Summary: WrapperModule seems a good idea but may introduce some surprising behavior to users, for example, it never registers enclosed modules as submodules and therefore it's unclear that's the state dict for the exported program should look like, because some people may argue to include every state in state dict but others want to keep them as constants.
Test Plan: CI
Reviewed By: tugsbayasgalan
Differential Revision: D54326331
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121042
Approved by: https://github.com/angelayi
Summary:
Previously we were renaming constants to `lifted_constant_tensor0` or equivalent. This PR changes things so that the constants retain the same FQN as in the original eager module.
Actually, `symbolic_trace` already is supposed to do this, but the code path is not triggered when used from `make_fx`, since we don't pass an actual `nn.Module` instance to `trace()`, but rather a multiply-wrapped-functionalized-lambda-thing.
So, I reproduced the essential logic outside of make_fx, at the export layer.
Test Plan: added a unit test
Differential Revision: D54221616
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120664
Approved by: https://github.com/SherlockNoMad
Recently we made it possible to serialize ExportedPrograms with fake parameters/buffers/etc.
The serialization regime was kind of whacky; basically we serialized a stub and reassembled the FakeTensor using metadata that we had stashed elsewhere in the Graph state.
This was bad for a few reasons:
- Storing the metadata separately from the actual serialized object caused situations where you could have one but not the other. An example case is if you had a FakeTensor contained inside a TorchBind object—there was no obviously place to store the metadata for this. This actually happens—TensorQueue in fbgemm does this.
- It created an annoying cycle: we had to deserialize the Graph's tensor metadata in order to deserialize (potentially faked) constants, but we need constants in order to deserialize the Graph.
This fixes all that. The basic idea is to patch the reducer function for FakeTensor at serialization time, and serialize a copy of the FakeTensor metadata. We already are policing BC for the TensorMeta schema struct so it's not a net increase in the BC surface.
As a bonus, I fixed a weird bug with torchbind tracing where we were accidentally reinterpreting a torch.ScriptObject as a torch.ScriptModule (which was the root cause of some weird behavior @bahuang was seeing last week).
Differential Revision: [D53601251](https://our.internmc.facebook.com/intern/diff/D53601251/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119531
Approved by: https://github.com/zhxchen17
- Added support for serializig the auto_functionalization op, which
required adding the functions `serialize_arbitrary_inputs` and
`serialize_arbitrary_outputs` which will serialize the inputs/outputs
without needing a schema, since HOOs do not have a schema.
- Added support for serializing user input mutations
- Added support for serializing operator inputs. They just get turned
into strings.
Differential Revision: [D53331039](https://our.internmc.facebook.com/intern/diff/D53331039)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118810
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
Summary:
Class FQN is needed when unpacking CustomObj instance.
For all other Arguments, e.g. Tensor, TensorList, SymInt, we always know their exact type. However, CustomObjArgument had an opaque type.
Adding this field also helps unveiling the type of this opaque object.
Test Plan: CI
Differential Revision: D53029847
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118158
Approved by: https://github.com/zhxchen17
Summary:
https://docs.google.com/document/d/1QJJEGnj2nHGPODlw38BEG3KLLCOTfdOVjPrNQbz_LM8/edit#bookmark=id.lp80wfshq130
Changes:
* `torch.export` will return a functional ATen graph but not lowered to core aten decompositions (CompositeImplicitAutograd decomps still run)
* `exported_program.run_decompositions(decomposition_table)` will optionally take a decomposition table, and run decompositions on the exported program, returning a new exported program. By default we will run the Core ATen decomposition table.
Calling convention for Executorch stays the same:
```
pre_autograd_graph = capture_pre_autograd_graph(f, args, ...)
aten_graph_no_decomps = torch.export.export(pre_autograd_graph, args, ...)
# Within to_edge we decompose to core aten and then convert to edge
edge_graph = exir.to_edge(aten_graph_no_decomps)
```
Test Plan: CI
Differential Revision: D50172210
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111030
Approved by: https://github.com/ydwu4
