Knapsack-based Data Pipeline

This presents a step-by-step optimization strategy to solve the GPU idle state and excessive padding problems that occur during multimodal model training.

The existing Naive Padding approach pads all data to match the longest sequence in the batch, and this process can result in up to 60% computational waste. To improve this, Constrained Padding can be applied by setting a global maximum length and dropping data that exceeds it, but inefficiency due to the fixed length still remains.

Finally, it proposes a Smart Packing method that uses the Knapsack Problem algorithm to fit as many sequences as possible within the batch's maximum token limit (max_length). This method minimizes padding and maximizes data density per batch, increasing GPU utilization.

The related implementation code is provided via a separate GitHub repository.