lightning/tests/models/test_hooks.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 functools import partial, update_wrapper
from inspect import getmembers, isfunction
from unittest import mock
from unittest.mock import ANY, PropertyMock

import pytest
import torch
from torch.utils.data import DataLoader

from pytorch_lightning import __version__, Callback, LightningDataModule, LightningModule, Trainer
from tests.helpers import BoringDataModule, BoringModel, RandomDataset
from tests.helpers.runif import RunIf


@pytest.mark.parametrize("max_steps", [1, 2, 3])
def test_on_before_zero_grad_called(tmpdir, max_steps):
    class CurrentTestModel(BoringModel):
        on_before_zero_grad_called = 0

        def on_before_zero_grad(self, optimizer):
            self.on_before_zero_grad_called += 1

    model = CurrentTestModel()

    trainer = Trainer(default_root_dir=tmpdir, max_steps=max_steps, max_epochs=2)
    assert 0 == model.on_before_zero_grad_called
    trainer.fit(model)
    assert max_steps == model.on_before_zero_grad_called

    model.on_before_zero_grad_called = 0
    trainer.test(model)
    assert 0 == model.on_before_zero_grad_called


def test_training_epoch_end_metrics_collection(tmpdir):
    """Test that progress bar metrics also get collected at the end of an epoch."""
    num_epochs = 3

    class CurrentModel(BoringModel):
        def training_step(self, *args, **kwargs):
            output = super().training_step(*args, **kwargs)
            self.log_dict({"step_metric": torch.tensor(-1), "shared_metric": 100}, logger=False, prog_bar=True)
            return output

        def training_epoch_end(self, outputs):
            epoch = self.current_epoch
            # both scalar tensors and Python numbers are accepted
            self.log_dict(
                {f"epoch_metric_{epoch}": torch.tensor(epoch), "shared_metric": 111}, logger=False, prog_bar=True
            )

    model = CurrentModel()
    trainer = Trainer(max_epochs=num_epochs, default_root_dir=tmpdir, overfit_batches=2)
    trainer.fit(model)
    assert trainer.state.finished, f"Training failed with {trainer.state}"
    metrics = trainer.progress_bar_callback.get_metrics(trainer, model)

    # metrics added in training step should be unchanged by epoch end method
    assert metrics["step_metric"] == -1
    # a metric shared in both methods gets overwritten by epoch_end
    assert metrics["shared_metric"] == 111
    # metrics are kept after each epoch
    for i in range(num_epochs):
        assert metrics[f"epoch_metric_{i}"] == i


def test_training_epoch_end_metrics_collection_on_override(tmpdir):
    """Test that batch end metrics are collected when training_epoch_end is overridden at the end of an epoch."""

    class OverriddenModel(BoringModel):
        def __init__(self):
            super().__init__()
            self.len_outputs = 0

        def on_train_epoch_start(self):
            self.num_train_batches = 0

        def training_epoch_end(self, outputs):
            self.len_outputs = len(outputs)

        def on_train_batch_end(self, outputs, batch, batch_idx):
            self.num_train_batches += 1

    class NotOverriddenModel(BoringModel):
        def on_train_epoch_start(self):
            self.num_train_batches = 0

        def on_train_batch_end(self, outputs, batch, batch_idx):
            self.num_train_batches += 1

    overridden_model = OverriddenModel()
    not_overridden_model = NotOverriddenModel()
    not_overridden_model.training_epoch_end = None

    trainer = Trainer(max_epochs=1, default_root_dir=tmpdir, overfit_batches=2)

    trainer.fit(overridden_model)
    assert overridden_model.len_outputs == overridden_model.num_train_batches


