Use global lock instead of per instance lock. #31404
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Multiple "trainers" could each create different instances of DistributedOptimizer, which means we can still have a race condition unless we do a trully global per worker lock. Differential Revision: [D19154248](https://our.internmc.facebook.com/intern/diff/D19154248/) [ghstack-poisoned]
pritamdamania87
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pritamdamania87
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Multiple "trainers" could each create different instances of DistributedOptimizer, which means we can still have a race condition unless we do a trully global per worker lock. Differential Revision: [D19154248](https://our.internmc.facebook.com/intern/diff/D19154248/) ghstack-source-id: 95874624 Pull Request resolved: #31404
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This is a resubmission of #30117. |
mrshenli
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Dec 18, 2019
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This pull request has been merged in 359c39b. |
wuhuikx
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Jan 30, 2020
Summary: Pull Request resolved: pytorch#31404 Multiple "trainers" could each create different instances of DistributedOptimizer, which means we can still have a race condition unless we do a trully global per worker lock. ghstack-source-id: 95874624 Test Plan: run unit tests -- unfortunatelly due to the non-deterministic behavior it's not clear how to unit test this properly. Differential Revision: D19154248 fbshipit-source-id: fab6286c17212f534f1bd1cbdf9f0de002d48c74
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Stack from ghstack:
Multiple "trainers" could each create different instances of DistributedOptimizer, which means we can still have a race condition unless we do a trully global per worker lock.
Differential Revision: D19154248