[MLPerf] Add DLRM-DCNv2

[abheesht17]

TODOs:

• Need to make it work for multi-host: Multi-host training does not work for distributed embeddings #143.
• Currently uses dummy data, need to shift it to using the actual dataset. Already added code for loading actual data, but the path we are reading already has "batched" data. Can go ahead with this, or can load "unbatched" data and batch that up.
• Eval dataset
• Check if random seed is being correctly passed. We need to make sure that the initialiser returns different values for different layers (since we are passing SeedGenerator, I think we are good, but good to verify manually).
• Instead of separate files for configs, maybe have separate functions for configs.
• Is this a worry?

 08:44:12.046114: W jax_tpu_embedding/sparsecore/lib/core/input_preprocessing_util.cc:251] No Coo Buffer Size provided for table cat_14_table_cat_15_table_cat_23_table_cat_24_table_cat_25_table_cat_33_table_cat_34_table_cat_35_table_cat_36_table, the default value (6144) may be too large and can cause OOM. Utilize the stats returned from the sparse dense matmul preprocessing API.

• Set up metric logging and checkpointing.

[gemini-code-assist]

Summary of Changes

Hello @abheesht17, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces the DLRM-DCNv2 model as an MLPerf example, designed for efficient training on TPUs. It provides the model architecture, configuration files for dataset, model, and training hyperparameters, a dummy data loader, and a comprehensive shell script to set up the TPU environment and execute the training process. The current implementation uses dummy data and has known areas for future improvement, such as multi-host support and actual dataset integration.

Highlights

• New Model Implementation: Adds the DLRM-DCNv2 model, a deep learning architecture combining deep neural networks with a cross-network for learning explicit feature interactions, commonly used in recommendation systems.
• TPU Integration: Leverages Keras's distributed training capabilities with JAX backend and keras_rs.layers.DistributedEmbedding for efficient large-scale embedding table handling on TPUs, including SparseCore.
• Modular Configuration: Introduces a structured configuration system using keras.utils.Config for defining dataset features, model parameters (embedding dimensions, MLP layers, DCN layers), and training hyperparameters.
• Automated Setup Script: Provides a run.sh script to automate the entire setup process on Google Cloud TPU VMs, from VM creation and environment setup to dependency installation and model execution.
• Dummy Data Support: Includes a dataloader.py to generate dummy data, enabling initial testing and development of the DLRM-DCNv2 model before integration with real datasets.

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[gemini-code-assist]

Code Review

This pull request introduces a DLRM-DCNv2 model implementation for MLPerf benchmarks, including configurations, a dummy dataloader, the model definition, and a run script. The overall structure is good, but there are several critical and high-severity issues that need to be addressed. Specifically, there are bugs related to weight initialization due to seed reuse in loops, which will lead to layers having identical weights. There's also a potential runtime error in the model's forward pass due to improper handling of an empty list during tensor concatenation. Additionally, the main training script has a hardcoded number of epochs, ignoring the value from the configuration. I've also included some medium-severity suggestions to improve code maintainability and script robustness. Please review the detailed comments.

[hertschuh]

Some comments about the bash script

[abheesht17]

Status: Moved to multi-threaded dataloader because TPU was idle for long times between train steps because of embedding layer preprocessing which is outside of tf.data.

However, it doesn't immediately solve the issue, because host-to-device transfer is stalling it. Merging this for now, will open another PR on top of this.