Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42963
* Adds code printing for enum type
* Enhance enum type to include all contained enum names and values
* Adds code parsing for enum type in deserialization
* Enabled serialization/deserialization test in most TestCases. (With a few dangling issues to be addressed in later PRs to avoid this PR grows too large)
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D23223281
Pulled By: gmagogsfm
fbshipit-source-id: 716d1866b7770dfb7bd8515548cfe7dc4c4585f7
Summary:
Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript.
This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work.
Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message.
This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907
Reviewed By: ngimel
Differential Revision: D22778301
Pulled By: gmagogsfm
fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad
Summary:
* Add EnumType and AnyEnumType as first-class jit type
* Add Enum-typed IValue
* Enhanced aten::eq to support Enum
Supported:
Enum-typed function targuments
using Enum type and comparing them
TODO:
Add PyThon sugared value for Enum
Support getting name/value attrs of enums
Support Enum-typed return values
Support enum values of different types in same Enum class
Support serialization and deserialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41390
Reviewed By: eellison
Differential Revision: D22524388
Pulled By: gmagogsfm
fbshipit-source-id: 1627154a64e752d8457cd53270f3d14aea4b1150
Summary:
Assert in OptionalType::create for valid TypePtr to catch all uses, as well as in python resolver to propagate slightly more helpful error message.
Closes https://github.com/pytorch/pytorch/issues/40713.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41509
Reviewed By: suo
Differential Revision: D22563710
Pulled By: wconstab
fbshipit-source-id: ee6314b1694a55c1ba7c8251260ea120be148b17
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40902
See the bottom of this stack for context.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22360210
Pulled By: suo
fbshipit-source-id: 4275127173a36982ce9ad357aa344435b98e1faf
Summary:
Fix for https://github.com/pytorch/vision/issues/2320 - still need to fix whatever reverting this change breaks
EDIT: reverting this change doesnt seem to break anything, and fixes the torchvision issue
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40041
Reviewed By: eellison
Differential Revision: D22067586
Pulled By: fmassa
fbshipit-source-id: 4b235fd3a69665dcc5689f12310097be31b40a28
Summary:
Remove PY3 and PY34 checks from `torch/testing/_internal/common_utils.py`
Remove PY35 global var from `torch.jit.annotations`
Always call `try_get_real_signature` in `torch/jit/annotations.py`
Use `map` instead of `imap`, since Python-2 is no longer support, so map is always lazy.
Remove all pre Python-3.6 checks from `torch/_six.py` and `torch/_appdirs.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39879
Differential Revision: D22037811
Pulled By: malfet
fbshipit-source-id: af0c79f976569c2059d39ecb49c6b8285161734f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38211
Just because the annotations are inline doesn't mean the files type
check; most of the newly annotated files have type errors and I
added exclusions for them in mypy.ini. The payoff of moving
all of these modules inline is I can delete the relevant code
generation logic for the pyi files (which was added ignore
annotations that weren't actually relevant anymore.)
For the most part the translation was completely mechanical, but there
were two hairy issues. First, I needed to work around a Python 3.6 and
earlier bug where Generic has a nontrivial metaclass. This fix is in
torch/jit/__init__.py. Second, module.py, we need to apply the same
fix for avoiding contravariance checks that the pyi file used to have;
this is done by declaring forward as a variable (rather than a
function), which appears to be sufficient enough to get mypy to not
contravariantly check input arguments.
Because we aren't actually typechecking these modules in most
cases, it is inevitable that some of these type annotations are wrong.
I slavishly copied the old annotations from the pyi files unless there
was an obvious correction I could make. These annotations will probably
need fixing up later.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21497397
Pulled By: ezyang
fbshipit-source-id: 2b08bacc152c48f074e7edc4ee5dce1b77d83702
Summary:
Currently torch.Tensor subclasses (like torch.nn.Parameter) isn't a supported type annotation to torch script inputs. This PR allows it to be treated like torch.Tensor for compilation.
Closes https://github.com/pytorch/pytorch/issues/38235
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39487
Differential Revision: D21885827
Pulled By: gmagogsfm
fbshipit-source-id: 1ec51829b132b7b0293a6c526d73497b23dae113
Summary:
Make it so that non-nn Module classes do not need to be annotated with `torch.jit.script`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38050
Differential Revision: D21482654
Pulled By: eellison
fbshipit-source-id: 22689e4d7a33f6e1574b9495cff29a1fe6abb910
Summary:
**Summary**
This commit detects and prohibits the case in which `typing.List` is
used as an annotation without a type argument (i.e. `typing.List[T]`).
At present, `typing.List` is always assumed to have one argument, and
when it is used without one, `typing.List.__args__[0]` is nonempty and
set to some `typing.TypeVar` instance, which has no JIT type equivalent.
Consequently, trying to convert `typing.List` to a JIT type results in
a `c10::ListType` with `nullptr` for its element type, which can cause
a segmentation fault.
This is fixed by returning a `ListType` from
`jit.annotations.try_ann_to_type` only if the element type is converted
successfully to a JIT type and returning `None` otherwise.
