677 lines
22 KiB
Python
677 lines
22 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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from unittest import mock
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from unittest.mock import PropertyMock
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import pytest
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import torch
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from torch.utils.data import DataLoader
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from pytorch_lightning import Trainer
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from tests.helpers import BoringDataModule, BoringModel, RandomDataset
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from tests.helpers.runif import RunIf
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@pytest.mark.parametrize('max_steps', [1, 2, 3])
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def test_on_before_zero_grad_called(tmpdir, max_steps):
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class CurrentTestModel(BoringModel):
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on_before_zero_grad_called = 0
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def on_before_zero_grad(self, optimizer):
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self.on_before_zero_grad_called += 1
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model = CurrentTestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_steps=max_steps,
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max_epochs=2,
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)
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assert 0 == model.on_before_zero_grad_called
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trainer.fit(model)
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assert max_steps == model.on_before_zero_grad_called
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model.on_before_zero_grad_called = 0
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trainer.test(model)
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assert 0 == model.on_before_zero_grad_called
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def test_training_epoch_end_metrics_collection(tmpdir):
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""" Test that progress bar metrics also get collected at the end of an epoch. """
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num_epochs = 3
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class CurrentModel(BoringModel):
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def training_step(self, *args, **kwargs):
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output = super().training_step(*args, **kwargs)
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self.log_dict({'step_metric': torch.tensor(-1), 'shared_metric': 100}, logger=False, prog_bar=True)
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return output
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def training_epoch_end(self, outputs):
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epoch = self.current_epoch
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# both scalar tensors and Python numbers are accepted
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self.log_dict(
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{
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f'epoch_metric_{epoch}': torch.tensor(epoch),
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'shared_metric': 111
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},
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logger=False,
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prog_bar=True,
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)
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model = CurrentModel()
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trainer = Trainer(
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max_epochs=num_epochs,
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default_root_dir=tmpdir,
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overfit_batches=2,
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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metrics = trainer.progress_bar_dict
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# metrics added in training step should be unchanged by epoch end method
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assert metrics['step_metric'] == -1
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# a metric shared in both methods gets overwritten by epoch_end
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assert metrics['shared_metric'] == 111
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# metrics are kept after each epoch
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for i in range(num_epochs):
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assert metrics[f'epoch_metric_{i}'] == i
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def test_training_epoch_end_metrics_collection_on_override(tmpdir):
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""" Test that batch end metrics are collected when training_epoch_end is overridden at the end of an epoch. """
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class OverriddenModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.len_outputs = 0
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def on_train_epoch_start(self):
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self.num_train_batches = 0
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def training_epoch_end(self, outputs):
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self.len_outputs = len(outputs)
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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self.num_train_batches += 1
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class NotOverriddenModel(BoringModel):
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def on_train_epoch_start(self):
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self.num_train_batches = 0
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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self.num_train_batches += 1
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overridden_model = OverriddenModel()
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not_overridden_model = NotOverriddenModel()
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not_overridden_model.training_epoch_end = None
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trainer = Trainer(
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max_epochs=1,
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default_root_dir=tmpdir,
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overfit_batches=2,
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)
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trainer.fit(overridden_model)
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assert overridden_model.len_outputs == overridden_model.num_train_batches
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@RunIf(min_gpus=1)
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@mock.patch("pytorch_lightning.accelerators.accelerator.Accelerator.lightning_module", new_callable=PropertyMock)
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def test_apply_batch_transfer_handler(model_getter_mock):
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expected_device = torch.device('cuda', 0)
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class CustomBatch:
