Ignore the updates when weights are 0s and return the default value #283
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…ytorch#283) Summary: Pull Request resolved: pytorch#283 For a multi-task multi-label (MTML) model, sometimes we intentionally set weights = 0 for the model effectively ignore the data. In terms of metrics calculation, we should - ignore this update if weights for all tasks are 0 - ignore the metric result and output 0 (metric's default value) if the weights for a tasks are 0 Previously if weights = 0, there would be some NAN values for metrics and triggered metric health related alerts. This change fixes it Differential Revision: D36114064 fbshipit-source-id: bb684849f737fa9a68eeae7c76509c5656818b34
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This pull request was exported from Phabricator. Differential Revision: D36114064 |
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This pull request was exported from Phabricator. Differential Revision: D36114064 |
yachyv7
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May 4, 2022
…ytorch#283) Summary: Pull Request resolved: pytorch#283 For a multi-task multi-label (MTML) model, sometimes we intentionally set weights = 0 for the model effectively ignore the data. In terms of metrics calculation, we should - ignore this update if weights for all tasks are 0 - ignore the metric result and output 0 (metric's default value) if the weights for a tasks are 0 Previously if weights = 0, there would be some NAN values for metrics and triggered metric health related alerts. This change fixes it Differential Revision: D36114064 fbshipit-source-id: 0e243bd96cdc33c9ea7f5399631950fa869a1e59
yachyv7
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May 5, 2022
…ytorch#283) Summary: Pull Request resolved: pytorch#283 For a multi-task multi-label (MTML) model, sometimes we intentionally set weights = 0 for the model effectively ignore the data. In terms of metrics calculation, we should - ignore this update if weights for all tasks are 0 - ignore the metric result and output 0 (metric's default value) if the weights for a tasks are 0 Previously if weights = 0, there would be some NAN values for metrics and triggered metric health related alerts. This change fixes it Differential Revision: D36114064 fbshipit-source-id: e709885aebd743cd008a48debc43f31c041c5cf5
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This pull request was exported from Phabricator. Differential Revision: D36114064 |
…ytorch#283) Summary: Pull Request resolved: pytorch#283 For a multi-task multi-label (MTML) model, sometimes we intentionally set weights = 0 for the model effectively ignore the data. In terms of metrics calculation, we should - ignore this update if weights for all tasks are 0 - ignore the metric result and output 0 (metric's default value) if the weights for a tasks are 0 Previously if weights = 0, there would be some NAN values for metrics and triggered metric health related alerts. This change fixes it Reviewed By: fegin Differential Revision: D36114064 fbshipit-source-id: 144b06d9bcf4954107738463cda1f41f23a88c5f
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This pull request was exported from Phabricator. Differential Revision: D36114064 |
samiwilf
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Oct 25, 2022
…ytorch#283) Summary: X-link: facebookresearch/dlrm#283 Remove the constraint that ranks must iterate through batches of the exact same size for the exact same number of iterations. Now each rank's input batch can be a different size containing a different number of samples, and each rank can forward pass or train fewer or more batches than other ranks. Differential Revision: D40676549 fbshipit-source-id: 47174289e88d7d13339a9b16325b4275bc0aa628
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Summary:
For a multi-task multi-label (MTML) model, sometimes we intentionally set weights = 0 for the model effectively ignore the data. In terms of metrics calculation, we should
Previously if weights = 0, there would be some NAN values for metrics and triggered metric health related alerts. This change fixes it
Differential Revision: D36114064