Summary:
Replace _dynamo.config with an object instead of module
Current usage patterns of setting and reading fields on config will work
unchanged.
Only changes needed going forward:
1. import torch._dynamo.config will not work. However, just doing
import torch._dynamo is sufficient to access dynamo config
as torch._dynamo.config.
2. Files inside of _dynamo folder need to access config via
from torch._dynamo.config_util import config instead of
from torch._dynamo import config. Because _dynamo/__init__.py
imports some of the files so it would be circular import.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96455
Approved by: https://github.com/williamwen42
Previously they printed like `torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass`; now they print as `torch._dynamo.testing.StaticDefaultDynamicShapesUnspecTests`
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95806
Approved by: https://github.com/dagitses
Cleaning FQN for `FullyShardedDataParallel(use_orig_params=True)` can cause some discrepancies with respect to the FQN compared to manually looping over `named_modules()` and `named_parameters()` together.
There is no requirement for the FQNs to be clean when using wrapper FSDP + `use_orig_params=True`. We can leave clean FQNs to `fully_shard`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91767
Approved by: https://github.com/zhaojuanmao
This PR introduces a new function we can pass to torch._dynamo.optimize - guard_failure_fn. Usage is in the PR, and the one stacked on top of it, but the gist of it is that it emits failed guard reason strings alongside code. This is useful for tests and debugging, as it gives far finer grained assertions and control than the compile counter alone.
This is a resubmit of https://github.com/pytorch/pytorch/pull/90129
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90371
Approved by: https://github.com/ezyang
This is an API change, so please review carefully.
With this PR, torchdynamo returns an `OptimizedModule` class object, a subclass of `torch.nn.Module`, when asked to optimize a `nn.Module` object. Most of the methods are redirected to the original `nn.Module`, which is installed as `_mod` in the `OptimizedModule`.
This is helpful for many cases
```
mod = MockModule()
opt_mod = torch._dynamo.optimize()(mod)
print(opt_mod) # Works
opt_mod = opt_mod.to(device="cuda")
print(opt_mod) # Works
opt_mod(input) # Triggers recompile if necessary, earlier we were shedding the TorchDynamo wrapper
opt_mod.parameters() # Refers to the original module
```
Topics unclear to me
* I have overridden many methods to raise NotImplementedError. A careful review of those will be good.
* hooks
* For the optimized forward, should we call torchdynamo optimization on `__call__` or `forward`
* What else to test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88629
Approved by: https://github.com/Chillee, https://github.com/jansel, https://github.com/msaroufim
