lightning/tests/loops/test_evaluation_loop.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from unittest import mock

import torch
from torch.utils.data.dataloader import DataLoader

from pytorch_lightning import Trainer
from pytorch_lightning.loops import EvaluationEpochLoop
from pytorch_lightning.utilities.model_helpers import is_overridden
from tests.helpers.boring_model import BoringModel, RandomDataset
from tests.helpers.runif import RunIf


@mock.patch("pytorch_lightning.loops.dataloader.evaluation_loop.EvaluationLoop._on_evaluation_epoch_end")
def test_on_evaluation_epoch_end(eval_epoch_end_mock, tmpdir):
    """Tests that `on_evaluation_epoch_end` is called for `on_validation_epoch_end` and `on_test_epoch_end`
    hooks."""
    model = BoringModel()

    trainer = Trainer(
        default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, max_epochs=2, enable_model_summary=False
    )

    trainer.fit(model)
    # sanity + 2 epochs
    assert eval_epoch_end_mock.call_count == 3

    trainer.test()
    # sanity + 2 epochs + called once for test
    assert eval_epoch_end_mock.call_count == 4


@mock.patch(
    "pytorch_lightning.trainer.connectors.logger_connector.logger_connector.LoggerConnector.log_eval_end_metrics"
)
def test_log_epoch_metrics_before_on_evaluation_end(update_eval_epoch_metrics_mock, tmpdir):
    """Test that the epoch metrics are logged before the `on_evaluation_end` hook is fired."""
    order = []
    update_eval_epoch_metrics_mock.side_effect = lambda: order.append("log_epoch_metrics")

    class LessBoringModel(BoringModel):
        def on_validation_end(self):
            order.append("on_validation_end")
            super().on_validation_end()

    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1, enable_model_summary=False, num_sanity_val_steps=0)
    trainer.fit(LessBoringModel())

    assert order == ["log_epoch_metrics", "on_validation_end"]


@RunIf(min_gpus=1)
def test_memory_consumption_validation(tmpdir):
    """Test that the training batch is no longer in GPU memory when running validation."""

    initial_memory = torch.cuda.memory_allocated(0)

    class BoringLargeBatchModel(BoringModel):
        @property
        def num_params(self):
            return sum(p.numel() for p in self.parameters())

        def train_dataloader(self):
            # batch target memory >= 100x boring_model size
            batch_size = self.num_params * 100 // 32 + 1
            return DataLoader(RandomDataset(32, 5000), batch_size=batch_size)

        def val_dataloader(self):
            return self.train_dataloader()

        def training_step(self, batch, batch_idx):
            # there is a batch and the boring model, but not two batches on gpu, assume 32 bit = 4 bytes
            lower = 101 * self.num_params * 4
            upper = 201 * self.num_params * 4
            current = torch.cuda.memory_allocated(0)
            assert lower < current
            assert current - initial_memory < upper
            return super().training_step(batch, batch_idx)

        def validation_step(self, batch, batch_idx):
            # there is a batch and the boring model, but not two batches on gpu, assume 32 bit = 4 bytes
            lower = 101 * self.num_params * 4
            upper = 201 * self.num_params * 4
            current = torch.cuda.memory_allocated(0)
            assert lower < current
            assert current - initial_memory < upper
            return super().validation_step(batch, batch_idx)

    torch.cuda.empty_cache()
    trainer = Trainer(
        accelerator="gpu",
        devices=1,
        default_root_dir=tmpdir,
        fast_dev_run=2,
        move_metrics_to_cpu=True,
        enable_model_summary=False,
    )
    trainer.fit(BoringLargeBatchModel())


def test_evaluation_loop_doesnt_store_outputs_if_epoch_end_not_overridden(tmpdir):
    did_assert = False

    class TestModel(BoringModel):
        def on_test_batch_end(self, outputs, *_):
            # check `test_step` returns something
            assert outputs is not None

    class TestLoop(EvaluationEpochLoop):
        def on_advance_end(self):
            # should be empty
            assert not self._outputs
            # sanity check
            nonlocal did_assert
            did_assert = True
            super().on_advance_end()

    model = TestModel()
    model.test_epoch_end = None
    assert not is_overridden("test_epoch_end", model)

    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=3)
    trainer.test_loop.replace(epoch_loop=TestLoop)
    trainer.test(model)
    assert did_assert