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def __init__(self, data):
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self.samples = data[0]
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self.targets = data[1]
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class CurrentTestModel(BoringModel):
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rank = 0
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transfer_batch_to_device_hook_rank = None
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on_before_batch_transfer_hook_rank = None
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on_after_batch_transfer_hook_rank = None
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def on_before_batch_transfer(self, batch, dataloader_idx):
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assert dataloader_idx is None
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self.on_before_batch_transfer_hook_rank = self.rank
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self.rank += 1
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batch.samples += 1
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return batch
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def on_after_batch_transfer(self, batch, dataloader_idx):
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assert dataloader_idx is None
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assert batch.samples.device == batch.targets.device == expected_device
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self.on_after_batch_transfer_hook_rank = self.rank
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self.rank += 1
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batch.targets *= 2
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return batch
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def transfer_batch_to_device(self, batch, device, dataloader_idx):
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assert dataloader_idx is None
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self.transfer_batch_to_device_hook_rank = self.rank
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self.rank += 1
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batch.samples = batch.samples.to(device)
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batch.targets = batch.targets.to(device)
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return batch
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model = CurrentTestModel()
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batch = CustomBatch((torch.zeros(5, 32), torch.ones(5, 1, dtype=torch.long)))
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trainer = Trainer(gpus=1)
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# running .fit() would require us to implement custom data loaders, we mock the model reference instead
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model_getter_mock.return_value = model
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batch_gpu = trainer.accelerator.batch_to_device(batch, expected_device)
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assert model.on_before_batch_transfer_hook_rank == 0
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assert model.transfer_batch_to_device_hook_rank == 1
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assert model.on_after_batch_transfer_hook_rank == 2
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assert batch_gpu.samples.device == batch_gpu.targets.device == expected_device
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assert torch.allclose(batch_gpu.samples.cpu(), torch.ones(5, 32))
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assert torch.allclose(batch_gpu.targets.cpu(), torch.ones(5, 1, dtype=torch.long) * 2)
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@RunIf(min_gpus=2, special=True)
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def test_transfer_batch_hook_ddp(tmpdir):
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"""
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Test custom data are properly moved to the right device using ddp
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"""
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class CustomBatch:
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def __init__(self, data):
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self.samples = data[0]
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def to(self, device, **kwargs):
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self.samples = self.samples.to(device, **kwargs)
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return self
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def collate_fn(batch):
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return CustomBatch(batch)
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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assert batch.samples.device == self.device
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assert isinstance(batch_idx, int)
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def train_dataloader(self):
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return torch.utils.data.DataLoader(RandomDataset(32, 64), collate_fn=collate_fn)
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model = TestModel()
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model.validation_step = None
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model.training_epoch_end = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=0,
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max_epochs=1,
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weights_summary=None,
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accelerator="ddp",
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gpus=2,
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)
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trainer.fit(model)
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@pytest.mark.parametrize('max_epochs,batch_idx_', [(2, 5), (3, 8), (4, 12)])
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def test_on_train_batch_start_hook(max_epochs, batch_idx_):
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class CurrentModel(BoringModel):
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def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
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if batch_idx == batch_idx_:
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return -1
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model = CurrentModel()
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trainer = Trainer(max_epochs=max_epochs)
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trainer.fit(model)
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if batch_idx_ > len(model.val_dataloader()) - 1:
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assert trainer.train_loop.batch_idx == len(model.val_dataloader()) - 1
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assert trainer.global_step == len(model.val_dataloader()) * max_epochs
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else:
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assert trainer.train_loop.batch_idx == batch_idx_
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assert trainer.global_step == (batch_idx_ + 1) * max_epochs
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def test_trainer_model_hook_system(tmpdir):
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"""Test the LightningModule hook system."""
