提出配置优先框架，系统化检测和减少跨后端深度学习部署的差异。
Toward Reproducible Cross-Backend Compatibility for Deep Learning: A Configuration-First Framework with Three-Tier Verification
- 用YAML解耦实验与代码，支持多种模型格式
- 三阶段验证发现72%运行通过，严格阈值下差异多发
- 确定性适配器可显著提升一致性，适合多平台部署者
本文提出一种配置优先的框架，用于评估深度学习系统在CPU、GPU及编译运行时上的跨后端兼容性。该框架通过YAML文件解耦实验与代码，支持库级与仓库级模型，采用三级验证协议：张量级接近度、激活对齐度和任务级指标。在多个模型和容忍度设置下共执行672次检查，结果显示72.0%的运行通过验证，多数差异出现在更严格的阈值下。研究发现检测模型和编译后端尤其容易出现漂移，常源于非确定性后处理。进一步表明，使用确定性适配器和选择性回退可显著提升一致性，且性能损失极小。据我们所知，这是首个系统量化并缓解深度学习跨后端漂移的统一框架，为异构运行时的可复现部署提供可靠方法。
原文摘要 · Abstract (English)
This paper presents a configuration-first framework for evaluating cross-backend compatibility in deep learning systems deployed on CPU, GPU, and compiled runtimes. The framework decouples experiments from code using YAML, supports both library and repository models, and employs a three-tier verification protocol covering tensor-level closeness, activation alignment, and task-level metrics. Through 672 checks across multiple models and tolerance settings, we observe that 72.0% of runs pass, with most discrepancies occurring under stricter thresholds. Our results show that detection models and compiled backends are particularly prone to drift, often due to nondeterministic post-processing. We further demonstrate that deterministic adapters and selective fallbacks can substantially improve agreement without significant performance loss. To our knowledge, this is the first unified framework that systematically quantifies and mitigates cross-backend drift in deep learning, providing a reproducible methodology for dependable deployment across heterogeneous runtimes.
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