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https://github.com/pytorch/pytorch.git
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bc3e2e03cd
This reverts commit 6ae4e3a8d249a96d9a8bbfba389d0509783e11e1. Reverted https://github.com/pytorch/pytorch/pull/112851 on behalf of https://github.com/facebook-github-bot due to Diff reverted internally ([comment](https://github.com/pytorch/pytorch/pull/112851#issuecomment-1799539354))
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
# Owner(s): ["module: unknown"]
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import os.path
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import sys
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import tempfile
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import torch
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from torch import ops
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from model import Model, get_custom_op_library_path
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from torch.testing._internal.common_utils import TestCase, run_tests
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class TestCustomOperators(TestCase):
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def setUp(self):
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self.library_path = get_custom_op_library_path()
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ops.load_library(self.library_path)
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def test_custom_library_is_loaded(self):
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self.assertIn(self.library_path, ops.loaded_libraries)
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def test_abstract_impl_pystub_faketensor(self):
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from functorch import make_fx
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x = torch.randn(3, device='cpu')
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self.assertNotIn("my_custom_ops", sys.modules.keys())
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with self.assertRaises(torch._subclasses.fake_tensor.UnsupportedOperatorException):
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gm = make_fx(torch.ops.custom.nonzero.default, tracing_mode="symbolic")(x)
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torch.ops.import_module("my_custom_ops")
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gm = make_fx(torch.ops.custom.nonzero.default, tracing_mode="symbolic")(x)
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self.assertExpectedInline("""\
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def forward(self, arg0_1):
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nonzero = torch.ops.custom.nonzero.default(arg0_1); arg0_1 = None
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return nonzero
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""".strip(), gm.code.strip())
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def test_abstract_impl_pystub_meta(self):
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x = torch.randn(3, device="meta")
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self.assertNotIn("my_custom_ops2", sys.modules.keys())
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with self.assertRaisesRegex(NotImplementedError, r"import the 'my_custom_ops2'"):
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y = torch.ops.custom.sin.default(x)
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torch.ops.import_module("my_custom_ops2")
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y = torch.ops.custom.sin.default(x)
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def test_calling_custom_op_string(self):
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output = ops.custom.op2("abc", "def")
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self.assertLess(output, 0)
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output = ops.custom.op2("abc", "abc")
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self.assertEqual(output, 0)
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def test_calling_custom_op(self):
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output = ops.custom.op(torch.ones(5), 2.0, 3)
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self.assertEqual(type(output), list)
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self.assertEqual(len(output), 3)
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for tensor in output:
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self.assertTrue(tensor.allclose(torch.ones(5) * 2))
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output = ops.custom.op_with_defaults(torch.ones(5))
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self.assertEqual(type(output), list)
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self.assertEqual(len(output), 1)
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self.assertTrue(output[0].allclose(torch.ones(5)))
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def test_calling_custom_op_with_autograd(self):
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x = torch.randn((5, 5), requires_grad=True)
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y = torch.randn((5, 5), requires_grad=True)
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output = ops.custom.op_with_autograd(x, 2, y)
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self.assertTrue(output.allclose(x + 2 * y + x * y))
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go = torch.ones((), requires_grad=True)
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output.sum().backward(go, False, True)
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grad = torch.ones(5, 5)
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self.assertEqual(x.grad, y + grad)
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self.assertEqual(y.grad, x + grad * 2)
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# Test with optional arg.
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x.grad.zero_()
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y.grad.zero_()
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z = torch.randn((5, 5), requires_grad=True)
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output = ops.custom.op_with_autograd(x, 2, y, z)
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self.assertTrue(output.allclose(x + 2 * y + x * y + z))
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go = torch.ones((), requires_grad=True)
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output.sum().backward(go, False, True)
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self.assertEqual(x.grad, y + grad)
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self.assertEqual(y.grad, x + grad * 2)
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self.assertEqual(z.grad, grad)
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def test_calling_custom_op_with_autograd_in_nograd_mode(self):
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with torch.no_grad():
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x = torch.randn((5, 5), requires_grad=True)
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y = torch.randn((5, 5), requires_grad=True)
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output = ops.custom.op_with_autograd(x, 2, y)
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self.assertTrue(output.allclose(x + 2 * y + x * y))
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def test_calling_custom_op_inside_script_module(self):
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model = Model()
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output = model.forward(torch.ones(5))
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self.assertTrue(output.allclose(torch.ones(5) + 1))
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def test_saving_and_loading_script_module_with_custom_op(self):
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model = Model()
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# Ideally we would like to not have to manually delete the file, but NamedTemporaryFile
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# opens the file, and it cannot be opened multiple times in Windows. To support Windows,
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# close the file after creation and try to remove it manually.
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file = tempfile.NamedTemporaryFile(delete=False)
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try:
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file.close()
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model.save(file.name)
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loaded = torch.jit.load(file.name)
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finally:
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os.unlink(file.name)
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output = loaded.forward(torch.ones(5))
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self.assertTrue(output.allclose(torch.ones(5) + 1))
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if __name__ == "__main__":
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run_tests()
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