implemented 'torch.distributions.constraints.symmetric' checking if the tensor is symmetric at last 2 dimension. (#68644) #69123
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…he tensor is symmetric at last 2 dimension. (#68644) Summary: Implemented submodule for #68050 Opened cleaned, final version of PR for #68240 Explanation: I am trying to contribute to PyTorch by implementing distributions for symmetric matrices like Wishart distribution and Inverse Wishart distribution. Although there is a LKJ distribution for the Cholesky decomposition of correlation matrices, it only represents equivalence to restricted form of Wishart distribution. [https://arxiv.org/abs/1809.04746](https://arxiv.org/abs/1809.04746) Thus, I started implementing Wishart distribution and Inverse Wishart distribution seperately. I added a short code about the 'torch.distributions.constraints.symmetric', which was not included in 'torch.distributions.constraints' previously. i.e., 'torch.distributions.constraints' contains module like 'positive_definite' constraints, but it just assumes symmetricity of the input matrix. [Link](https://github.com/pytorch/pytorch/blob/1adeeabdc0c8832420c091c5c668843768530d7f/torch/distributions/constraints.py#L466) So, I think it will be better if we have constraint checking symmetricity of the tensors in PyTorch. We may further utilize it like `constraints.stack([constraints.symmetric, constraints.positive_definite])` for the constraint of the covariance matrix in Multivariate Normal distribution, for example, to check if the random matrix is a symmetric positive definite matrix. cc fritzo neerajprad alicanb nikitaved Reviewed By: jbschlosser Differential Revision: D32599540 Pulled By: neerajprad fbshipit-source-id: 9227f7e9931834a548a88da69e4f2e9af7732cfe [ghstack-poisoned]
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Stack from ghstack:
Summary:
Implemented submodule for #68050
Opened cleaned, final version of PR for #68240
Explanation:
I am trying to contribute to PyTorch by implementing distributions for symmetric matrices like Wishart distribution and Inverse Wishart distribution. Although there is a LKJ distribution for the Cholesky decomposition of correlation matrices, it only represents equivalence to restricted form of Wishart distribution. https://arxiv.org/abs/1809.04746 Thus, I started implementing Wishart distribution and Inverse Wishart distribution seperately.
I added a short code about the 'torch.distributions.constraints.symmetric', which was not included in 'torch.distributions.constraints' previously. i.e., 'torch.distributions.constraints' contains module like 'positive_definite' constraints, but it just assumes symmetricity of the input matrix. Link So, I think it will be better if we have constraint checking symmetricity of the tensors in PyTorch.
We may further utilize it like
constraints.stack([constraints.symmetric, constraints.positive_definite])for the constraint of the covariance matrix in Multivariate Normal distribution, for example, to check if the random matrix is a symmetric positive definite matrix.
cc fritzo neerajprad alicanb nikitaved
Reviewed By: jbschlosser
Differential Revision: D32599540
Pulled By: neerajprad
fbshipit-source-id: 9227f7e9931834a548a88da69e4f2e9af7732cfe