Model-Preserving Adaptive Rounding
The main goal of post-training quantization (PTQ) is to produced a compressed
model whose output distribution is as close to the original model's as
possible. To do this tractably, almost all LLM PTQ algorithms quantize linear
layers by independently minimizing the immediate activation error. However,
this localized objective ignores the effect of subsequent layers, so reducing
it does not necessarily give a closer model. In this work, we introduce Yet
Another Quantization Algorithm (YAQA), an adaptive rounding algorithm that uses
Kronecker-factored approximations of each linear layer's Hessian with respect
to the full model KL divergence. YAQA consists of two components:
Kronecker-factored sketches of the full layerwise Hessian that can be tractably
computed for hundred-billion parameter LLMs, and a quantizer-independent
rounding algorithm that uses these sketches and comes with theoretical
guarantees. Across a wide range of models and quantizers, YAQA empirically
reduces the KL divergence to the original model by approx 30% while
achieving state of the art performance on downstream tasks.
683a57be57009ee6f3c7a0d2
Albert Tseng
claimed_verified
2025-06-02T07:43:31.763Z
649e3f263914db6cf8e8ab1f
/avatars/35298884b7633c67a9bcff0a32d31211.svg
Albert Tseng
user
at676
683a57be57009ee6f3c7a0d3
Zhaofeng Sun
extracted_pending
2025-05-31T01:13:35.081Z
67aee52fe680b6d64f1d1646
/avatars/334b73779ce2e62c41071d91cc1ffc5e.svg
Zhaofeng Sun
user
thuEcstasy1
683a57be57009ee6f3c7a0d4
Christopher De Sa
Model-Preserving Adaptive Rounding
The main goal of post-training quantization (PTQ) is to produced a compressed
model whose output distribution is as close to the original model's as
possible. To do this tractably, almost all LLM PTQ algorithms quantize linear
layers by independently minimizing the immediate activation error. However,
this localized objective ignores the effect of subsequent layers, so reducing
it does not necessarily give a closer model. In this work, we introduce Yet
Another Quantization Algorithm (YAQA), an adaptive rounding algorithm that uses
Kronecker-factored approximations of each linear layer's Hessian with respect
to the full model KL divergence. YAQA consists of two components:
Kronecker-factored sketches of the full layerwise Hessian that can be tractably
computed for hundred-billion parameter LLMs, and a quantizer-independent
rounding algorithm that uses these sketches and comes with theoretical
guarantees. Across a wide range of models and quantizers, YAQA empirically
reduces the KL divergence to the original model by approx 30% while
achieving state of the art performance on downstream tasks.
YAQA, an adaptive rounding algorithm using Kronecker-factored approximations of the full model's Hessian, reduces KL divergence and improves performance for post-training quantization of large language models.
post-training quantization LLM PTQ linear layers activation error Kronecker-factored approximations Hessian full model KL divergence quantizer-independent rounding algorithm theoretical guarantees