@RunIf(min_gpus=1)
@mock.patch(
    "pytorch_lightning.strategies.Strategy.lightning_module",
    new_callable=PropertyMock,
)
def test_apply_batch_transfer_handler(model_getter_mock):
    expected_device = torch.device("cuda", 0)

    class CustomBatch:
        def __init__(self, data):
            self.samples = data[0]
            self.targets = data[1]

    class CurrentTestModel(BoringModel):
        rank = 0
        transfer_batch_to_device_hook_rank = None
        on_before_batch_transfer_hook_rank = None
        on_after_batch_transfer_hook_rank = None

        def on_before_batch_transfer(self, batch, dataloader_idx):
            assert dataloader_idx == 0
            self.on_before_batch_transfer_hook_rank = self.rank
            self.rank += 1
            batch.samples += 1
            return batch

        def on_after_batch_transfer(self, batch, dataloader_idx):
            assert dataloader_idx == 0
            assert batch.samples.device == batch.targets.device == expected_device
            self.on_after_batch_transfer_hook_rank = self.rank
            self.rank += 1
            batch.targets *= 2
            return batch

        def transfer_batch_to_device(self, batch, device, dataloader_idx):
            assert dataloader_idx == 0
            self.transfer_batch_to_device_hook_rank = self.rank
            self.rank += 1
            batch.samples = batch.samples.to(device)
            batch.targets = batch.targets.to(device)
            return batch

    model = CurrentTestModel()
    batch = CustomBatch((torch.zeros(5, 32), torch.ones(5, 1, dtype=torch.long)))

    trainer = Trainer(gpus=1)
    # running .fit() would require us to implement custom data loaders, we mock the model reference instead

    model_getter_mock.return_value = model
    batch_gpu = trainer.strategy.batch_to_device(batch, expected_device)

    assert model.on_before_batch_transfer_hook_rank == 0
    assert model.transfer_batch_to_device_hook_rank == 1
    assert model.on_after_batch_transfer_hook_rank == 2
    assert batch_gpu.samples.device == batch_gpu.targets.device == expected_device
    assert torch.allclose(batch_gpu.samples.cpu(), torch.ones(5, 32))
    assert torch.allclose(batch_gpu.targets.cpu(), torch.ones(5, 1, dtype=torch.long) * 2)


@RunIf(min_gpus=2, standalone=True)
def test_transfer_batch_hook_ddp(tmpdir):
    """Test custom data are properly moved to the right device using ddp."""

    class CustomBatch:
        def __init__(self, data):
            self.samples = data[0]

        def to(self, device, **kwargs):
            self.samples = self.samples.to(device, **kwargs)
            return self

    def collate_fn(batch):
        return CustomBatch(batch)

    class TestModel(BoringModel):
        def training_step(self, batch, batch_idx):
            assert batch.samples.device == self.device
            assert isinstance(batch_idx, int)

        def train_dataloader(self):
            return torch.utils.data.DataLoader(RandomDataset(32, 64), collate_fn=collate_fn)

    model = TestModel()
    model.validation_step = None
    model.training_epoch_end = None
    trainer = Trainer(
        default_root_dir=tmpdir,
        limit_train_batches=2,
        limit_val_batches=0,
        max_epochs=1,
        enable_model_summary=False,
        strategy="ddp",
        gpus=2,
    )
    trainer.fit(model)


def get_members(cls):
    return {h for h, _ in getmembers(cls, predicate=isfunction) if not h.startswith("_")}


class HookedCallback(Callback):
    def __init__(self, called):
        def call(hook, fn, *args, **kwargs):
            out = fn(*args, **kwargs)
            d = {"name": f"Callback.{hook}"}
            if args:
                d["args"] = args
            if kwargs:
                d["kwargs"] = kwargs
            called.append(d)
            return out

        for h in get_members(Callback):
            attr = getattr(self, h)
            partial_h = partial(call, h, attr)
            update_wrapper(partial_h, attr)
            setattr(self, h, partial_h)

    def on_save_checkpoint(*args, **kwargs):
        return {"foo": True}


class HookedModel(BoringModel):
    def __init__(self, called):
        super().__init__()
        pl_module_hooks = get_members(LightningModule)
        # remove non-hooks
        pl_module_hooks.difference_update({"optimizers", "log", "log_dict"})
        # remove most `nn.Module` hooks
        module_hooks = get_members(torch.nn.Module)
        module_hooks.difference_update({"forward", "zero_grad", "train"})
        pl_module_hooks.difference_update(module_hooks)

