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https://github.com/zebrajr/pytorch.git
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4057d005ff
This PR adds the bare minimum functionality to get torchbind working in an e2e testable way on PT2. It implements: * ProxyTensor support * Simple torch.export support (proxytensor-only path, e.g. non-strict). * add some tests exercising the path. Because all this is not fully baked, I hide the functionality behind a feature flag (`enable_torchbind_tracing()`) so it does not affect regular users for now. Still on the agenda: * Dynamo support * Actual FakeMode support * Mutability support Hoping to get this first bit in as a standalone, as it will unblock some more extensive experimentation/testing going on internally. Differential Revision: [D51825372](https://our.internmc.facebook.com/intern/diff/D51825372/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/117697 Approved by: https://github.com/SherlockNoMad
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
# Owner(s): ["module: export"]
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import unittest
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import torch
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from torch._higher_order_ops.torchbind import enable_torchbind_tracing
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from torch.export import export
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from torch.testing._internal.common_utils import (
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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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run_tests,
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skipIfTorchDynamo,
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TestCase,
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)
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@skipIfTorchDynamo("torchbind not supported with dynamo yet")
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class TestExportTorchbind(TestCase):
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def setUp(self):
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if IS_MACOS:
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raise unittest.SkipTest("non-portable load_library call used in test")
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elif IS_SANDCASTLE or IS_FBCODE:
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torch.ops.load_library(
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"//caffe2/test/cpp/jit:test_custom_class_registrations"
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)
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elif 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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else:
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lib_file_path = find_library_location("libtorchbind_test.so")
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torch.ops.load_library(str(lib_file_path))
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def _test_export_same_as_eager(self, f, args, kwargs=None, strict=True):
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kwargs = kwargs or {}
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with enable_torchbind_tracing():
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exported_program = export(f, args, kwargs, strict=strict)
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reversed_kwargs = {key: kwargs[key] for key in reversed(kwargs)}
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self.assertEqual(exported_program(*args, **kwargs), f(*args, **kwargs))
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self.assertEqual(
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exported_program(*args, **reversed_kwargs), f(*args, **reversed_kwargs)
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)
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def test_none(self):
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.attr = torch.classes._TorchScriptTesting._Foo(10, 20)
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def forward(self, x, n):
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return x + self.attr.add_tensor(x)
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self._test_export_same_as_eager(
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MyModule(), (torch.ones(2, 3), None), strict=False
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)
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def test_attribute(self):
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.attr = torch.classes._TorchScriptTesting._Foo(10, 20)
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def forward(self, x):
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return x + self.attr.add_tensor(x)
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self._test_export_same_as_eager(MyModule(), (torch.ones(2, 3),), strict=False)
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def test_attribute_as_custom_op_argument(self):
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.attr = torch.classes._TorchScriptTesting._Foo(10, 20)
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def forward(self, x):
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return x + torch.ops._TorchScriptTesting.takes_foo(self.attr, x)
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self._test_export_same_as_eager(MyModule(), (torch.ones(2, 3),), strict=False)
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def test_input(self):
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, cc):
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return x + cc.add_tensor(x)
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cc = torch.classes._TorchScriptTesting._Foo(10, 20)
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self._test_export_same_as_eager(
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MyModule(), (torch.ones(2, 3), cc), strict=False
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)
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def test_input_as_custom_op_argument(self):
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, cc):
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return x + torch.ops._TorchScriptTesting.takes_foo(cc, x)
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cc = torch.classes._TorchScriptTesting._Foo(10, 20)
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self._test_export_same_as_eager(
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MyModule(), (torch.ones(2, 3), cc), strict=False
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)
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if __name__ == "__main__":
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run_tests()
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