This resolves a bug in eager where if an old state dict is loaded (without the capturable flag) but the original dict had the capturable flag, then state_steps would be on cuda but we would take the non-capturable path. We now fallback to eager if capturable=False.
Current design doc and discussion: https://docs.google.com/document/d/1DmmbiaSp16CDZtGw1qzXKHFTY_0gqc0xpnBdviXq0vk/edit#heading=h.871u7bvwz7ze
Note on the actual fallback logic - there was an issue with torchscript originally not handling *args, **kwargs properly, after rectifying that by using `functools.wraps`, there was an additional bug with scoping which required the single tensor implementation to be in the global scope at the time of the fallback closure being created. I pass in the single tensor function to the `_disable_dynamo_if_unsupported` decorator to workaround this bug.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123619
Approved by: https://github.com/janeyx99
Finishes the work started in https://github.com/pytorch/pytorch/pull/118697. Thanks @MarouaneMaatouk for the attempt, but due to inactivity I have opened this PR for Adamax. Note that the new capturable implementation is much simpler and I've modified the foreach capturable impl--it now calls fewer kernels and is more easily comparable to forloop.
Next steps:
* This PR discovered two bugs: #121178 and #121238.
* Move the now hefty graph optim tests in test_cuda to use OptimInfo.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121183
Approved by: https://github.com/albanD
This PR fixes several bugs, listed in priority:
1. `load_state_dict` with a nontensor step was incorrect for capturable and fused implementations since we don't create the tensors on the right device in `__setstate__`. This has been fixed.
2. The most recently added capturable implementations forgot the check that all tensors should be on CUDA for eager. We've now added those checks
3. The most recent change in Adamax only adds capturable for foreach but will silently be incorrect for forloop/single-tensor. I've added erroring and modified testing with many many many skips for that. Honestly my preference after this PR has only been further cemented that we should just do the single tensor and multi tensor capturable implementations together in the future. @mlazos
4. The conditional for adding cuda-supported configs for the optimizer infos was incorrect! So we hadn't been testing capturable! This also stands rectified and was the trigger for this PR in the first place.
5. In a similar way, the conditional for `_get_optim_inputs_including_global_cliquey_kwargs` was incorrect sometimes as well. This has also been corrected.
The following is not a bug, but is just something to make life simpler by not needing to handle Nones: `optim_input_funcs` must now mandatorily take in a `device`, which could be a string or a torch.device.
Details for posterity:
4. Running the test_foreach_matches_forloop test and printing the configs that get printed yields capturable getting included, which is correct.
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (5d50138f)]$ python test/test_optim.py -k test_foreach_matches_forloop_AdamW_cuda
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
_torch_pytree._register_pytree_node(
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={}, desc=default
params=None, kwargs={'lr': 0.01}, desc=non-default lr
params=None, kwargs={'weight_decay': 0.1}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'maximize': True}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True}, desc=amsgrad
params=None, kwargs={'capturable': True}, desc=capturable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True}, desc=capturable, amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True}, desc=Tensor lr with capturable and amsgrad
.
----------------------------------------------------------------------
Ran 1 test in 19.229s
OK
```
5. Running the test_optimizer_can_be_printed test (which calls `_get_optim_inputs_including_global_cliquey_kwargs`) and printing what gets run is also now correct.
```
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={'differentiable': False}, desc=default
params=None, kwargs={'differentiable': True}, desc=default & differentiable
params=None, kwargs={'lr': 0.01, 'differentiable': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'differentiable': True}, desc=non-default lr & differentiable
params=None, kwargs={'weight_decay': 0.1, 'differentiable': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'differentiable': True}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': True}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': True}, desc=amsgrad & differentiable
.params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': False}, desc=default
params=None, kwargs={'foreach': True, 'differentiable': False, 'fused': False}, desc=default & foreach
params=None, kwargs={'foreach': False, 'differentiable': True, 'fused': False}, desc=default & differentiable
params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': True}, desc=default & fused
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'foreach': True, 'differentiable': False, 'fused': False}, desc=non-default lr & foreach
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': True, 'fused': False}, desc=non-default lr & differentiable
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': True}, desc=non-default lr & fused
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'foreach': True, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay & foreach
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': True, 'fused': False}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': True}, desc=nonzero weight_decay & fused
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=maximize & foreach
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=maximize & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=amsgrad & fused
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable
params=None, kwargs={'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable & foreach
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable & differentiable
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable, amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable, amsgrad & fused
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad & foreach
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=Tensor lr with capturable and amsgrad & differentiable
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=Tensor lr with capturable and amsgrad & fused
.
