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complex_autogradmodule: autogradRelated to torch.autograd, and the autograd engine in generalRelated to torch.autograd, and the autograd engine in generalmodule: complexRelated to complex number support in PyTorchRelated to complex number support in PyTorchmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmultriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
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The error information is as follows:
RuntimeError: eig_ backward: Backward calculation does not support complex eigenvalues at the moment.
The details are as follows:
One step in my loss function is to calculate the eigenvalues of the matrix. I used the torch.eig function, but later when I used the reverse derivation of the loss function, the program was stuck in the position of the torch.eig function, because sometimes the matrix has complex eigenvalues. Under what circumstances, the torch.eig function does not support reverse derivation! How can I solve it???
Please help me!!!
cc @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @anjali411 @dylanbespalko @mruberry @jianyuh @heitorschueroff @walterddr @IvanYashchuk
maremita
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complex_autogradmodule: autogradRelated to torch.autograd, and the autograd engine in generalRelated to torch.autograd, and the autograd engine in generalmodule: complexRelated to complex number support in PyTorchRelated to complex number support in PyTorchmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmultriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module