Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines

2026-10-02

AI Summary

This study addresses the non-deterministic data loading in online stateful data pipelines caused by topology changes, failure recovery, and backend heterogeneity. It proposes Zephon, a system enabling resilient and deterministic data loading. The core innovations include a novel topology-agnostic stream partitioning scheme and an ordered decision serialization mechanism, which achieve constant-overhead checkpoint recovery by persisting only bounded in-flight states. These techniques are integrated with stateless parallel processing, backend abstraction, and incremental checkpointing to form a comprehensive solution. Experiments demonstrate that Zephon sustains high throughput under both text and multimodal workloads while providing online determinism guarantees unattainable by existing approaches.

Abstract

Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costly ablations are caused by the parameter they changed rather than non-determinism in the training data sequence. The data loader must provide elastic determinism, i.e., a deterministic sequence of global training data batches despite changes to the GPU topology across runs (e.g., due to GPU scarcity), frequent checkpoint-resume cycles, and different data processing execution backends. Achieving this is difficult because modern foundation model data pipelines tokenize, pack, and mix samples online, introducing stateful n-to-m transformations that break sample indexing. Existing data loaders largely assume indexable 1-to-1 pipelines, and the common workaround of offline materialization is expensive and, for some modalities such as video, infeasible. We present Zephon, a data loader for foundation models that supports online, stateful pipelines while providing elastic determinism and efficient resumption from checkpoints. It partitions the global stream into topology-independent lanes, serializes ordering decisions while parallelizing stateless work on interchangeable backends, and checkpoints only bounded in-flight state so recovery cost does not grow with training progress. We evaluate Zephon on text and vision-language workloads and show that it achieves competitive throughput while providing a combination of guarantees that no existing loader offers for online, stateful pipelines.

Problem

• deterministic data loading
• foundation models
• elastic determinism
• stateful pipelines
• checkpoint resumption

Innovation

• Elastic Determinism
• Stateful Data Pipeline
• Foundation Model
• Checkpoint Resumption
• Data Loader