mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
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
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# Owner(s): ["module: custom-operators"]
|
|
|
|
import torch
|
|
from torch._dynamo.test_case import run_tests, TestCase
|
|
from torch._library.opaque_object import register_opaque_type
|
|
|
|
|
|
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)
|
|
|
|
|
|
class TestOpaqueObject(TestCase):
|
|
def setUp(self):
|
|
self.lib = torch.library.Library("_TestOpaqueObject", "FRAGMENT") # noqa: TOR901
|
|
|
|
register_opaque_type(OpaqueQueue, "_TestOpaqueObject_OpaqueQueue")
|
|
|
|
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)
|
|
|
|
self.lib.define(
|
|
"queue_pop(_TestOpaqueObject_OpaqueQueue a) -> Tensor",
|
|
)
|
|
|
|
def pop_impl(queue: OpaqueQueue) -> torch.Tensor:
|
|
assert isinstance(queue, OpaqueQueue)
|
|
return queue.pop()
|
|
|
|
self.lib.impl("queue_pop", pop_impl, "CompositeExplicitAutograd")
|
|
|
|
@torch.library.custom_op(
|
|
"_TestOpaqueObject::queue_size",
|
|
mutates_args=[],
|
|
)
|
|
def size_impl(queue: OpaqueQueue) -> int:
|
|
assert isinstance(queue, OpaqueQueue)
|
|
return queue.size()
|
|
|
|
super().setUp()
|
|
|
|
def tearDown(self):
|
|
self.lib._destroy()
|
|
|
|
super().tearDown()
|
|
|
|
def test_ops(self):
|
|
queue = OpaqueQueue([], torch.zeros(3))
|
|
|
|
torch.ops._TestOpaqueObject.queue_push(queue, torch.ones(3) + 1)
|
|
size = torch.ops._TestOpaqueObject.queue_size(queue)
|
|
self.assertEqual(size, 1)
|
|
popped = torch.ops._TestOpaqueObject.queue_pop(queue)
|
|
self.assertEqual(popped, torch.ones(3) + 1)
|
|
size = torch.ops._TestOpaqueObject.queue_size(queue)
|
|
self.assertEqual(size, 0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|