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>
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
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
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
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`:
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/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
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/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
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/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
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/content/pytorch/torch/cuda/random.py:88 in public function `manual_seed`:
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/content/pytorch/torch/cuda/random.py:88 in public function `manual_seed`:
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/content/pytorch/torch/cuda/random.py:110 in public function `manual_seed_all`:
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/content/pytorch/torch/cuda/random.py:110 in public function `manual_seed_all`:
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/content/pytorch/torch/cuda/random.py:128 in public function `seed`:
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/content/pytorch/torch/cuda/random.py:128 in public function `seed`:
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/content/pytorch/torch/cuda/random.py:146 in public function `seed_all`:
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/content/pytorch/torch/cuda/random.py:146 in public function `seed_all`:
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/content/pytorch/torch/cuda/random.py:167 in public function `initial_seed`:
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18
```
```
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/content/pytorch/torch/cuda/random.py:1 at module level:
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```
2) **File: torch/cuda/amp/autocast_mode.py**
```
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/content/pytorch/torch/cuda/amp/autocast_mode.py:23 in public method `__init__`:
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/content/pytorch/torch/cuda/amp/autocast_mode.py:38 in public method `__enter__`:
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```
```
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/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__`:
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/content/pytorch/torch/cuda/amp/autocast_mode.py:38 in public method `__enter__`:
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```
3) **File: torch/cuda/amp/grad_scaler.py**
```
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/content/pytorch/torch/cuda/amp/grad_scaler.py:39 in public class `OptState`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:544 in public method `get_growth_factor`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:563 in public method `set_backoff_factor`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:563 in public method `set_backoff_factor`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:570 in public method `get_growth_interval`:
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/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`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:576 in public method `set_growth_interval`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:592 in public method `is_enabled`:
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/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`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:624 in public method `load_state_dict`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:649 in public method `__getstate__`:
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/content/pytorch/torch/cuda/amp/grad_scaler.py:665 in public method `__setstate__`:
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```
```
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`:
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/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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```
```
After:
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```
5) **File: torch/optim/__init__.py**
```
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1
```
```
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0
```
6) **File: torch/optim/lbfgs.py**
```
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D100: Missing docstring in public module
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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__`:
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/content/pytorch/torch/optim/lbfgs.py:285 in public method `step`:
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```
```
After:
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```
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`:
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/content/pytorch/torch/optim/sparse_adam.py:8 in public method `__init__`:
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/content/pytorch/torch/optim/sparse_adam.py:40 in public method `step`:
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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`:
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/content/pytorch/torch/optim/sparse_adam.py:8 in public method `__init__`:
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3
```
8) **File:torch/optim/adadelta.py**
```
Before:
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/content/pytorch/torch/optim/adadelta.py:82 in public method `step`:
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/content/pytorch/torch/optim/adadelta.py:193 in public function `adadelta`:
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6
```
```
After:
/content/pytorch/torch/optim/adadelta.py:1 at module level:
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4
```
9) **File: torch/optim/adagrad.py**
```
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/content/pytorch/torch/optim/adagrad.py:201 in public function `adagrad`:
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7
```
```
After:
/content/pytorch/torch/optim/adagrad.py:1 at module level:
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/content/pytorch/torch/optim/adagrad.py:78 in public method `share_memory`:
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5
```
10) **File: torch/optim/adam.py**
```
Before:
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7
```
```
After:
/content/pytorch/torch/optim/adam.py:1 at module level:
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/content/pytorch/torch/optim/adam.py:65 in public method `__setstate__`:
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4
```
11) **File: torch/optim/adamax.py**
```
Before:
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6
```
```
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```
12) **File: torch/optim/adamw.py**
```
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/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
