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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129764 Approved by: https://github.com/ezyang
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
# Owner(s): ["oncall: jit"]
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import torch
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import torch.nn.utils.parametrize as parametrize
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from torch import nn
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from torch.testing._internal.jit_utils import JitTestCase
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if __name__ == "__main__":
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raise RuntimeError(
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"This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead."
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)
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class TestParametrization(JitTestCase):
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# Define some parametrization
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class Symmetric(nn.Module):
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def forward(self, X):
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return X.triu() + X.triu(1).mT
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def test_traceable(self):
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r"""Test the jit scripting and tracing of a parametrized model."""
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model = nn.Linear(5, 5)
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parametrize.register_parametrization(model, "weight", self.Symmetric())
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x = torch.randn(3, 5)
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y = model(x)
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# Check the tracing works. Because traced functions cannot be called
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# directly, we run the comparison on the activations.
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traced_model = torch.jit.trace_module(model, {"forward": x})
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y_hat = traced_model(x)
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self.assertEqual(y, y_hat)
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# Check traced model works with caching
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with parametrize.cached():
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y_hat = traced_model(x)
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self.assertEqual(y, y_hat)
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# Check the tracing throws an error when caching
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with self.assertRaisesRegex(RuntimeError, "Cannot trace a model while caching"):
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with parametrize.cached():
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traced_model = torch.jit.trace_module(model, {"forward": x})
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def test_scriptable(self):
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# TODO: Need to fix the scripting in parametrizations
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# Currently, all the tests below will throw torch.jit.Error
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model = nn.Linear(5, 5)
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parametrize.register_parametrization(model, "weight", self.Symmetric())
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x = torch.randn(3, 5)
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y = model(x)
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with self.assertRaises(torch.jit.Error):
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# Check scripting works
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scripted_model = torch.jit.script(model)
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y_hat = scripted_model(x)
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self.assertEqual(y, y_hat)
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with parametrize.cached():
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# Check scripted model works when caching
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y_hat = scripted_model(x)
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self.assertEqual(y, y_hat)
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# Check the scripting process throws an error when caching
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with self.assertRaisesRegex(RuntimeError, "Caching is not implemented"):
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scripted_model = torch.jit.trace_module(model)
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