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
Summary: Exposes `dynamic_shapes` api at multiple levels so it's easier to replace the old API `dynamic_dim()` with the new API `Dim()`.
Test Plan: CI
Differential Revision: D53246409
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118695
Approved by: https://github.com/ydwu4
Added support for constant outputs. We will just embed the constant directly into the output, like `return (x, 1)`.
Also adds support for None input/outputs. For None inputs we address it the same way we do to constants, which is that a placeholder with no users will be inserted into the graph, and the None will be embedded into whatever operator is using the None. For None outputs, we will also address the same way we do constants, which is that we embed it into the output, like `return (x, None)`.
Differential Revision: D52881070
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117894
Approved by: https://github.com/zhxchen17
This PR adds the bare minimum functionality to get torchbind working in an e2e testable way on PT2.
It implements:
* ProxyTensor support
* Simple torch.export support (proxytensor-only path, e.g. non-strict).
* add some tests exercising the path.
Because all this is not fully baked, I hide the functionality behind a feature flag (`enable_torchbind_tracing()`) so it does not affect regular users for now.
Still on the agenda:
* Dynamo support
* Actual FakeMode support
* Mutability support
Hoping to get this first bit in as a standalone, as it will unblock some more extensive experimentation/testing going on internally.
Differential Revision: [D51825372](https://our.internmc.facebook.com/intern/diff/D51825372/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117697
Approved by: https://github.com/SherlockNoMad
Summary:
As current export doesn't support training, so grad mode ops doesn't
make sense. To avoid the confusion, we choose to early error if there
exist grad mode ops.
Test Plan:
python test/export/test_safeguard.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116339
Approved by: https://github.com/tugsbayasgalan
Summary:
Currently we have a very ugly specialization on edge dialect in verifier like the following:
```
# TODO Remove this branch.
if ep.dialect == "EDGE": # !!! Don't change this allowlist. !!!
pass
else:
raise e
```
In this diff we do some additional work to make signature checking also work in exir. We decouple the transformation stack in torch export and exir so that different layers of the stack can evolve in their own fashion and the team can divide and conquer them seperately.
Test Plan: CI
Differential Revision: D52499225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116705
Approved by: https://github.com/tugsbayasgalan
AOTInductor currently relies of export_to_torch_ir to generate a graph, and passes it to inductor to generate the .so. They would like the FQN to be consistent so that they can easily find/update the weights in the .so.
Note that since export flattens all modules in to a single computational graph, we will change the FQNs in the original module by replacing all periods with underscores. For example, `foo.child1param`, which points to a submodule named `foo`'s parameter named `child1param`, will be renamed to `foo_child1param` since we no longer have the submodule `foo`. This is done just by doing `name.replace(".", "_")`.
Outputted AOTInductor c++ code: https://www.internalfb.com/phabricator/paste/view/P900120950?lines=377-355%2C354
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115462
Approved by: https://github.com/tugsbayasgalan
