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arXiv AI

Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism

arXiv:2606. 09377v1 Announce Type: cross Abstract: Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $\alpha$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator.

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Hugging Face Trending Papers
Jun 8

Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism

Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $α$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the \texttt{auto\_LiRPA}\,/\,$α,β$-CROWN verification framework.

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arXiv Machine Learning
Sep 18

Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training

The paper introduces Block Parallelism (BP) and Context‑Sharded Block Parallelism (CSBP) to improve training efficiency for Block Diffusion Language Models (BDLMs) with long contexts. By assigning each corrupted‑block computation to a separate rank and sharding the shared clean sequence, CSBP reduces communication overhead and memory usage while preserving training semantics. Experiments on 16 H200 GPUs and 8 H100 GPUs show throughput gains of up to 1.61× and 7.59×, respectively, and higher benchmark pass rates in practical fine‑tuning scenarios.

By Tarun Suresh, Pranshu Chaturvedi, Hangoo Kang, Parth Shroff, Ishan S. Khare, Hermann Kumbong, Azalia Mirhoseini
llmsdiffusionfine-tuningefficiency
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arXiv Machine Learning
4d ago

Scaling Zero-Order Pretraining through Model Sharding

arXiv:2609.37899v1 Announce Type: new Abstract: Zero-order optimization (ZO) trains without backpropagation, making it relevant to forward-only hardware and non-differentiable loss, but its gradient...

By Francois Chaubard, Mykel J. Kochenderfer, Chris R\'e
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arXiv Machine Learning
Sep 22

Neural Spectral Capacity: Measuring and Designing Architectures from Network Specification Alone

The paper introduces Neural Spectral Capacity (NSC), a closed‑form metric derived from the singular‑value spectrum of weight matrices that can be computed solely from a network’s architectural specification. Unlike traditional measures such as #Params and #FLOPs, NSC captures architectural structure (depth, width, head, FFN allocations) and can be evaluated without instantiating the model, data, or gradients. Using a dynamic‑programming solver (NSC‑DP), the authors demonstrate that NSC can efficiently identify architectures that outperform existing training‑free proxies across Transformer and CNN families, and achieve state‑of‑the‑art results in tasks such as WikiText‑103 and commonsense reasoning with LLaMA‑7B. whyItMatters":"NSC provides a fast, architecture‑only proxy that outperforms conventional metrics and training‑free proxies, enabling more effective design and pruning of large models without costly training or data."

By Chenyu Zhu, Ruoyu Zhao, Zhichao Lu
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