Restoring a checkpoint (from model checkpoint) via `trainer.test(model_module, ckpt_path='best')` doesn't restore associated `current_epoch` within the trainer

Nobody has claimed this yet.

### Bug description

When a checkpoint (presumably the 'best' one saved by model checkpoint monitor) is restored to be used for testing via `trainer.test(model_module, ckpt_path='best')` the `current_epoch` member of the trainer is not restored to the one associated with the checkpoint, even though the checkpoint file stores the correct epoch value.

I am not sure whether this is intended (seems rather incorrect to me). I asked a question about this problem at the Discord forum before, but got no response, so I decided to open an issue here.

### What version are you seeing the problem on?

v2.2

### How to reproduce the bug

```python
import os

import torch
from lightning.pytorch import LightningModule, Trainer, callbacks, seed_everything
from torch.utils.data import DataLoader, Dataset

class RandomDataset(Dataset):
def __init__(self, size, length, offset = 0):
self.len = length
self.data = torch.randn(length, size) + offset

def __getitem__(self, index):
return self.data[index]

def __len__(self):
return self.len

class BoringModel(LightningModule):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(32, 2)

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

def training_step(self, batch, batch_idx):
loss = self(batch).sum()
self.log("train_loss", loss)
return {"loss": loss}

def validation_step(self, batch, batch_idx):
loss = self(batch).sum()
self.log("valid_loss", loss)

def test_step(self, batch, batch_idx):
loss = self(batch).sum()
print(f'CURRENT EPOCH: {self.current_epoch}')
self.log("test_loss", loss)

def configure_optimizers(self):
return torch.optim.SGD(self.layer.parameters(), lr=0.1)

def run():
seed_everything(10)

train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
val_data = DataLoader(RandomDataset(32, 64, offset=16), batch_size=2)
test_data = DataLoader(RandomDataset(32, 64), batch_size=2)

model = BoringModel()
trainer = Trainer(
default_root_dir=os.getcwd(),
num_sanity_val_steps=0,
limit_train_batches=1,
limit_val_batches=1,
limit_test_batches=1,
max_epochs=2,
enable_model_summary=False,
callbacks=[(mc := callbacks.ModelCheckpoint(monitor="valid_loss", verbose=True))],
)
trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
trainer.test(model, dataloaders=test_data, ckpt_path="best")
print(f'CKPT EPOCH: {torch.load(mc.best_model_path, map_location="cpu")["epoch"]}')

if __name__ == "__main__":
run()
```

### Error messages and logs

```
Epoch 0: 100%|███████████████████████████████████████████| 32/32 [00:00<00:00, 277.12it/s, v_num=7]
Epoch 0, global step 32: 'valid_loss' reached 183.69585 (best 183.69585), saving model to '/Users/maciej/Desktop/lightning_logs/version_7/checkpoints/epoch=0-step=32.ckpt' as top 1
Epoch 1: 100%|███████████████████████████████████████████| 32/32 [00:00<00:00, 432.74it/s, v_num=7]
Epoch 1, global step 64: 'valid_loss' was not in top 1
`Trainer.fit` stopped: `max_epochs=2` reached.
Epoch 1: 100%|███████████████████████████████████████████| 32/32 [00:00<00:00, 410.05it/s, v_num=7]
Restoring states from the checkpoint path at /Users/maciej/Desktop/lightning_logs/version_7/checkpoints/epoch=0-step=32.ckpt
Loaded model weights from the checkpoint at /Users/maciej/Desktop/lightning_logs/version_7/checkpoints/epoch=0-step=32.ckpt
/Users/maciej/.local/share/pyenv/versions/3.11.7/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.
Testing DataLoader 0: 0%| | 0/1 [00:00
Current environment