----------------------------------------------------------------------
Ran 2 tests in 11.112s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118326
Approved by: https://github.com/mlazos
This PR fixes several bugs, listed in priority:
1. `load_state_dict` with a nontensor step was incorrect for capturable and fused implementations since we don't create the tensors on the right device in `__setstate__`. This has been fixed.
2. The most recently added capturable implementations forgot the check that all tensors should be on CUDA for eager. We've now added those checks
3. The most recent change in Adamax only adds capturable for foreach but will silently be incorrect for forloop/single-tensor. I've added erroring and modified testing with many many many skips for that. Honestly my preference after this PR has only been further cemented that we should just do the single tensor and multi tensor capturable implementations together in the future. @mlazos
4. The conditional for adding cuda-supported configs for the optimizer infos was incorrect! So we hadn't been testing capturable! This also stands rectified and was the trigger for this PR in the first place.
5. In a similar way, the conditional for `_get_optim_inputs_including_global_cliquey_kwargs` was incorrect sometimes as well. This has also been corrected.
The following is not a bug, but is just something to make life simpler by not needing to handle Nones: `optim_input_funcs` must now mandatorily take in a `device`, which could be a string or a torch.device.
Details for posterity:
4. Running the test_foreach_matches_forloop test and printing the configs that get printed yields capturable getting included, which is correct.
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (5d50138f)]$ python test/test_optim.py -k test_foreach_matches_forloop_AdamW_cuda
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
_torch_pytree._register_pytree_node(
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={}, desc=default
params=None, kwargs={'lr': 0.01}, desc=non-default lr
params=None, kwargs={'weight_decay': 0.1}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'maximize': True}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True}, desc=amsgrad
params=None, kwargs={'capturable': True}, desc=capturable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True}, desc=capturable, amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True}, desc=Tensor lr with capturable and amsgrad
.
----------------------------------------------------------------------
Ran 1 test in 19.229s
OK
```
5. Running the test_optimizer_can_be_printed test (which calls `_get_optim_inputs_including_global_cliquey_kwargs`) and printing what gets run is also now correct.
```
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={'differentiable': False}, desc=default
params=None, kwargs={'differentiable': True}, desc=default & differentiable
params=None, kwargs={'lr': 0.01, 'differentiable': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'differentiable': True}, desc=non-default lr & differentiable
params=None, kwargs={'weight_decay': 0.1, 'differentiable': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'differentiable': True}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': True}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': True}, desc=amsgrad & differentiable
.params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': False}, desc=default
params=None, kwargs={'foreach': True, 'differentiable': False, 'fused': False}, desc=default & foreach
params=None, kwargs={'foreach': False, 'differentiable': True, 'fused': False}, desc=default & differentiable
params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': True}, desc=default & fused
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'foreach': True, 'differentiable': False, 'fused': False}, desc=non-default lr & foreach
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': True, 'fused': False}, desc=non-default lr & differentiable
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': True}, desc=non-default lr & fused
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'foreach': True, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay & foreach
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': True, 'fused': False}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': True}, desc=nonzero weight_decay & fused
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=maximize & foreach
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=maximize & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=amsgrad & fused
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable
params=None, kwargs={'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable & foreach
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable & differentiable
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable, amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable, amsgrad & fused
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad & foreach
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=Tensor lr with capturable and amsgrad & differentiable
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=Tensor lr with capturable and amsgrad & fused
.
----------------------------------------------------------------------
Ran 2 tests in 11.112s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118326
Approved by: https://github.com/mlazos
This PR does what it says and more.
