This is a cleaner implementation of opaque objects (https://github.com/pytorch/pytorch/pull/162660). Instead now we just need to do:
Call `register_opaque_type` to register the type as being "opaque" and allowed by custom ops. You also need to pass a unique name that maps to the type.
```python
class OpaqueQueue:
def __init__(self, queue: list[torch.Tensor], init_tensor_: torch.Tensor) -> None:
super().__init__()
self.queue = queue
self.init_tensor_ = init_tensor_
def push(self, tensor: torch.Tensor) -> None:
self.queue.append(tensor)
def pop(self) -> torch.Tensor:
if len(self.queue) > 0:
return self.queue.pop(0)
return self.init_tensor_
def size(self) -> int:
return len(self.queue)
register_opaque_type(OpaqueQueue, "_TestOpaqueObject_OpaqueQueue")
```
When creating the custom op, the schema will then use the unique name:
```python
self.lib = torch.library.Library("_TestOpaqueObject", "FRAGMENT")
torch.library.define(
"_TestOpaqueObject::queue_push",
"(_TestOpaqueObject_OpaqueQueue a, Tensor b) -> ()",
tags=torch.Tag.pt2_compliant_tag,
lib=self.lib,
)
@torch.library.impl(
"_TestOpaqueObject::queue_push", "CompositeExplicitAutograd", lib=self.lib
)
def push_impl(queue: OpaqueQueue, b: torch.Tensor) -> None:
assert isinstance(queue, OpaqueQueue)
queue.push(b)
```
Using the custom op:
```python
queue = OpaqueQueue([], torch.zeros(3))
torch.ops._TestOpaqueObject.queue_push(queue, torch.ones(3))
self.assertTrue(queue.size(), 1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165004
Approved by: https://github.com/albanD
This is a cleaner implementation of opaque objects (https://github.com/pytorch/pytorch/pull/162660). Instead now we just need to do:
Call `register_opaque_type` to register the type as being "opaque" and allowed by custom ops. You also need to pass a unique name that maps to the type.
```python
class OpaqueQueue:
def __init__(self, queue: list[torch.Tensor], init_tensor_: torch.Tensor) -> None:
super().__init__()
self.queue = queue
self.init_tensor_ = init_tensor_
def push(self, tensor: torch.Tensor) -> None:
self.queue.append(tensor)
def pop(self) -> torch.Tensor:
if len(self.queue) > 0:
return self.queue.pop(0)
return self.init_tensor_
def size(self) -> int:
return len(self.queue)
register_opaque_type(OpaqueQueue, "_TestOpaqueObject_OpaqueQueue")
```
When creating the custom op, the schema will then use the unique name:
```python
self.lib = torch.library.Library("_TestOpaqueObject", "FRAGMENT")
torch.library.define(
"_TestOpaqueObject::queue_push",
"(_TestOpaqueObject_OpaqueQueue a, Tensor b) -> ()",
tags=torch.Tag.pt2_compliant_tag,
lib=self.lib,
)
@torch.library.impl(
"_TestOpaqueObject::queue_push", "CompositeExplicitAutograd", lib=self.lib
)
def push_impl(queue: OpaqueQueue, b: torch.Tensor) -> None:
assert isinstance(queue, OpaqueQueue)
queue.push(b)
```
Using the custom op:
```python
queue = OpaqueQueue([], torch.zeros(3))
torch.ops._TestOpaqueObject.queue_push(queue, torch.ones(3))
self.assertTrue(queue.size(), 1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165004
Approved by: https://github.com/albanD
This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
Over time, a large number of the existing type ignores have become irrelevant/unused/dead as a result of improvements in annotations and type checking.
Having these `# type: ignore` linger around is not ideal for two reasons:
- They are verbose/ugly syntatically.
- They could hide genuine bugs in the future, if a refactoring would actually introduce a bug but it gets hidden by the ignore.
I'm counting over 1500 unused ignores already. This is a first PR that removes some of them. Note that I haven't touched type ignores that looked "conditional" like the import challenge mentioned in https://github.com/pytorch/pytorch/pull/60006#issuecomment-2480604728. I will address these at a later point, and eventually would enable `warn_unused_ignores = True` in the mypy configuration as discussed in that comment to prevent accumulating more dead ignores going forward.
This PR should have no effect on runtime at all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142325
Approved by: https://github.com/Skylion007, https://github.com/janeyx99
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
ghstack dependencies: #131777
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
I run into this a lot. I can imagine that it would look opaque to users,
so made it more friendly
Old error message: "ValueError: infer_schema(func): Return has unsupported type <class 'inspect._empty'>."
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129896
Approved by: https://github.com/yushangdi
Fixes [#129370](https://github.com/pytorch/pytorch/issues/129370)
Suggest correct a List type annotation when input is in Tuple type. To avoid confusion, we only suggest a type if the type is supported.
Example:
Tuple[int, int] -> List[int]
Tuple[Tensor, Tensor, Optional[Tensor]] -> List[Optional[Tensor]]
Tuple[int, ...] -> List[int]
ValueError: infer_schema(func): Parameter y has unsupported type typing.Tuple[torch.Tensor, torch.Tensor, typing.Optional[torch.Tensor]]. Tuple type annotation is not supported. Please try to use a List instead. For example, typing.List[typing.Optional[torch.Tensor]].
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129417
Approved by: https://github.com/zou3519
This PR:
- moves some of the dtype-string utilities into ScalarType.{h, cpp}
- adds a new utility to get a mapping from dtype name to the C++ dtype
- the perser now checks if the string is a dtype name; if it is then it
pulls the c++ dtype from the mapping.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129189
Approved by: https://github.com/albanD
ghstack dependencies: #129177, #129178, #129179
Fixes#105157
Bug source: `from __future__ import annotations` converts type annotation to strings to make forwards references easier. However, existing custom ops do not consider strings to be valid types.
Fix: We check if the argument and return type annotation is string type. If so, we try to use `eval` to convert it to a type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128809
Approved by: https://github.com/zou3519
