Summary: In some cases we don't have information from the old IR about submodule ordering, in this case unflattener should still work in best effort mode.
Differential Revision: D55642005
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123192
Approved by: https://github.com/angelayi
Summary:
Make sure the order of submodules is the same as the original eager module.
bypass-github-export-checks
Test Plan: buck test mode/opt caffe2/test:test_export -- -r test_unflatten_submodule_ordering
Differential Revision: D55251277
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122507
Approved by: https://github.com/tugsbayasgalan
Summary: In non-strict mode of torch.export() we didn't set those `is_compiling()` to `True` which is needed by some models.
Test Plan: Unit tests and manual testing.
Differential Revision: D53624452
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119602
Approved by: https://github.com/suo
Summary:
X-link: https://github.com/pytorch/executorch/pull/1817
Basic support for non-persistent buffers, which are buffers that do not show up in the state dict.
One weird twist is that most of our other systems (FX, aot_export, dynamo) have completely buggy handling of non-persistent buffers. I tried to go on a wild goose chase to fix them all, but it got to be too much. So I introduced some sad rewrite passes in `_export` make the final state dict correctly align with the original module's state dict.
This exposed some bugs/ambiguous handling of parameters/buffers in existing test code. For example, `TestSaveLoad.test_save_buffer` traced over a module that was not in the root module hierarchy and caused some weird behavior. I think we should error explicitly on use cases like this: https://github.com/pytorch/pytorch/issues/118410. For now I just rewrote the tests or skipped them.
As a side effect, this diff tightened up quite a few sloppy behaviors around state dict handling:
- Tensor attributes were getting promoted to be buffers—bad!
- Tracing through a module not in the children of the root module would add its parameters/buffers to the state dict—bad!
This behavior is unlikely to show up in user code since the model would be totally broken, but did show up in a bunch of tests.
#buildmore
Test Plan:
unit tests
sandcastle
Differential Revision: D53340041
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118969
Approved by: https://github.com/guangy10, https://github.com/huydhn, https://github.com/titaiwangms
Summary:
X-link: https://github.com/pytorch/executorch/pull/1769
Basic support for non-persistent buffers, which are buffers that do not show up in the state dict.
One weird twist is that most of our other systems (FX, aot_export, dynamo) have completely buggy handling of non-persistent buffers. I tried to go on a wild goose chase to fix them all, but it got to be too much. So I introduced some sad rewrite passes in `_export` make the final state dict correctly align with the original module's state dict.
This exposed some bugs/ambiguous handling of parameters/buffers in existing test code. For example, `TestSaveLoad.test_save_buffer` traced over a module that was not in the root module hierarchy and caused some weird behavior. I think we should error explicitly on use cases like this: https://github.com/pytorch/pytorch/issues/118410. For now I just rewrote the tests or skipped them.
Test Plan: added a unit test
Differential Revision: D53253905
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118722
Approved by: https://github.com/SherlockNoMad, https://github.com/angelayi
This PR rewrites two paths to use the newly-added keypaths API in pytree:
First: we were hand-rolling a tree_map during fakification because we wanted to track sources. This PR uses keypaths instead, which can do the same thing without needing custom code.
Second: our constraint error formatting was referencing placeholder names in error messages. These placeholder names are not otherwise user-visible, so they are super confusing to users (e.g. "which input does arg1_3 correspond to?"). This diff uses the `keystr` API to format the error message.
This necessitated some small refactors—generating the keystr is expensive so doing it in an f-string was very bad.
It can also be further improved—we can inspect the signature so that instead of `*args[0]` we can give people the actual argument name, which would be the ideal UX. But leaving that for later.
Differential Revision: [D53139358](https://our.internmc.facebook.com/intern/diff/D53139358/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118609
Approved by: https://github.com/zhxchen17
ghstack dependencies: #118607, #118608
tree_flatten_spec is bad; it isn't synced up with `register_pytree_node` so it will not handle arbitrary custom pytrees. It's also not really maintained.
We only use it for two purposes:
- To retain kwarg ordering stability, so that if the user passes in kwargs in a different order things will still work.
- To do "structural" checks that ignore types.
In both cases, tree_flatten_spec is probably *not* the ideal way to implement the desired behavior.
## kwargs ordering
- tree_flatten_spec overwrites the behavior of ALL dictionaries, not just kwargs. This is not correct, dictionary ordering is meaningful in Python, and it's pretty trivial to write a program that relies on dict ordering.
- For kwargs, we do sort of expect that the order in which arguments are passed shouldn't matter. BUT there is one exception: `**kwargs`. In fact, [PEP 468](https://peps.python.org/pep-0468/) was introduced specifically to clarify that ordering does matter when the function being called uses `**kwargs`.
In this diff I introduce a utility function that *only* reorders kwargs. This gets us most of the way to correct—dicts are no longer reordered, but kwargs can be passed in any order.
A "fully correct" solution would need fix the corner case from PEP468. We could detect whether the top-level fn being traced uses `**kwargs` (via `inspect`), then serialize a flag for it. In ExportedProgram, we would check that flag and only re-order if `**kwargs` was unused; otherwise error if the key order doesn't match. This is a super corner case though, so I'll file it as a followup task.
## structural equivalence checking
This is another use case, where again `tree_flatten_spec` is too broad. Generally we want to treat a precise two types as the same, not override the behavior of comparison generally. So I introduce an `is_equivalent` util for this purpose.
Differential Revision: [D53168420](https://our.internmc.facebook.com/intern/diff/D53168420/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118608
Approved by: https://github.com/zhxchen17
ghstack dependencies: #118607
