lightning/tests/tests_pytorch/trainer/test_config_validator.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.
import pytest
import torch

import pytorch_lightning as pl
from lightning.fabric.utilities.warnings import PossibleUserWarning
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests_pytorch.conftest import mock_cuda_count


def test_wrong_train_setting(tmpdir):
    """Test that an error is raised when no `training_step()` is defined."""
    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)

    with pytest.raises(MisconfigurationException, match=r"No `training_step\(\)` method defined."):
        model = BoringModel()
        model.training_step = None
        trainer.fit(model)


def test_wrong_configure_optimizers(tmpdir):
    """Test that an error is thrown when no `configure_optimizers()` is defined."""
    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)

    with pytest.raises(MisconfigurationException, match=r"No `configure_optimizers\(\)` method defined."):
        model = BoringModel()
        model.configure_optimizers = None
        trainer.fit(model)


def test_fit_val_loop_config(tmpdir):
    """When either val loop or val data are missing raise warning."""
    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)

    # no val data has val loop
    with pytest.warns(UserWarning, match=r"You passed in a `val_dataloader` but have no `validation_step`"):
        model = BoringModel()
        model.validation_step = None
        trainer.fit(model)

    # has val loop but no val data
    with pytest.warns(PossibleUserWarning, match=r"You defined a `validation_step` but have no `val_dataloader`"):
        model = BoringModel()
        model.val_dataloader = None
        trainer.fit(model)


def test_eval_loop_config(tmpdir):
    """When either eval step or eval data is missing."""
    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)

    # has test data but no val step
    model = BoringModel()
    model.validation_step = None
    with pytest.raises(MisconfigurationException, match=r"No `validation_step\(\)` method defined"):
        trainer.validate(model)

    # has test data but no test step
    model = BoringModel()
    model.test_step = None
    with pytest.raises(MisconfigurationException, match=r"No `test_step\(\)` method defined"):
        trainer.test(model)

    # has predict data but no predict_step
    model = BoringModel()
    model.predict_step = None
    with pytest.raises(MisconfigurationException, match=r"`predict_step` cannot be None."):
        trainer.predict(model)

    # has predict data but no forward
    model = BoringModel()
    model.forward = None
    with pytest.raises(MisconfigurationException, match=r"requires `forward` method to run."):
        trainer.predict(model)


@pytest.mark.parametrize("datamodule", [False, True])
def test_trainer_predict_verify_config(tmpdir, datamodule):
    class TestModel(LightningModule):
        def __init__(self):
            super().__init__()
            self.layer = torch.nn.Linear(32, 2)

        def forward(self, x):
            return self.layer(x)

    class TestLightningDataModule(LightningDataModule):
        def __init__(self, dataloaders):
            super().__init__()
            self._dataloaders = dataloaders

        def test_dataloader(self):
            return self._dataloaders

        def predict_dataloader(self):
            return self._dataloaders

    data = [torch.utils.data.DataLoader(RandomDataset(32, 2)), torch.utils.data.DataLoader(RandomDataset(32, 2))]
    if datamodule:
        data = TestLightningDataModule(data)

    model = TestModel()
    trainer = Trainer(default_root_dir=tmpdir)
    results = trainer.predict(model, data)

    assert len(results) == 2
    assert results[0][0].shape == torch.Size([1, 2])


def test_trainer_manual_optimization_config():
    """Test error message when requesting Trainer features unsupported with manual optimization."""
    model = BoringModel()
    model.automatic_optimization = False

    trainer = Trainer(gradient_clip_val=1.0)
    with pytest.raises(MisconfigurationException, match="Automatic gradient clipping is not supported"):
        trainer.fit(model)

    trainer = Trainer(accumulate_grad_batches=2)
    with pytest.raises(MisconfigurationException, match="Automatic gradient accumulation is not supported"):
        trainer.fit(model)


@pytest.mark.parametrize("trainer_kwargs", [{"accelerator": "ipu"}, {"accelerator": "gpu", "strategy": "dp"}])
@pytest.mark.parametrize("hook", ["transfer_batch_to_device", "on_after_batch_transfer"])
def test_raise_exception_with_batch_transfer_hooks(monkeypatch, hook, trainer_kwargs, tmpdir):
    """Test that an exception is raised when overriding batch_transfer_hooks."""
    if trainer_kwargs.get("accelerator") == "gpu":
        match_pattern = rf"Overriding `{hook}` is not .* in DP mode."
        mock_cuda_count(monkeypatch, 2)
    elif trainer_kwargs.get("accelerator") == "ipu":
        match_pattern = rf"Overriding `{hook}` is not .* with IPUs"
        monkeypatch.setattr(pl.accelerators.ipu.IPUAccelerator, "is_available", lambda: True)
        monkeypatch.setattr(pl.strategies.ipu, "_IPU_AVAILABLE", lambda: True)

    def custom_method(self, batch, *_, **__):
        batch = batch + 1
        return batch

    trainer = Trainer(default_root_dir=tmpdir, **trainer_kwargs)

    model = BoringModel()
    setattr(model, hook, custom_method)

    with pytest.raises(MisconfigurationException, match=match_pattern):
        trainer.fit(model)