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vLLM 0.30.0 mis-scales LoRA adapters, degrading model performance

By PulseAugur Editorial · [1 sources] ·

A bug in vLLM version 0.30.0 causes it to incorrectly scale LoRA adapters, leading to a significant degradation in performance. The issue stems from vLLM ignoring the `rank_pattern` and `alpha_pattern` settings in adapter configuration files, which are designed to allow different scaling factors for different modules within an adapter. Instead, vLLM applies a single, uniform scaling factor derived only from the base `r` and `lora_alpha` values. This mis-scaling was demonstrated to increase perplexity by approximately 40% on a mixture-of-experts model and caused a 70x larger deviation in prompt log-probabilities compared to the PEFT library on a smaller test model. The problem is subtle and may go unnoticed as vLLM still loads the adapters without warning, and the generated text does not immediately appear incorrect. AI

IMPACT This bug in vLLM can lead to significant performance degradation for models using LoRA adapters, potentially impacting inference quality and efficiency.

RANK_REASON Bug report detailing incorrect behavior in an inference serving framework.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

vLLM 0.30.0 mis-scales LoRA adapters, degrading model performance

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Bug report detailing incorrect behavior in an inference serving framework.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · The Homelab Postmortem ·

    vLLM ignores LoRA rank_pattern and alpha_pattern and serves the adapter at the wrong scale

    <p><strong>TL;DR</strong>: A PEFT LoRA adapter can give individual modules their own rank and alpha through <code>rank_pattern</code> and <code>alpha_pattern</code> in <code>adapter_config.json</code>, and PEFT scales each module by its own <code>alpha_m / r_m</code>. vLLM 0.30.0…

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