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https://github.com/zebrajr/pytorch.git
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773d80747c
- Rename `test_pytorch_common` -> `pytorch_test_common`, `test_onnx_common` -> `onnx_test_common`, removing the test_ prefix to show that the files are not test cases - Remove import * in `test_pytorch_common` and adjust to import from `testing._internal.common_utils` (where functions are actually defined) instead - Import modules only in `test_pytorch_onnx_onnxruntime` (too many to handle in a single PR in other tests) (The skips are exceptions) Pull Request resolved: https://github.com/pytorch/pytorch/pull/81141 Approved by: https://github.com/BowenBao
110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
# Owner(s): ["module: onnx"]
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from verify import verify
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import caffe2.python.onnx.backend as backend
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import torch
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from torch.autograd import Function
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from torch.nn import Module, Parameter
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from torch.testing._internal import common_utils
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class TestVerify(common_utils.TestCase):
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maxDiff = None
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def assertVerifyExpectFail(self, *args, **kwargs):
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try:
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verify(*args, **kwargs)
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except AssertionError as e:
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if str(e):
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# substring a small piece of string because the exact message
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# depends on system's formatting settings
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# self.assertExpected(str(e)[:60])
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# NB: why we comment out the above check? because numpy keeps
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# changing the error format, and we have to keep updating the
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# expect files let's relax this constraint
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return
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else:
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raise
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# Don't put this in the try block; the AssertionError will catch it
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self.assertTrue(False, msg="verify() did not fail when expected to")
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def test_result_different(self):
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class BrokenAdd(Function):
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@staticmethod
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def symbolic(g, a, b):
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return g.op("Add", a, b)
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@staticmethod
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def forward(ctx, a, b):
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return a.sub(b) # yahaha! you found me!
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class MyModel(Module):
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def forward(self, x, y):
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return BrokenAdd().apply(x, y)
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x = torch.tensor([1, 2])
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y = torch.tensor([3, 4])
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self.assertVerifyExpectFail(MyModel(), (x, y), backend)
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def test_jumbled_params(self):
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class MyModel(Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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y = x * x
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self.param = Parameter(torch.tensor([2.0]))
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return y
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x = torch.tensor([1, 2])
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with self.assertRaisesRegex(RuntimeError, "state_dict changed"):
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verify(MyModel(), x, backend)
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def test_dynamic_model_structure(self):
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class MyModel(Module):
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def __init__(self):
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super().__init__()
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self.iters = 0
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def forward(self, x):
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if self.iters % 2 == 0:
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r = x * x
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else:
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r = x + x
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self.iters += 1
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return r
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x = torch.tensor([1, 2])
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self.assertVerifyExpectFail(MyModel(), x, backend)
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def test_embedded_constant_difference(self):
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class MyModel(Module):
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def __init__(self):
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super().__init__()
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self.iters = 0
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def forward(self, x):
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r = x[self.iters % 2]
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self.iters += 1
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return r
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x = torch.tensor([[1, 2], [3, 4]])
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self.assertVerifyExpectFail(MyModel(), x, backend)
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def test_explicit_test_args(self):
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class MyModel(Module):
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def forward(self, x):
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if x.data.sum() == 1.0:
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return x + x
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else:
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return x * x
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x = torch.tensor([[6, 2]])
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y = torch.tensor([[2, -1]])
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self.assertVerifyExpectFail(MyModel(), x, backend, test_args=[(y,)])
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if __name__ == "__main__":
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common_utils.run_tests()
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