Trainer.predict and Trainer.test reset model state to evaluation

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### Bug description

I want to have dropout active in evaluation mode to generate random outputs. For this, I set the model to train mode before performing the prediction. However, using lightning's `Trainer.predict` resets the model to evaluation mode, essentially disabling dropout, which leads to deterministic outputs. Running the prediction on the raw model works as expected.

Note: Contrary to the version dropdown selection, I am running version 2.6.0.

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

master

### Reproduced in studio

_No response_

### How to reproduce the bug

```python
# %%
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, TensorDataset

# %%
class SimpleModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(10, 1)
self.dropout = torch.nn.Dropout(0.5)

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

def test_step(self, batch, batch_idx):
x, y = batch
out = self(x)
loss = torch.nn.functional.mse_loss(out, y)
self.log("test_loss", loss)
return loss

def predict_step(self, batch, batch_idx):
x, y = batch
return self(x)

# Data
X = torch.randn(100, 10)
y = torch.randn(100, 1)
ds = TensorDataset(X, y)
dl = DataLoader(ds, batch_size=10)

# Model
pl.seed_everything(42)
model = SimpleModel()
trainer = pl.Trainer(accelerator="cpu", devices=1)

# %%
print("--- Evaluate model using Trainer ---")
print("--- Run 1: train(False) ---")
model.train(False)
predictions = trainer.predict(model, dataloaders=dl)
y_pred_deterministic = torch.cat(predictions)

print("--- Run 2: train(True) ---")
model.train(True)
predictions = trainer.predict(model, dataloaders=dl)
y_pred_stochastic = torch.cat(predictions)

are_same = torch.allclose(y_pred_deterministic, y_pred_stochastic)
print(f"Predictions are the same: {are_same}")

print("------------------------------------")
# %%
print("--- Evaluate model using Raw Model Loop ---")
print("--- Run 3: raw train(True) ---")
model.train(False)
with torch.no_grad():
raw_predictions = [model(x) for x, _ in dl]
y_pred_raw_deterministic = torch.cat(raw_predictions)

print("--- Run 4: raw train(True) ---")
model.train(True)
with torch.no_grad():
raw_predictions = [model(x) for x, _ in dl]
y_pred_raw_stochastic = torch.cat(raw_predictions)

are_same = torch.allclose(y_pred_raw_deterministic, y_pred_raw_stochastic)
print(f"Raw predictions are the same: {are_same}")

# %%
```

