arXiv Machine Learning
The Quantization Benefits of Residual-Free Transformers
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arXiv Machine Learning
Sep 25
FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates
FlashLoop is a training‑free inference framework for Looped Transformers that reduces cross‑loop redundancy by employing token‑sparse updates, sparse attention, and KV‑residual quantization. It exploits observations that, as loops progress, state changes concentrate on a small token subset, attention differences are dominated by a sparse key subset, and KV residuals become amenable to low‑bit quantization. The method achieves lossless accuracy with up to 1.64× speedup and 6× KV‑cache memory reduction across several Looped Transformer models.
arXiv Machine Learning
Oct 8
ResidualQuant: KV Cache Quantization for Looped Transformers with 2-Bit Residuals
ResidualQuant introduces a 2‑bit residual quantization scheme for KV caches in looped Transformers, leveraging the similarity of KV states across loops. By using the final‑loop KV states as a reference and applying least‑square scaling, rotations, and loop‑wise mixed precision, the method achieves accurate quantization down to INT2. Experiments on various looped Transformer models show that ResidualQuant improves the accuracy‑memory tradeoff, retaining near BF16 accuracy while reducing KV storage by 80.7% and boosting decode throughput and batch size on an RTX 5090.
arXiv Machine Learning
Jun 5
SpanNorm: Reconciling Training Stability and Performance in Deep Transformers
arXiv:2601. 22580v2 Announce Type: replace-cross Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures.
arXiv Machine Learning
Oct 21
TTS-Q: Speaker-Aware Post-Training Quantization of Text-to-Speech Diffusion Transformers
The paper presents TTS‑Q, a post‑training quantization method that reduces the size of large diffusion transformer TTS models to 4‑bit precision while preserving speaker timbre and intelligibility. By identifying hard‑to‑quantize weights and activations and applying a mixed‑precision schedule that gives higher precision to late‑step feed‑forward activations, TTS‑Q restores timbre loss observed in standard 4‑bit PTQ. The approach also offers a calibrated‑W4A4 reference that lowers GPU memory usage, making TTS diffusion transformers more viable for on‑device, assistive, and real‑time applications.
arXiv Machine Learning
Oct 12
TTS-Q: Perception-Guided Post-Training Quantization of Text-to-Speech Diffusion Transformers
The paper introduces TTS‑Q, a perception‑guided post‑training quantization method for zero‑shot text‑to‑speech diffusion transformers. It identifies that 4‑bit weight‑and‑activation quantization erodes speaker timbre, and addresses this by selecting hard‑to‑quantize items and applying a mixed‑precision schedule that gives higher precision to late‑step feed‑forward activations. The resulting approach restores timbre while preserving intelligibility and reduces GPU memory usage for resource‑constrained deployment.
arXiv Machine Learning
Oct 22
InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
InfoQuant is a train‑free method that reshapes activation distributions to be more compatible with low‑bit quantizers. It uses Peak Suppression Orthogonal Transformation (PSOT) and adaptive outlier‑token selection to reduce numerical range while maintaining dispersion, thereby lowering quantization error. Across several large language model families, InfoQuant outperforms existing post‑training quantization and end‑to‑end training baselines, preserving 97% of floating‑point accuracy on average and narrowing the LLaMA‑2 13B performance gap by 42%.