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Summary: Updated the `ParameterConstraints` in the TorchRec benchmarking script to include pooling factors, number of poolings, and batch sizes. This enhancement allows for more detailed configuration of embedding tables, improving the flexibility and precision of sharding strategies in distributed training scenarios. Differential Revision: D76440004
…ctionSparseNN models. Summary: Refactored the training benchmarking by moving generative helper functinos into separate util file and added a model configuration that supports SparseNN, TowerSparseNN, TowerCollectionSparseNN models. Future commits will add support for DeepFM and DLRM models. Differential Revision: D76539867
This pull request was exported from Phabricator. Differential Revision: D76559261 |
…rate, momentum, and weight decay configurations. (pytorch#3094) Summary: Pull Request resolved: pytorch#3094 This commit introduces enhancements to the optimizer configuration in TorchRec. It now supports specifying the optimizer type, learning rate, momentum, and weight decay. These changes provide more flexibility and control over the training process, allowing users to fine-tune their models with different optimization strategies and hyperparameters. Differential Revision: D76559261
This pull request was exported from Phabricator. Differential Revision: D76559261 |
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Summary: This commit introduces enhancements to the optimizer configuration in TorchRec. It now supports specifying the optimizer type, learning rate, momentum, and weight decay. These changes provide more flexibility and control over the training process, allowing users to fine-tune their models with different optimization strategies and hyperparameters.
Differential Revision: D76559261