        def call(hook, fn, *args, **kwargs):
            out = fn(*args, **kwargs)
            d = {"name": hook}
            if args:
                d["args"] = args
            elif hook == "train":
                # DeepSpeed calls `train(mode)` but we do not. Standardize
                # https://github.com/microsoft/DeepSpeed/pull/571
                d["args"] = (True,)
            if kwargs:
                d["kwargs"] = kwargs
            called.append(d)
            return out

        for h in pl_module_hooks:
            attr = getattr(self, h)
            partial_h = partial(call, h, attr)
            update_wrapper(partial_h, attr)
            setattr(self, h, partial_h)

    def validation_epoch_end(self, *args, **kwargs):
        # `BoringModel` does not have a return for `validation_step_end` so this would fail
        pass

    def test_epoch_end(self, *args, **kwargs):
        # `BoringModel` does not have a return for `test_step_end` so this would fail
        pass

    def _train_batch(self, *args, **kwargs):
        if self.automatic_optimization:
            return self._auto_train_batch(*args, **kwargs)
        return self._manual_train_batch(*args, **kwargs)

    @staticmethod
    def _auto_train_batch(
        trainer, model, batches, device=torch.device("cpu"), current_epoch=0, current_batch=0, **kwargs
    ):
        using_native_amp = kwargs.get("amp_backend") == "native"
        using_deepspeed = kwargs.get("strategy") == "deepspeed"
        out = []
        for i in range(current_batch, batches):
            out.extend(
                [
                    dict(name="on_before_batch_transfer", args=(ANY, 0)),
                    dict(name="transfer_batch_to_device", args=(ANY, device, 0)),
                    dict(name="on_after_batch_transfer", args=(ANY, 0)),
                    dict(name="Callback.on_batch_start", args=(trainer, model)),
                    dict(name="Callback.on_train_batch_start", args=(trainer, model, ANY, i)),
                    dict(name="on_train_batch_start", args=(ANY, i)),
                    dict(name="forward", args=(ANY,)),
                    dict(name="training_step", args=(ANY, i)),
                    dict(name="training_step_end", args=(dict(loss=ANY),)),
                    dict(name="Callback.on_before_zero_grad", args=(trainer, model, ANY)),
                    dict(name="on_before_zero_grad", args=(ANY,)),
                    dict(name="optimizer_zero_grad", args=(current_epoch, i, ANY, 0)),
                    dict(name="Callback.on_before_backward", args=(trainer, model, ANY)),
                    dict(name="on_before_backward", args=(ANY,)),
                    # DeepSpeed handles backward internally
                    *([dict(name="backward", args=(ANY, ANY, 0))] if not using_deepspeed else []),
                    dict(name="Callback.on_after_backward", args=(trainer, model)),
                    dict(name="on_after_backward"),
                    # note: unscaling happens here in the case of AMP
                    dict(name="Callback.on_before_optimizer_step", args=(trainer, model, ANY, 0)),
                    dict(name="on_before_optimizer_step", args=(ANY, 0)),
                    *([dict(name="log_grad_norm", args=ANY)] if not using_deepspeed else []),
                    dict(
                        name="clip_gradients",
                        args=(ANY,),
                        kwargs=dict(gradient_clip_val=None, gradient_clip_algorithm=None),
                    ),
                    dict(
                        name="configure_gradient_clipping",
                        args=(ANY, 0),
                        kwargs=dict(gradient_clip_val=None, gradient_clip_algorithm=None),
                    ),
                    # this is after because it refers to the `LightningModule.optimizer_step` hook which encapsulates
                    # the actual call to `PrecisionPlugin.optimizer_step`
                    dict(
                        name="optimizer_step",
                        args=(current_epoch, i, ANY, 0, ANY),
                        kwargs=dict(on_tpu=False, using_lbfgs=False, using_native_amp=using_native_amp),
                    ),
                    *(
                        [dict(name="lr_scheduler_step", args=(ANY, 0, None))]
                        if i == (trainer.num_training_batches - 1)
                        else []
                    ),
                    dict(name="Callback.on_train_batch_end", args=(trainer, model, dict(loss=ANY), ANY, i)),
                    dict(name="on_train_batch_end", args=(dict(loss=ANY), ANY, i)),
                    dict(name="Callback.on_batch_end", args=(trainer, model)),
                ]
            )
        return out

