Adam part of: https://github.com/pytorch/pytorch/issues/110506
TODO:
- If this approach is validated as a good one, it an also be applied to all other optimizers which convert `complex` via list comprehensions
### Results:
`NUM_PARAMS=200, foreach=True`
- main: dynamo: 43s, inductor: 31s, total: 74s
- this PR: dynamo: 3.5s, inductor: 30s, total: 34s (dynamo speedup: 12.3x, overall speedup: 34s, 2.1x)
`NUM_PARAMS=1000, foreach=True, has_complex shortcut`:
```
<class 'torch.optim.adam.Adam'> {'lr': 0.01, 'foreach': True} torch.float32 TorchDynamo compilation metrics:
Function Runtimes (s)
------------------------------------ -------------------------------
_compile.<locals>.compile_inner 0.0329, 50.0806, 0.0041
OutputGraph.call_user_compiler 44.9924
```
`NUM_PARAMS=1000, foreach=True`:
```
<class 'torch.optim.adam.Adam'> {'lr': 0.01, 'foreach': True} torch.float32 TorchDynamo compilation metrics:
Function Runtimes (s)
------------------------------------ -------------------------------
_compile.<locals>.compile_inner 0.0389, 58.6069, 0.0043
OutputGraph.call_user_compiler 44.1425
```
### Discussion
- `has_complex` shortcut provides additional 2x dynamo speedup. It is not necessary to achieve a significant overall speedup.
CC: @janeyx99 @mlazos
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110607
Approved by: https://github.com/janeyx99, https://github.com/lezcano
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
Not sure, how it worked before, but if arguments must be annotated is optional if they are defaulted to None
Towards enabling mypy-1.4.1 in lintrunner
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 5e1b9f4</samp>
> _We annotate the arguments of doom_
> _To show the `None` values of gloom_
> _We improve the type checking and readability_
> _With `Optional` annotations of metal-ity_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105022
Approved by: https://github.com/izaitsevfb, https://github.com/huydhn, https://github.com/Skylion007
When using KeyedOptimizer.init_state(), some optimizers initializes the states even if the param is empty (size() == 0) while some optimizer avoid initializing the states. There is no way FSDP can tell. Instead, FSDP should look up `optim.state`. Fortunatelly, `optim.state` does not rely on FQNs which some internal users change the FQNs.
Differential Revision: [D47285562](https://our.internmc.facebook.com/intern/diff/D47285562/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104765
Approved by: https://github.com/fduwjj
Summary:
The current `optim_state_dict()` does not require users to call `optim.state_dict()` first while `optim_state_dict_to_load()` requires users to call `optim.load_state_dict()`. This PR make both APIs provide the option for users not having to call the extra API.
This PR also changes the arguments order of `optim_state_dict_to_load` which is a breaking change. So we should do this asap before the API is adopted in production cases.
Test Plan: CI
Differential Revision: D43925068
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96534
Approved by: https://github.com/rohan-varma
Attempts to fix#92656
BC-breaking! This changes the default of zero_grad in optim and in nn to default set grads to None instead of zero tensors. We are changing the default because there are proven perf wins and existing code has typically not regressed due to this change. (will probably have to flesh out this note more).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92731
Approved by: https://github.com/ngimel
Allow _apply_optim_in_backward to work with DDP.
Example:
```
dist.init_process_group("nccl", rank=rank, world_size=2)
torch.cuda.set_device(rank)
e = enc().cuda(rank)
_apply_optimizer_in_backward(
optimizer_class=torch.optim.SGD,
params=e.parameters(),
optimizer_kwargs={"lr": 0.03},
)
e = DDP(e, device_ids=[rank])
inp = torch.randn(1, 10, device=rank)
e(inp).sum().backward()
```
Constraints:
1. Custom communication hook not yet supported
2. _apply_optim_in_backward needs to be called _before_ wrapping model in DDP.
3. DDP will remove the gradient hooks _apply_optim_in_backward registers, so these gradient hooks will not be fired and cannot be used.
4. All DDP managed parameters have grads set to None by default once optimizer is applied. There is no support for setting only some parameter grads to None, this must be done manually by user (and DDP_OVERLAPPED_OPTIM_SET_GRADS_TO_NONE=0 needs to be set.)
Differential Revision: [D41329694](https://our.internmc.facebook.com/intern/diff/D41329694/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D41329694/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89194
Approved by: https://github.com/zhaojuanmao
1. Add param_group check logic and unit test
2. Remove unnecessary check for conditional param update
3. Return the param_group from the inner optimizer so that when param_group is None or not all params are specified, we still return the expected result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91147
Approved by: https://github.com/fegin
In pytorch, the optim state_dict will always use number to index optimizer state_dict for parameters.
Now composability workstream need a FQN based way to index optimizer state_dict for parameters..
For example, SGD optimizer might have something in its `state_dict` like:
```
{'state':
{0:
{'momentum_buffer': tensor(...)},
{1:
{'momentum_buffer': tensor(...)},
...
}
'param_groups':
[{'lr': 0.001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False, 'params': [0, 1, 2, 3, 4, 5, 6, 7]}]
}
```
And in NamedOptimizer we want the `state_dict` can be:
```
{'state':
{'net1.0.weight':
{'momentum_buffer': tensor(...)},
{'net1.0.bias':
{'momentum_buffer': tensor(...)},
...
}
'param_groups':
[{'lr': 0.001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False, 'params': ['net1.0.weight', 'net1.0.bias', 'net2.0.weight', 'net2.0.bias', 'net3.weight', 'net3.bias', 'net4.1.weight', 'net4.1.bias']}]
}
```
We also want to support load_state_dict to enable optim `state_dict` override for NameOptimizer.
For the next couple PR/diffs, we also need to:
1. To make `NamedOptimizer` working with FSDP (like registering a hook for model wrapped with FSDP) and other PTD/PT components.
2. Make `NamedOptimizer` works well with apply_optim_in_backward
3. Upstream also `CombinedOptimizer`.
Differential Revision: [D41432088](https://our.internmc.facebook.com/intern/diff/D41432088/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D41432088/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89480
Approved by: https://github.com/rohan-varma
Summary:
Upstreaming this as part of sharing common APIs. This is just a plain
move, any changes needed to support DDP / FSDP will come in follow up diffs.
Test Plan: CI
Reviewed By: zhaojuanmao
Differential Revision: D40564646
fbshipit-source-id: 619c434e02196812f8d4db1e40d07290e08b18f9
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88539
Approved by: https://github.com/awgu
