When stub files (`*.pyi`) were removed from `optim` (#125556, #125452), some types that existed are no longer available. This pull request adds them back.
Just for reference, these types are used in `pytorch-lightning`'s `LightningCLI`. Command line interfaces are created automatically, and having type hints make them nicer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136185
Approved by: https://github.com/janeyx99
Link various classes and functions of the `optim.swa.util` to make doc content accessible from the `torch.optim` doc.
Currently, if you click the link,
https://pytorch.org/docs/stable/optim.html#module-torch.optim.swa_utils it goes to a blank, bottom of the page section of `torch.optim`.
Also,
`torch.optim.swa_utils.AveragedModel` and `torch.optim.swa_utils.SWALR` classes as well as `torch.optim.swa_utils.update_bn()` and `optim.swa_utils.get_ema_multi_avg_fn` are not linked to doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133393
Approved by: https://github.com/janeyx99
There could be some cases where the params have the meta device when calling optimizer's dunder init and those params are materialized in the first computation. This change would allow such situation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131153
Approved by: https://github.com/mlazos, https://github.com/janeyx99
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
This PR adds the foreach impl for Adafactor knowing that there are many ways to improve its runtime perf today (by adding more foreach support). After this PR:
- we have a foreach flag for Adafactor
- It is NOT the default. Why not? It is only slightly faster + uses O(n) more memory where n is the number of params in your max param group. People tend to use Adafactor for memory efficiency.
Next steps:
- make torch.compile possible on it
- make it faster (by adding more foreach apis)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132336
Approved by: https://github.com/albanD
ghstack dependencies: #133360
This PR fixes a bug in `test_correct_module_names` introduced in #130497. It also addresses post-fix test failures in:
* `torch/ao/quantization/__init__.py` - set the correct `__module__` for several public API helpers
* `torch/library.py` - add `register_vmap` to `__all__`
* `torch/nn/attention/flex_attention.py` - make `round_up_to_multiple` private by prepending an underscore
* `torch/storage.py` - introduce `__all__` to avoid `Self` being re-exported as a public API
* `torch/distributed/pipelining/schedules.py` - add `ZeroBubbleAlgorithm` to `__all__`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131386
Approved by: https://github.com/albanD
This PR fixes a bug in `test_correct_module_names` introduced in #130497. It also addresses post-fix test failures in:
* `torch/ao/quantization/__init__.py` - set the correct `__module__` for several public API helpers
* `torch/library.py` - add `register_vmap` to `__all__`
* `torch/nn/attention/flex_attention.py` - make `round_up_to_multiple` private by prepending an underscore
* `torch/storage.py` - introduce `__all__` to avoid `Self` being re-exported as a public API
* `torch/distributed/pipelining/schedules.py` - add `ZeroBubbleAlgorithm` to `__all__`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131386
Approved by: https://github.com/albanD
#109581
At this point, the vanilla implementation (the default) is good.
Docs: https://docs-preview.pytorch.org/pytorch/pytorch/129905/generated/torch.optim.Adafactor.html#torch.optim.Adafactor
Specifically, the impl in this PR, which attempts to replicate the paper,
```
optim = torch.optim.Adafactor([weight])
```
is close enough to https://pytorch-optimizers.readthedocs.io/en/latest/optimizer/#pytorch_optimizer.AdaFactor
```
optim_c = AdaFactor([weight], betas=(0, 0.999), scale_parameter=False)
```
is close enough to https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adafactor
```
optim = keras.optimizers.Adafactor(learning_rate=0.01)
```
The three results respectively for the same randomly generated weights:
```
# ours
tensor([[ 0.3807594, -0.3912092],
[ 0.0762539, 0.5377805],
[ 0.2459473, 0.4662207]])
# pytorch-optimizer
tensor([[ 0.3807592, -0.3912172],
[ 0.0762507, 0.5377818],
[ 0.2459457, 0.4662213]])
# keras
array([[ 0.38076326, -0.39121315],
[ 0.0762547 , 0.5377859 ],
[ 0.24594972, 0.46622536]], dtype=float32)
```
This gives me confidence to move forward in speeding up the implementation now that a baseline has been established. If you're curious about differences:
* keras assigns step_size (rho_t in their code) to `min(lr, 1 / sqrt(step)` whereas the OG impl uses a hardcoded 0.01 instead of lr. We do the same thing as keras, but our lr default is 0.01.
* We differ from the pytorch-optimizers default in that our default will not track momentum (thus `beta1=0`) and we do not apply parameter scaling.
<details>
Keras collab: https://colab.research.google.com/drive/1i3xF8ChL7TWKJGV_5v_5nMhXKnYmQQ06?usp=sharing
My script repro:
```
import torch
from pytorch_optimizer import AdaFactor
torch.set_printoptions(precision=7)
weight = torch.tensor([[ 0.37697506, -0.39500135],
[ 0.07246649, 0.53399765],
[ 0.24216151, 0.46243715]], dtype=torch.float32)
# bias = torch.tensor([0, 0], dtype=torch.float32)
weight.grad = torch.tensor([[-0.5940447, -0.7743838],
[-0.5940447, -0.7743838],
[-0.5940447, -0.7743838]], dtype=torch.float32)
# bias.grad = torch.tensor([-2.5027974, 1.5422692], dtype=torch.float32)
weight_c = weight.clone()
weight_c.grad = weight.grad.clone()
optim = torch.optim.Adafactor([weight])
optim.step()
print(weight)
optim_c = AdaFactor([weight_c], betas=(0, 0.999), scale_parameter=False)
optim_c.step()
print(weight_c)
```
<details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129905
Approved by: https://github.com/albanD
Fix docstrings in Learning Rate Scheduler.
The fix can be verified by running pydocstyle path-to-file --count
Related #112593
**BEFORE the PR:**
pydocstyle torch/optim/lr_scheduler.py --count
92
**AFTER the PR:**
pydocstyle torch/optim/lr_scheduler.py --count
0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128679
Approved by: https://github.com/janeyx99
I'm currently locked into jsonargparse version 4.19.0, and it complains when used in combination with LightningCLI (v2.0.8). This is because it cares about the types declared in google style docstrings. This causes a problem when it tries to parse how it should cast arguments to construct an instance of an LRScheduler class because the docstrings declare the "verbose" parameter as a bool, but the defaults recently changed to a string "deprecated". This means the type should really be `bool | str`.
This PR adds a `| str` to the docstring type in each learning rate scheduler class. This will prevent jsonargparse from complaining.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127943
Approved by: https://github.com/janeyx99
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127122
Approved by: https://github.com/kit1980
This continues the full deprecation after https://github.com/pytorch/pytorch/pull/114425. It's been 6 months! And I'm fairly certain no one is going to yell at me as this patch is not really used.
------
# BC Breaking note
As of this PR, SparseAdam will become consistent with the rest of our optimizers in that it will only accept containers of Tensors/Parameters/param groups and fully complete deprecation of this path. Hitherto, the SparseAdam constructor had allowed raw tensors as the params argument to the constructor. Now, if you write the following code, there will be an error similar to every other optim: "params argument given to the optimizer should be an iterable of Tensors or dicts"
```
import torch
param = torch.rand(16, 32)
optimizer = torch.optim.SparseAdam(param)
```
Instead you should replace the last line with
```
optimizer = torch.optim.SparseAdam([param])
```
to no longer error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127081
Approved by: https://github.com/soulitzer
