449 lines
16 KiB
Python
449 lines
16 KiB
Python
# Copyright The Lightning AI 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 call, Mock
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import pytest
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import torch
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from torch.utils.data.dataloader import DataLoader
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from torch.utils.data.sampler import BatchSampler, RandomSampler
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from lightning.fabric.accelerators.cuda import _clear_cuda_memory
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from lightning.pytorch import Trainer
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from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
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from tests_pytorch.helpers.runif import RunIf
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@mock.patch("lightning.pytorch.loops.evaluation_loop._EvaluationLoop._on_evaluation_epoch_end")
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def test_on_evaluation_epoch_end(eval_epoch_end_mock, tmpdir):
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"""Tests that `on_evaluation_epoch_end` is called for `on_validation_epoch_end` and `on_test_epoch_end`
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hooks."""
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, max_epochs=2, enable_model_summary=False
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)
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trainer.fit(model)
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# sanity + 2 epochs
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assert eval_epoch_end_mock.call_count == 3
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trainer.test()
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# sanity + 2 epochs + called once for test
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assert eval_epoch_end_mock.call_count == 4
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@pytest.mark.parametrize("use_batch_sampler", (False, True))
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def test_evaluation_loop_sampler_set_epoch_called(tmp_path, use_batch_sampler):
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"""Tests that set_epoch is called on the dataloader's sampler (if any) during training and validation."""
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def _get_dataloader():
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dataset = RandomDataset(32, 64)
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sampler = RandomSampler(dataset)
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sampler.set_epoch = Mock()
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if use_batch_sampler:
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batch_sampler = BatchSampler(sampler, 2, True)
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return DataLoader(dataset, batch_sampler=batch_sampler)
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return DataLoader(dataset, sampler=sampler)
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmp_path,
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limit_train_batches=1,
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limit_val_batches=1,
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max_epochs=2,
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enable_model_summary=False,
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enable_checkpointing=False,
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logger=False,
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)
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train_dataloader = _get_dataloader()
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val_dataloader = _get_dataloader()
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trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)
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train_sampler = train_dataloader.batch_sampler.sampler if use_batch_sampler else train_dataloader.sampler
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val_sampler = val_dataloader.batch_sampler.sampler if use_batch_sampler else val_dataloader.sampler
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# One for each epoch
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assert train_sampler.set_epoch.mock_calls == [call(0), call(1)]
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# One for each epoch + sanity check
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assert val_sampler.set_epoch.mock_calls == [call(0), call(0), call(1)]
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val_dataloader = _get_dataloader()
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trainer.validate(model, val_dataloader)
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val_sampler = val_dataloader.batch_sampler.sampler if use_batch_sampler else val_dataloader.sampler
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assert val_sampler.set_epoch.mock_calls == [call(2)]
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@mock.patch(
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"lightning.pytorch.trainer.connectors.logger_connector.logger_connector.LoggerConnector.log_eval_end_metrics"
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)
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def test_log_epoch_metrics_before_on_evaluation_end(update_eval_epoch_metrics_mock, tmpdir):
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"""Test that the epoch metrics are logged before the `on_evaluation_end` hook is fired."""
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order = []
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update_eval_epoch_metrics_mock.side_effect = lambda _: order.append("log_epoch_metrics")
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class LessBoringModel(BoringModel):
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def on_validation_end(self):
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order.append("on_validation_end")
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super().on_validation_end()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1, enable_model_summary=False, num_sanity_val_steps=0)
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trainer.fit(LessBoringModel())
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assert order == ["log_epoch_metrics", "on_validation_end"]
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@RunIf(min_cuda_gpus=1)
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def test_memory_consumption_validation(tmpdir):
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"""Test that the training batch is no longer in GPU memory when running validation.
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Cannot run with MPS, since there we can only measure shared memory and not dedicated, which device has how much
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memory allocated.