    @staticmethod
    def _manual_train_batch(trainer, model, batches, device=torch.device("cpu"), **kwargs):
        using_deepspeed = kwargs.get("strategy") == "deepspeed"
        out = []
        for i in range(batches):
            out.extend(
                [
                    dict(name="on_before_batch_transfer", args=(ANY, 0)),
                    dict(name="transfer_batch_to_device", args=(ANY, device, 0)),
                    dict(name="on_after_batch_transfer", args=(ANY, 0)),
                    dict(name="Callback.on_batch_start", args=(trainer, model)),
                    dict(name="Callback.on_train_batch_start", args=(trainer, model, ANY, i)),
                    dict(name="on_train_batch_start", args=(ANY, i)),
                    dict(name="forward", args=(ANY,)),
                    dict(name="Callback.on_before_backward", args=(trainer, model, ANY)),
                    dict(name="on_before_backward", args=(ANY,)),
                    # DeepSpeed handles backward internally
                    *([dict(name="backward", args=(ANY, None, None))] if not using_deepspeed else []),
                    dict(name="Callback.on_after_backward", args=(trainer, model)),
                    dict(name="on_after_backward"),
                    # `manual_backward` calls the previous 3
                    dict(name="manual_backward", args=(ANY,)),
                    dict(name="closure"),
                    dict(name="Callback.on_before_optimizer_step", args=(trainer, model, ANY, 0)),
                    dict(name="on_before_optimizer_step", args=(ANY, 0)),
                    *([dict(name="log_grad_norm", args=ANY)] if not using_deepspeed else []),
                    dict(name="training_step", args=(ANY, i)),
                    dict(name="training_step_end", args=(dict(loss=ANY),)),
                    dict(name="Callback.on_train_batch_end", args=(trainer, model, dict(loss=ANY), ANY, i)),
                    dict(name="on_train_batch_end", args=(dict(loss=ANY), ANY, i)),
                    dict(name="Callback.on_batch_end", args=(trainer, model)),
                ]
            )
        return out

    @staticmethod
    def _eval_epoch(fn, trainer, model, batches, key, device=torch.device("cpu")):
        outputs = {key: ANY}
        return [
            dict(name="Callback.on_epoch_start", args=(trainer, model)),
            dict(name="on_epoch_start"),
            dict(name=f"Callback.on_{fn}_epoch_start", args=(trainer, model)),
            dict(name=f"on_{fn}_epoch_start"),
            *HookedModel._eval_batch(fn, trainer, model, batches, key, device=device),
            dict(name=f"{fn}_epoch_end", args=([outputs] * batches,)),
            dict(name=f"Callback.on_{fn}_epoch_end", args=(trainer, model)),
            dict(name=f"on_{fn}_epoch_end"),
            dict(name="Callback.on_epoch_end", args=(trainer, model)),
            dict(name="on_epoch_end"),
        ]

    @staticmethod
    def _eval_batch(fn, trainer, model, batches, key, device=torch.device("cpu")):
        out = []
        outputs = {key: ANY}
        for i in range(batches):
            out.extend(
                [
                    dict(name="on_before_batch_transfer", args=(ANY, 0)),
                    dict(name="transfer_batch_to_device", args=(ANY, device, 0)),
                    dict(name="on_after_batch_transfer", args=(ANY, 0)),
                    dict(name=f"Callback.on_{fn}_batch_start", args=(trainer, model, ANY, i, 0)),
                    dict(name=f"on_{fn}_batch_start", args=(ANY, i, 0)),
                    dict(name="forward", args=(ANY,)),
                    dict(name=f"{fn}_step", args=(ANY, i)),
                    dict(name=f"{fn}_step_end", args=(outputs,)),
                    dict(name=f"Callback.on_{fn}_batch_end", args=(trainer, model, outputs, ANY, i, 0)),
                    dict(name=f"on_{fn}_batch_end", args=(outputs, ANY, i, 0)),
                ]
            )
        return out

