Add OptimType.NONE in SplitTBE (defuse bwd and optim) #1819
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This pull request was exported from Phabricator. Differential Revision: D44392172 |
sryap
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Summary: Pull Request resolved: pytorch#1819 This diff is the **backend** part This diff introduces `OptimType.NONE`. Unlike other `OptimType`s, `OptimType.NONE` does not perform the optimizer step during SplitTBE's backward pass. With `OptimType.NONE`, SplitTBE deduplicates output gradients in the backward pass and generates a sparse gradient tensor (PyTorch's `sparse_coo_tensor`) for the device's weight (FQN: `weights_dev`). Currently, `OptimType.NONE` only supports the case where the embedding dimensions of all embedding tables are identical. Differential Revision: D44392172 fbshipit-source-id: b1264e5a5032ebad051d5c5b739dd9ffec1d8a92
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This pull request was exported from Phabricator. Differential Revision: D44392172 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 12, 2023
Summary: Pull Request resolved: pytorch#1819 This diff is the **backend** part This diff introduces `OptimType.NONE`. Unlike other `OptimType`s, `OptimType.NONE` does not perform the optimizer step during SplitTBE's backward pass. With `OptimType.NONE`, SplitTBE deduplicates output gradients in the backward pass and generates a sparse gradient tensor (PyTorch's `sparse_coo_tensor`) for the device's weight (FQN: `weights_dev`). Currently, `OptimType.NONE` only supports the case where the embedding dimensions of all embedding tables are identical. Differential Revision: D44392172 fbshipit-source-id: e01cd97b9ea0aab2e0f7004e2323d98f83751099
|
This pull request was exported from Phabricator. Differential Revision: D44392172 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 12, 2023
Summary: Pull Request resolved: pytorch#1819 This diff is the **backend** part This diff introduces `OptimType.NONE`. Unlike other `OptimType`s, `OptimType.NONE` does not perform the optimizer step during SplitTBE's backward pass. With `OptimType.NONE`, SplitTBE deduplicates output gradients in the backward pass and generates a sparse gradient tensor (PyTorch's `sparse_coo_tensor`) for the device's weight (FQN: `weights_dev`). Currently, `OptimType.NONE` only supports the case where the embedding dimensions of all embedding tables are identical. Differential Revision: D44392172 fbshipit-source-id: d62b11a29ab221c3a706f57a2ed146cc5c624096
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This pull request was exported from Phabricator. Differential Revision: D44392172 |
Summary: Pull Request resolved: pytorch#1819 This diff is the **backend** part This diff introduces `OptimType.NONE`. Unlike other `OptimType`s, `OptimType.NONE` does not perform the optimizer step during SplitTBE's backward pass. With `OptimType.NONE`, SplitTBE deduplicates output gradients in the backward pass and generates a sparse gradient tensor (PyTorch's `sparse_coo_tensor`) for the device's weight (FQN: `weights_dev`). Currently, `OptimType.NONE` only supports the case where the embedding dimensions of all embedding tables are identical. Differential Revision: D44392172 fbshipit-source-id: 52d746963b772f6ddaada7630cdf4b53d1e71ed3
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This pull request was exported from Phabricator. Differential Revision: D44392172 |
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This pull request has been merged in edc57b1. |
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Summary:
This diff is the backend part
This diff introduces
OptimType.NONE. Unlike otherOptimTypes,OptimType.NONEdoes not perform the optimizer step during SplitTBE'sbackward pass. With
OptimType.NONE, SplitTBE deduplicates outputgradients in the backward pass and generates a sparse gradient tensor
(PyTorch's
sparse_coo_tensor) for the device's weight (FQN:weights_dev).Currently,
OptimType.NONEonly supports the case where the embeddingdimensions of all embedding tables are identical.
Differential Revision: D44392172