Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
Follow up work:
1. disallow `default` as an overload name for aten operators.
2. Add a method to obtain a list of all overloads (exclude the ones registered by JIT)
3. Add methods/properties to `OpOverload` to access more schema information (types of input and output args etc)
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33469839
Pulled By: anjali411
fbshipit-source-id: c3fc43460f1c7c9651c64b4d46337be21c400621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
TODO: disallow `default` as an overload name for aten operators.
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33262228
Pulled By: anjali411
fbshipit-source-id: 600dbf511514ea9b41aea3e6b1bc1102dab08909
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32521
Not all ops support the templated unboxing wrappers yet. For the ones that don't,
let's use the codegen'ed unboxing wrappers from register_aten_ops.cpp, but register
them with c10 directly instead of JIT.
The `use_c10_dispatcher` setting in `native_functions.yaml` now has a new option 'with_codegenerated_unboxing_wrapper' which means we take the codegened unboxing wrapper from register_aten_ops.cpp and stuff it into c10. This new argument is the default, 'unboxed_only' is not the default anymore. For the (very few) ops that don't support boxed dispatch yet (i.e. ops taking TensorOptions arguments), we set them to 'unboxed_only' and they follow the old behavior of having register_aten_ops.cpp register the jit op.
Next steps here are (1) to make TensorOptions work with boxed dispatch and remove the `unboxed_only` option from `use_c10_dispatcher`, so that all ops go through the new path and (2) make the new path template-only and remove codegen from it (see https://github.com/pytorch/pytorch/issues/32366).
First experiments show that
- For a small JITted model that calls add (i.e. a op with just two arguments that are both tensors) on two tensors in a loop, we see a 2-4% performance improvement (~35-50ns) when compared to the old path. This is a simple op that takes two tensor arguments and no non-tensor arguments, so iterating over it in boxed dispatch is cheap.
- For a small JITted model that calls avgpool1d (i.e. an op that has one tensor arg and 5 non-tensor args) on a tensor in a loop, we see a 3-4% performance regression (~60ns) when compared to the old path. This is an op that takes only one tensor argument and then 6 non-tensor arguments. Unboxed dispatch doesn’t have to look at those but boxed dispatch still needs to iterate over them.
This performance difference is likely due to boxed dispatch iterating over all arguments in a loop and unboxed dispatch not having to look at non-tensor arguments.
ghstack-source-id: 99161484
Test Plan: unit tests that call existing ops through JIT
Differential Revision: D18672405
fbshipit-source-id: bf2a7056082dfad61e7e83e9eeff337060eb6944
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29334
As title
Test Plan: Imported from OSS
Differential Revision: D18358592
Pulled By: suo
fbshipit-source-id: d7afbce52ddd008ae9c42aeda6be24e35086ef01
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29249
This splits out all the tests that are "easy", leaving `TestJit`,
`TestScript`, the autogenerated tests, and a small docs test.
Splitting those into reasonable chunks is more effort which is less
mechanical.
Differential Revision: D18339007
Test Plan: Imported from OSS
Pulled By: suo
fbshipit-source-id: 69164b9f9a2c379fe8923a846c98dd3c37ccb70e
