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cdc56d0b6c
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37700 Certain autograd functions can have optional Tensor arguments. For this purpose it would be nice to support c10::optional<Tensor> as an argument for C++ autograd functions. I've added the appropriate overload to ExtractVariables to ensure this works. For an example, you can look at D21272807 in terms of how this is used. ghstack-source-id: 103541789 Test Plan: waitforbuildbot Differential Revision: D21363491 fbshipit-source-id: 0c8665e9bfe279e6b9ab84a889524fea11fa971c
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
import os.path
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import tempfile
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import unittest
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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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class TestCustomOperators(unittest.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_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.assertTrue(torch.allclose(x.grad, y + grad))
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self.assertTrue(torch.allclose(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.assertTrue(torch.allclose(x.grad, y + grad))
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self.assertTrue(torch.allclose(y.grad, x + grad * 2))
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self.assertTrue(torch.allclose(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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unittest.main()
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