Add iter singular value into TBE optimizer state
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Summary: X-link: pytorch/torchrec#2474 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: spcyppt Differential Revision: D63909559
csmiler
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Summary: X-link: pytorch/torchrec#2474 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559
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Summary: X-link: pytorch/FBGEMM#3228 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559
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This pull request was exported from Phabricator. Differential Revision: D63909559 |
csmiler
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Oct 10, 2024
Summary: X-link: pytorch/torchrec#2474 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559
csmiler
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Oct 10, 2024
Summary: X-link: pytorch/FBGEMM#3228 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559
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Summary: X-link: pytorch/FBGEMM#3228 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559
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Summary: X-link: pytorch/torchrec#2474 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559
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This pull request was exported from Phabricator. Differential Revision: D63909559 |
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Summary: X-link: pytorch/torchrec#2474 Pull Request resolved: #3228 X-link: facebookresearch/FBGEMM#326 When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the `iter` number is a single value tensor, which **cannot be tracked and checkpointed properly** (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!) Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim). By doing so, single-value `iter` can be properly checkpointed just like other states, ensuring correct reloading of states and continuous training. This diff checkpoints `iter` for rowwise_adagrad with GWD. The next diff would checkpoint `iter` for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb. Reviewed By: iamzainhuda, spcyppt Differential Revision: D63909559 fbshipit-source-id: e14c1dc3e8f87bfc4cc95f2321b358526719d88f
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Summary:
X-link: https://github.com/facebookresearch/FBGEMM/pull/326
When the optimizer states for sharded embedding tables are tracked in TorchRec, they are assumed to be either point-wise (same shape as the embedding table, for example, Adam's exp_avg), or row-wise (same length as the embedding hashsize, for example, rowwise_adagrad's momentum/sum). However, there may be other formats, a single value for each table. Specifically, for Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb and GWD, the
iternumber is a single value tensor, which cannot be tracked and checkpointed properly (this also means that there is a bug in Adam/Partial_rowwise_adam/Lamb/Partial_rowwise_lamb usages!)Here we support tracking and checkpointing single-value states, by constructing ShardMetadata with rowwise-sharding and replicating the single-value for each Sharded param (this is similar to how the rowwise state for colume-wise sharded tables are concatenated along row-dim).
By doing so, single-value
itercan be properly checkpointed just like other states, ensuring correct reloading of states and continuous training.Differential Revision: D63909559