```
* CUDA:
- GPU: None
- available: False
- version: None
* Lightning:
- lightning: 2.2.0.post0
- lightning-utilities: 0.10.1
- pytorch-lightning: 2.2.0.post0
- torch: 2.2.1
- torchmetrics: 1.3.1
- torchvision: 0.17.1
* Packages:
- aiohttp: 3.9.3
- aiohttp-retry: 2.8.3
- aiosignal: 1.3.1
- amqp: 5.2.0
- annotated-types: 0.6.0
- antlr4-python3-runtime: 4.9.3
- anyio: 4.3.0
- appdirs: 1.4.4
- appnope: 0.1.4
- argon2-cffi: 23.1.0
- argon2-cffi-bindings: 21.2.0
- arrow: 1.3.0
- astroid: 3.1.0
- asttokens: 2.4.1
- async-lru: 2.0.4
- asyncssh: 2.14.2
- atpublic: 4.0
- attrs: 23.2.0
- autopep8: 2.0.4
- babel: 2.14.0
- backcall: 0.2.0
- beautifulsoup4: 4.12.3
- billiard: 4.2.0
- black: 24.2.0
- bleach: 6.1.0
- blinker: 1.7.0
- celery: 5.3.6
- certifi: 2024.2.2
- cffi: 1.16.0
- charset-normalizer: 3.3.2
- click: 8.1.7
- click-didyoumean: 0.3.0
- click-plugins: 1.1.1
- click-repl: 0.3.0
- colorama: 0.4.6
- comm: 0.2.1
- configobj: 5.0.8
- contourpy: 1.2.0
- cryptography: 42.0.5
- cycler: 0.12.1
- dash: 2.15.0
- dash-core-components: 2.0.0
- dash-html-components: 2.0.0
- dash-table: 5.0.0
- debugpy: 1.8.1
- decorator: 5.1.1
- defusedxml: 0.7.1
- dictdiffer: 0.9.0
- dill: 0.3.8
- diskcache: 5.6.3
- distlib: 0.3.8
- distro: 1.9.0
- docopt: 0.6.2
- dpath: 2.1.6
- dulwich: 0.21.7
- dvc: 3.48.0
- dvc-data: 3.13.0
- dvc-http: 2.32.0
- dvc-objects: 5.0.0
- dvc-render: 1.0.1
- dvc-studio-client: 0.20.0
- dvc-task: 0.3.0
- einops: 0.7.0
- entrypoints: 0.4
- executing: 2.0.1
- fastjsonschema: 2.19.1
- filelock: 3.13.1
- flake8: 7.0.0
- flask: 3.0.2
- flatten-dict: 0.4.2
- flufl.lock: 7.1.1
- fonttools: 4.49.0
- fqdn: 1.5.1
- frozenlist: 1.4.1
- fsspec: 2024.2.0
- funcy: 2.0
- gitdb: 4.0.11
- gitpython: 3.1.42
- grandalf: 0.8
- greenlet: 3.0.3
- gto: 1.7.0
- h11: 0.14.0
- h5py: 3.10.0
- httpcore: 1.0.4
- httpx: 0.27.0
- hydra-core: 1.3.2
- idna: 3.6
- imageio: 2.34.0
- importlib-metadata: 7.0.1
- iniconfig: 2.0.0
- ipdb: 0.13.13
- ipykernel: 6.29.3
- ipython: 8.12.3
- isoduration: 20.11.0
- isort: 5.13.2
- iterative-telemetry: 0.0.8
- itsdangerous: 2.1.2
- jedi: 0.19.1
- jinja2: 3.1.3
- joblib: 1.3.2
- json5: 0.9.17
- jsonpointer: 2.4
- jsonschema: 4.21.1
- jsonschema-specifications: 2023.12.1
- jupyter-client: 8.6.0
- jupyter-core: 5.7.1
- jupyter-events: 0.9.0
- jupyter-lsp: 2.2.3
- jupyter-server: 2.12.5
- jupyter-server-mathjax: 0.2.6
- jupyter-server-terminals: 0.5.2
- jupyterlab: 4.1.2
- jupyterlab-pygments: 0.3.0
- jupyterlab-server: 2.25.3
- kiwisolver: 1.4.5
- kombu: 5.3.5
- lazy-loader: 0.3
- lightning: 2.2.0.post0
- lightning-utilities: 0.10.1
- markdown-it-py: 3.0.0
- markupsafe: 2.1.5
- matplotlib: 3.8.3
- matplotlib-inline: 0.1.6
- mccabe: 0.7.0
- mdurl: 0.1.2
- mistune: 3.0.2
- mpmath: 1.3.0
- msgpack: 1.0.7
- multidict: 6.0.5
- mypy: 1.8.0
- mypy-extensions: 1.0.0
