Reducing Padding Overhead with Sequence Bucketing

Issue

When training on mixed-length sequences, dynamic padding causes:

• Wasted GPU memory from excessive padding
• Unstable throughput due to sequence-length variance
• Slower token-level processing in attention-heavy models

This inefficiency was noticeable during large-scale pretraining and fine-tuning runs.

Solution

We implemented bucketing to organize samples by similar length before batching:

1. Sort + Bucket Strategy

• Used a sortish sampler to roughly group sequences by length (e.g., sentence tokens).
• Applied batch shuffling within each bucket to avoid overfitting.
• Integrated with PyTorch DataLoader for seamless batching.

2. Tools

• sortish_sampler (FastAI or custom)
• Native collate_fn with sequence padding per bucket

🚀 Outcome

• Reduced padding by up to 40–60% in long-sequence tasks
• Smoothed GPU memory consumption across epochs
• Boosted throughput by ~1.5×, especially on large batches

💡 Takeaway

Sequence bucketing is a simple but powerful optimization for NLP:

• It increases training speed and memory efficiency
• Essential for large token models with highly variable input lengths