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

* CUDA:
- GPU: None
- available: False
- version: 12.8
* Lightning:
- lightning-utilities: 0.15.2
- pytorch-lightning: 2.6.0
- torch: 2.9.1
- torchinfo: 1.8.0
- torchmetrics: 1.8.2
- torchview: 0.2.7
* Packages:
- absl-py: 2.3.1
- aiohappyeyeballs: 2.6.1
- aiohttp: 3.13.2
- aiosignal: 1.4.0
- alembic: 1.17.2
- alibi-detect: 0.13.0
- altair: 6.0.0
- annotated-doc: 0.0.4
- annotated-types: 0.7.0
- anyio: 4.12.0
- appdirs: 1.4.4
- argon2-cffi: 25.1.0
- argon2-cffi-bindings: 25.1.0
- arrow: 1.4.0
- astor: 0.8.1
- asttokens: 3.0.1
- astunparse: 1.6.3
- async-lru: 2.0.5
- attrs: 25.4.0
- autocommand: 2.2.2
- babel: 2.17.0
- backports-zstd: 1.2.0
- backports.tarfile: 1.2.0
- beautifulsoup4: 4.14.3
- bleach: 6.3.0
- blinker: 1.9.0
- bokeh: 3.8.1
- brotli: 1.2.0
- cachetools: 6.2.2
- cartes: 0.8.5
- cartopy: 0.25.0
- catalogue: 2.0.10
- certifi: 2025.11.12
- cffi: 2.0.0
- charset-normalizer: 3.4.4
- cheroot: 11.1.2
- click: 8.2.1
- cloudpickle: 3.1.2
- cmdstanpy: 1.3.0
- coloredlogs: 15.0.1
- comm: 0.2.3
- contourpy: 1.3.3
- cramjam: 2.11.0
- cryptography: 46.0.3
- cycler: 0.12.1
- databricks-sdk: 0.73.0
- debugpy: 1.8.17
- decorator: 5.2.1
- defusedxml: 0.7.1
- dill: 0.3.9
- dm-tree: 0.1.9
- docker: 7.1.0
- etils: 1.13.0
- executing: 2.2.1
- fastapi: 0.124.0
- fastjsonschema: 2.21.2
- fastparquet: 2024.11.0
- filelock: 3.20.0
- flask: 3.1.2
- flask-cors: 6.0.1
- flatbuffers: 25.9.23
- flexcache: 0.3
- flexparser: 0.4
- fonttools: 4.61.0
- fqdn: 1.5.1
- frozenlist: 1.8.0
- fsspec: 2025.12.0
- gast: 0.7.0
- gcsfs: 2025.12.0
- geopandas: 1.1.1
- gitdb: 4.0.12
- gitpython: 3.1.45
- google-api-core: 2.28.1
- google-auth: 2.43.0
- google-auth-oauthlib: 1.2.2
- google-cloud-core: 2.5.0
- google-cloud-storage: 3.6.0
- google-cloud-storage-control: 1.8.0
- google-crc32c: 1.7.1
- google-pasta: 0.2.0
- google-resumable-media: 2.8.0
- googleapis-common-protos: 1.72.0
- graphene: 3.4.3
- graphql-core: 3.2.7
- graphql-relay: 3.2.0
- graphviz: 0.21
- greenlet: 3.3.0
- grpc-google-iam-v1: 0.14.3
- grpcio: 1.76.0
- grpcio-status: 1.76.0
- gunicorn: 23.0.0
- gviz-api: 1.10.0
- h11: 0.16.0
- h2: 4.3.0
- h5py: 3.15.1
- hf-xet: 1.2.0
- holidays: 0.86
- hpack: 4.1.0
- httpcore: 1.0.9
- httpx: 0.28.1
- huggingface-hub: 0.36.0
- humanfriendly: 10.0
- hyperframe: 6.1.0
- idna: 3.11
- imageio: 2.37.2
- importlib-metadata: 8.7.0
- importlib-resources: 6.5.2
- impunity: 1.0.5
- inflate64: 1.0.4
- inflect: 7.3.1
- iniconfig: 2.3.0
- ipykernel: 7.1.0
- ipython: 9.8.0
- ipython-pygments-lexers: 1.1.1
- ipywidgets: 8.1.8
- isoduration: 20.11.0
- itsdangerous: 2.2.0
- jaraco-functools: 4.3.0
- jaraco.classes: 3.4.0
- jaraco.collections: 5.1.0
- jaraco.context: 6.0.1
- jaraco.functools: 4.0.1
- jaraco.text: 3.12.1
- jedi: 0.19.2
