feat: support gradient accumulation in spark torch estimator #3681
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Signed-off-by: Li Jiang <bnujli@gmail.com>
Signed-off-by: Li Jiang <bnujli@gmail.com>
Signed-off-by: Li Jiang <bnujli@gmail.com>
Signed-off-by: Li Jiang <bnujli@gmail.com>
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Please rebase with latest master, which includes a fix for the CI. |
Signed-off-by: Li Jiang <bnujli@gmail.com>
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Thanks @EnricoMi , done! |
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OOO this week. I will take a look when I'm back. |
Unit Test Results (with flaky tests) 1 169 files - 35 1 169 suites - 35 11h 33m 24s ⏱️ - 9m 14s Results for commit 6d33ba4. ± Comparison against base commit 25ed803. ♻️ This comment has been updated with latest results. |
Tixxx
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Sep 9, 2022
Tixxx
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Signed-off-by: Li Jiang <bnujli@gmail.com>
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do loss.div_ only when backward_passes_per_step > 1 Signed-off-by: Li Jiang <bnujli@gmail.com>
Tixxx
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chongxiaoc
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Description
We can use
backward_passes_per_stepin torch, although we need to adjust the training code to make it work as expected. However, in spark torch estimator, there is no implementation for supporting it.In this PR, we added support to spark torch estimator, thus we can apply gradient accumlation by simply set up param
backward_passes_per_stepin TorchEstimator. Example and test are modified accordingly.Review process to land