1. We increase coverage by a LOT! Previously, complex was not tested for many many configs, including foreach + maximize at the same time. Or the fused impls. Or just random configs people forgot about.
2. I rearranged the maximize conditional and the _view_as_real to preserve list-ness. This is needed for _view_as_real to function properly, I did add a comment in the Files Changed. This new order also just...makes more aesthetic sense.
3. Note that LBFGS and SparseAdam are skipped--they don't support complex and now we know.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118160
Approved by: https://github.com/mikaylagawarecki
Should allow for uses cases mentioned in #110940
This would allow scalars to also be float64s in the foreach implementation. The fused implementation would still create a float32 step on Adam and AdamW. This PR also does NOT worry about performance and is mainly for enablement.
Next steps:
- Relax the constraint on fused adam(w) and allow torch.float64 scalars there
- Allow _performant_ mixed dtypes in foreach (a bigger project in itself).
This PR will conflict with my other PRs, I will figure out a landing order
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115841
Approved by: https://github.com/albanD
Fixes#112592
1) **File: torch/cuda/random.py**
```
Before:
/content/pytorch/torch/cuda/random.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/random.py:21 in public function `get_rng_state`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/random.py:43 in public function `get_rng_state_all`:
D202: No blank lines allowed after function docstring (found 1)
/content/pytorch/torch/cuda/random.py:43 in public function `get_rng_state_all`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/random.py:54 in public function `set_rng_state`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D208: Docstring is over-indented
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D209: Multi-line docstring closing quotes should be on a separate line
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D414: Section has no content ('Args')
/content/pytorch/torch/cuda/random.py:88 in public function `manual_seed`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:88 in public function `manual_seed`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:110 in public function `manual_seed_all`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:110 in public function `manual_seed_all`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:128 in public function `seed`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:128 in public function `seed`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:146 in public function `seed_all`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:146 in public function `seed_all`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:167 in public function `initial_seed`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
18
```
```
After:
/content/pytorch/torch/cuda/random.py:1 at module level:
D100: Missing docstring in public module
1
```
2) **File: torch/cuda/amp/autocast_mode.py**
```
Before: /content/pytorch/torch/cuda/amp/autocast_mode.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/autocast_mode.py:18 in public class `autocast`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/autocast_mode.py:23 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/autocast_mode.py:38 in public method `__enter__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:44 in public method `__exit__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:49 in public method `__call__`:
D102: Missing docstring in public method
/content/pytorch/torch/cuda/amp/autocast_mode.py:90 in public function `custom_fwd`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/autocast_mode.py:90 in public function `custom_fwd`:
D400: First line should end with a period (not 'f')
/content/pytorch/torch/cuda/amp/autocast_mode.py:90 in public function `custom_fwd`:
D401: First line should be in imperative mood; try rephrasing (found 'Helper')
/content/pytorch/torch/cuda/amp/autocast_mode.py:130 in public function `custom_bwd`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/autocast_mode.py:130 in public function `custom_bwd`:
D400: First line should end with a period (not 'f')
/content/pytorch/torch/cuda/amp/autocast_mode.py:130 in public function `custom_bwd`:
D401: First line should be in imperative mood; try rephrasing (found 'Helper')
12
```
```
After:
/content/pytorch/torch/cuda/amp/autocast_mode.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/autocast_mode.py:23 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/autocast_mode.py:38 in public method `__enter__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:44 in public method `__exit__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:49 in public method `__call__`:
D102: Missing docstring in public method
5
```
3) **File: torch/cuda/amp/grad_scaler.py**
```
Before: /content/pytorch/torch/cuda/amp/grad_scaler.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/grad_scaler.py:17 in private class `_MultiDeviceReplicator`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:39 in public class `OptState`:
D101: Missing docstring in public class
/content/pytorch/torch/cuda/amp/grad_scaler.py:50 in public class `GradScaler`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:50 in public class `GradScaler`:
D400: First line should end with a period (not 'g')
/content/pytorch/torch/cuda/amp/grad_scaler.py:115 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/grad_scaler.py:354 in public method `step`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:456 in public method `update`:
D401: First line should be in imperative mood (perhaps 'Update', not 'Updates')
