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Optimize CUDA svd and eig #9083
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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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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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eig, wheneigenvector=False, do not force the row-major-contiguous input to be column-major-contiguous becauseAandA^Thave same evalues.For
svd, when input is row-major-contiguous, calculatesvdas ifsvdis done on the input's transpose, and then returnr0^T, r1, r2, where(r0, r1, r2)are from magmagesdd.cc @ngimel @jianyuh @nikitaved @pearu @mruberry @heitorschueroff @walterddr @IvanYashchuk @VitalyFedyunin