Improves typing so that all the optimizer subclasses (which all of them that subtype step) do not erase their type signature when this decorator is used. Now *kwarg values and returns will propogate
This complements @tsunghsienlee PR #153367 as the type signature of step() was being erased on all the optimizer subclasses by this untyped decorator
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153374
Approved by: https://github.com/janeyx99, https://github.com/tsunghsienlee
As per this [discussion](https://discuss.pytorch.org/t/a-question-about-requiredparameter/137977), I figured that `_RequiredParameter` is no longer used.
The `required` object was initially introduced in this [PR](4db6667923) as the `SGD` optimizer did not offer a default value for the learning rate. However there isn't a single place in the code base using `_RequiredParameter`, nor `required`. I am therefore removing unused `_RequiredParameter` and `required`.
Everything not included in this PR is Not a Contribution.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144771
Approved by: https://github.com/janeyx99
A proposal addressing Issue #1489: **Optimizer should track parameter names and not id.**
(also mentioned in here: [[RFC] Introducing FQNs/clarity eyeglasses to optim state_dict](https://dev-discuss.pytorch.org/t/rfc-introducing-fqns-clarity-to-optim-state-dict/1552)
## Summary
This PR introduces a backward-compatible enhancement where optimizers track parameter names instead of just their id.
Optimizers can be initialized with `named_parameters()` as:
```python
optimizer = optim.SGD(model.named_parameters(), lr=0.01, momentum=0.9)
```
This allows for greater clarity and ease when handling optimizers, as the parameters' names are preserved within the optimizer’s `state_dict` as:
```
state_dict =
{
'state': {
0: {'momentum_buffer': tensor(...), ...},
1: {'momentum_buffer': tensor(...), ...},
},
'param_groups': [
{
'lr': 0.01,
'weight_decay': 0,
...
'params': [0,1]
'param_names' ['layer.weight', 'layer.bias'] (optional)
}
]
}
```
Loading `state_dict` is not changed (backward-compatible) and the `param_names` key will be ignored.
## Key Features
#### Named Parameters in Optimizer Initialization:
Optimizers can accept the output of `model.named_parameters()` during initialization, allowing them to store parameter names directly.
#### Parameter Names in `state_dict`:
The parameter names are saved as a list in the optimizer’s `state_dict` with key `param_names`, alongside the `params` indices, ensuring seamless tracking of both names and parameters.
## Backward Compatibility
#### No Breaking Changes:
This change is fully backward-compatible. The added `param_names` key in the optimizer's `state_dict` is ignored when loading a state to the optimizer.
#### Customization with Hooks:
For more control, the loaded state_dict can be modified using a custom `register_load_state_dict_pre_hook`, providing flexibility for different design needs.
## Documentation Updates
Please refer to the documentation changes for more details on how this feature is implemented and how it can be used effectively.
## Solution Example:
A suggested solution to the problem mentioned in #1489, for the same parameters but in a different order.
The following `register_load_state_dict_pre_hook` should be added to the optimizer before loading to enable loading the state dict :
```python
def adapt_state_dict_ids(optimizer, state_dict):
# assuming a single param group.
current_state_group = optimizer.state_dict()['param_groups'][0]
loaded_state_group = state_dict['param_groups'][0]
# same number of params, same names, only different ordering
current_state_name_to_id_mapping = {} # mapping -- param_name: id
for i, name in enumerate(current_state_group['param_names']):
current_state_name_to_id_mapping[name] = current_state_group['params'][i]
# changing the ids of the loaded state dict to match the order of the given state dict.
for i, name in enumerate(current_state_group['param_names']):
loaded_state_group['params'][i] = current_state_name_to_id_mapping[name]
return state_dict
```
In this code, the loaded `state_dict` ids are adapted to match the order of the current optimizer `state_dict`.
Both the previous and the current optimizers are required to be initiated with `named_parameters()` to have the 'param_names' key in the dict.
### Note
This is my first contribution to PyTorch, and I wish to receive feedback or suggestions for improvement.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134107
Approved by: https://github.com/janeyx99
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
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>
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