**Test Plan**
I ran the code from the issue (https://github.com/pytorch/pytorch/issues/37530) that reported this problem and also ran some unit tests.
*Before*
```
$ python3 segfault.py
Segmentation fault (core dumped)
```
*After*
```
$ python3 segfault.py
Traceback (most recent call last):
...
RuntimeError:
Unknown type name 'List':
File "segfault.py", line 9
classmethod
def cat(cls, box_lists: List):
~~~~ <--- HERE
return cls(torch.cat([x for x in box_lists]))
'Boxes.cat' is being compiled since it was called from 'Boxes'
File "segfault.py", line 13
def f(t: torch.Tensor):
b = Boxes(t)
~~~~~ <--- HERE
c = Boxes(torch.tensor([3, 4]))
return Boxes.cat([b, c])
'Boxes' is being compiled since it was called from 'f'
File "segfault.py", line 13
def f(t: torch.Tensor):
b = Boxes(t)
~~~~~~~~~~~ <--- HERE
c = Boxes(torch.tensor([3, 4]))
return Boxes.cat([b, c])
```
**Fixes**
This pull request fixes https://github.com/pytorch/pytorch/issues/37530.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38130
Differential Revision: D21485284
Pulled By: SplitInfinity
fbshipit-source-id: 9b51ef6340485a24c8b7cfb85832d4668b8ac51a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).
Test Plan: CI
Differential Revision: D20842886
Pulled By: dreiss
fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
Summary:
This adds some machinery so that we use Python to resolve types to a value and the corresponding resolution logic in `annotations.py` instead of using the string.
This PR also `slowTests` a random test since it was taking > 1 min whereas all the other tests take < 10 seconds.
Fixes#31864Fixes#31950
](https://our.intern.facebook.com/intern/diff/20144407/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29623
Pulled By: driazati
Differential Revision: D20144407
fbshipit-source-id: ef3699f6b86039d8b4646ffc42c21bd1132d1681
Summary:
This hooks up `inspect` so that Python functions get their parameters
names attached instead of naming them `0, 1, 2, ...`. This also fixes
issue #28537 where `ignore` functions were improperly typing `self`.
](https://our.intern.facebook.com/intern/diff/19256434/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29300
Pulled By: driazati
Differential Revision: D19256434
fbshipit-source-id: 6a1fe7bd0afab708b8439517798955d0abfeb44c
Summary:
Uses new overload mechanism for rnns, making it so that python & torchscript go through the same path and using an API that is in line with the one specified
in https://docs.python.org/3/library/typing.html#typing.overload
This brings the TorchScriptable rnns closer to the base implementation; unifying them should be done in a follow up PR but there are still a few limitations that make it difficult to do so.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29614
Differential Revision: D18486982
Pulled By: eellison
fbshipit-source-id: aaaea66a4a7f12d2e46199ca254f9e8f7475500e
Summary:
Uses new overload mechanism for rnns, making it so that python & torchscript go through the same path and using an API that is in line with the one specified
in https://docs.python.org/3/library/typing.html#typing.overload
This brings the TorchScriptable rnns closer to the base implementation; unifying them should be done in a follow up PR but there are still a few limitations that make it difficult to do so.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29614
Differential Revision: D18458751
Pulled By: eellison
fbshipit-source-id: 07c71838f21cb5425e8d6dbd4a512f774c8c2970
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26572
Combined with isinstance specialization this allows a degree of polymorphic
functions to work without needing to use our weirder overload hacks.
We do not define any operators on Any, so the only thing you can do with it
is to put it in containers or type refine it using an isinstance check.
Any is restricted from appearing in non-argument position because we
cannot restore type tags if it ends up as a field in a class.
Test Plan: Imported from OSS
Differential Revision: D17530643
Pulled By: zdevito
fbshipit-source-id: f06f78ce84819f7773953a492f3d4c49219ee94c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26666
Changes:
- Introduce a `ConcreteModuleType` concept. This acts both as the key into the type
cache, and as the source of truth for `ModuleValue::attr` queries. It needs
to do both jobs because that's how we ensure correctness (if the types are
different, it's because `ModuleValue::attr` would return different things).
- Now `recursive_script` will first construct a `ConcreteModuleType` and search for a
pre-existing type before starting compilation.
- All previous paths to creating a `ScriptModule` (including inheriting from
`ScriptModule`) are now rewritten to go through `create_script_module`, so
that we have only a single place where construction happens.
Behavioral changes:
- Big change to `torch.jit.ScriptModule` inheritance: all attributes are now
recursively scripted if possible, matching recursive scripting semantics.
This makes it hard to keep something from being scripted (for example, a
Python submodule). Possibly we'll need an `ignore()` type thing for
attributes. In particular, this adds `self.training` to *every* ScriptModule, since
it's present on every `nn.Module`.
- I believe this change to be transparent to existing users of the inheritance API, since if you had an attribute that is unscriptable that you never used, there is no error. In some cases, we will create new attributes (even if they are unused), which will increase serialized model size from before.