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"""
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def get_memory():
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_clear_cuda_memory()
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return torch.cuda.memory_allocated(0)
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initial_memory = get_memory()
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class BoringLargeBatchModel(BoringModel):
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@property
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def num_params(self):
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return sum(p.numel() for p in self.parameters())
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def train_dataloader(self):
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# batch target memory >= 100x boring_model size
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batch_size = self.num_params * 100 // 32 + 1
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return DataLoader(RandomDataset(32, 5000), batch_size=batch_size)
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def val_dataloader(self):
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return self.train_dataloader()
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def training_step(self, batch, batch_idx):
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# there is a batch and the boring model, but not two batches on gpu, assume 32 bit = 4 bytes
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lower = 101 * self.num_params * 4
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upper = 201 * self.num_params * 4
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current = get_memory()
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assert lower < current
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assert current - initial_memory < upper
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return super().training_step(batch, batch_idx)
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def validation_step(self, batch, batch_idx):
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# there is a batch and the boring model, but not two batches on gpu, assume 32 bit = 4 bytes
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lower = 101 * self.num_params * 4
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upper = 201 * self.num_params * 4
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current = get_memory()
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assert lower < current
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assert current - initial_memory < upper
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return super().validation_step(batch, batch_idx)
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_clear_cuda_memory()
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trainer = Trainer(
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accelerator="gpu",
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devices=1,
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default_root_dir=tmpdir,
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fast_dev_run=2,
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enable_model_summary=False,
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)
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trainer.fit(BoringLargeBatchModel())
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def test_evaluation_loop_dataloader_iter_multiple_dataloaders(tmp_path):
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trainer = Trainer(
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default_root_dir=tmp_path,
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limit_val_batches=1,
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enable_model_summary=False,
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enable_checkpointing=False,
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logger=False,
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)
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class MyModel(BoringModel):
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def validation_step(self, dataloader_iter, batch_idx, dataloader_idx=0):
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...
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model = MyModel()
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with pytest.raises(NotImplementedError, match="dataloader_iter.*is not supported with multiple dataloaders"):
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trainer.validate(model, {"a": [0, 1], "b": [2, 3]})
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def test_invalid_dataloader_idx_raises_step(tmp_path):
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trainer = Trainer(default_root_dir=tmp_path, fast_dev_run=True)
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class ExtraDataloaderIdx(BoringModel):
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def validation_step(self, batch, batch_idx, dataloader_idx):
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...
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def test_step(self, batch, batch_idx, dataloader_idx):
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...
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model = ExtraDataloaderIdx()
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with pytest.raises(RuntimeError, match="have included `dataloader_idx` in `ExtraDataloaderIdx.validation_step"):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="have included `dataloader_idx` in `ExtraDataloaderIdx.test_step"):
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trainer.test(model)
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class GoodDefault(BoringModel):
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def validation_step(self, batch, batch_idx, dataloader_idx=0):
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...
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def test_step(self, batch, batch_idx, dataloader_idx=0):
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...
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model = GoodDefault()
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trainer.validate(model)
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trainer.test(model)
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class ExtraDlIdxOtherName(BoringModel):
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def validation_step(self, batch, batch_idx, dl_idx):
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...
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def test_step(self, batch, batch_idx, dl_idx):
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...
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model = ExtraDlIdxOtherName()
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# different names are not supported
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with pytest.raises(TypeError, match="missing 1 required positional argument: 'dl_idx"):
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trainer.validate(model)
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with pytest.raises(TypeError, match="missing 1 required positional argument: 'dl_idx"):
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trainer.test(model)
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class MultipleDataloader(BoringModel):
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def val_dataloader(self):
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return [super().val_dataloader(), super().val_dataloader()]
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def test_dataloader(self):
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return [super().test_dataloader(), super().test_dataloader()]
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model = MultipleDataloader()
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `MultipleDataloader.validation_step"):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `MultipleDataloader.test_step"):
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trainer.test(model)
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class IgnoringModel(MultipleDataloader):
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def validation_step(self, batch, batch_idx, *_):
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...
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def test_step(self, batch, batch_idx, *_):
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...
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model = IgnoringModel()
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trainer.validate(model)
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trainer.test(model)
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class IgnoringModel2(MultipleDataloader):
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def validation_step(self, batch, batch_idx, **_):
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...
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def test_step(self, batch, batch_idx, **_):
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...
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model = IgnoringModel2()
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `IgnoringModel2.validation_step"):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `IgnoringModel2.test_step"):
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trainer.test(model)
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def test_invalid_dataloader_idx_raises_batch_start(tmp_path):
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trainer = Trainer(default_root_dir=tmp_path, fast_dev_run=True)
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class ExtraDataloaderIdx(BoringModel):
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def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
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...
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def on_test_batch_start(self, batch, batch_idx, dataloader_idx):
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...
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model = ExtraDataloaderIdx()
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with pytest.raises(
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RuntimeError, match="have included `dataloader_idx` in `ExtraDataloaderIdx.on_validation_batch_start"
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):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="have included `dataloader_idx` in `ExtraDataloaderIdx.on_test_batch_start"):
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trainer.test(model)
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class GoodDefault(BoringModel):
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def on_validation_batch_start(self, batch, batch_idx, dataloader_idx=0):
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...
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def on_test_batch_start(self, batch, batch_idx, dataloader_idx=0):
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...
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model = GoodDefault()
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trainer.validate(model)
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trainer.test(model)
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class ExtraDlIdxOtherName(BoringModel):
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def on_validation_batch_start(self, batch, batch_idx, dl_idx):
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...
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def on_test_batch_start(self, batch, batch_idx, dl_idx):
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...
