{"action":"created","issue":{"url":"https://api.github.com/repos/pytorch/pytorch/issues/5354","repository_url":"https://api.github.com/repos/pytorch/pytorch","labels_url":"https://api.github.com/repos/pytorch/pytorch/issues/5354/labels{/name}","comments_url":"https://api.github.com/repos/pytorch/pytorch/issues/5354/comments","events_url":"https://api.github.com/repos/pytorch/pytorch/issues/5354/events","html_url":"https://github.com/pytorch/pytorch/issues/5354","id":299473918,"number":5354,"title":"[feature request] Batch linear algebra operations","user":{"login":"Taha-Bahadori","id":31113375,"avatar_url":"https://avatars3.githubusercontent.com/u/31113375?v=4","gravatar_id":"","url":"https://api.github.com/users/Taha-Bahadori","html_url":"https://github.com/Taha-Bahadori","followers_url":"https://api.github.com/users/Taha-Bahadori/followers","following_url":"https://api.github.com/users/Taha-Bahadori/following{/other_user}","gists_url":"https://api.github.com/users/Taha-Bahadori/gists{/gist_id}","starred_url":"https://api.github.com/users/Taha-Bahadori/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Taha-Bahadori/subscriptions","organizations_url":"https://api.github.com/users/Taha-Bahadori/orgs","repos_url":"https://api.github.com/users/Taha-Bahadori/repos","events_url":"https://api.github.com/users/Taha-Bahadori/events{/privacy}","received_events_url":"https://api.github.com/users/Taha-Bahadori/received_events","type":"User","site_admin":false},"labels":[{"id":424131849,"url":"https://api.github.com/repos/pytorch/pytorch/labels/enhancement","name":"enhancement","color":"84b6eb","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2018-02-22T19:15:49Z","updated_at":"2018-03-26T16:41:12Z","closed_at":null,"author_association":"NONE","body":"Linear algebra operations such as `svd()` and `symeig()` only support a single matrix input (e.g.,  a matrix of size `M x N`).   I request to extend these functions to support running these operations on batches in parallel on a single GPU machine.  That is, the input will take the form of `B x M x N` sized tensors and the function will calculate `B` operations in parallel on a GPU.\r\n\r\n**Simpler feature request**: Given that It seems that they support batch QR decomposition, can you implement the batch version of `torch.qr()`?\r\nhttp://icl.cs.utk.edu/projectsfiles/magma/doxygen/group__group__orthogonal__batched.html\r\n\r\nGiven batch QR, I can compute batch `torch.symeig()` using [the famous QR algorithm](https://en.wikipedia.org/wiki/QR_algorithm). \r\n\r\n"},"comment":{"url":"https://api.github.com/repos/pytorch/pytorch/issues/comments/376231483","html_url":"https://github.com/pytorch/pytorch/issues/5354#issuecomment-376231483","issue_url":"https://api.github.com/repos/pytorch/pytorch/issues/5354","id":376231483,"user":{"login":"SsnL","id":5674597,"avatar_url":"https://avatars2.githubusercontent.com/u/5674597?v=4","gravatar_id":"","url":"https://api.github.com/users/SsnL","html_url":"https://github.com/SsnL","followers_url":"https://api.github.com/users/SsnL/followers","following_url":"https://api.github.com/users/SsnL/following{/other_user}","gists_url":"https://api.github.com/users/SsnL/gists{/gist_id}","starred_url":"https://api.github.com/users/SsnL/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/SsnL/subscriptions","organizations_url":"https://api.github.com/users/SsnL/orgs","repos_url":"https://api.github.com/users/SsnL/repos","events_url":"https://api.github.com/users/SsnL/events{/privacy}","received_events_url":"https://api.github.com/users/SsnL/received_events","type":"User","site_admin":false},"created_at":"2018-03-26T16:41:12Z","updated_at":"2018-03-26T16:41:12Z","author_association":"CONTRIBUTOR","body":"I'm sorry, but I have higher priority tasks to finish. I've added this on my list of things to do, but it'll probably at lease a couple weeks later. If you or anyone else want to implement it, I am happy to review it."}}
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