64 lines
2.1 KiB
Python
64 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 import Trainer
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from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils
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from tests.base.boring_model import BoringModel
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from tests.base.develop_utils import pl_multi_process_test
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@pytest.mark.skipif(not XLADeviceUtils.tpu_device_exists(), reason="test requires TPU machine")
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@pl_multi_process_test
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def test_resume_training_on_cpu(tmpdir):
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""" Checks if training can be resumed from a saved checkpoint on CPU"""
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# Train a model on TPU
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model = BoringModel()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=8,)
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trainer.fit(model)
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model_path = trainer.checkpoint_callback.best_model_path
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# Verify saved Tensors are on CPU
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ckpt = torch.load(model_path)
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weight_tensor = list(ckpt["state_dict"].values())[0]
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assert weight_tensor.device == torch.device("cpu")
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# Verify that training is resumed on CPU
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trainer = Trainer(
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resume_from_checkpoint=model_path,
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checkpoint_callback=True,
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max_epochs=1,
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default_root_dir=tmpdir,
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)
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result = trainer.fit(model)
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assert result == 1
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@pytest.mark.skipif(not XLADeviceUtils.tpu_device_exists(), reason="test requires TPU machine")
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@pl_multi_process_test
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def test_if_test_works_after_train(tmpdir):
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""" Ensure that .test() works after .fit() """
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# Train a model on TPU
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model = BoringModel()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=8, default_root_dir=tmpdir)
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trainer.fit(model)
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assert trainer.test() == 1
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