- jeepney: 0.9.0
- jinja2: 3.1.6
- joblib: 1.5.2
- json5: 0.12.1
- jsonpointer: 3.0.0
- jsonschema: 4.25.1
- jsonschema-specifications: 2025.9.1
- jupyter: 1.1.1
- jupyter-client: 8.6.3
- jupyter-console: 6.6.3
- jupyter-core: 5.9.1
- jupyter-events: 0.12.0
- jupyter-lsp: 2.3.0
- jupyter-server: 2.17.0
- jupyter-server-terminals: 0.5.3
- jupyterlab: 4.5.0
- jupyterlab-pygments: 0.3.0
- jupyterlab-server: 2.28.0
- jupyterlab-widgets: 3.0.16
- keopscore: 2.2.3
- keras: 3.12.0
- keras-tuner: 1.4.8
- keyring: 25.7.0
- kiwisolver: 1.4.9
- kt-legacy: 1.0.5
- lark: 1.3.1
- lazy-loader: 0.4
- libclang: 18.1.1
- librt: 0.7.3
- lightning-utilities: 0.15.2
- llvmlite: 0.46.0
- lxml: 6.0.2
- lz4: 4.4.5
- mako: 1.3.10
- markdown: 3.10
- markdown-it-py: 4.0.0
- markupsafe: 3.0.3
- matplotlib: 3.10.7
- matplotlib-inline: 0.2.1
- mdurl: 0.1.2
- metar: 1.11.0
- minio: 7.2.20
- mistune: 3.1.4
- ml-dtypes: 0.5.4
- mlflow: 3.5.1
- mlflow-skinny: 3.5.1
- mlflow-tracing: 3.5.1
- more-itertools: 10.8.0
- mpmath: 1.3.0
- msgpack: 1.1.2
- multidict: 6.7.0
- multivolumefile: 0.2.3
- mypy: 1.19.0
- mypy-extensions: 1.1.0
- namex: 0.1.0
- narwhals: 2.13.0
- nbclient: 0.10.2
- nbconvert: 7.16.6
- nbformat: 5.10.4
- nest-asyncio: 1.6.0
- networkx: 3.6.1
- notebook: 7.5.0
- notebook-shim: 0.2.4
- numba: 0.63.0
- numpy: 2.3.5
- nvidia-cublas-cu12: 12.8.4.1
- nvidia-cuda-cupti-cu12: 12.8.90
- nvidia-cuda-nvcc-cu12: 12.9.86
- nvidia-cuda-nvrtc-cu12: 12.8.93
- nvidia-cuda-runtime-cu12: 12.8.90
- nvidia-cudnn-cu12: 9.10.2.21
- nvidia-cufft-cu12: 11.3.3.83
- nvidia-cufile-cu12: 1.13.1.3
- nvidia-curand-cu12: 10.3.9.90
- nvidia-cusolver-cu12: 11.7.3.90
- nvidia-cusparse-cu12: 12.5.8.93
- nvidia-cusparselt-cu12: 0.7.1
- nvidia-nccl-cu12: 2.27.5
- nvidia-nvjitlink-cu12: 12.8.93
- nvidia-nvshmem-cu12: 3.3.20
- nvidia-nvtx-cu12: 12.8.90
- oauthlib: 3.3.1
- onnxruntime: 1.23.2
- openap: 2.4
- opencv-python: 4.11.0.86
- opentelemetry-api: 1.39.0
- opentelemetry-proto: 1.39.0
- opentelemetry-sdk: 1.39.0
- opentelemetry-semantic-conventions: 0.60b0
- opt-einsum: 3.4.0
- optree: 0.18.0
- orjson: 3.11.5
- overrides: 7.7.0
- packaging: 25.0
- pandas: 2.3.3
- pandocfilters: 1.5.1
- parso: 0.8.5
- pathspec: 0.12.1
- patsy: 1.0.2
- pexpect: 4.9.0
- pillow: 10.4.0
- pint: 0.25.2
- pitot: 0.3.2
- platformdirs: 4.5.1
- plotly: 6.5.0
- pluggy: 1.6.0
- prometheus-client: 0.23.1
- prompt-toolkit: 3.0.52
- propcache: 0.4.1
- properscoring: 0.1
- prophet: 1.2.1
- proto-plus: 1.26.1
- protobuf: 6.33.2
- psutil: 7.1.3
- ptyprocess: 0.7.0
- pure-eval: 0.2.3
- py7zr: 1.0.0
- pyarrow: 21.0.0
- pyasn1: 0.6.1
- pyasn1-modules: 0.4.2
- pybcj: 1.0.7
- pybind11: 3.0.1
- pycparser: 2.23
- pycryptodome: 3.23.0
- pycryptodomex: 3.23.0
- pydantic: 2.12.5
- pydantic-core: 2.41.5
- pygments: 2.19.2
- pyjwt: 2.10.1
