Add forward mode differentiation for torch.linalg.cholesky and transpose (#62159)
authorIvan Yashchuk <ivan.yashchuk@aalto.fi>
Wed, 8 Sep 2021 16:34:46 +0000 (09:34 -0700)
committerFacebook GitHub Bot <facebook-github-bot@users.noreply.github.com>
Wed, 8 Sep 2021 16:44:30 +0000 (09:44 -0700)
commitdd8f6ac59784472d499d1594ca4e348f92337861
tree6032875d5fe5f84fe0a5b1032ec43cbd9d38dc29
parenta2934b38f8e9264fe24651ca179c281f25b33eac
Add forward mode differentiation for torch.linalg.cholesky and transpose (#62159)

Summary:
This PR adds forward mode differentiation for `torch.linalg.cholesky`, `torch.linalg.cholesky_ex`, and `transpose` functions.
Complex tests for Cholesky fail because for some reason the gradcheck sends matrices full of zeros to `cholesky_jvp` function.

cc ezyang albanD zou3519 gqchen pearu nikitaved soulitzer Lezcano Varal7 jianyuh mruberry heitorschueroff walterddr IvanYashchuk xwang233

Pull Request resolved: https://github.com/pytorch/pytorch/pull/62159

Reviewed By: mrshenli

Differential Revision: D30776829

Pulled By: albanD

fbshipit-source-id: 32e5539ed6423eed8c18cce16271330ab0ea8d5e
tools/autograd/derivatives.yaml
torch/csrc/autograd/FunctionsManual.cpp
torch/csrc/autograd/FunctionsManual.h
torch/testing/_internal/common_methods_invocations.py