123 lines
3.9 KiB
Python
123 lines
3.9 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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import pytest
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import torch
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from torch import nn
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from pytorch_lightning import Trainer
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from pytorch_lightning.trainer.states import TrainerState
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from pytorch_lightning.utilities import _TPU_AVAILABLE
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.utils import pl_multi_process_test
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class WeightSharingModule(BoringModel):
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def __init__(self):
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super().__init__()
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self.layer_1 = nn.Linear(32, 10, bias=False)
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self.layer_2 = nn.Linear(10, 32, bias=False)
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self.layer_3 = nn.Linear(32, 10, bias=False)
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self.layer_3.weight = self.layer_1.weight
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def forward(self, x):
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x = self.layer_1(x)
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x = self.layer_2(x)
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x = self.layer_3(x)
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return x
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@pytest.mark.skipif(not _TPU_AVAILABLE, 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(
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checkpoint_callback=True,
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max_epochs=1,
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tpu_cores=8,
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)
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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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trainer.fit(model)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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@pytest.mark.skipif(not _TPU_AVAILABLE, 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(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True)
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trainer.fit(model)
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assert trainer.test(model) == 1
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_weight_tying_warning(tmpdir, capsys=None):
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"""
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Ensure a warning is thrown if model parameter lengths do not match
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post moving to device.
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"""
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model = WeightSharingModule()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
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with pytest.warns(UserWarning, match=r'The model layers do not match after moving to the target device.'):
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result = trainer.fit(model)
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assert result
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_if_weights_tied(tmpdir, capsys=None):
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"""
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Test if weights are properly tied on `on_post_move_to_device`.
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Ensure no warning for parameter mismatch is thrown.
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"""
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class Model(WeightSharingModule):
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def on_post_move_to_device(self):
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self.layer_3.weight = self.layer_1.weight
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model = Model()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
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with pytest.warns(UserWarning) as warnings:
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result = trainer.fit(model)
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assert result
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assert not list(filter(lambda x: 'The model layers do not match' in str(x), warnings.list))
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assert trainer.test(model) == 1
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