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2b4ef6b4d6
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 |
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| .. | ||
| __init__.py | ||
| autograd.py | ||
| custom_ops.py | ||
| fake_class_registry.py | ||
| fake_impl.py | ||
| fake_profile.py | ||
| infer_schema.py | ||
| opaque_object.py | ||
| simple_registry.py | ||
| triton.py | ||
| utils.py | ||