Summary:
Adds qtensor specific fields to the proto file so that they get serialized into the model.json
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23356
ghstack-source-id: 87263428
Differential Revision: D16473237
fbshipit-source-id: bf5b51d0863d036d30a1644a3c3b74516468224b
Summary:
Bumping up the producer_version in exported ONNX models in view of the next release. Updating tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23120
Reviewed By: zrphercule
Differential Revision: D16420917
Pulled By: houseroad
fbshipit-source-id: 6686b10523c102e924ecaf96fd3231240b4219a9
Summary:
`pickle` supports this and a lot of the quantized use cases for get/set
state follow this pattern
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23119
Pulled By: driazati
Differential Revision: D16391234
fbshipit-source-id: 9f63e0a1679daa61b17aa64b5995e2be23b07b50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22476
Dead code elimination assumes a valid jit graph because it checks if operators have side effects.
The onnx export path destroys the jit graph right before calling dead code elimination, but it actually doesn't care about side effects.
We can just call dead code elimination and disable side effect lookup and things should work.
Reviewed By: houseroad
Differential Revision: D16100172
fbshipit-source-id: 8c790055e0d76c4227394cafa93b07d1310f2cea
Summary:
~~This is work in progress due to its dependency on multiple pending PRs.~~
- [x] ONNX: Relax constraint on subgraph input/output type & shape check. https://github.com/onnx/onnx/pull/2009
- [x] PyTorch: Add infra to test_pytorch_onnx_caffe2.py to test ScriptModule models. https://github.com/pytorch/pytorch/pull/20256
This PR should partially resolve https://github.com/pytorch/pytorch/issues/17531. However, ideally we shouldn't need to put cast(and reshape) node to help the conversion for loop condition.
- Added cast node for condition values before entering loop node. The ONNX spec only accepts Bool type, while in PyTorch if the condition value is an output from other node it could potentially have any integral type.
- Tidying up the exported ONNX loop subgraph input type & shape. According to ONNX spec, input "M" is exported as 0-d scalar tensor with type int64. input "Cond" is exported as incomplete tensor of type Bool without shape information. This is because through out the iteration, the rank of condition value is dynamic, either 0-d or 1-d, as long as it holds a single value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20445
Differential Revision: D15534188
Pulled By: houseroad
fbshipit-source-id: d174e778529def05ee666afeee4b8fb27786e320
Summary:
In larger system environment, there's usually a need to store some information about how the model was created (e.g. from which process, workflow, by which user, etc). It's almost like JPEG metadata written by camera.
This PR adds a low-level c++ hook to allow population of additional files in zip container based on environment. The reason to have it a low-level hook instead of top-level API wrapper (e.g. `m.save_with_metadata`) is to capture all usages of the saving API transparently for user.
Let me know if there are concerns.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20863
Differential Revision: D15487941
Pulled By: dzhulgakov
fbshipit-source-id: 120c5a4c9758aa82846bb51a1207f923e3da1333
Summary:
Resubmit #20698 which got messed up.
Idea is that when PyTorch is used in a custom build environment (e.g. Facebook), it's useful to track usage of various APIs centrally. This PR introduces a simple very lightweight mechanism to do so - only first invocation of a trigger point would be logged. This is significantly more lightweight than #18235 and thus we can allow to put logging in e.g. TensorImpl.
Also adds an initial list of trigger points. Trigger points are added in such a way that no static initialization triggers them, i.e. just linking with libtorch.so will not cause any logging. Further suggestions of what to log are welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20745
Differential Revision: D15429196
Pulled By: dzhulgakov
fbshipit-source-id: a5e41a709a65b7ebccc6b95f93854e583cf20aca
Summary:
Adds support for `__getstate__` and `__setstate__` on modules that are called as part of export (`torch.save()`) and import (`torch.jit.load`).
* `__getstate__` and `__setstate__` must be TorchScript functions with the signatures `() -> T` and `(T) -> None` respectively
* The results of `__getstate__` are stored using the pickler in `states.pkl` with one for each module in definition order (`__getstate__` returns `None` by default if an imlpementation is not provided)
* This prevents sharing between `__getstate__` and attributes, but this should be fine since their use is mostly unrelated (attributes are for storing values to be used in script methods, `__getstate__` for running arbitrary computations during import)
Follow up
* Somehow replacing `__getstate__`/`__setstate__` with a `ScriptMethodStub` makes `MyScriptModule().__getstate__()` call `ScriptModule.__getstate__()` when used in Python. This should be fixed so semantics in Python are preserved, but it doesn't affect the typical usage.
](https://our.intern.facebook.com/intern/diff/15287161/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20242
Pulled By: driazati
Differential Revision: D15287161
fbshipit-source-id: b3f5f33ab74a21a89e6d15460af63aff75cab2d8
Summary:
This PR makes `torch.save` call out to the pickler which saves a tensor in the same format that `torch.save()` does, the file looks like `| pickle archive 1 (includes sizes, strides, requires_grad, etc...) | pickle archive 2 (list of tensor keys) | tensor binary data |` and can be read back in with `torch.load(my_file, pickle_module=torch.jit._pickle)`
Fixes#18003
Unpickling in the JIT for things such as model parallelism will be a follow up PR
](https://our.intern.facebook.com/intern/diff/15015160/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18154
Pulled By: driazati
Differential Revision: D15015160
fbshipit-source-id: ef76a44b8c243f4794cd7e245ec8305e965bc59f
Summary:
Strip the doc_string by default from the exported ONNX models (this string has the stack trace and information about the local repos and folders, which can be confidential).
The users can still generate the doc_string by specifying add_doc_string=True in torch.onnx.export().
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18882
Differential Revision: D14889684
Pulled By: houseroad
fbshipit-source-id: 26d2c23c8dc3f484544aa854b507ada429adb9b8
Summary:
Almost there, feel free to review.
these c10 operators are exported to _caffe2 domain.
TODO:
- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210
Reviewed By: zrphercule
Differential Revision: D14600916
Pulled By: houseroad
fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18469
ghimport-source-id: 73cb8b58f43f10b1dcfca805fd5b25c4fa977632
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18469 Create Object that represents a Module**
* #18468 slots with explicit value/setValue make more sense in future patches
* #18467 Make Object hold its ClassType
* #18379 Enforce single parent for script submodules
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.
This changes the underlying storage for script::Module to hold
a ivalue::Object which has slots for all the parameters and attributes.
NamedIValue and Slot are now merged together into one class Slot that stores
the tuple (ivalue::Object, offset) and can be used to read the name, type,
or value of the slot and also to set the value. This cleans up a bunch
of client uses.
This PR does not actually use the module object in any generated code.
A future PR will switch how code is generated to treat modules as
first class.
Differential Revision: D14613508
fbshipit-source-id: d853a7559f58d244de2ef54a781427fcd1060ed0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18468
ghimport-source-id: d4b41c521f2269a695e03c8e7d05d5542731ee48
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18469 Create Object that represents a Module
* **#18468 slots with explicit value/setValue make more sense in future patches**
* #18467 Make Object hold its ClassType
* #18379 Enforce single parent for script submodules
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.
Reviewed By: suo
Differential Revision: D14613509
fbshipit-source-id: 9f2208d0efd01465c78cebdc3e8365a9e0adf9ff
