Cheaply Evaluating Inference Efficiency Metrics for Autoregressive
  Transformer APIs
Authors: name: Deepak Narayanan; name: Keshav Santhanam; name: Peter Henderson; name: Rishi Bommasani; name: Tony Lee; name: Percy Liang
Published: 2023-05-03 21:51:42

Abstract: Large language models (LLMs) power many state-of-the-art systems in natural
language processing. However, these models are extremely computationally
expensive, even at inference time, raising the natural question: when is the
extra cost of deploying a larger model worth the anticipated boost in
capabilities? Better understanding this tradeoff fundamentally could benefit
from an inference efficiency metric that is both (i) easily comparable across
models from different providers, and (ii) representative of the true cost of
running queries in an isolated performance environment. Unfortunately, access
to LLMs today is largely restricted to black-box text generation APIs and raw
runtimes measured through this interface do not satisfy these desiderata: model
providers can apply various software and hardware optimizations orthogonal to
the model, and models served on shared infrastructure are susceptible to
performance contention. To circumvent these problems, we propose a new metric
for comparing inference efficiency across models. This metric puts models on
equal footing as though they were served (i) on uniform hardware and software,
and (ii) without performance contention. We call this metric the
idealized runtime, and we propose a methodology to efficiently estimate
this metric for autoregressive Transformer models. We also propose cost-aware
variants that incorporate the number of accelerators needed to serve the model.
Using these metrics, we compare ten state-of-the-art LLMs to provide the first
analysis of inference efficiency-capability tradeoffs; we make several
observations from this analysis, including the fact that the superior inference
runtime performance of certain APIs is often a byproduct of optimizations
within the API rather than the underlying model. Our methodology also
facilitates the efficient comparison of different software and hardware stacks.