    @staticmethod
    def _predict_batch(trainer, model, batches):
        out = []
        for i in range(batches):
            out.extend(
                [
                    dict(name="on_before_batch_transfer", args=(ANY, 0)),
                    dict(name="transfer_batch_to_device", args=(ANY, torch.device("cpu"), 0)),
                    dict(name="on_after_batch_transfer", args=(ANY, 0)),
                    dict(name="Callback.on_predict_batch_start", args=(trainer, model, ANY, i, 0)),
                    dict(name="on_predict_batch_start", args=(ANY, i, 0)),
                    dict(name="forward", args=(ANY,)),
                    dict(name="predict_step", args=(ANY, i)),
                    # TODO: `predict_step_end`
                    dict(name="Callback.on_predict_batch_end", args=(trainer, model, ANY, ANY, i, 0)),
                    dict(name="on_predict_batch_end", args=(ANY, ANY, i, 0)),
                ]
            )
        return out


@pytest.mark.parametrize(
    "kwargs",
    [
        {},
        # these precision plugins modify the optimization flow, so testing them explicitly
        pytest.param(dict(gpus=1, precision=16, amp_backend="native"), marks=RunIf(min_gpus=1)),
        pytest.param(dict(gpus=1, precision=16, amp_backend="apex"), marks=RunIf(amp_apex=True, min_gpus=1)),
        pytest.param(
            dict(gpus=1, precision=16, strategy="deepspeed"), marks=RunIf(deepspeed=True, min_gpus=1, standalone=True)
        ),
    ],
)
@pytest.mark.parametrize("automatic_optimization", (True, False))
def test_trainer_model_hook_system_fit(tmpdir, kwargs, automatic_optimization):
    called = []

    class TestModel(HookedModel):
        def __init__(self, *args):
            super().__init__(*args)
            self.automatic_optimization = automatic_optimization

        def training_step(self, batch, batch_idx):
            if self.automatic_optimization:
                return super().training_step(batch, batch_idx)
            loss = self.step(batch[0])
            opt = self.optimizers()
            opt.zero_grad()
            self.manual_backward(loss)
            opt.step(lambda: called.append({"name": "closure"}))
            return {"loss": loss}

