{"action":"created","issue":{"url":"https://api.github.com/repos/pytorch/pytorch/issues/8021","repository_url":"https://api.github.com/repos/pytorch/pytorch","labels_url":"https://api.github.com/repos/pytorch/pytorch/issues/8021/labels{/name}","comments_url":"https://api.github.com/repos/pytorch/pytorch/issues/8021/comments","events_url":"https://api.github.com/repos/pytorch/pytorch/issues/8021/events","html_url":"https://github.com/pytorch/pytorch/issues/8021","id":328373619,"node_id":"MDU6SXNzdWUzMjgzNzM2MTk=","number":8021,"title":"[feature request] Add TensorFlow's bfloat16","user":{"login":"Wulfsta","id":13378502,"node_id":"MDQ6VXNlcjEzMzc4NTAy","avatar_url":"https://avatars2.githubusercontent.com/u/13378502?v=4","gravatar_id":"","url":"https://api.github.com/users/Wulfsta","html_url":"https://github.com/Wulfsta","followers_url":"https://api.github.com/users/Wulfsta/followers","following_url":"https://api.github.com/users/Wulfsta/following{/other_user}","gists_url":"https://api.github.com/users/Wulfsta/gists{/gist_id}","starred_url":"https://api.github.com/users/Wulfsta/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Wulfsta/subscriptions","organizations_url":"https://api.github.com/users/Wulfsta/orgs","repos_url":"https://api.github.com/users/Wulfsta/repos","events_url":"https://api.github.com/users/Wulfsta/events{/privacy}","received_events_url":"https://api.github.com/users/Wulfsta/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2018-06-01T03:59:14Z","updated_at":"2018-09-11T16:02:21Z","closed_at":null,"author_association":"NONE","body":"TensorFlow defines a floating point number with 16 bits that is different from the IEEE 754 standard; one bit is for the sign, eight are exponent bits, and seven are mantissa bits. It is essentially a 32 bit float with less precision. More information can be found [here](https://github.com/tensorflow/tensorflow/blob/1e44b15ff02fe3bf4764189eb1b796602d669c3e/tensorflow/core/framework/bfloat16.h) and [here](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format). "},"comment":{"url":"https://api.github.com/repos/pytorch/pytorch/issues/comments/420326582","html_url":"https://github.com/pytorch/pytorch/issues/8021#issuecomment-420326582","issue_url":"https://api.github.com/repos/pytorch/pytorch/issues/8021","id":420326582,"node_id":"MDEyOklzc3VlQ29tbWVudDQyMDMyNjU4Mg==","user":{"login":"ezyang","id":13564,"node_id":"MDQ6VXNlcjEzNTY0","avatar_url":"https://avatars0.githubusercontent.com/u/13564?v=4","gravatar_id":"","url":"https://api.github.com/users/ezyang","html_url":"https://github.com/ezyang","followers_url":"https://api.github.com/users/ezyang/followers","following_url":"https://api.github.com/users/ezyang/following{/other_user}","gists_url":"https://api.github.com/users/ezyang/gists{/gist_id}","starred_url":"https://api.github.com/users/ezyang/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ezyang/subscriptions","organizations_url":"https://api.github.com/users/ezyang/orgs","repos_url":"https://api.github.com/users/ezyang/repos","events_url":"https://api.github.com/users/ezyang/events{/privacy}","received_events_url":"https://api.github.com/users/ezyang/received_events","type":"User","site_admin":false},"created_at":"2018-09-11T16:02:21Z","updated_at":"2018-09-11T16:02:21Z","author_association":"CONTRIBUTOR","body":"Well, we're working on complex numbers out of tree, and bfloat16 seems like it would be fairly similar..."}}
{"actor":{"display_login":"ezyang"}}