/content/pytorch/torch/cuda/amp/grad_scaler.py:529 in public method `get_scale`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:544 in public method `get_growth_factor`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:544 in public method `get_growth_factor`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:550 in public method `set_growth_factor`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:550 in public method `set_growth_factor`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:557 in public method `get_backoff_factor`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:557 in public method `get_backoff_factor`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:563 in public method `set_backoff_factor`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:563 in public method `set_backoff_factor`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:570 in public method `get_growth_interval`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:570 in public method `get_growth_interval`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:576 in public method `set_growth_interval`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:576 in public method `set_growth_interval`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:592 in public method `is_enabled`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:592 in public method `is_enabled`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:598 in public method `state_dict`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:598 in public method `state_dict`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:624 in public method `load_state_dict`:
D401: First line should be in imperative mood (perhaps 'Load', not 'Loads')
/content/pytorch/torch/cuda/amp/grad_scaler.py:649 in public method `__getstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/grad_scaler.py:665 in public method `__setstate__`:
D105: Missing docstring in magic method
28
```
```
After:
/content/pytorch/torch/cuda/amp/grad_scaler.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/grad_scaler.py:40 in public class `OptState`:
D101: Missing docstring in public class
/content/pytorch/torch/cuda/amp/grad_scaler.py:117 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/grad_scaler.py:647 in public method `__getstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/grad_scaler.py:663 in public method `__setstate__`:
D105: Missing docstring in magic method
5
```
4) **File: torch/optim/_functional.py**
```
Before:
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D400: First line should end with a period (not 'e')
1
```
```
After:
0
```
5) **File: torch/optim/__init__.py**
```
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D205: 1 blank line required between summary line and description (found 0)
1
```
```
After:
0
```
6) **File: torch/optim/lbfgs.py**
```
Before:
/content/pytorch/torch/optim/lbfgs.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/lbfgs.py:185 in public class `LBFGS`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/optim/lbfgs.py:185 in public class `LBFGS`:
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/content/pytorch/torch/optim/lbfgs.py:215 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/lbfgs.py:285 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
5
```
```
After:
/content/pytorch/torch/optim/lbfgs.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/lbfgs.py:217 in public method `__init__`:
D107: Missing docstring in __init__
2
```
7)**File: torch/optim/sparse_adam.py**
```
Before: /content/pytorch/torch/optim/sparse_adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/sparse_adam.py:7 in public class `SparseAdam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/sparse_adam.py:8 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/sparse_adam.py:40 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
4
```
```
After:
/content/pytorch/torch/optim/sparse_adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/sparse_adam.py:7 in public class `SparseAdam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/sparse_adam.py:8 in public method `__init__`:
D107: Missing docstring in __init__
3
```
8) **File:torch/optim/adadelta.py**
```
Before:
/content/pytorch/torch/optim/adadelta.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adadelta.py:11 in public class `Adadelta`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adadelta.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adadelta.py:44 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adadelta.py:82 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adadelta.py:193 in public function `adadelta`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/adadelta.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adadelta.py:11 in public class `Adadelta`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adadelta.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adadelta.py:44 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
9) **File: torch/optim/adagrad.py**
```
Before:
/content/pytorch/torch/optim/adagrad.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adagrad.py:11 in public class `Adagrad`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adagrad.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adagrad.py:63 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adagrad.py:78 in public method `share_memory`:
D102: Missing docstring in public method
/content/pytorch/torch/optim/adagrad.py:100 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adagrad.py:201 in public function `adagrad`:
D202: No blank lines allowed after function docstring (found 1)
7
```
```
After:
/content/pytorch/torch/optim/adagrad.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adagrad.py:11 in public class `Adagrad`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adagrad.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adagrad.py:63 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adagrad.py:78 in public method `share_memory`:
D102: Missing docstring in public method
5
```
10) **File: torch/optim/adam.py**
```
Before:
/content/pytorch/torch/optim/adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adam.py:14 in public class `Adam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adam.py:15 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adam.py:65 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adam.py:135 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adam.py:281 in public function `adam`:
D202: No blank lines allowed after function docstring (found 1)
