pytest
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import torch
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from tests.base import EvalModelTemplate
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from pytorch_lightning.core.decorators import auto_move_data
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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@pytest.mark.parametrize(['src_device', 'dest_device'], [
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pytest.param(torch.device('cpu'), torch.device('cpu')),
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pytest.param(torch.device('cpu', 0), torch.device('cuda', 0)),
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pytest.param(torch.device('cuda', 0), torch.device('cpu')),
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pytest.param(torch.device('cuda', 0), torch.device('cuda', 0)),
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])
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def test_auto_move_data(src_device, dest_device):
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""" Test that the decorator moves the data to the device the model is on. """
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class CurrentModel(EvalModelTemplate):
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pass
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# apply the decorator
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CurrentModel.forward = auto_move_data(CurrentModel.forward)
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model = CurrentModel()
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model = model.to(dest_device)
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model.prepare_data()
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loader = model.train_dataloader()
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x, y, = next(iter(loader))
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x = x.flatten(1)
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# test that data on source device gets moved to destination device
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x = x.to(src_device)
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assert model(x).device == dest_device, "Automoving data to same device as model failed"
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