Enable Global Weight Decay for VBE#2507
Closed
spcyppt wants to merge 1 commit into
Closed
Conversation
✅ Deploy Preview for pytorch-fbgemm-docs ready!
To edit notification comments on pull requests, go to your Netlify site configuration. |
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
force-pushed
the
export-D56200676
branch
from
9436f4f to
72f21ee
Compare
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Apr 17, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Differential Revision: D56200676
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
May 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
1 similar comment
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jul 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jul 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jul 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jul 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jul 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jul 2, 2024
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
Contributor
|
This pull request was exported from Phabricator. Differential Revision: D56200676 |
Contributor
|
This pull request has been merged in 114bb0d. |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Feb 26, 2026
Summary: Pull Request resolved: pytorch#2507 Enable Global weight decay for VBE --- **Usage:** set ``` optimizer = OptimType.EXACT_ROWWISE_ADAGRAD weight_decay_mode = WeightDecayMode.DECOUPLE_GLOBAL # for VBE, pass batch_size_per_feature_per_rank # Example: num_features = 2, num_ranks = 4 batch_size_per_feature_per_rank = [ [1, 2, 8, 3] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 0 [6, 10, 3, 5] # batch sizes for [Rank 0, Rank 1, Rank 2, Rank 3] in Feature 1 ] ``` e.g., ``` tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=[ (E, D, managed_option, ComputeDevice.CUDA) for (E, D) in zip(Es, Ds) ], optimizer=OptimType.EXACT_ROWWISE_ADAGRAD, learning_rate=0.1, eps=0.1, output_dtype=output_dtype, pooling_mode=pooling_mode, weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL, ) output = tbe(indices, offsets, batch_size_per_feature_per_rank=batch_size_per_feature_per_rank) ``` Relevant diffs: D53866750 D55660277 D55660762 Reviewed By: sryap Differential Revision: D56200676
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary:
Enable Global weight decay for VBE
Usage:
set
e.g.,
Relevant diffs:
D53866750
D55660277
D55660762
Differential Revision: D56200676