    model = TestModel(called)
    callback = HookedCallback(called)
    train_batches = 2
    val_batches = 2
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        limit_train_batches=train_batches,
        limit_val_batches=val_batches,
        enable_progress_bar=False,
        enable_model_summary=False,
        callbacks=[callback],
        track_grad_norm=1,
        **kwargs,
    )
    assert called == [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
    ]
    trainer.fit(model)
    saved_ckpt = {
        "callbacks": ANY,
        "epoch": 0,
        "global_step": train_batches,
        "lr_schedulers": ANY,
        "optimizer_states": ANY,
        "pytorch-lightning_version": __version__,
        "state_dict": ANY,
        "loops": ANY,
    }
    if kwargs.get("amp_backend") == "native":
        saved_ckpt["native_amp_scaling_state"] = ANY
    elif kwargs.get("amp_backend") == "apex":
        saved_ckpt["amp_scaling_state"] = ANY
    device = torch.device("cuda:0" if "gpus" in kwargs else "cpu")
    expected = [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
        dict(name="configure_callbacks"),
        dict(name="prepare_data"),
        dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
        # DeepSpeed needs the batch size to figure out throughput logging
        *([dict(name="train_dataloader")] if kwargs.get("strategy") == "deepspeed" else []),
        dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage="fit")),
        dict(name="setup", kwargs=dict(stage="fit")),
        dict(name="configure_sharded_model"),
        dict(name="Callback.on_configure_sharded_model", args=(trainer, model)),
        dict(name="configure_optimizers"),
        dict(name="Callback.on_fit_start", args=(trainer, model)),
        dict(name="on_fit_start"),
        dict(name="Callback.on_pretrain_routine_start", args=(trainer, model)),
        dict(name="on_pretrain_routine_start"),
        dict(name="Callback.on_pretrain_routine_end", args=(trainer, model)),
        dict(name="on_pretrain_routine_end"),
        dict(name="Callback.on_sanity_check_start", args=(trainer, model)),
        dict(name="on_val_dataloader"),
        dict(name="val_dataloader"),
        dict(name="train", args=(False,)),
        dict(name="on_validation_model_eval"),
        dict(name="zero_grad"),
        dict(name="Callback.on_validation_start", args=(trainer, model)),
        dict(name="on_validation_start"),
        *model._eval_epoch("validation", trainer, model, val_batches, "x", device=device),
        dict(name="Callback.on_validation_end", args=(trainer, model)),
        dict(name="on_validation_end"),
        dict(name="train", args=(True,)),
        dict(name="on_validation_model_train"),
        dict(name="Callback.on_sanity_check_end", args=(trainer, model)),
        # duplicate `train` because `_run_train` calls it again in case validation wasn't run
        dict(name="train", args=(True,)),
        dict(name="on_train_dataloader"),
        dict(name="train_dataloader"),
        dict(name="Callback.on_train_start", args=(trainer, model)),
        dict(name="on_train_start"),
        dict(name="Callback.on_epoch_start", args=(trainer, model)),
        dict(name="on_epoch_start"),
        dict(name="Callback.on_train_epoch_start", args=(trainer, model)),
        dict(name="on_train_epoch_start"),
        *model._train_batch(trainer, model, train_batches, device=device, **kwargs),
        dict(name="train", args=(False,)),
        dict(name="on_validation_model_eval"),
        dict(name="zero_grad"),
        dict(name="Callback.on_validation_start", args=(trainer, model)),
        dict(name="on_validation_start"),
        *model._eval_epoch("validation", trainer, model, val_batches, "x", device=device),
        dict(name="Callback.on_validation_end", args=(trainer, model)),
        dict(name="on_validation_end"),
        dict(name="train", args=(True,)),
        dict(name="on_validation_model_train"),
        dict(name="training_epoch_end", args=([dict(loss=ANY)] * train_batches,)),
        dict(name="Callback.on_train_epoch_end", args=(trainer, model)),
        # `ModelCheckpoint.save_checkpoint` is called here from `Callback.on_train_epoch_end`
        dict(name="Callback.on_save_checkpoint", args=(trainer, model, saved_ckpt)),
        dict(name="on_save_checkpoint", args=(saved_ckpt,)),
        dict(name="on_train_epoch_end"),
        dict(name="Callback.on_epoch_end", args=(trainer, model)),
        dict(name="on_epoch_end"),
        dict(name="Callback.on_train_end", args=(trainer, model)),
        dict(name="on_train_end"),
        dict(name="Callback.on_fit_end", args=(trainer, model)),
        dict(name="on_fit_end"),
        dict(name="Callback.teardown", args=(trainer, model), kwargs=dict(stage="fit")),
        dict(name="teardown", kwargs=dict(stage="fit")),
    ]
    assert called == expected


def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
    # initial training to get a checkpoint
    model = BoringModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=1,
        limit_val_batches=0,
        enable_progress_bar=False,
        enable_model_summary=False,
        callbacks=[HookedCallback([])],
    )
    trainer.fit(model)
    best_model_path = trainer.checkpoint_callback.best_model_path

    # resume from checkpoint with HookedModel
    called = []
    model = HookedModel(called)
    callback = HookedCallback(called)

    # already performed 1 step, resume and do 2 more
    train_batches = 2
    steps_after_reload = 1 + train_batches
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=steps_after_reload,
        limit_val_batches=0,
        enable_progress_bar=False,
        enable_model_summary=False,
        callbacks=[callback],
        track_grad_norm=1,
    )
    assert called == [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
    ]

