{"action":"created","issue":{"url":"https://api.github.com/repos/twitter/torch-autograd/issues/19","labels_url":"https://api.github.com/repos/twitter/torch-autograd/issues/19/labels{/name}","comments_url":"https://api.github.com/repos/twitter/torch-autograd/issues/19/comments","events_url":"https://api.github.com/repos/twitter/torch-autograd/issues/19/events","html_url":"https://github.com/twitter/torch-autograd/issues/19","id":117486167,"number":19,"title":"Variable sized input and target tensors","user":{"login":"noa","id":139875,"avatar_url":"https://avatars.githubusercontent.com/u/139875?v=3","gravatar_id":"","url":"https://api.github.com/users/noa","html_url":"https://github.com/noa","followers_url":"https://api.github.com/users/noa/followers","following_url":"https://api.github.com/users/noa/following{/other_user}","gists_url":"https://api.github.com/users/noa/gists{/gist_id}","starred_url":"https://api.github.com/users/noa/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/noa/subscriptions","organizations_url":"https://api.github.com/users/noa/orgs","repos_url":"https://api.github.com/users/noa/repos","events_url":"https://api.github.com/users/noa/events{/privacy}","received_events_url":"https://api.github.com/users/noa/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":13,"created_at":"2015-11-18T00:48:45Z","updated_at":"2015-11-19T07:47:32Z","closed_at":null,"body":"In some cases it's useful to have variable sized input and target tensors and batch sizes, e.g. RNNs. This was reasonably efficient before the Nov 16 overhaul but I'm now seeing extreme slowdowns after the overhaul. Any plans to introduce a flag to switch between the pre- and post-overhaul behaviors, or suggestions on how to improve performance? I assume that moving to fixed BPTT dimension and batch size would address the speed issue (to match the provided LM example), but maybe there's another way to improve performance without changing the data?"},"comment":{"url":"https://api.github.com/repos/twitter/torch-autograd/issues/comments/157978813","html_url":"https://github.com/twitter/torch-autograd/issues/19#issuecomment-157978813","issue_url":"https://api.github.com/repos/twitter/torch-autograd/issues/19","id":157978813,"user":{"login":"noa","id":139875,"avatar_url":"https://avatars.githubusercontent.com/u/139875?v=3","gravatar_id":"","url":"https://api.github.com/users/noa","html_url":"https://github.com/noa","followers_url":"https://api.github.com/users/noa/followers","following_url":"https://api.github.com/users/noa/following{/other_user}","gists_url":"https://api.github.com/users/noa/gists{/gist_id}","starred_url":"https://api.github.com/users/noa/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/noa/subscriptions","organizations_url":"https://api.github.com/users/noa/orgs","repos_url":"https://api.github.com/users/noa/repos","events_url":"https://api.github.com/users/noa/events{/privacy}","received_events_url":"https://api.github.com/users/noa/received_events","type":"User","site_admin":false},"created_at":"2015-11-19T07:47:32Z","updated_at":"2015-11-19T07:47:32Z","body":"@luketwitter Great, #23 seems to have done the trick. With CUDA I'm down to ~300ms per step past 100 batches or so, and I'm not seeing the big 50s+ delays in early steps either. Thanks!  "}}