Add embedding weights FP16 supports in dense TBE #1343
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This pull request was exported from Phabricator. Differential Revision: D39712548 |
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Summary: Pull Request resolved: pytorch#1343 For data parallelism dense TBE evaluation. Reduce memory pressure and avoid OOM. Differential Revision: D39712548 fbshipit-source-id: b19fe9d4e5e6aa2358ae8efbc148d7fa03f6c48b
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Summary: Pull Request resolved: pytorch#1343 For data parallelism dense TBE evaluation. Reduce memory pressure and avoid OOM. Differential Revision: D39712548 fbshipit-source-id: eb965c9e12dfeb27854d93cbfcd010a5efcaba26
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Summary: Pull Request resolved: pytorch#1343 For data parallelism dense TBE evaluation. Reduce memory pressure and avoid OOM. Differential Revision: D39712548 fbshipit-source-id: 3e1193364e19c35c6c45c7d0394201b157f02e56
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Summary: Pull Request resolved: pytorch#1343 For data parallelism dense TBE evaluation. Reduce memory pressure and avoid OOM. Differential Revision: D39712548 fbshipit-source-id: cba504023daa21eca13d13f8963765bc75b76a4e
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This pull request was exported from Phabricator. Differential Revision: D39712548 |
Summary: Pull Request resolved: pytorch#1343 For data parallelism dense TBE evaluation. Reduce memory pressure and avoid OOM. Differential Revision: D39712548 fbshipit-source-id: faa28cebb5ba2db60014dff2cb18cd49715db333
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Summary: For data parallelism dense TBE evaluation. Reduce memory pressure and avoid OOM.
Differential Revision: D39712548