dense_to_jagged add Meta and Autograd backend #1477
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This pull request was exported from Phabricator. Differential Revision: D41498148 |
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Summary: Pull Request resolved: pytorch#1477 Add Meta and Autograd backend for dense_to_jagged Differential Revision: D41498148 fbshipit-source-id: 246c24f67aa71ba90c39b4462255324072a33e87
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Summary: Pull Request resolved: pytorch#1477 Add Meta and Autograd backend for dense_to_jagged Differential Revision: D41498148 fbshipit-source-id: efa5fe3b1b4bf61389cba086639d5afaf7aeda12
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Summary: Pull Request resolved: pytorch#1477 Add Meta and Autograd backend for dense_to_jagged Differential Revision: D41498148 fbshipit-source-id: 6459e642431d20a7990e145b05047059e1c680e4
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Summary: Pull Request resolved: pytorch#1477 Add Meta and Autograd backend for dense_to_jagged Differential Revision: D41498148 fbshipit-source-id: cd92d03727b6406a9db597f3fa08d1554620363d
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Summary: Pull Request resolved: pytorch#1477 Add Meta and Autograd backend for dense_to_jagged and jagged_dense_elementwise_add_jagged_output Note total_L is required since Meta/Fake tensor cannot calculate total_L through offsets. Reviewed By: jianyuh Differential Revision: D41498148 fbshipit-source-id: 7b6f4b4dcba0fed1b6233f865d5452fcad777360
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Summary: Add Meta and Autograd backend for dense_to_jagged
Differential Revision: D41498148