48 lines
2.1 KiB
Python
48 lines
2.1 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 pytest
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import torch
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from pytorch_lightning.utilities.apply_func import move_data_to_device
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from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
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from tests.helpers.runif import RunIf
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from tests.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
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@pytest.mark.parametrize("include_lengths", [False, True])
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@pytest.mark.parametrize("device", [torch.device("cuda", 0)])
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@pytest.mark.skipif(not _TORCHTEXT_LEGACY, reason="torchtext.legacy is deprecated.")
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@RunIf(min_gpus=1)
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def test_batch_move_data_to_device_torchtext_include_lengths(include_lengths, device):
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data_iterator, _ = get_dummy_torchtext_data_iterator(num_samples=3, batch_size=3, include_lengths=include_lengths)
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data_iter = iter(data_iterator)
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batch = next(data_iter)
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with pytest.deprecated_call(match="The `torchtext.legacy.Batch` object is deprecated"):
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batch_on_device = move_data_to_device(batch, device)
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if include_lengths:
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# tensor with data
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assert batch_on_device.text[0].device == device
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# tensor with length of data
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assert batch_on_device.text[1].device == device
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else:
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assert batch_on_device.text.device == device
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@pytest.mark.parametrize("include_lengths", [False, True])
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@pytest.mark.skipif(not _TORCHTEXT_LEGACY, reason="torchtext.legacy is deprecated.")
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def test_batch_move_data_to_device_torchtext_include_lengths_cpu(include_lengths):
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test_batch_move_data_to_device_torchtext_include_lengths(include_lengths, torch.device("cpu"))
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