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class HookedModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.called = []
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def on_after_backward(self):
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self.called.append("on_after_backward")
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super().on_after_backward()
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def on_before_zero_grad(self, *args, **kwargs):
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self.called.append("on_before_zero_grad")
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super().on_before_zero_grad(*args, **kwargs)
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def on_epoch_start(self):
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self.called.append("on_epoch_start")
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super().on_epoch_start()
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def on_epoch_end(self):
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self.called.append("on_epoch_end")
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super().on_epoch_end()
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def on_fit_start(self):
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self.called.append("on_fit_start")
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super().on_fit_start()
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def on_fit_end(self):
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self.called.append("on_fit_end")
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super().on_fit_end()
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def on_hpc_load(self, *args, **kwargs):
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self.called.append("on_hpc_load")
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super().on_hpc_load(*args, **kwargs)
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def on_hpc_save(self, *args, **kwargs):
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self.called.append("on_hpc_save")
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super().on_hpc_save(*args, **kwargs)
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def on_load_checkpoint(self, *args, **kwargs):
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self.called.append("on_load_checkpoint")
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super().on_load_checkpoint(*args, **kwargs)
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def on_save_checkpoint(self, *args, **kwargs):
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self.called.append("on_save_checkpoint")
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super().on_save_checkpoint(*args, **kwargs)
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def on_pretrain_routine_start(self):
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self.called.append("on_pretrain_routine_start")
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super().on_pretrain_routine_start()
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def on_pretrain_routine_end(self):
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self.called.append("on_pretrain_routine_end")
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super().on_pretrain_routine_end()
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def on_train_start(self):
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self.called.append("on_train_start")
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super().on_train_start()
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def on_train_end(self):
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self.called.append("on_train_end")
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super().on_train_end()
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def on_before_batch_transfer(self, *args, **kwargs):
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self.called.append("on_before_batch_transfer")
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return super().on_before_batch_transfer(*args, **kwargs)
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def transfer_batch_to_device(self, *args, **kwargs):
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self.called.append("transfer_batch_to_device")
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return super().transfer_batch_to_device(*args, **kwargs)
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def on_after_batch_transfer(self, *args, **kwargs):
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self.called.append("on_after_batch_transfer")
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return super().on_after_batch_transfer(*args, **kwargs)
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def on_train_batch_start(self, *args, **kwargs):
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self.called.append("on_train_batch_start")
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super().on_train_batch_start(*args, **kwargs)
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def on_train_batch_end(self, *args, **kwargs):
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self.called.append("on_train_batch_end")
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super().on_train_batch_end(*args, **kwargs)
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def on_train_epoch_start(self):
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self.called.append("on_train_epoch_start")
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super().on_train_epoch_start()
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def on_train_epoch_end(self):
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self.called.append("on_train_epoch_end")
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super().on_train_epoch_end()
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def on_validation_start(self):
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self.called.append("on_validation_start")
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super().on_validation_start()
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def on_validation_end(self):
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self.called.append("on_validation_end")
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super().on_validation_end()
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def on_validation_batch_start(self, *args, **kwargs):
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self.called.append("on_validation_batch_start")
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super().on_validation_batch_start(*args, **kwargs)
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def on_validation_batch_end(self, *args, **kwargs):
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self.called.append("on_validation_batch_end")
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super().on_validation_batch_end(*args, **kwargs)
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def on_validation_epoch_start(self):
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self.called.append("on_validation_epoch_start")
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super().on_validation_epoch_start()
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def on_validation_epoch_end(self, *args, **kwargs):
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self.called.append("on_validation_epoch_end")
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super().on_validation_epoch_end(*args, **kwargs)
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def on_test_start(self):
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self.called.append("on_test_start")
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super().on_test_start()
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def on_test_batch_start(self, *args, **kwargs):
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self.called.append("on_test_batch_start")
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super().on_test_batch_start(*args, **kwargs)
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def on_test_batch_end(self, *args, **kwargs):
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self.called.append("on_test_batch_end")
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super().on_test_batch_end(*args, **kwargs)
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def on_test_epoch_start(self):
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self.called.append("on_test_epoch_start")
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super().on_test_epoch_start()
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def on_test_epoch_end(self, *args, **kwargs):
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self.called.append("on_test_epoch_end")
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super().on_test_epoch_end(*args, **kwargs)
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def on_validation_model_eval(self):
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self.called.append("on_validation_model_eval")
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super().on_validation_model_eval()
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def on_validation_model_train(self):
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self.called.append("on_validation_model_train")
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super().on_validation_model_train()