/content/pytorch/torch/optim/adam.py:281 in public function `adam`:
D205: 1 blank line required between summary line and description (found 0)
7
```
```
After:
/content/pytorch/torch/optim/adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adam.py:14 in public class `Adam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adam.py:15 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adam.py:65 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
11) **File: torch/optim/adamax.py**
```
Before:
/content/pytorch/torch/optim/adamax.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamax.py:12 in public class `Adamax`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamax.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamax.py:47 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adamax.py:91 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adamax.py:203 in public function `adamax`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/adamax.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamax.py:12 in public class `Adamax`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamax.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamax.py:47 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
12) **File: torch/optim/adamw.py**
```
Before:
/content/pytorch/torch/optim/adamw.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamw.py:12 in public class `AdamW`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamw.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamw.py:73 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adamw.py:153 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adamw.py:304 in public function `adamw`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/adamw.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamw.py:12 in public class `AdamW`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamw.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamw.py:73 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
13) **File: torch/optim/asgd.py**
```
Before:
/content/pytorch/torch/optim/asgd.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/asgd.py:17 in public class `ASGD`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/asgd.py:18 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/asgd.py:52 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/asgd.py:107 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/asgd.py:195 in public function `asgd`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/asgd.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/asgd.py:17 in public class `ASGD`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/asgd.py:18 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/asgd.py:52 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
Resolved docstring errors as listed. I initially changed in the main branch of forked repo which caused changes to appear in my PR to other issue. I have fixed that and hope this PR won't have any conflicts.
Kindly review @svekars @jbschlosser.
In case of any other issues please let me know. Thanks!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112964
Approved by: https://github.com/kit1980
This is a reland of https://github.com/pytorch/pytorch/pull/100007 with a build fix for Windows debug builds.
`at::native::ParamsHash` only works on structs with standard layout, but `std::string` isn't one in Visual C++ debug builds, which one can easily verified by running something like:
```cpp
#define _DEBUG
#include <type_traits>
#include <string>
static_assert(std::is_standard_layout_v<std::string>, "Oh noes");
```
If above conditon is not met, instead of printing a static_assert output, VC++ raises a very cryptic compilation errors, see https://github.com/pytorch/pytorch/pull/100007#discussion_r1227116292 for more detail.
Also, using `std::hash` for string should result in a faster hash function.
(cherry picked from commit 74b7a6c75e)
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at 5914771</samp>
This pull request introduces a new function `_group_tensors_by_device_and_dtype` that can group tensors by their device and dtype, and updates the `foreach` utilities and several optimizers to use this function. The goal is to improve the performance, readability, and compatibility of the code that handles tensors with different properties. The pull request also adds a test case and type annotations for the new function, and some error checks for the `fused` argument in Adam and AdamW.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103912
Approved by: https://github.com/janeyx99
Big OOP correction continued. Also added a test this time to verify the defaulting was as expected.
The key here is realizing that the grouping for foreach already assumes that the non-param tensorlists follow suit in dtype and device, so it is too narrow to check that _all_ tensors were on CUDA. The main leeway this allowed was state_steps, which are sometimes cpu tensors. Since foreach _can_ handle cpu tensors, this should not introduce breakage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95820
Approved by: https://github.com/albanD
Rolling back the default change for Adam and rectifying the docs to reflect that AdamW never defaulted to fused.
Since our fused implementations are relatively newer, let's give them a longer bake-in time before flipping the switch for every user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95241
Approved by: https://github.com/ngimel
This allows it so that ONLY when the users don't set anything for foreach or fused do we switch the default and cascades adam so that we default to fused, then foreach, then single-tensor.
To clarify:
* if the user puts True in foreach _only_, it will run the foreach implementation.
* if the user puts True in fused _only_, it will run the fused implementation.
* if the user puts True in foreach AND for fused, it will run the fused implementation.
And:
* if the user puts False in foreach _only_, it will run the single tensor implementation.
* if the user puts False in fused _only_, it will still run the single tensor implementation.
* if the user puts False in foreach AND for fused, it will run the single tensor implementation.
