Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference

• URL: http://arxiv.org/abs/2508.09505v1
• Date: Wed, 13 Aug 2025 05:33:25 GMT
• Title: Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
• Authors: Zhanghan Wang, Ding Ding, Hang Zhu, Haibin Lin, Aurojit Panda,
• Abstract summary: Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU.<n>In this paper, we describe an approach to statically identify such bugs by checking model refinement.<n>Our approach, implemented in GraphGuard, uses iterative rewriting to prove model refinement.
• Score: 5.699231128144775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed models are generally produced by programmers who take a sequential model specification and apply several distribution strategies to distribute state and computation across GPUs. Unfortunately, bugs can be introduced in the process, and a distributed model implementation's outputs might differ from the sequential model's outputs. In this paper, we describe an approach to statically identify such bugs by checking model refinement, that is, can the sequential model's outputs be reconstructed from the distributed model's outputs? Our approach, implemented in GraphGuard, uses iterative rewriting to prove model refinement. Our approach can scale to today's large models and deployments: we evaluate it using GPT and Llama-3. Further, it provides actionable output that aids in bug localization.

Related papers

• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Every Step Counts: Decoding Trajectories as Authorship Fingerprints of dLLMs [63.82840470917859]
  We show that the decoding mechanism of dLLMs can be used as a powerful tool for model attribution.<n>We propose a novel information extraction scheme called the Directed Decoding Map (DDM), which captures structural relationships between decoding steps and better reveals model-specific behaviors.
  arXiv  Detail & Related papers  (2025-10-02T06:25:10Z)
• Model Integrity when Unlearning with T2I Diffusion Models [11.321968363411145]
  We propose approximate Machine Unlearning algorithms to reduce the generation of specific types of images, characterized by samples from a forget distribution'' We then propose unlearning algorithms that demonstrate superior effectiveness in preserving model integrity compared to existing baselines.
  arXiv  Detail & Related papers  (2024-11-04T13:15:28Z)
• Hierarchical Blockmodelling for Knowledge Graphs [0.5530212768657544]
  We use blockmodels for the purpose of hierarchical entity clustering on knowledge graphs. The integration of the Nested Chinese Restaurant Process and the Stick Breaking Process into the generative model allows for the induction of hierarchical clusterings. We evaluate our model on synthetic and real-world datasets and quantitatively compare against benchmark models.
  arXiv  Detail & Related papers  (2024-08-28T09:04:15Z)
• Heat Death of Generative Models in Closed-Loop Learning [63.83608300361159]
  We study the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset. We show that, unless a sufficient amount of external data is introduced at each iteration, any non-trivial temperature leads the model to degenerate.
  arXiv  Detail & Related papers  (2024-04-02T21:51:39Z)
• ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis [7.979501926410114]
  ConvTimeNet is a hierarchical pure convolutional model designed for time series analysis.<n>It adaptively perceives local patterns of temporally dependent basic units in a data-driven manner.<n>A large kernel mechanism is employed to ensure that convolutional blocks can be deeply stacked.
  arXiv  Detail & Related papers  (2024-03-03T12:05:49Z)
• DORE: Document Ordered Relation Extraction based on Generative Framework [56.537386636819626]
  This paper investigates the root cause of the underwhelming performance of the existing generative DocRE models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models.
  arXiv  Detail & Related papers  (2022-10-28T11:18:10Z)
• How to Learn when Data Gradually Reacts to Your Model [10.074466859579571]
  We propose a new algorithm, Stateful Performative Gradient Descent (Stateful PerfGD), for minimizing the performative loss even in the presence of these effects. Our experiments confirm that Stateful PerfGD substantially outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-13T22:05:26Z)
• Sampling from Arbitrary Functions via PSD Models [55.41644538483948]
  We take a two-step approach by first modeling the probability distribution and then sampling from that model. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models.
  arXiv  Detail & Related papers  (2021-10-20T12:25:22Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Model Reuse with Reduced Kernel Mean Embedding Specification [70.044322798187]
  We present a two-phase framework for finding helpful models for a current application. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a specification for the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification.
  arXiv  Detail & Related papers  (2020-01-20T15:15:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.