Principle:FMInference FlexLLMGen HELM Batch Construction
| Knowledge Sources | |
|---|---|
| Domains | Benchmark_Integration, Batch_Processing |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A batching strategy that groups HELM scenario evaluation requests into fixed-size batches with uniform sequence length padding for efficient GPU inference.
Description
HELM scenarios produce variable-length prompts, but FlexLLMGen's OptLM.generate() requires fixed-size batches matching gpu_batch_size * num_gpu_batches. The batch construction process groups requests by generation parameters (temperature, max_tokens, stop sequences), pads all prompts to a uniform length, and creates numpy arrays suitable for the generate() API. This enables efficient batched evaluation of diverse HELM scenarios on limited GPU hardware.
Usage
Used internally by the HELM execution pipeline to prepare request_states for batched generation. The pad_to_seq_len parameter controls padding length (auto-computed from the longest prompt in each batch if not specified).
Theoretical Basis
Batched inference requires uniform tensor shapes. Variable-length prompts are left-padded (padding_side="left") to the longest sequence in the batch, with attention masks ensuring padded positions don't affect computation.