Changes:
- #95200
1. Recognize `.py.in` and `.pyi.in` files as Python in VS Code for a better development experience.
2. Fix deep setting merge in `tools/vscode_settings.py`.
- #95267
3. Use `Namedtuple` rather than `namedtuple + __annotations__` for `torch.nn.utils.rnn.PackedSequence_`:
`namedtuple + __annotations__`:
```python
PackedSequence_ = namedtuple('PackedSequence_',
['data', 'batch_sizes', 'sorted_indices', 'unsorted_indices'])
# type annotation for PackedSequence_ to make it compatible with TorchScript
PackedSequence_.__annotations__ = {'data': torch.Tensor, 'batch_sizes': torch.Tensor,
'sorted_indices': Optional[torch.Tensor],
'unsorted_indices': Optional[torch.Tensor]}
```
`Namedtuple`: Python 3.6+
```python
class PackedSequence_(NamedTuple):
data: torch.Tensor
batch_sizes: torch.Tensor
sorted_indices: Optional[torch.Tensor]
unsorted_indices: Optional[torch.Tensor]
```
- => this PR: #95268
4. Sort import statements and remove unnecessary imports in `.pyi`, `.pyi.in` files.
5. Format `.pyi`, `.pyi.in` files and remove unnecessary ellipsis `...` in type stubs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95268
Approved by: https://github.com/huydhn
Changes:
1. `typing_extensions -> typing-extentions` in dependency. Use dash rather than underline to fit the [PEP 503: Normalized Names](https://peps.python.org/pep-0503/#normalized-names) convention.
```python
import re
def normalize(name):
return re.sub(r"[-_.]+", "-", name).lower()
```
2. Import `Literal`, `Protocal`, and `Final` from standard library as of Python 3.8+
3. Replace `Union[Literal[XXX], Literal[YYY]]` to `Literal[XXX, YYY]`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94490
Approved by: https://github.com/ezyang, https://github.com/albanD
In `torchvision` we started to use tensor subclasses. With the current annotations, this minimal example throws three errors when checking with `mypy`:
```py
from typing import Type, TypeVar, Any, Optional, Union
import torch
T = TypeVar("T", bound="TensorSubclass")
class TensorSubclass(torch.Tensor):
def __new__(
cls: Type[T],
data: Any,
*,
dtype: Optional[torch.dtype] = None,
device: Optional[Union[torch.device, str, int]] = None,
) -> T:
return torch.as_tensor(data, dtype=dtype, device=device).as_subclass(cls)
```
```
main.py:16:16: error: Incompatible return value type (got "Tensor", expected "T") [return-value]
main.py:16:58: error: Argument "device" to "as_tensor" has incompatible type "Union[device, str, int, None]"; expected "Optional[device]" [arg-type]
main.py:16:78: error: Argument 1 to "as_subclass" of "_TensorBase" has incompatible type "Type[T]"; expected "Tensor" [arg-type]
```
I'll explain inline why the old annotations are wrong.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86105
Approved by: https://github.com/albanD
Summary:
This PR introduces `SymInt` type to Pytorch which will be used by LTC and AOTAutograd for tracing size arithmetic and tests.
`SymInt` is a C++ union structure [int64_t, SymbolicIntNode*] that wraps around an int64_t field where the value of the field could be an index into a list of `shared_ptr<SymbolicIntNode>` or a real int.
This PR doesn't add any support for actually tracing symbolic ints. i.e. data_ for now can only contain real ints.
```
Goal 1: just to show we can add a type to PyTorch core. (wraps int) LANDEABLE
Finalize the naming - symint
Want the name to be short
Does invoke “size” - NO
SInt/SymInt/SymbolicInt
SInt could mean signed int
sym_int or symint or SymInt (originally it was “int”; capitalized implies object semantics, whereas lowercase implies value semantics)
JIT schema - symint
C++ - symint
```
See more details here: https://docs.google.com/document/d/1iiLNwR5ohAsw_ymfnOpDsyF6L9RTUaHMpD8 (d843f63f2a)YLw-jxEw
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74861
Reviewed By: qihqi, ngimel
Differential Revision: D35226230
Pulled By: Krovatkin
fbshipit-source-id: 34acf342bd50fcaa4d8d5dd49c2fd6a98823a5b3
(cherry picked from commit 218643f63ef181cabb92d13a6e837eb64f2dda3c)
Currently, `NamedTuple` return types are created in `torch/_VF.pyi` instead of
typing being added for the symbols in in `torch/return_types.py`. This also
fixes the type names to match the actual names in the python code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74199
Approved by: https://github.com/ezyang
Summary:
The overloads are a little tricky here. It's important that the overloads are such that it's unambiguous what
`torch.nonzero(x)` will resolve to - so just specify defaults for one of the overloads. Also, `out` is left out of the second overload
because a non-None value for `out` is not valid in combination with `as_tuple=True`.
Closes gh-51434
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51635
Reviewed By: zhangguanheng66
Differential Revision: D26279203
Pulled By: walterddr
fbshipit-source-id: 8459c04fc9fbf7fc5f31b3f631aaac2f98b17ea6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49055
Removed the majority of the TODO hacks that I added to the original pyi PR to maintain byte-for-byte compatibility.
I left a few of the divergences between pyi deprecated vs. native signatures, since (a) they're smaller and (b) it might make more sense to kill the deprecated functions at some point entirely.
Test Plan: Imported from OSS
Reviewed By: ljk53
Differential Revision: D25410847
Pulled By: bdhirsh
fbshipit-source-id: cf07cdda92f7492cd83d363cbb810e3810f6b8c8
Summary:
Related to https://github.com/pytorch/pytorch/issues/40397
Inspired by ezyang's comment at https://github.com/pytorch/pytorch/issues/40397#issuecomment-648233001, this PR attempts to leverage using `__all__` to explicitly export private functions from `_VariableFunctions.pyi` in order to make `mypy` aware of them after:
```
if False:
from torch._C._VariableFunctions import *
```
The generation of the `__all__` template variable excludes some items from `unsorted_function_hints`, as it seems that those without hints end up not being explicitly included in the `.pyi` file: I leaned on the side of caution and opted for having `__all__` consistent with the definitions inside the file. Additionally, added some pretty-printing to avoid having an extremely long line.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40499
Differential Revision: D22240716
Pulled By: ezyang
fbshipit-source-id: 77718752577a82b1e8715e666a8a2118a9d3a1cf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38157
This removes the error prone process of assembling `torch/__init__.pyi`
(and frequently forgetting to expose things), since now we can simply
rely on the true source file to get things done. Most of the old
codegen in gen_pyi.py is now rerouted to various files:
- `torch/_C/__init__.pyi` (the dumping pile of all misc bindings)
- `torch/_C/_nn.pyi` (NN function bindings)
- `torch/_C/_VariableFunctions.pyi` (torch function bindings)
`torch.types` grew a bunch more definitions that previously where
defined in `torch/__init__.pyi`
Some miscellaneous changes
- Fixed a bug where we treat single TensorList argument as implying
varargs are accepted. This is actually only supported on IntList.
This means we can correctly generate a stub for dequantize.
- Add missing manual stub for nonzero
- Switched torch/onnx/operators.py to directly refer to _C module,
since apparently mypy doesn't think that methods prefixed with
underscores get reexported. This may be a recurring theme; maybe
we need to find a better way to solve it.
Because I was really lazy, I dumped namedtuple definitions in both
`torch._C` and `torch._C._VariableFunctions`. This is definitely wrong.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21497400
Pulled By: ezyang
fbshipit-source-id: 07b126141c82efaca37be27c07255cb2b9b3f064
