979b8c9efa551e8c948a4aca145367a2d87ac8d6,test/distributions/test_multivariate_normal.py,TestMultivariateNormal,test_multivariate_normal_correlated_samples,#TestMultivariateNormal#Any#,187

Before Change

        covmat = torch.diag(torch.tensor([1, 0.75, 1.5], device=device))
        mvn = MultivariateNormal(mean=mean, covariance_matrix=NonLazyTensor(covmat))

        base_samples = mvn.get_base_samples(torch.Size((3, 4)))
        self.assertTrue(mvn.sample(base_samples=base_samples).shape == torch.Size([3, 4, 3]))

        base_samples = mvn.get_base_samples()
        self.assertTrue(mvn.sample(base_samples=base_samples).shape == torch.Size([3]))

After Change

    def test_multivariate_normal_correlated_samples(self, cuda=False):
        device = torch.device("cuda") if cuda else torch.device("cpu")
        for dtype in (torch.float, torch.double):
            mean = torch.tensor([0, 1, 2], device=device, dtype=dtype)
            covmat = torch.diag(torch.tensor([1, 0.75, 1.5], device=device, dtype=dtype))
            mvn = MultivariateNormal(mean=mean, covariance_matrix=NonLazyTensor(covmat))
            base_samples = mvn.get_base_samples(torch.Size([3, 4]))
            self.assertTrue(mvn.sample(base_samples=base_samples).shape == torch.Size([3, 4, 3]))
            base_samples = mvn.get_base_samples()
            self.assertTrue(mvn.sample(base_samples=base_samples).shape == torch.Size([3]))

    def test_multivariate_normal_correlated_samples_cuda(self):
        if torch.cuda.is_available():
            with least_used_cuda_device():
                self.test_multivariate_normal_correlated_samples(cuda=True)

Instances