Commit Graph

4 Commits

Author SHA1 Message Date
Tom Ritchford
d8c8ba2440 Fix unused Python variables in test/[e-z]* (#136964)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136964
Approved by: https://github.com/justinchuby, https://github.com/albanD
2024-12-18 23:02:30 +00:00
Yuanhao Ji
604c9c5601 Enable UFMT on all of test/jit (#123623)
Partially addresses #123062

Ran lintrunner on:

- `test/jit`

with command:

```bash
lintrunner -a --take UFMT --all-files
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/123623
Approved by: https://github.com/ezyang
2024-04-11 23:45:05 +00:00
Aaron Gokaslan
8fce9a09cd [BE]: pyupgrade Python to 3.8 - imports and object inheritance only (#94308)
Apply parts of pyupgrade to torch (starting with the safest changes).
This PR only does two things: removes the need to inherit from object and removes unused future imports.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94308
Approved by: https://github.com/ezyang, https://github.com/albanD
2023-02-07 21:10:56 +00:00
Ivan Kobzarev
2fc73622f8 [jit] Support Awaitable type (#90863)
We want to make TorchRec sharded models TorchScriptable.

TorchRec sharded models uses generic types Awaitable[W] and LazyAwaitable[W] (https://github.com/pytorch/torchrec/blob/main/torchrec/distributed/types.py#L212).
In sharded model those types are used instead of contained type W, having the initialization function that produces object of type W.

At the moment when the first attribute of W is requested - `LazyAwaitable[W]` will call its initialization function (on the same stack), cache the result inside and work transparently as an object of W. So we can think about it as a delayed object initialization.

To support this behavior in TorchScript - we propose a new type to TorchScript - `Await`.
In eager mode it works the same as `LazyAwaitable[W]` in TorchRec, being dynamically typed - acting as a type `W` while it is `Await[W]`.

Within torchscript it is `Await[W]` and can be only explicitly converted to W, using special function `torch.jit.awaitable_wait(aw)`.
Creation of this `Await[W]` is done via another special function `torch.jit.awaitable(func, *args)`.

The semantic is close to `torch.jit.Future`, fork, wait and uses the same jit mechanics (inline fork Closures) with the difference that it does not start this function in parallel on fork. It only stores as a lambda inside IValue that will be called on the same thread when `torch.jit.awaitable_wait` is called.

For example (more examples in this PR `test/jit/test_await.py`)
```
      def delayed(z: Tensor) -> Tensor:
          return Tensor * 3

      @torch.jit.script
      def fn(x: Tensor):
          aw: Await[int] = torch.jit._awaitable(delayed, 99)
          a = torch.eye(2)
          b = torch.jit._awaitable_wait(aw)
          return a + b + x
```

Functions semantics:

`_awaitable(func -> Callable[Tuple[...], W], *args, **kwargs) -> Await[W]`

Creates Await object, owns args and kwargs. Once _awaitable_wait calls, executes function func and owns the result of the function. Following _awaitable_wait calls will return this result from the first function call.

`_awaitable_wait(Await[W]) -> W`
Returns either cached result of W if it is not the first _awaitable_wait call to this Await object or calls specified function if the first.

`_awaitable_nowait(W) -> Await[W]`

Creates trivial Await[W] wrapper on specified object To be type complaint for the corner cases.

Differential Revision: [D42502706](https://our.internmc.facebook.com/intern/diff/D42502706)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90863
Approved by: https://github.com/davidberard98
2023-01-30 17:38:59 +00:00