Summary:
It seems we have multiple places deserializing torchbind objects. Moving the code around so that every load essentially share the same implementation.
Also added a test case "package_reader_testing" which load back the archive file in Python and eagerly validate the numerical result.
Test Plan: buck test mode/opt sigmoid/inference/test:e2e_test_cpu
Reviewed By: SherlockNoMad
Differential Revision: D61235770
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133463
Approved by: https://github.com/ydwu4
Summary:
There is an annoying inconsistency in how we pickle custom objs.
`torch.save` will invoke regular pickle, for which we have bound `__setstate__`/`__getstate__` methods on `torch.ScriptObject`: https://fburl.com/code/4howyl4u.
This serializes in a different format than TorchScript does, which uses the TS C++ pickler.
The issue we were facing was using the Python pickler to save, and the C++ pickler to load. If we use the C++ pickler to both save and load (plus some plumbing to get type/object resolution to work correctly), then things should work.
Test Plan:
ran SherlockNoMad's repro
```
buck2 run 'fbcode//mode/dev-nosan' scripts/bahuang:export_torchbind -- --logging DBG
```
Got to a new error, which has to do with how we're initializing the graph, but will leave that for future diffs.
Reviewed By: SherlockNoMad
Differential Revision: D53248454
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118791
Approved by: https://github.com/qxy11, https://github.com/SherlockNoMad, https://github.com/khabinov
Summary: We are trying to use wired message to pass python objects like KJT. In order to make JIT be able to unpickle it, we need to provide a type resolver as well as an obj loader. This diff modify the interface to let we be able to do that.
Test Plan:
Rely on current CI to make sure existing usage doesn't break.
In the next diff, test e2e
Differential Revision: D49438569
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109730
Approved by: https://github.com/davidberard98
As we live in C++17 world
This is a functional no-op, just
- `s/namespace at { namespace native {/namespace at::native {/`
- `s/namespace torch { namespace jit {/namespace torch::jit {/`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92100
Approved by: https://github.com/izaitsevfb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70338
Today Unpickler is used by both server and mobile for deserializing model, and it always fallback to mobile parser when there's no type resolver provided by user. However this is not intended as server and mobile type parser supports different things. In this diff we provide a default fallback using script parser and opt it out for all mobile cases.
ghstack-source-id: 146727330
(Note: this ignores all push blocking failures!)
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D33284352
fbshipit-source-id: 997c4f110b36eee6596e8f23f6a87bf91a4197ed
Summary:
Follow up to https://github.com/pytorch/pytorch/issues/68095
This also changes the files from the ATen folder to include c10's `Export.h` instead since they can't ever be exporting `TORCH_PYTHON_API`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69585
Reviewed By: mrshenli
Differential Revision: D32958594
Pulled By: albanD
fbshipit-source-id: 1ec7ef63764573fa2b486928955e3a1172150061
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57098
1. Separate `readArchiveAndTensors()` from `jit/import.cpp` to a new file `jit/import_read.cpp`.
2. Use `readArchiveAndTensors()` in `mobile/import.cpp`
ghstack-source-id: 127703081
3. Add a util function in cpp that could read .pkl files directly instead of loading the entire module
Test Plan: CI
Reviewed By: raziel, iseeyuan
Differential Revision: D28052193
fbshipit-source-id: c8d57f3270bdcf2e52a32f7c111899bd5da7cac2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54428
Using c10::ArrayRef as the parameter type makes the API more flexible and allows the caller to leverage small-buffer optimizations (e.g. c10::SmallVector, std::array) for performance critical cases.
Test Plan: No behavioral changes. Run the existing unit and integration tests.
Reviewed By: suo
Differential Revision: D27232222
fbshipit-source-id: 7b13bc6bd02257097ca119077028fbccc68cc925
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35720
When modules are saved, all relevant types are serialized according to
their qualified name with a compilation unit. Since qualified names are
guaranteed to be unique within a compilation unit, this normally works
fine.
On load, all types are registered in a compilation unit owned by the
script::Module. Type names are not unique across compilation units, so
if you load two modules with colliding type names, make them submodules
of yet another module, and save that module, there is the potential of a
name collision. See the added tests for examples if that description is
confusing.
The solution is to unique type names when serializing code by mangling
them if we detect a name collision.
Test Plan: Imported from OSS
Differential Revision: D20749423
Pulled By: suo
fbshipit-source-id: a8827ff1d4a89f3e7964dbbb49b4381863da3e6a
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
