BrainSurgery：可重現且可靠的聲明式權重操作用於模型編輯與升級再利用

摘要

随着深度学习模型的规模不断增大，管理、检查及修改大型检查点的难度日益提升。研究人员经常需要调整模型权重以进行层重构、精度转换、低秩分解和架构调试，但这些工作流程往往依赖于脆弱的临时Python脚本。在此，我们介绍BrainSurgery，一个用于对神经网络检查点进行稳健且可复现的“张量手术”的工具，并通过系统演示涵盖从模型升级到LoRA提取的四个示例和三个案例研究。通过抽象存储格式与内存管理，BrainSurgery能够通过声明式YAML计划执行复杂的变换。它支持通过表达性正则表达式和结构化定位进行结构修改、数学变换和张量重塑，同时内置断言检查张量形状、数据类型和数值，以防止静默错误。我们期望BrainSurgery凭借其可复现且经过验证的操作，为未来研究提供坚实基础。

English

As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python scripts. Here, we introduce BrainSurgery, a tool for robust and reproducible "tensor surgery" on neural network checkpoints, and provide a system demonstration covering four examples and three case studies from model upcycling to LoRA extraction. By abstracting storage formats and memory management, BrainSurgery executes complex transformations through declarative YAML plans. It supports structural modifications, mathematical transformations, and tensor reshaping through expressive regex and structural targeting, while built-in assertions validate tensor shapes, data types, and values to prevent silent errors. We envision that BrainSurgery will provide a strong foundation for future research through its reproducible and validated operations.