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See #127836 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127846 Approved by: https://github.com/ezyang ghstack dependencies: #127842, #127843, #127844, #127845
133 lines
3.7 KiB
Python
133 lines
3.7 KiB
Python
# mypy: allow-untyped-defs
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import contextlib
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from typing import Optional
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import torch
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_TORCHBIND_IMPLS_INITIALIZED = False
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_TENSOR_QUEUE_GLOBAL_TEST: Optional[torch.ScriptObject] = None
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def init_torchbind_implementations():
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global _TORCHBIND_IMPLS_INITIALIZED
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global _TENSOR_QUEUE_GLOBAL_TEST
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if _TORCHBIND_IMPLS_INITIALIZED:
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return
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load_torchbind_test_lib()
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register_fake_operators()
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register_fake_classes()
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_TENSOR_QUEUE_GLOBAL_TEST = _empty_tensor_queue()
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_TORCHBIND_IMPLS_INITIALIZED = True
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def _empty_tensor_queue() -> torch.ScriptObject:
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return torch.classes._TorchScriptTesting._TensorQueue(
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torch.empty(
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0,
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).fill_(-1)
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)
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# put these under a function because the corresponding library might not be loaded yet.
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def register_fake_operators():
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@torch.library.register_fake("_TorchScriptTesting::takes_foo_python_meta")
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def fake_takes_foo(foo, z):
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return foo.add_tensor(z)
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@torch.library.register_fake("_TorchScriptTesting::queue_pop")
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def fake_queue_pop(tq):
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return tq.pop()
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@torch.library.register_fake("_TorchScriptTesting::queue_push")
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def fake_queue_push(tq, x):
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return tq.push(x)
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@torch.library.register_fake("_TorchScriptTesting::queue_size")
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def fake_queue_size(tq):
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return tq.size()
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def meta_takes_foo_list_return(foo, x):
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a = foo.add_tensor(x)
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b = foo.add_tensor(a)
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c = foo.add_tensor(b)
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return [a, b, c]
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def meta_takes_foo_tuple_return(foo, x):
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a = foo.add_tensor(x)
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b = foo.add_tensor(a)
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return (a, b)
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torch.ops._TorchScriptTesting.takes_foo_list_return.default.py_impl(
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torch._C.DispatchKey.Meta
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)(meta_takes_foo_list_return)
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torch.ops._TorchScriptTesting.takes_foo_tuple_return.default.py_impl(
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torch._C.DispatchKey.Meta
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)(meta_takes_foo_tuple_return)
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torch.ops._TorchScriptTesting.takes_foo.default.py_impl(torch._C.DispatchKey.Meta)(
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lambda cc, x: cc.add_tensor(x)
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)
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def register_fake_classes():
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@torch._library.register_fake_class("_TorchScriptTesting::_Foo")
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class FakeFoo:
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def __init__(self, x: int, y: int):
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self.x = x
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self.y = y
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@classmethod
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def __obj_unflatten__(cls, flattend_foo):
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return cls(**dict(flattend_foo))
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def add_tensor(self, z):
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return (self.x + self.y) * z
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@torch._library.register_fake_class("_TorchScriptTesting::_ContainsTensor")
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class FakeContainsTensor:
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def __init__(self, t: torch.Tensor):
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self.t = t
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@classmethod
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def __obj_unflatten__(cls, flattend_foo):
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return cls(**dict(flattend_foo))
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def get(self):
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return self.t
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def load_torchbind_test_lib():
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import unittest
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from torch.testing._internal.common_utils import ( # type: ignore[attr-defined]
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find_library_location,
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IS_FBCODE,
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IS_MACOS,
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IS_SANDCASTLE,
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IS_WINDOWS,
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)
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if IS_SANDCASTLE or IS_FBCODE:
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torch.ops.load_library("//caffe2/test/cpp/jit:test_custom_class_registrations")
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elif IS_MACOS:
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raise unittest.SkipTest("non-portable load_library call used in test")
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else:
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lib_file_path = find_library_location("libtorchbind_test.so")
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if IS_WINDOWS:
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lib_file_path = find_library_location("torchbind_test.dll")
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torch.ops.load_library(str(lib_file_path))
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@contextlib.contextmanager
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def _register_py_impl_temporarily(op_overload, key, fn):
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try:
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op_overload.py_impl(key)(fn)
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yield
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finally:
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del op_overload.py_kernels[key]
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op_overload._dispatch_cache.clear()
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