MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness

With the advances in artificial intelligence, Mix-of-Experts (MoE) has become the main form of Large Language Models (LLMs), and its demand for model compression is increasing. Quantization is an effective method that not only compresses the models but also significantly accelerates their performance. Existing quantization methods have gradually shifted the focus from parameter scaling to the analysis of data distributions. However, their analysis is designed for dense LLMs and relies on the simple one-model-all-data mapping, which is unsuitable for MoEs. This paper proposes a new quantization framework called MoQa. MoQa decouples the data-model distribution complexity of MoEs in multiple analysis stages, quantitively revealing the dynamics during sparse data activation, data-parameter mapping, and inter-expert correlations. Based on these, MoQa identifies particular experts' and parameters' significance with optimal data-model distribution awareness and proposes a series of fine-grained mix-quantization strategies adaptive to various data activation and expert combination scenarios. Moreover, MoQa discusses the limitations of existing quantization and analyzes the impact of each stage analysis, showing novel insights for MoE quantization. Experiments show that MoQa achieves a 1.69~2.18 perplexity decrease in language modeling tasks and a 1.58%~8.91% accuracy improvement in zero-shot inference tasks. We believe MoQa will play a role in future MoE construction, optimization, and compression.

OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection

Recent studies have illuminated that Large Language Models (LLMs) exhibit substantial potential in the realm of RTL (Register Transfer Level) code generation, with notable advancements evidenced by commercial models such as GPT-4 and Claude3-Opus. Despite their proficiency, these commercial LLMs often raise concerns regarding privacy and security. Conversely, open-source LLMs, which offer solutions to these concerns, have inferior performance in RTL code generation tasks to commercial models due to the lack of highquality open-source RTL datasets. To address this issue, we introduce OriGen, a fully open-source framework featuring self-reflection capabilities and a dataset augmentation methodology for generating high-quality, large-scale RTL code. We propose a novel code-to-code augmentation methodology that leverages knowledge distillation to enhance the quality of the open-source RTL code datasets. Additionally, OriGen is capable of correcting syntactic errors by leveraging a self-reflection process based on feedback from the compiler. The self-reflection ability of the model is facilitated by a carefully constructed dataset, which comprises a comprehensive collection of samples. Experimental results demonstrate that OriGen remarkably outperforms other open-source alternatives in RTL code generation, surpassing the previous best-performing LLM by 9.8% on the VerilogEval-Human benchmark. Furthermore, OriGen exhibits superior capabilities in self-reflection and error rectification, surpassing GPT-4 by 18.1% on the benchmark designed to evaluate the capability of self-reflection.