Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
Apply modularization pass to exported program exporting. The only two things that needs to be taken care of are (1) the extra call stack generated by `torch.export.export` and (2) lifted placeholder has call stack (different from original placeholder).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119498
Approved by: https://github.com/thiagocrepaldi
Since PyTorch 2.1, torch.export API was introduced and the term "export"
got overloaded due to the already existing torch.onnx.export API.
The torch.onnx.dynamo_export API was introduced on pyTorch 2.0 and it
exposed a torch.onnx.ExportOutput which now can be confused with
torch.export.export output
To prevent such ambiguity and standardize names around the new
torch.export.ExportedProgram, this PR renames torch.onnx.ExportOutput to
torch.onnx.ONNXProgram
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112263
Approved by: https://github.com/BowenBao
ghstack dependencies: #112444
Introduce `Modularize` pass that analyzes the flat `fx.GraphModule` and creates nested
layers of sub `fx.GraphModule`s along with the `call_module` fx nodes that invokes them.
The analysis is done on the meta data "nn_module_stack", which captures the `nn.Module`
each flat `fx.Node` belongs to.
`FxOnnxInterpreter` is updated to support `call_module`. The related sub module linked
by `node.target` is exported as an ONNX model local function. The `call_module` node itself
is exported as an ONNX node, associated with the ONNX model local function by op_type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105618
Approved by: https://github.com/justinchuby
Simplifies the logic to not depend on info within the exception raised. Due to changes
in onnx dispatcher, the diagnostic within exception raised is now different, which broke
this pass in retrieving the unsupported fx node kind. Adds proper unittest.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105156
Approved by: https://github.com/thiagocrepaldi
