Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50832
Please see the previous diff in this stack for the motivation to do so. This makes the same change but for the non-mobile codebase.
ghstack-source-id: 120184012
Test Plan: Sandcastle + Build
Reviewed By: raziel, iseeyuan
Differential Revision: D25979986
fbshipit-source-id: 7708f4f6a50cb16d7a23651e5655144d277d0a4f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48802
Current torch::jit::load API only supports unique_ptr ReadAdaptInterface input, but for some cases, torch::jit::load may not be the only consumer of the reader adapter. This diff enables an overload of torch::jit::load to load shared_ptr PyTorchStreamReader.
Reviewed By: malfet, houseroad
Differential Revision: D25241904
fbshipit-source-id: aa403bac9ed820cc0e94342aebfe524a1d5bf913
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40718
Currently only constant except tensor must be inlined during serialization.
Tensor are stored in the contant table. This patch generalizes this capability
to any IValue. This is particularly useful for non ASCII string literal that
cannot be inlined.
Test Plan: Imported from OSS
Differential Revision: D22298169
Pulled By: bzinodev
fbshipit-source-id: 88cc59af9cc45e426ca8002175593b9e431f4bac
Summary:
Clearly expressing a type is inferred by PyTorch instead of explicitly annotated by user makes many error messages more user-friendly
Currently Type has two string conversion methods. str() for IR printing and python_str() for serialization and error message generation. If we want to include more information in type printing while maintaining serialization/deserialization correctness, we need to split python_str() into annotation_str() and repr_str().
annotation_str is solely responsible for serialization, it strictly matches format of python type annotation. repr_str() is responsible for generating a human-readable error message that includes information like "this type is inferred, not explicitly annotated"
Closes https://github.com/pytorch/pytorch/issues/39449
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39544
Differential Revision: D21978759
Pulled By: gmagogsfm
fbshipit-source-id: 733566f5a62e748b5ca4bb3c5943ebb6d5b664d0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37472
Our convention is for `findX` to return an optional version and `getX`
to assert that the X is there. Fix up `getMethod` to be consistent with
this convention.
Test Plan: Imported from OSS
Differential Revision: D21297543
Pulled By: suo
fbshipit-source-id: b40f56231cc8183e61bbb01fe5c0c113bcb6464d
Summary:
fmt is a formatting library for C++. It has several properties that make it nice
for inclusion in PyTorch:
- Widely used
- Basically copies how Python does it
- Support for all the compilers and platforms we care about
- Standards track (C++20)
- Small code size
- Header only
This PR includes it as a submodule and sets up the build.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37356
Differential Revision: D21262619
Pulled By: suo
fbshipit-source-id: 1d9a1a5ed08a634213748e7b02fc718ef8dac4c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37251
This was broken by recent changes to how we serialize with type tags. We
save a name (like `Dict[str, MyNamedTuple]`) and then relied on the
mobile type parser to resolve that name back into a set of types.
This doesn't work for any NamedTypes as the mobile type parser doesn't
know how to resolve those. The unpickler allows the caller to inject a
type resolver in for this purpose, use that so that when importing in a
non-mobile environment you get the right results.
A second problem also had to be fixed: the SourceImporter type loader
would only load named types directly (e.g. `MyNamedTuple`) and choked if
it was a general type that contained a named tupe (e.g.
`List[MyNamedTuple]`). Fixed that and renamed `loadNamedType` to
`loadType` for clarity.
Test Plan: Imported from OSS
Differential Revision: D21235213
Pulled By: suo
fbshipit-source-id: 16db0f4c5e91a890d67a8687cc8ababa6b94b0f4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
Stacked PRs
* #33474 - [jit] Remove list specializations from pickler
* **#33255 - [jit] Add type tags to lists/dicts in pickle**
This adds a global call to `torch.jit._pickle.restore_type_tags` for
lists and dicts so that we can preserve their types after serialization.
](https://our.intern.facebook.com/intern/diff/20346780/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33255
Pulled By: driazati
Differential Revision: D20346780
fbshipit-source-id: c8534954ef4adb2e3c880401acbee30cd284f3db
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33294
1. Serialize bytecode of __setstate__ and run it when loading the model.
2. One use case is quantization. To test this use case a few operators are registered temporarily for lite interpreter. The "_" prefix registration will be removed when the operators are all migrated to mobile.
Test Plan: Imported from OSS
Differential Revision: D20162898
Pulled By: iseeyuan
fbshipit-source-id: 7a3180807bf38fbce594d86993896861f12bb58c
Summary:
Stacked PRs
* #33474 - [jit] Remove list specializations from pickler
* **#33255 - [jit] Add type tags to lists/dicts in pickle**
This adds a global call to `torch.jit._pickle.restore_type_tags` for
lists and dicts so that we can preserve their types after serialization.
](https://our.intern.facebook.com/intern/diff/19868637/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33255
Pulled By: driazati
Reviewed By: xman1979, Tianshu-Bao
Differential Revision: D19868637
fbshipit-source-id: 2f1826e6679a786ca209198690269f399a542c04