    trainer.fit(model, ckpt_path=best_model_path)
    loaded_ckpt = {
        "callbacks": ANY,
        "epoch": 0,
        "global_step": 1,
        "lr_schedulers": ANY,
        "optimizer_states": ANY,
        "pytorch-lightning_version": __version__,
        "state_dict": ANY,
        "loops": ANY,
    }
    saved_ckpt = {**loaded_ckpt, "global_step": steps_after_reload, "epoch": 1}
    expected = [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
        dict(name="configure_callbacks"),
        dict(name="prepare_data"),
        dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
        dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage="fit")),
        dict(name="setup", kwargs=dict(stage="fit")),
        dict(name="on_load_checkpoint", args=(loaded_ckpt,)),
        dict(name="Callback.on_load_checkpoint", args=(trainer, model, {"foo": True})),
        dict(name="configure_sharded_model"),
        dict(name="Callback.on_configure_sharded_model", args=(trainer, model)),
        dict(name="configure_optimizers"),
        dict(name="Callback.on_fit_start", args=(trainer, model)),
        dict(name="on_fit_start"),
        dict(name="Callback.on_pretrain_routine_start", args=(trainer, model)),
        dict(name="on_pretrain_routine_start"),
        dict(name="Callback.on_pretrain_routine_end", args=(trainer, model)),
        dict(name="on_pretrain_routine_end"),
        dict(name="train", args=(True,)),
        dict(name="on_train_dataloader"),
        dict(name="train_dataloader"),
        # even though no validation runs, we initialize the val dataloader for properties like `num_val_batches`
        dict(name="on_val_dataloader"),
        dict(name="val_dataloader"),
        dict(name="Callback.on_train_start", args=(trainer, model)),
        dict(name="on_train_start"),
        dict(name="Callback.on_epoch_start", args=(trainer, model)),
        dict(name="on_epoch_start"),
        dict(name="Callback.on_train_epoch_start", args=(trainer, model)),
        dict(name="on_train_epoch_start"),
        *model._train_batch(trainer, model, steps_after_reload, current_batch=1, current_epoch=1),
        dict(name="training_epoch_end", args=([dict(loss=ANY)] * train_batches,)),
        dict(name="Callback.on_train_epoch_end", args=(trainer, model)),
        dict(name="Callback.on_save_checkpoint", args=(trainer, model, saved_ckpt)),
        dict(name="on_save_checkpoint", args=(saved_ckpt,)),
        dict(name="on_train_epoch_end"),
        dict(name="Callback.on_epoch_end", args=(trainer, model)),
        dict(name="on_epoch_end"),
        dict(name="Callback.on_train_end", args=(trainer, model)),
        dict(name="on_train_end"),
        dict(name="Callback.on_fit_end", args=(trainer, model)),
        dict(name="on_fit_end"),
        dict(name="Callback.teardown", args=(trainer, model), kwargs=dict(stage="fit")),
        dict(name="teardown", kwargs=dict(stage="fit")),
    ]
    assert called == expected


@pytest.mark.parametrize("batches", (0, 2))
@pytest.mark.parametrize(
    ["verb", "noun", "dataloader", "key"], [("validate", "validation", "val", "x"), ("test", "test", "test", "y")]
)
def test_trainer_model_hook_system_eval(tmpdir, batches, verb, noun, dataloader, key):
    called = []
    model = HookedModel(called)
    callback = HookedCallback(called)
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        limit_val_batches=batches,
        limit_test_batches=batches,
        enable_progress_bar=False,
        enable_model_summary=False,
        callbacks=[callback],
    )
    assert called == [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
    ]
    fn = getattr(trainer, verb)
    fn(model, verbose=False)
    hooks = [
        dict(name="train", args=(False,)),
        dict(name=f"on_{noun}_model_eval"),
        dict(name="zero_grad"),
        dict(name=f"Callback.on_{noun}_start", args=(trainer, model)),
        dict(name=f"on_{noun}_start"),
        *model._eval_epoch(noun, trainer, model, batches, key),
        dict(name=f"Callback.on_{noun}_end", args=(trainer, model)),
        dict(name=f"on_{noun}_end"),
        dict(name="train", args=(True,)),
        dict(name=f"on_{noun}_model_train"),
    ]
    expected = [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
        dict(name="configure_callbacks"),
        dict(name="prepare_data"),
        dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
        dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage=verb)),
        dict(name="setup", kwargs=dict(stage=verb)),
        dict(name="configure_sharded_model"),
        dict(name="Callback.on_configure_sharded_model", args=(trainer, model)),
        dict(name=f"on_{dataloader}_dataloader"),
        dict(name=f"{dataloader}_dataloader"),
        *(hooks if batches else []),
        dict(name="Callback.teardown", args=(trainer, model), kwargs=dict(stage=verb)),
        dict(name="teardown", kwargs=dict(stage=verb)),
    ]
    assert called == expected


