Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24281
These are not just classes anymore, rename
Test Plan: Imported from OSS
Differential Revision: D16800564
Pulled By: suo
fbshipit-source-id: 8b8d508944c26a8916fc7642df43f22583dfcf82
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24077
This replaces all uses of DimensionedTensorType with ProfiledTensorType.
For places where we propagate shape information, we still follow the
dimension-only propagation rules, meaning that even if full size information
is known on inputs the outputs will only have dimension information.
This fixes several bugs in existing implentations that this change uncovered:
* requires_grad was not propgated correctly across loops
* requires_grad on ProfiledTensorType returned false when requires_grad information
is unknown but the conservative result is true
* some equality code on ProfiledTensorType contained bugs.
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D16729581
Pulled By: zdevito
fbshipit-source-id: bd9f823c1c6b1d06a236a1b5b2b2fcdf0245edce
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23846
This moves a test from Python to cpp, and in doing so lets us clean up a
bunch of otherwise unused code.
Test Plan: Imported from OSS
Differential Revision: D16684390
Pulled By: suo
fbshipit-source-id: fca81ca14d1ac9e4d6b47ae5eecaa42b38d69147
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23845
These are not just classes anymore, rename
Test Plan: Imported from OSS
Differential Revision: D16684391
Pulled By: suo
fbshipit-source-id: af0024c0b7fbcca68785ec3fc6dc288ec46a1b84
Summary:
I accidentally removed this in a merge, breaking a test. Fix for master
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24003
Differential Revision: D16707108
Pulled By: eellison
fbshipit-source-id: 8b59f46e7932b88a7ae246a261c4daf17f23995f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23886
This is a series of PRs that will allow us to support adding [padding to conv](https://github.com/pytorch/pytorch/pull/22484) and also reduce the friction of adding method overloads that was brought up in https://github.com/pytorch/pytorch/pull/23266.
Support for overloaded functions following the specification in [PEP 484](https://www.python.org/dev/peps/pep-0484/#function-method-overloading).
The usage is:
```
torch.jit.overload
def add(x: int, y: int) -> int: ...
torch.jit.overload
def add(x: float, y: float) -> float: ...
def add:
return x + y
```
Follow up PRs:
- Add same API for methods
- A couple of cleanups for functions:
- don't require default params specified on the overload as well
- potentially error if invocation could be matched to multiple overloads. now it just chooses the first one, mypy does the same thing currently
Test Plan: Imported from OSS
Differential Revision: D16694863
Pulled By: eellison
fbshipit-source-id: f94f2933bc1c97fa58f31846acfe962b0630068c
Summary:
Before calling `__setstate__` when loading a module, we need to disable
the optimizer since the module's type does not match the values on the
stack (all the tensors will be `UndefinedTensor`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23698
Pulled By: driazati
Differential Revision: D16690935
fbshipit-source-id: 71e2238fd25cd16271af478ef21a3cf4e514a462
Summary:
formatting in advance of pr that touches this file bc there is a lot of formatting noise :'(
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23840
Differential Revision: D16659311
Pulled By: eellison
fbshipit-source-id: 7dedaccf9b9c455f97efdcce1c58515eb155d261
Summary:
This fixes up the test suite (mostly just adding `ignore` decorations
to tests that need to call Python function) so that it all passes with
recursive script enabled.
The main user-facing result of this change is that Python functions are
compiled without any decorators, so non-TorchScriptable code must be
decorated with `torch.jit.ignore` (or
`torch.jit.ignore(drop_on_export=True` to maintain the functionality of
the current `ignore`)
Details can be found in #20939
](https://our.intern.facebook.com/intern/diff/16277608/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22887
Pulled By: driazati
Differential Revision: D16277608
fbshipit-source-id: 0abd0dc4291cf40651a1719bff813abb2b559640
Summary:
The error for `test_error_stack_module`:
```
Traceback (most recent call last):
File "../test.py", line 35, in <module>
scripted = torch.jit.script(M())
File "/home/davidriazati/other/pytorch/torch/jit/__init__.py", line 1119, in script
return _convert_to_script_module(obj)
File "/home/davidriazati/other/pytorch/torch/jit/__init__.py", line 1825, in _convert_to_script_module
raise e
RuntimeError:
d(int x) -> int:
Expected a value of type 'int' for argument 'x' but instead found type 'str'.
:
at ../test.py:11:12
def c(x):
return d("hello") + d(x)
~ <--- HERE
'c' is being compiled since it was called from 'b'
at ../test.py:14:12
def b(x):
return c(x)
~~~ <--- HERE
'b' is being compiled since it was called from 'forward'
at ../test.py:22:16
def forward(self, x):
return b(x)
~~~ <--- HERE
'forward' is being compiled since it was called from 'forward'
at ../test.py:31:20
def forward(self, x):
return x + self.submodule(x)
~~~~~~~~~~~~~~~~ <--- HERE
```
This also unifies our error reporting in the front end with `ErrorReport`
TODO
* Include module names in message, #22207 should make this easy
](https://our.intern.facebook.com/intern/diff/16060781/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22280
Pulled By: driazati
Differential Revision: D16060781
fbshipit-source-id: c42968b53aaddb774ac69d5abbf7e60c23df8eed
Summary:
The code in `python_sugared_value.cpp` to recursively compile methods
was not being tested, so this adds a test for it and fixes some errors
in it
It was necessary to disable any hooks set since (at least in our tests) they would try to export
a half-finished graph since they were being called on recursively
compiled methods
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21862
Differential Revision: D15860314
Pulled By: driazati
fbshipit-source-id: e8afe9d4c75c345b6e1471072d67c5e335b61337
Summary:
Resolves https://github.com/pytorch/lockdown/issues/18
This implements NamedTuple by taking advantage of the existing `names` field in `TupleType`.
TODO: This currently doesn't retain the NamedTuple-ness through serialization. Discussed with suo offline, we can probably make a way to define an anonymous NamedTuple in script (e.g. `NamedTuple('Foo', [('a', int), ('b', float), ('c', List[float])])` and serialize that
TODO: implement support for calling the constructor with kwargs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21428
Differential Revision: D15741564
Pulled By: jamesr66a
fbshipit-source-id: c077cbcea1880675ca6deb340a9ec78f824a136c
Summary:
- [x] Add tests after https://github.com/pytorch/pytorch/pull/20256 is merged
- Support exporting ScriptModule with inputs/outputs of arbitrarily constructed tuples.
- Moved the assigning of output shapes to after graph conversion to ONNX is completed. By then all tuples in the IR has already been lowered by the pass ```_jit_pass_lower_all_tuples```. If assigning output shapes is required to happen before that, we'll need to hand parse the tuple structures in the graph, and repeat the same logic in ```_jit_pass_lower_all_tuples```. Handling inputs is easier because all tuple information is encoded within the input tensor type.
- Swap the order of ```_jit_pass_lower_all_tuples``` and ```_jit_pass_erase_number_types```. Ops like ```prim::TupleIndex``` relies on index being a scalar. ```_jit_pass_erase_number_types``` will convert these kind of scalars to tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20784
Reviewed By: zrphercule
Differential Revision: D15484171
Pulled By: houseroad
fbshipit-source-id: 4767a84038244c929f5662758047af6cb92228d3
