Add SplitTBE optimizer (defuse bwd and optim) #1821
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
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Summary: Pull Request resolved: pytorch#1821 Differential Revision: D44772326 fbshipit-source-id: 268d468d731a6c8e629fa4c54d79860d4fe10a79
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
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Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension Usage: ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Differential Revision: D44772326 fbshipit-source-id: fbe8c873c88a7783ab1341ab26f16142afea4ee7
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
added a commit
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this pull request
Jun 12, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension Usage: ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: 4d6e54bc0d94cdc6cec3469cd16b805daa5a3394
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
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Jun 20, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: bce4950db51f0335e7d13229550eb8d073e31161
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 20, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: bb70a403a3d9e89938e39021426343a617ad0bb5
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
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Jun 21, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: cbd0f96df11ba90d2109c92914b7304d95de2cad
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 21, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: 96b541d57aad88d22d60e3c005f9011b81f3b8ac
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 21, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: 6313cfc192effe7ff260e2040442a5f3141cfbb2
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 21, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: 5f6447d7df1ede2c8c7004a38aa6e294dcba8e0b
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
sryap
added a commit
to sryap/FBGEMM
that referenced
this pull request
Jun 22, 2023
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: 464edcad7ec0df7f8653fb1ecaa3201e1e7527ce
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
Summary: Pull Request resolved: pytorch#1821 This diff adds the sparse optimizer op support in FBGEMM GPU. Before this diff, FBGEMM GPU only provided the optimizer support via TBE backward (i.e., TBE's backward was fused with the optimizer step). However, fused backward and optimizer prevented many exploration usecases. Thus, in this diff, we provide individual sparse optimizer operators. We call them "`SplitTBE` optimizers" as they are only applicable for `SplitTBE`'s parameters. **Limitations**: - Only support `SplitTBE`'s parameters - Only support `rowwise_adagrad` - All embedding tables must have the same embedding dimension **Usage:** ``` from fbgemm_gpu.split_embedding_optimizer_ops import ( SplitEmbeddingArgs, SplitEmbeddingOptimizerParams, SplitEmbeddingRowwiseAdagrad, ) # Init SplitTBE split_tbe = SplitTableBatchedEmbeddingBagsCodegen( embedding_specs=embedding_specs, optimizer=OptimType.NONE, feature_table_map=feature_table_map, ) # Create arguments for SplitTBE optimizer params = SplitEmbeddingOptimizerParams(weights_dev=cc.weights_dev) embedding_args = SplitEmbeddingArgs( weights_placements=cc.weights_placements, weights_offsets=cc.weights_offsets, max_D=cc.max_D, ) # Init SplitTBE optimizer optim = SplitEmbeddingRowwiseAdagrad( params, embedding_args, embedding_specs, feature_table_map, learning_rate=lr, eps=eps, stochastic_rounding=stochastic_rounding, ) # Invoke optimizer's step optim.step() ``` Reviewed By: jianyuh Differential Revision: D44772326 fbshipit-source-id: 95adb292ee3a248c540b51f6ca3686dfb461c0a6
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This pull request was exported from Phabricator. Differential Revision: D44772326 |
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This pull request has been merged in 96c3711. |
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Differential Revision: D44772326