Speeding Up Evaluation with Cached Tokenization

Issue

Evaluation during fine-tuning slowed dramatically due to:

• Repeated tokenizer calls at each validation step
• Duplicate pre-processing even on static evaluation datasets
• Wasted CPU cycles and memory allocation

Solution

We implemented tokenization caching ahead of evaluation:

1. Pre-tokenize Static Datasets

• Tokenized all evaluation samples before training
• Saved token IDs using pickle or torch.save to disk

2. Efficient On-Demand Loading

• Loaded tokenized sequences as Tensors or NumPy arrays
• Avoided re-tokenizing already-seen text during eval

🚀 Outcome

• Evaluation loop speed increased by 2× to 3×
• CPU usage dropped, freeing resources for GPU training
• Enabled faster feedback on validation accuracy

💡 Takeaway

Never tokenize the same data twice during long fine-tunes.
Pre-tokenizing saves time, memory, and compute – especially on large validation sets.