add documentation on multi test dataloaders

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…ing-AI#21235)

Bumps [mypy](https://github.com/python/mypy) from 1.18.1 to 1.18.2.
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- [Commits](python/mypy@v1.18.1...v1.18.2)

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…r subclass model mode (Lightning-AI#21246)

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Bumps [click](https://github.com/pallets/click) from 8.2.1 to 8.3.0.
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…>=1.12.0,<1.24.0 in /requirements (Lightning-AI#21252)

build(deps): update onnxruntime requirement in /requirements

Updates the requirements on [onnxruntime](https://github.com/microsoft/onnxruntime) to permit the latest version.
- [Release notes](https://github.com/microsoft/onnxruntime/releases)
- [Changelog](https://github.com/microsoft/onnxruntime/blob/main/docs/ReleaseManagement.md)
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….0 in /requirements (Lightning-AI#21267)

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Updates the requirements on [ipython[notebook]](https://github.com/ipython/ipython) to permit the latest version.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](ipython/ipython@rel-0.8.4...9.6.0)

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Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 6 to 7.
- [Release notes](https://github.com/astral-sh/setup-uv/releases)
- [Commits](astral-sh/setup-uv@v6...v7)

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…ements (Lightning-AI#21283)

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Bumps [pytest-rerunfailures](https://github.com/pytest-dev/pytest-rerunfailures) from 16.0.1 to 16.1.
- [Changelog](https://github.com/pytest-dev/pytest-rerunfailures/blob/master/CHANGES.rst)
- [Commits](pytest-dev/pytest-rerunfailures@16.0.1...16.1)

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* fix: update pytest-rerunfailures version constraints for Python compatibility

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- [github.com/astral-sh/ruff-pre-commit: v0.12.2 → v0.13.3](astral-sh/ruff-pre-commit@v0.12.2...v0.13.3)
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…ghtning-AI#21234)

Bumps [coverage](https://github.com/nedbat/coveragepy) from 7.10.6 to 7.10.7.
- [Release notes](https://github.com/nedbat/coveragepy/releases)
- [Changelog](https://github.com/nedbat/coveragepy/blob/master/CHANGES.rst)
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* fix pylint E1120 error.

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…g package (Lightning-AI#21275)

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Restructure the "Synchronize validation and test logging" section in
accelerator_prepare.rst into a problem-framing intro plus three
subsections (sync_dist, TorchMetrics, manual all_gather), a decision
table, and a common-pitfalls list.

Directly addresses the custom-metric case: accumulate per-step outputs,
call all_gather at epoch end, and compute the metric. The "my compute
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…ightning-AI#21709)

Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.13.0 to 1.14.0.
- [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases)
- [Commits](pypa/gh-action-pypi-publish@ed0c539...cef2210)

---
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…I#21750)

Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 7.6.0 to 8.1.0.
- [Release notes](https://github.com/astral-sh/setup-uv/releases)
- [Commits](astral-sh/setup-uv@37802ad...0880764)

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…htning-AI#21686)

* Fix toggle_optimizer breaking under torch.compile (Lightning-AI#21513)

`LightningModule.toggle_optimizer` and `untoggle_optimizer` mutate
`requires_grad` on parameters to implement multi-optimizer gradient
masking. Dynamo/AOTAutograd does not support `setattr()` on
`Tensor.requires_grad` because it can change a tensor's leaf-ness
mid-graph, so when the `LightningModule` is wrapped with
`torch.compile` tracing either graph-breaks with
"Unsupported: setattr() on Tensor.requires_grad" or raises a
`KeyError` on the internal `param_requires_grad_state` mapping when
the traced parameter references diverge from those held by
`trainer.optimizers`.

Decorate both helpers with `@torch.compiler.disable` (the same
pattern already used for logging bookkeeping in
`logger_connector/result.py`) so they run as opaque Python when
called from a compiled `training_step`. Eager behavior is unchanged.

Adds a CPU regression test that compiles a two-optimizer
`LightningModule` calling `toggle_optimizer` / `untoggle_optimizer`
in `training_step` and exercises one training iteration, plus a
CHANGELOG entry.

* Narrow test_toggle_untoggle to check compiler.disable attribute (Lightning-AI#21513)

The previous regression test compiled a `LightningModule` end-to-end
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limitation: tracing `self.trainer.strategy._lightning_optimizers`
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across all CI platforms and torch versions.

The shipped fix — `@torch.compiler.disable` on `toggle_optimizer` /
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to verify; it only guarantees Dynamo skips those two methods.
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methods carry the `_torchdynamo_disable` marker installed by
`torch.compiler.disable`, following the same `has_dynamo(fn)` pattern
already used by `tests/utilities/test_compile.py::test_compile_uncompile`.

Toggle/untoggle functional correctness remains covered by the existing
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`test_toggle_untoggle_3_optimizers_shared_parameters` tests in this
file.

---------

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…g-AI#21707)

* feat: add filter_keys​ to log only specified device stats

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…c & Trainer (Lightning-AI#21746)

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* added arguments

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* Fix: add weights_only parameter to LearningRateFinder and propagate through LR finder call chain

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…ontract (Lightning-AI#21774)

* fix(strategy): pass CPU device to FSDP device_id on CPU

Passing `device_id=None` (which is `root_device.index` for CPU) triggers a guard in PyTorch >= 2.5 ("FSDP needs a non-CPU accelerator device"). Passing the actual `torch.device("cpu")` avoids this guard and allows FSDP to run on CPU.

- Update `FSDPStrategy` in both Fabric and PyTorch to pass `self.root_device` if it is a CPU device, otherwise `self.root_device.index`.
- Add unit tests to verify `device_id` selection for CPU and GPU devices.

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* Profile dataloader initialization and update profiler docs

* docs: update Advanced Profiler example to use run_training_batch

* update

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