def test_trainer_model_hook_system_predict(tmpdir):
    called = []
    model = HookedModel(called)
    callback = HookedCallback(called)
    batches = 2
    trainer = Trainer(
        default_root_dir=tmpdir, limit_predict_batches=batches, enable_progress_bar=False, callbacks=[callback]
    )
    assert called == [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
    ]
    trainer.predict(model)
    expected = [
        dict(name="Callback.on_init_start", args=(trainer,)),
        dict(name="Callback.on_init_end", args=(trainer,)),
        dict(name="configure_callbacks"),
        dict(name="prepare_data"),
        dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
        dict(name="Callback.setup", args=(trainer, model), kwargs=dict(stage="predict")),
        dict(name="setup", kwargs=dict(stage="predict")),
        dict(name="configure_sharded_model"),
        dict(name="Callback.on_configure_sharded_model", args=(trainer, model)),
        dict(name="on_predict_dataloader"),
        dict(name="predict_dataloader"),
        dict(name="train", args=(False,)),
        dict(name="on_predict_model_eval"),
        dict(name="zero_grad"),
        dict(name="Callback.on_predict_start", args=(trainer, model)),
        dict(name="on_predict_start"),
        dict(name="Callback.on_predict_epoch_start", args=(trainer, model)),
        dict(name="on_predict_epoch_start"),
        *model._predict_batch(trainer, model, batches),
        # TODO: `predict_epoch_end`
        dict(name="Callback.on_predict_epoch_end", args=(trainer, model, [[ANY] * batches])),
        dict(name="on_predict_epoch_end", args=([[ANY] * batches],)),
        dict(name="Callback.on_predict_end", args=(trainer, model)),
        dict(name="on_predict_end"),
        # TODO: `on_predict_model_train`
        dict(name="Callback.teardown", args=(trainer, model), kwargs=dict(stage="predict")),
        dict(name="teardown", kwargs=dict(stage="predict")),
    ]
    assert called == expected


# TODO: add test for tune


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(model)

    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, called):
            super().__init__()

            def call(hook, fn, *args, **kwargs):
                out = fn(*args, **kwargs)
                d = {"name": hook}
                if args:
                    d["args"] = args
                if kwargs:
                    d["kwargs"] = kwargs
                called.append(d)
                return out

            for h in get_members(LightningDataModule):
                attr = getattr(self, h)
                partial_h = partial(call, h, attr)
                update_wrapper(partial_h, attr)
                setattr(self, h, partial_h)

    model = BoringModel()
    batches = 2
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        limit_train_batches=batches,
        limit_val_batches=batches,
        limit_test_batches=batches,
        limit_predict_batches=batches,
        enable_progress_bar=False,
        enable_model_summary=False,
        reload_dataloaders_every_n_epochs=1,
    )

    called = []
    dm = HookedDataModule(called)
    trainer.fit(model, datamodule=dm)
    expected = [
        dict(name="prepare_data"),
        dict(name="setup", kwargs=dict(stage="fit")),
        dict(name="val_dataloader"),
        dict(name="train_dataloader"),
        dict(name="state_dict"),
        dict(name="on_save_checkpoint", args=(ANY,)),
        dict(name="teardown", kwargs=dict(stage="fit")),
    ]
    assert called == expected

    called = []
    dm = HookedDataModule(called)
    trainer.validate(model, datamodule=dm, verbose=False)
    expected = [
        dict(name="prepare_data"),
        dict(name="setup", kwargs=dict(stage="validate")),
        dict(name="val_dataloader"),
        dict(name="teardown", kwargs=dict(stage="validate")),
    ]
    assert called == expected

    called = []
    dm = HookedDataModule(called)
    trainer.test(model, datamodule=dm, verbose=False)
    expected = [
        dict(name="prepare_data"),
        dict(name="setup", kwargs=dict(stage="test")),
        dict(name="test_dataloader"),
        dict(name="teardown", kwargs=dict(stage="test")),
    ]
    assert called == expected

    called = []
    dm = HookedDataModule(called)
    trainer.predict(model, datamodule=dm)
    expected = [
        dict(name="prepare_data"),
        dict(name="setup", kwargs=dict(stage="predict")),
        dict(name="predict_dataloader"),
        dict(name="teardown", kwargs=dict(stage="predict")),
    ]
    assert called == expected