I also didn't trust myself that much with the helper function, so I ran some local asserts on _default_to_fused_or_foreach. The only point left to really test is the type(p) -- torch.Tensor but I think the distributed tests will catch that in CI.
```
cuda_only_fp_list = [
torch.rand((1, 2), device="cuda", dtype=torch.float32),
torch.rand((1, 2), device="cuda", dtype=torch.float64),
torch.rand((1, 2), device="cuda", dtype=torch.float16),
torch.rand((1, 2), device="cuda", dtype=torch.bfloat16),
]
cuda_only_int_list = [
torch.randint(1024, (1, 2), device="cuda", dtype=torch.int64),
]
cpu_list = [
torch.rand((1, 2), device="cpu", dtype=torch.float32),
torch.rand((1, 2), device="cpu", dtype=torch.float64),
torch.rand((1, 2), device="cpu", dtype=torch.float16),
]
none_list = [None]
# differentiable should always make it return false for both
assert _default_to_fused_or_foreach([cuda_only_fp_list], True, True) == (False, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list], True, False) == (False, False)
# cpu lists should always make it return false for both
assert _default_to_fused_or_foreach([cuda_only_fp_list, cpu_list], False, True) == (False, False)
assert _default_to_fused_or_foreach([cpu_list], False, True) == (False, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list, cpu_list], False, False) == (False, False)
assert _default_to_fused_or_foreach([cpu_list], False, False) == (False, False)
# has fused triggers correctly
assert _default_to_fused_or_foreach([cuda_only_fp_list], False, True) == (True, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list], False, False) == (False, True)
# ints always goes to foreach
assert _default_to_fused_or_foreach([cuda_only_fp_list, cuda_only_int_list], False, True) == (False, True)
assert _default_to_fused_or_foreach([cuda_only_fp_list, cuda_only_int_list], False, False) == (False, True)
# Nones don't error
assert _default_to_fused_or_foreach([cuda_only_fp_list, none_list], False, True) == (True, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list, cuda_only_int_list, none_list], False, True) == (False, True)
assert _default_to_fused_or_foreach([none_list], False, True) == (True, False)
assert _default_to_fused_or_foreach([none_list], False, False) == (False, True)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93184
Approved by: https://github.com/albanD
@mlazos: skips `item()` calls if compiling with dynamo, by defining a helper function `_get_value` which either returns the result of `.item()` or the scalar cpu tensor if compiling with dynamo. This was done because removing `item()` calls significantly regresses eager perf. Additionally, `_dispatch_sqrt` calls the appropriate sqrt function (math.sqrt, or torch.sqrt).
Fixes https://github.com/pytorch/torchdynamo/issues/1083
This PR will no longer be needed once symint support is default.
This PR closes all remaining graph breaks in the optimizers (!!)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88173
Approved by: https://github.com/albanD
### Description
Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function.
### Testing
There shouldn't be any testing required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487
Approved by: https://github.com/albanD
Generator comprehensions with any/all are less verbose and potentially help to save memory/CPU : https://eklitzke.org/generator-comprehensions-and-using-any-and-all-in-python
To make JIT work with this change, I added code to convert GeneratorExp to ListComp. So the whole PR is basically NoOp for JIT, but potentially memory and speed improvement for eager mode.
Also I removed a test from test/jit/test_parametrization.py. The test was bad and had a TODO to actually implement and just tested that UnsupportedNodeError is thrown, and with GeneratorExp support a different error would be thrown.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78142
Approved by: https://github.com/malfet, https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71333
Updated
- Adagrad
- Adamax
- Adam
- AdamW
- RAdam
make multi_tensor functionals take `state_steps: List[Tensor]` instead of taking `states: List[Dict]`
make `state_steps: List[int]s -> state_steps:List[Tensor]` where each is a Singleton tensor so step can be updated within the functional
(NAdam and ASGD) were updated in separate diffs to fold their handling of state into the functionals
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D33767872
Pulled By: mikaylagawarecki
fbshipit-source-id: 9baa7cafb6375eab839917df9287c65a437891f2
(cherry picked from commit 831c02b3d0)