- pykeops: 2.2.3
- pynverse: 0.1.4.6
- pyod: 2.0.6
- pyogrio: 0.12.1
- pyopensky: 2.15
- pyparsing: 3.2.5
- pyppmd: 1.2.0
- pyproj: 3.7.2
- pyshp: 3.0.3
- pytest: 9.0.2
- python-dateutil: 2.9.0.post0
- python-dotenv: 1.2.1
- python-json-logger: 4.0.0
- pytorch-lightning: 2.6.0
- pytz: 2025.2
- pyyaml: 6.0.3
- pyzmq: 27.1.0
- pyzstd: 0.19.0
- quantile-forest: 1.4.1
- ray: 2.52.1
- referencing: 0.37.0
- regex: 2025.11.3
- requests: 2.32.5
- requests-oauthlib: 2.0.0
- rfc3339-validator: 0.1.4
- rfc3986-validator: 0.1.1
- rfc3987-syntax: 1.1.0
- rich: 14.2.0
- rpds-py: 0.30.0
- rs1090: 0.4.14
- rsa: 4.9.1
- ruff: 0.14.8
- safetensors: 0.7.0
- scikit-image: 0.25.2
- scikit-learn: 1.7.2
- scipy: 1.16.3
- seaborn: 0.13.2
- secretstorage: 3.5.0
- send2trash: 1.8.3
- setuptools: 80.9.0
- sfoutils: 0.2.0
- shap: 0.50.0
- shapely: 2.1.2
- six: 1.17.0
- sklearn-quantile: 0.1.1
- slicer: 0.0.8
- smmap: 5.0.2
- soupsieve: 2.8
- sqlalchemy: 2.0.44
- sqlparse: 0.5.4
- stack-data: 0.6.3
- stanio: 0.5.1
- starlette: 0.50.0
- statsmodels: 0.14.6
- sympy: 1.14.0
- tensorboard: 2.20.0
- tensorboard-data-server: 0.7.2
- tensorboard-plugin-profile: 2.21.3
- tensorboardx: 2.6.4
- tensorflow: 2.20.0
- tensorflow-docs: 2025.12.2.70325
- tensorflow-probability: 0.25.0
- termcolor: 3.2.0
- terminado: 0.18.1
- texttable: 1.7.0
- tf-keras: 2.20.1
- threadpoolctl: 3.6.0
- tifffile: 2025.10.16
- tinycss2: 1.4.0
- tokenizers: 0.21.4
- toml: 0.10.2
- tomli: 2.0.1
- torch: 2.9.1
- torchinfo: 1.8.0
- torchmetrics: 1.8.2
- torchview: 0.2.7
- tornado: 6.5.2
- tqdm: 4.67.1
- traffic: 2.13.post16.dev0+9e00a83
- traitlets: 5.14.3
- transformers: 4.51.3
- trino: 0.336.0
- triton: 3.5.1
- tudcolors: 0.0.1
- typeguard: 4.3.0
- types-protobuf: 6.32.1.20251105
- types-requests: 2.32.4.20250913
- types-tensorflow: 2.18.0.20251008
- typing-extensions: 4.15.0
- typing-inspection: 0.4.2
- tzdata: 2025.2
- tzlocal: 5.3.1
- uri-template: 1.3.0
- urllib3: 2.6.1
- uvicorn: 0.38.0
- wcwidth: 0.2.14
- webcolors: 25.10.0
- webencodings: 0.5.1
- websocket-client: 1.9.0
- werkzeug: 3.1.4
- wheel: 0.45.1
- widgetsnbextension: 4.0.15
- wrapt: 2.0.1
- xprof: 2.21.3
- xyzservices: 2025.11.0
- yarl: 1.22.0
- zipp: 3.23.0
- zstandard: 0.25.0
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor:
- python: 3.13.9
- release: 6.17.9-200.fc42.x86_64
- version: #1 SMP PREEMPT_DYNAMIC Mon Nov 24 22:28:05 UTC 2025

### More info

_No response_

cc @ethanwharris

Start with the Trainer.predict and Trainer.test entry points and run the provided Python reproduction against PyTorch Lightning 2.6.0. Trace where evaluation mode is applied during these calls; done means a model explicitly set to train mode can retain stochastic dropout behavior while predictions or tests run.

Written by the indexing model from the issue text.