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de7219e8a7
Generator comprehensions with any/all are less verbose and potentially help to save memory/CPU : https://eklitzke.org/generator-comprehensions-and-using-any-and-all-in-python To make JIT work with this change, I added code to convert GeneratorExp to ListComp. So the whole PR is basically NoOp for JIT, but potentially memory and speed improvement for eager mode. Also I removed a test from test/jit/test_parametrization.py. The test was bad and had a TODO to actually implement and just tested that UnsupportedNodeError is thrown, and with GeneratorExp support a different error would be thrown. Pull Request resolved: https://github.com/pytorch/pytorch/pull/78142 Approved by: https://github.com/malfet, https://github.com/albanD
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
# Owner(s): ["oncall: jit"]
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import torch
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from torch import nn
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import torch.nn.utils.parametrize as parametrize
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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("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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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,
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'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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