- nbclient: 0.9.0
- nbconvert: 7.16.1
- nbdime: 4.0.1
- nbformat: 5.9.2
- neovim: 0.3.1
- nest-asyncio: 1.6.0
- networkx: 3.2.1
- notebook-shim: 0.2.4
- numpy: 1.26.4
- omegaconf: 2.3.0
- orjson: 3.9.15
- overrides: 7.7.0
- packaging: 23.2
- pandas: 2.2.1
- pandocfilters: 1.5.1
- parso: 0.8.3
- pathspec: 0.12.1
- pexpect: 4.9.0
- pickleshare: 0.7.5
- pillow: 10.2.0
- pip: 23.2.1
- pipreqs: 0.5.0
- platformdirs: 3.11.0
- plotly: 5.19.0
- pluggy: 1.4.0
- prometheus-client: 0.20.0
- prompt-toolkit: 3.0.43
- psutil: 5.9.8
- ptyprocess: 0.7.0
- pure-eval: 0.2.2
- pycodestyle: 2.11.1
- pycparser: 2.21
- pydantic: 2.6.3
- pydantic-core: 2.16.3
- pydocstyle: 6.3.0
- pydot: 2.0.0
- pyflakes: 3.2.0
- pygit2: 1.14.1
- pygments: 2.17.2
- pygtrie: 2.5.0
- pylint: 3.1.0
- pynvim: 0.5.0
- pyparsing: 3.1.1
- pytest: 8.0.2
- pytest-mock: 3.12.0
- python-dateutil: 2.8.2
- python-json-logger: 2.0.7
- pytoolconfig: 1.3.1
- pytorch-lightning: 2.2.0.post0
- pytz: 2024.1
- pyyaml: 6.0.1
- pyzmq: 25.1.2
- referencing: 0.33.0
- rentry: 1.0.1
- requests: 2.31.0
- retrying: 1.3.4
- rfc3339-validator: 0.1.4
- rfc3986-validator: 0.1.1
- rich: 13.7.0
- rope: 1.12.0
- rpds-py: 0.18.0
- ruamel.yaml: 0.18.6
- ruamel.yaml.clib: 0.2.8
- scikit-image: 0.22.0
- scikit-learn: 1.4.1.post1
- scipy: 1.12.0
- scmrepo: 3.1.0
- semver: 3.0.2
- send2trash: 1.8.2
- setuptools: 65.5.0
- shortuuid: 1.0.11
- shtab: 1.7.0
- six: 1.16.0
- smmap: 5.0.1
- sniffio: 1.3.1
- snowballstemmer: 2.2.0
- soupsieve: 2.5
- sqltrie: 0.11.0
- stack-data: 0.6.3
- sympy: 1.12
- tabulate: 0.9.0
- tenacity: 8.2.3
- terminado: 0.18.0
- threadpoolctl: 3.3.0
- tifffile: 2024.2.12
- tinycss2: 1.2.1
- tomlkit: 0.12.4
- torch: 2.2.1
- torchmetrics: 1.3.1
- torchvision: 0.17.1
- tornado: 6.4
- tqdm: 4.66.2
- traitlets: 5.14.1
- typer: 0.9.0
- types-python-dateutil: 2.8.19.20240106
- typing-extensions: 4.10.0
- tzdata: 2024.1
- uri-template: 1.3.0
- urllib3: 2.2.1
- vine: 5.1.0
- virtualenv: 20.25.1
- voluptuous: 0.14.2
- wcwidth: 0.2.13
- webcolors: 1.13
- webencodings: 0.5.1
- websocket-client: 1.7.0
- werkzeug: 3.0.1
- wheel: 0.42.0
- yarg: 0.1.9
- yarl: 1.9.4
- zc.lockfile: 3.0.post1
- zipp: 3.17.0
* System:
- OS: Darwin
- architecture:
- 64bit
-
- processor: arm
- python: 3.11.7
- release: 23.2.0
- version: Darwin Kernel Version 23.2.0: Wed Nov 15 21:53:18 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T6000
```

### More info

_No response_

cc @lantiga

Start with the Trainer.test call using ckpt_path='best', the ModelCheckpoint callback, and the test_step that prints current_epoch. Trace how the selected checkpoint's stored epoch is loaded during testing, then add a regression test using the supplied reproduction to verify that current_epoch matches the checkpoint value.

Written by the indexing model from the issue text.