Use it to unwrap any functorch-wrapped tensor. I don't recommend using
the output in a program since it breaks the semantics of the transforms,
but it seems useful for debugging.
I will note that some people have wanted to get intermediate values out
of an e.g. grad transform, so this might be a way to do that...
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146528
Approved by: https://github.com/Chillee
This basically undoes some workarounds introduced in #119926, the
root causes of which have been fixed by #142078 and other changes in
Dynamo.
Now that Dynamo traces the spec comparison code, the test also needs update:
- removing the `_jvp_treespec_compare` calls in fx graph
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142081
Approved by: https://github.com/zou3519
ghstack dependencies: #142078, #142080
This basically undoes most of the workarounds introduced in #119405, the
root causes of which have been fixed by #142078 and other changes in
Dynamo.
Now that Dynamo traces the spec comparison code, the test also needs update:
1. renaming `o` to `pimals_out`
2. removing the `_vjp_treespec_compare` calls in fx graph
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142080
Approved by: https://github.com/zou3519
ghstack dependencies: #142078
Fixes#124550
Also moves `graph.eliminate_dead_code()` call to a few lines after
`_inline_module(...)` in `const_fold.py`
* Test plan:
Add a new test on `test_eager_transforms.py` to ensure the reported
issue was indeed fixed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133364
Approved by: https://github.com/zou3519
There's no reason to ban them for vmap or jvp, because without the
{grad, vjp} transforms those just act above PyTorch autograd, which will
end up saving regular Tensors.
Test Plan:
- some tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131191
Approved by: https://github.com/drisspg
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
List of changes:
- Replace JVP_NESTING by torch._C._functorch.maybe_current_level()
- Remove all increment nesting functions from wrap_fx_proxy_cls
- fwAD.make_dual receives the dual_level as keyword argument
- Add jvp_increment_nesting, set_fwd_grad_enabled and dual_level context managers to dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119926
Approved by: https://github.com/zou3519
List of changes:
- Replace JVP_NESTING by torch._C._functorch.maybe_current_level()
- Remove all increment nesting functions from wrap_fx_proxy_cls
- fwAD.make_dual receives the dual_level as keyword argument
- Add jvp_increment_nesting, set_fwd_grad_enabled and dual_level context managers to dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119926
Approved by: https://github.com/zou3519
List of changes:
- Replace JVP_NESTING by torch._C._functorch.maybe_current_level()
- Remove all increment nesting functions from wrap_fx_proxy_cls
- fwAD.make_dual receives the dual_level as keyword argument
- Add jvp_increment_nesting, set_fwd_grad_enabled and dual_level context managers to dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119926
Approved by: https://github.com/zou3519
This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
We now have two types of functionalization, C++ Functionalization (through the `Functionalize` dispatch key), and python functionalization (through the `FunctionalTensorMode` torch_dispatch mode).
This means that all higher order ops need custom functionalization rules for the python variant too. I added them here, as well as a helper function `dispatch_functionalize()` - equivalent to `torch.func.functionalize()`, except that it uses `FunctionalTensorMode`.
In theory we could have secretly switched `torch.func.functionalize` to use `FunctionalTensorMode`. This would be BC-breaking, though, since `FunctionalTensorMode` isn't composable with the other functorch transforms (the functorch layer-mode stack doesn't know how to re-order torch_dispatch modes arbitrarily).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108656
Approved by: https://github.com/zou3519
ghstack dependencies: #109024, #109248