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model = ExtraDlIdxOtherName()
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# different names are not supported
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with pytest.raises(TypeError, match="missing 1 required positional argument: 'dl_idx"):
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trainer.validate(model)
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with pytest.raises(TypeError, match="missing 1 required positional argument: 'dl_idx"):
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trainer.test(model)
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class MultipleDataloader(BoringModel):
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def validation_step(self, batch, batch_idx, dataloader_idx=0):
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...
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def test_step(self, batch, batch_idx, dataloader_idx=0):
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...
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def on_validation_batch_start(self, batch, batch_idx):
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...
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def on_test_batch_start(self, batch, batch_idx):
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...
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def val_dataloader(self):
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return [super().val_dataloader(), super().val_dataloader()]
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def test_dataloader(self):
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return [super().test_dataloader(), super().test_dataloader()]
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model = MultipleDataloader()
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with pytest.raises(
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RuntimeError, match="no `dataloader_idx` argument in `MultipleDataloader.on_validation_batch_start"
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):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `MultipleDataloader.on_test_batch_start"):
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trainer.test(model)
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class IgnoringModel(MultipleDataloader):
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def on_validation_batch_start(self, batch, batch_idx, *_):
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...
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def on_test_batch_start(self, batch, batch_idx, *_):
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...
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model = IgnoringModel()
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trainer.validate(model)
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trainer.test(model)
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class IgnoringModel2(MultipleDataloader):
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def on_validation_batch_start(self, batch, batch_idx, **_):
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...
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def on_test_batch_start(self, batch, batch_idx, **_):
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...
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model = IgnoringModel2()
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `IgnoringModel2.on_validation_batch_start"):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `IgnoringModel2.on_test_batch_start"):
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trainer.test(model)
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def test_invalid_dataloader_idx_raises_batch_end(tmp_path):
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trainer = Trainer(default_root_dir=tmp_path, fast_dev_run=True)
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class ExtraDataloaderIdx(BoringModel):
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def on_validation_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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...
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def on_test_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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...
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model = ExtraDataloaderIdx()
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with pytest.raises(
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RuntimeError, match="have included `dataloader_idx` in `ExtraDataloaderIdx.on_validation_batch_end"
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):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="have included `dataloader_idx` in `ExtraDataloaderIdx.on_test_batch_end"):
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trainer.test(model)
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class GoodDefault(BoringModel):
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def on_validation_batch_end(self, outputs, batch, batch_idx, dataloader_idx=0):
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...
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def on_test_batch_end(self, outputs, batch, batch_idx, dataloader_idx=0):
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...
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model = GoodDefault()
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trainer.validate(model)
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trainer.test(model)
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class ExtraDlIdxOtherName(BoringModel):
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def on_validation_batch_end(self, outputs, batch, batch_idx, dl_idx):
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...
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def on_test_batch_end(self, outputs, batch, batch_idx, dl_idx):
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...
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model = ExtraDlIdxOtherName()
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# different names are not supported
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with pytest.raises(TypeError, match="missing 1 required positional argument: 'dl_idx"):
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trainer.validate(model)
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with pytest.raises(TypeError, match="missing 1 required positional argument: 'dl_idx"):
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trainer.test(model)
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class MultipleDataloader(BoringModel):
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def validation_step(self, batch, batch_idx, dataloader_idx=0):
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...
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def test_step(self, batch, batch_idx, dataloader_idx=0):
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...
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def on_validation_batch_end(self, outputs, batch, batch_idx):
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...
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def on_test_batch_end(self, outputs, batch, batch_idx):
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...
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def val_dataloader(self):
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return [super().val_dataloader(), super().val_dataloader()]
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def test_dataloader(self):
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return [super().test_dataloader(), super().test_dataloader()]
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model = MultipleDataloader()
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with pytest.raises(
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RuntimeError, match="no `dataloader_idx` argument in `MultipleDataloader.on_validation_batch_end"
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):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `MultipleDataloader.on_test_batch_end"):
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trainer.test(model)
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class IgnoringModel(MultipleDataloader):
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def on_validation_batch_end(self, outputs, batch, batch_idx, *_):
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...
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def on_test_batch_end(self, outputs, batch, batch_idx, *_):
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...
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model = IgnoringModel()
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trainer.validate(model)
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trainer.test(model)
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class IgnoringModel2(MultipleDataloader):
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def on_validation_batch_end(self, outputs, batch, batch_idx, **_):
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...
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def on_test_batch_end(self, outputs, batch, batch_idx, **_):
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...
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model = IgnoringModel2()
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `IgnoringModel2.on_validation_batch_end"):
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trainer.validate(model)
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with pytest.raises(RuntimeError, match="no `dataloader_idx` argument in `IgnoringModel2.on_test_batch_end"):
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trainer.test(model)
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