Test Plan: Imported from OSS
Differential Revision: D17551196
Pulled By: suo
fbshipit-source-id: b476d1c9feb3ddfd63406d90989aaf9dfe890591
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26734
This PR added the python assignment for interface as an attribute in the
module, it enables any object that implicitly inheriting the specific
interface to be able to be assigned to the interface type in python.
Serialization support for interface/class assignment will be done in the
follow up PR
Test Plan: Imported from OSS
Differential Revision: D17742708
Pulled By: wanchaol
fbshipit-source-id: a0a2d8c74b60ed3fa6c05e1b0d49b7ad1abc670b
Summary:
When used as annotations on Python functions, `NamedTuple`s go through our Python annotation -> type mapping which previously had no way of lookup up `NamedTuple`s (which are created lazily by checking if the type has certain properties, so the lookup is creating the `TupleType` from scratch). This PR threads through the necessary data to make them work.
Fixes#26437
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26443
Pulled By: driazati
Differential Revision: D17486441
fbshipit-source-id: a6bbb543ff05a5abe61f1a7f68db9ecdb652b358
Summary:
If source code is not available due to packaging (e.g. sources are compiled to .pyc), TorchScript produces very obscure error message. This tries to make it nicer and allow to customize message by overriding _utils_internal.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25415
Test Plan: Really hard to unittest properly. Did one off testing by compiling to .pyc and checking the message.
Differential Revision: D17118238
Pulled By: dzhulgakov
fbshipit-source-id: 3cbfee0abddc8613000680548bfe0b8ed52a36b0
Summary:
Previously these were left out which would lead to confusing messages,
now it looks something like:
```
torch.jit.frontend.UnsupportedNodeError: import statements aren't
supported
:
at ../test.py:13:9
def bad_fn(self):
import pdb
~~~~~~ <--- HERE
'__torch__.X' is being compiled since it was called from 'fn'
at ../test.py:16:12
def fn(x):
return X(10)
~~~~ <--- HERE
```
Fixes#23453
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23454
Pulled By: driazati
Differential Revision: D16567930
fbshipit-source-id: 251b6f91f37a2816e06bb4c803f9bc172fa1d91b
Summary:
Previously these were left out which would lead to confusing messages,
now it looks something like:
```
torch.jit.frontend.UnsupportedNodeError: import statements aren't
supported
:
at ../test.py:13:9
def bad_fn(self):
import pdb
~~~~~~ <--- HERE
'__torch__.X' is being compiled since it was called from 'fn'
at ../test.py:16:12
def fn(x):
return X(10)
~~~~ <--- HERE
```
Fixes#23453
](https://our.intern.facebook.com/intern/diff/16526027/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23454
Pulled By: driazati
Differential Revision: D16526027
fbshipit-source-id: 109f2968430dbf51ee91b1b3409badfd557d19a4
Summary:
This PR adds a check that prints a warning if a type annotation prefix isn't what mypy expects.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20884
Differential Revision: D15511043
Pulled By: Krovatkin
fbshipit-source-id: 9038e074807832931faaa5f4e69628f94f51fd72
Summary:
TensorList, DoubleList, and BoolList were missing from the pickler, so
this adds them.
As a follow up a lot of the code for these could be templated and cut
down
](https://our.intern.facebook.com/intern/diff/15299106/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20191
Pulled By: driazati
Differential Revision: D15299106
fbshipit-source-id: f10c0c9af9d60a6b7fb8d93cea9f550b1a7e2415
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
resize_ and resize_as resize the input tensor. because our shape analysis
is flow invariant, we don't do shape analysis on any op that relies on a Tensor that can alias a resized Tensor.
E.g. in the following graph the x += 10 x may have been resized.
```
torch.jit.script
def test(x, y):
for i in range(10):
x += 10
x.resize_as_([1 for i in int(range(torch.rand())))
return x
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17518
Differential Revision: D14249835
Pulled By: eellison
fbshipit-source-id: f281b468ccb8c29eeb0f68ca5458cc7246a166d9
Summary:
Made the change requested in #15555
PR was failing build due to a time out error while getting packages using pip.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16416
Differential Revision: D13833873
Pulled By: soumith
fbshipit-source-id: e2200e9e8015558fcd359dfa3d025b25802d62b5
Summary:
Adds `List` to eval environment for type lines and allows `List` to be used on PythonOps (follows the same style as the `Tuple` code), fixes#15661
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15721
Differential Revision: D13578540
Pulled By: driazati
fbshipit-source-id: fce54dc3c0931d8b017b2e3483f0ac53826dda94
Summary:
This PR adds a polyfill for `typing.List` for Python versions that don't
support `typing` as a builtin. It also moves the type defintions from
`annotations.py` so that they can be used in `torch.nn`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14482
Differential Revision: D13237570
Pulled By: driazati
fbshipit-source-id: 6575b7025c2d98198aee3b170f9c4323ad5314bd
