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Summary: See https://github.com/pytorch/torchdynamo/issues/1475 Not allowing any new mutations happen inside forward() function during export. Test Plan: Run `python test/dynamo/test_export.py` and make sure it passes Added new unit tests (3 positive tests and 4 negative tests) Here's what the actual error looks like ``` File "/home/mnachin/local/miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 322, in step getattr(self, inst.opname)(inst) File "/home/mnachin/local/miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 835, in STORE_ATTR assert not self.export, f"Mutating module attribute {inst.argval} during export." AssertionError: Mutating module attribute a during export. from user code: File "/data/users/mnachin/pytorch/test/dynamo/test_export_mutations.py", line 25, in forward self.a = self.a.to(torch.float64) Set torch._dynamo.config.verbose=True for more information ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/88354 Approved by: https://github.com/tugsbayasgalan, https://github.com/jansel
120 lines
4.0 KiB
Python
120 lines
4.0 KiB
Python
# Owner(s): ["module: dynamo"]
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import torch
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import torch._dynamo.test_case
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import torch._dynamo.testing
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class MutationExportTests(torch._dynamo.test_case.TestCase):
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def check_failure_on_export(self, mod, *args):
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with self.assertRaises(AssertionError):
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torch._dynamo.export(mod, *args)
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def check_same_with_export(self, mod, arg):
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real_result = mod(arg)
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graph, _ = torch._dynamo.export(mod, arg)
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result = graph(arg)
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self.assertTrue(torch._dynamo.utils.same(result, real_result))
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def test_module_attribute_mutation_violation_positive_1(self):
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# Mutating attribute with a Tensor type
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = torch.Tensor(3, 2)
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def forward(self, x):
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self.a = self.a.to(torch.float64)
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return x.sum() + self.a.sum()
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self.check_failure_on_export(Foo(), torch.Tensor(3, 2))
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def test_module_attribute_mutation_violation_positive_2(self):
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# Mutating attribute with a scalar type
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = 2
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def forward(self, x):
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self.a = self.a * 3
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return x.sum() + self.a
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self.check_failure_on_export(Foo(), torch.Tensor(3, 2))
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def test_module_attribute_mutation_violation_positive_3(self):
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# Setting a new attribute inside forward()
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = torch.Tensor(3, 2)
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def forward(self, x):
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self.b = 2
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return x.sum() + self.a.sum() + self.b
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self.check_failure_on_export(Foo(), torch.Tensor(3, 2))
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def test_module_attribute_mutation_violation_negative_1(self):
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# Mutating attribute with a Tensor type inside __init__ but
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# not in forward()
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = torch.Tensor(3, 2)
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def forward(self, x):
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return x.sum() + self.a.to(torch.float64).sum()
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self.check_same_with_export(Foo(), torch.Tensor(3, 2))
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def test_module_attribute_mutation_violation_negative_2(self):
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# Mutating attribute with a Tensor type inside __init__ twice
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = torch.Tensor(3, 2)
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self.a = self.a.to(torch.float64)
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def forward(self, x):
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return x.sum() + self.a.sum()
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self.check_same_with_export(Foo(), torch.Tensor(3, 2))
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def test_module_attribute_mutation_violation_negative_3(self):
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# Mutating local variable inside forward()
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = torch.Tensor(3, 2)
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def forward(self, x):
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b = 1
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b = b * 5
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return x.sum() + self.a.sum() + b
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self.check_same_with_export(Foo(), torch.Tensor(3, 2))
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def test_module_attribute_mutation_violation_negative_4(self):
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# Mutating attribute with a Tensor type
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# But not exporting but using eager mode as well as dynamo optimize mode
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.a = torch.Tensor(3, 2)
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def forward(self, x):
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self.a = self.a.to(torch.float64)
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return x.sum() + self.a.sum()
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mod = Foo()
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arg = torch.Tensor(3, 2)
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real_result = mod(arg)
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opt_mod = torch._dynamo.optimize("eager", nopython=True)(mod)
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self.assertTrue(torch._dynamo.utils.same(opt_mod(arg), real_result))
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if __name__ == "__main__":
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from torch._dynamo.test_case import run_tests
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run_tests()
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