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9594d9bcfd
Summary: ~~Let's see whether this trigger and fix the problem~~ remove the expect files from test_verify Pull Request resolved: https://github.com/pytorch/pytorch/pull/16674 Reviewed By: zrphercule Differential Revision: D13930668 Pulled By: houseroad fbshipit-source-id: 092157af07f475cf3809c95a4fe586e050c53b7e
125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
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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import caffe2.python.onnx.backend as backend
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from verify import verify
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from test_pytorch_common import TestCase, run_tests
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import unittest
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class TestVerify(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(MyModel, self).__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_modifying_params(self):
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class MyModel(Module):
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def __init__(self):
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super(MyModel, self).__init__()
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self.param = Parameter(torch.tensor([2.0]))
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def forward(self, x):
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y = x * x
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self.param.data.add_(1.0)
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return y
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x = torch.tensor([1, 2])
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self.assertVerifyExpectFail(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(MyModel, self).__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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@unittest.skip("Indexing is broken by #3725")
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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(MyModel, self).__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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run_tests()
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