Summary:
Previously we design the GraphSignature format as a bunch of inputs and outputs node names. After a discussion in the design meeting we decide to change the format to make signature more self-contained. Now the signature format look like the following:
```
[
InputSpec(
kind=InputKind.USER_INPUT,
arg=TensorArgument(name="arg0_1"),
target=None,
),
...
]
```
Test Plan: CI
Reviewed By: angelayi
Differential Revision: D49876258
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111017
Approved by: https://github.com/angelayi
While the `range_constraints` that is initially derived by processing of constraints only contains symbols that appear in the graph module, eventually the `range_constraints` that are in the exported program seem to contain more symbols than those that appear in the graph module. Clearly this is a regression, because the example of "Expressing Dynamism" in our public docs (https://pytorch.org/docs/stable/export.html#expressing-dynamism) does not show the extra symbols in `range_constraints`, but running the example does.
The problem seems to arise when we are running `_transform` passes, where we regenerate the `range_constraints` from the `shape_env`. However, as a rule, symbols that have `replacements` are actually replaced (by other expressions, including constants or other symbols), so they should never appear in the graph module. Thus we can filter such symbols out from `range_constraints` as well.
Differential Revision: [D49969620](https://our.internmc.facebook.com/intern/diff/D49969620/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110644
Approved by: https://github.com/zhxchen17
Summary:
https://docs.google.com/document/d/1QJJEGnj2nHGPODlw38BEG3KLLCOTfdOVjPrNQbz_LM8/edit#bookmark=id.lp80wfshq130
`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.
Splitting up this diff with the following one (D49742989) to make migrating Executorch easier:
1. Land this diff
1. Wait for a pytorch nightly to include this diff
1. Update executorch's pytorch nightly
1. Land the following diff to have export() return no decomps
Test Plan: Tested in following diff
Differential Revision: D49743208
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110236
Approved by: https://github.com/gmagogsfm
Summary:
X-link: https://github.com/pytorch/executorch/pull/359
When exporting using enable_aot (through the torch.export path), we want to lift all constant tensors as buffers to the exported program. The ScalarToTensor pass in EXIR's aten_to_edge passes will create some constant tensors in the graph, so we will need to run a lift_constant_tensors pass afterwards.
Note that this only needs to be applied when exporting using the torch.export path because in the original path, nothing is lifted.
Test Plan: CI
Differential Revision: D49207492
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109382
Approved by: https://github.com/cccclai