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def on_test_model_eval(self):
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self.called.append("on_test_model_eval")
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super().on_test_model_eval()
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def on_test_model_train(self):
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self.called.append("on_test_model_train")
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super().on_test_model_train()
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def on_test_end(self):
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self.called.append("on_test_end")
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super().on_test_end()
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def setup(self, stage=None):
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self.called.append(f"setup_{stage}")
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super().setup(stage=stage)
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def teardown(self, stage=None):
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self.called.append(f"teardown_{stage}")
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super().teardown(stage)
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model = HookedModel()
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_val_batches=1,
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limit_train_batches=2,
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limit_test_batches=1,
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progress_bar_refresh_rate=0,
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weights_summary=None,
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)
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assert model.called == []
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trainer.fit(model)
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expected = [
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'setup_fit',
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'on_fit_start',
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'on_pretrain_routine_start',
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'on_pretrain_routine_end',
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'on_validation_model_eval',
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'on_validation_start',
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'on_epoch_start',
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'on_validation_epoch_start',
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'on_validation_batch_start',
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'on_before_batch_transfer',
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'transfer_batch_to_device',
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'on_after_batch_transfer',
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'on_validation_batch_end',
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'on_validation_epoch_end',
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'on_epoch_end',
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'on_validation_end',
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'on_validation_model_train',
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'on_train_start',
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'on_epoch_start',
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'on_train_epoch_start',
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'on_train_batch_start',
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'on_before_batch_transfer',
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'transfer_batch_to_device',
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'on_after_batch_transfer',
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'on_before_zero_grad',
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'on_after_backward',
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'on_train_batch_end',
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'on_train_batch_start',
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'on_before_batch_transfer',
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'transfer_batch_to_device',
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'on_after_batch_transfer',
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'on_before_zero_grad',
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'on_after_backward',
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'on_train_batch_end',
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'on_train_epoch_end',
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'on_epoch_end',
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'on_validation_model_eval',
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'on_validation_start',
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'on_epoch_start',
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'on_validation_epoch_start',
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'on_validation_batch_start',
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'on_before_batch_transfer',
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'transfer_batch_to_device',
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'on_after_batch_transfer',
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'on_validation_batch_end',
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'on_validation_epoch_end',
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'on_epoch_end',
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'on_save_checkpoint',
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'on_validation_end',
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'on_validation_model_train',
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'on_train_end',
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'on_fit_end',
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'teardown_fit',
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]
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assert model.called == expected
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model = HookedModel()
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trainer.validate(model, verbose=False)
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expected = [
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'setup_validate',
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'on_validation_model_eval',
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'on_validation_start',
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'on_epoch_start',
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'on_validation_epoch_start',
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'on_validation_batch_start',
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'on_before_batch_transfer',
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'transfer_batch_to_device',
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|
'on_after_batch_transfer',
|
|
'on_validation_batch_end',
|
|
'on_validation_epoch_end',
|
|
'on_epoch_end',
|
|
'on_validation_end',
|
|
'on_validation_model_train',
|
|
'teardown_validate',
|
|
]
|
|
assert model.called == expected
|
|
|
|
model = HookedModel()
|
|
trainer.test(model, verbose=False)
|
|
|
|
expected = [
|
|
'setup_test',
|
|
'on_test_model_eval',
|
|
'on_test_start',
|
|
'on_epoch_start',
|
|
'on_test_epoch_start',
|
|
'on_test_batch_start',
|
|
'on_before_batch_transfer',
|
|
'transfer_batch_to_device',
|
|
'on_after_batch_transfer',
|
|
'on_test_batch_end',
|
|
'on_test_epoch_end',
|
|
'on_epoch_end',
|
|
'on_test_end',
|
|
'on_test_model_train',
|
|
'teardown_test',
|
|
]
|
|
assert model.called == expected
|
|
|
|
|
|
def test_hooks_with_different_argument_names(tmpdir):
|
|
"""
|
|
Test that argument names can be anything in the hooks
|
|
"""
|
|
|
|
class CustomBoringModel(BoringModel):
|
|
|
|
def assert_args(self, x, batch_nb):
|
|
assert isinstance(x, torch.Tensor)
|
|
assert x.size() == (1, 32)
|
|
assert isinstance(batch_nb, int)
|
|
|
|
def training_step(self, x1, batch_nb1):
|
|
self.assert_args(x1, batch_nb1)
|
|
return super().training_step(x1, batch_nb1)
|
|
|
|
def validation_step(self, x2, batch_nb2):
|
|
self.assert_args(x2, batch_nb2)
|
|
return super().validation_step(x2, batch_nb2)
|
|
|
|
def test_step(self, x3, batch_nb3, dl_idx3):
|
|
self.assert_args(x3, batch_nb3)
|
|
assert isinstance(dl_idx3, int)
|
|
return super().test_step(x3, batch_nb3)
|
|
|
|
def predict(self, x4, batch_nb4, dl_idx4):
|
|
self.assert_args(x4, batch_nb4)
|
|
assert isinstance(dl_idx4, int)
|
|
return super().predict(x4, batch_nb4, dl_idx4)
|
|
|
|
def test_dataloader(self):
|
|
return [DataLoader(RandomDataset(32, 64)), DataLoader(RandomDataset(32, 64))]
|
|
|
|
def predict_dataloader(self):
|
|
return [DataLoader(RandomDataset(32, 64)), DataLoader(RandomDataset(32, 64))]
|
|
|
|
model = CustomBoringModel()
|
|
model.test_epoch_end = None
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
fast_dev_run=5,
|
|
)
|
|
|
|
trainer.fit(model)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
trainer.test(ckpt_path=None)
|
|
|
|
preds = trainer.predict(model)
|
|
assert len(preds) == 2
|
|
assert all(len(x) == 5 for x in preds)
|
|
|
|
|
|
def test_trainer_datamodule_hook_system(tmpdir):
|
|
"""Test the LightningDataModule hook system."""
