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module: cudaRelated to torch.cuda, and CUDA support in generalRelated to torch.cuda, and CUDA support in generalmodule: 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 matmulmodule: performanceIssues related to performance, either of kernel code or framework glueIssues related to performance, either of kernel code or framework gluetriagedThis 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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For eig, when eigenvector=False, do not force the row-major-contiguous input to be column-major-contiguous because A and A^T have same evalues.
For svd, when input is row-major-contiguous, calculate svd as if svd is done on the input's transpose, and then return r0^T, r1, r2, where (r0, r1, r2) are from magma gesdd.
cc @ngimel @jianyuh @nikitaved @pearu @mruberry @heitorschueroff @walterddr @IvanYashchuk @VitalyFedyunin
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module: cudaRelated to torch.cuda, and CUDA support in generalRelated to torch.cuda, and CUDA support in generalmodule: 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 matmulmodule: performanceIssues related to performance, either of kernel code or framework glueIssues related to performance, either of kernel code or framework gluetriagedThis 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