Principle:Ggml org Llama cpp Batch Processing System
| Knowledge Sources | |
|---|---|
| Domains | Batch_Processing |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
The Batch Processing System is the principle of grouping multiple tokens or sequences into a single forward pass for efficient parallel inference.
Description
This principle covers the batch data structure and processing logic that allows multiple tokens from one or more sequences to be evaluated together in a single model forward pass. Batching is essential for throughput optimization, enabling the GPU to process many tokens simultaneously rather than one at a time. The batch structure encodes token IDs, positions, sequence assignments, and output flags for each token in the batch.
Usage
Apply this principle when implementing prompt processing (where many tokens are evaluated at once), parallel sequence generation (where multiple independent sequences share a single forward pass), or any scenario where multiple tokens need to be evaluated together for throughput.
Theoretical Basis
Batch processing exploits the parallelism inherent in GPU hardware by combining multiple independent computations into a single large matrix operation. Instead of running N separate forward passes for N tokens, a batch groups them into one pass where the matrix dimensions are scaled by the batch size. The batch structure must track per-token metadata including position IDs (for positional encoding), sequence IDs (to correctly mask attention between independent sequences), and logit output flags (to indicate which tokens in the batch need their output logits computed). Proper batch construction is critical for both correctness (attention masking) and performance (maximizing GPU utilization).