|
|
|
|
class HookedDataModule(BoringDataModule):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.called = []
|
|
|
|
def prepare_data(self):
|
|
self.called.append("prepare_data")
|
|
super().prepare_data()
|
|
|
|
def setup(self, stage=None):
|
|
self.called.append(f"setup_{stage}")
|
|
super().setup(stage=stage)
|
|
|
|
def teardown(self, stage=None):
|
|
self.called.append(f"teardown_{stage}")
|
|
super().teardown(stage=stage)
|
|
|
|
def train_dataloader(self):
|
|
self.called.append("train_dataloader")
|
|
return super().train_dataloader()
|
|
|
|
def test_dataloader(self):
|
|
self.called.append("test_dataloader")
|
|
return super().test_dataloader()
|
|
|
|
def val_dataloader(self):
|
|
self.called.append("val_dataloader")
|
|
return super().val_dataloader()
|
|
|
|
def predict_dataloader(self):
|
|
self.called.append("predict_dataloader")
|
|
|
|
def transfer_batch_to_device(self, *args, **kwargs):
|
|
self.called.append("transfer_batch_to_device")
|
|
return super().transfer_batch_to_device(*args, **kwargs)
|
|
|
|
def on_before_batch_transfer(self, *args, **kwargs):
|
|
self.called.append("on_before_batch_transfer")
|
|
return super().on_before_batch_transfer(*args, **kwargs)
|
|
|
|
def on_after_batch_transfer(self, *args, **kwargs):
|
|
self.called.append("on_after_batch_transfer")
|
|
return super().on_after_batch_transfer(*args, **kwargs)
|
|
|
|
model = BoringModel()
|
|
dm = HookedDataModule()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=1,
|
|
limit_val_batches=1,
|
|
limit_train_batches=2,
|
|
limit_test_batches=1,
|
|
progress_bar_refresh_rate=0,
|
|
weights_summary=None,
|
|
reload_dataloaders_every_epoch=True,
|
|
)
|
|
trainer.fit(model, datamodule=dm)
|
|
|
|
expected = [
|
|
'prepare_data', 'setup_fit', 'val_dataloader', 'on_before_batch_transfer', 'transfer_batch_to_device',
|
|
'on_after_batch_transfer', 'train_dataloader', 'on_before_batch_transfer', 'transfer_batch_to_device',
|
|
'on_after_batch_transfer', 'on_before_batch_transfer', 'transfer_batch_to_device', 'on_after_batch_transfer',
|
|
'val_dataloader', 'on_before_batch_transfer', 'transfer_batch_to_device', 'on_after_batch_transfer',
|
|
'teardown_fit'
|
|
]
|
|
assert dm.called == expected
|
|
|
|
dm = HookedDataModule()
|
|
trainer.validate(model, datamodule=dm, verbose=False)
|
|
|
|
expected = [
|
|
'prepare_data', 'setup_validate', 'val_dataloader', 'on_before_batch_transfer', 'transfer_batch_to_device',
|
|
'on_after_batch_transfer', 'teardown_validate'
|
|
]
|
|
assert dm.called == expected
|
|
|
|
dm = HookedDataModule()
|
|
trainer.test(model, datamodule=dm, verbose=False)
|
|
|
|
expected = [
|
|
'prepare_data', 'setup_test', 'test_dataloader', 'on_before_batch_transfer', 'transfer_batch_to_device',
|
|
'on_after_batch_transfer', 'teardown_test'
|
|
]
|
|
assert dm.called == expected
|