TorchAmi: Generalized CPU/GPU implementation of algorithmic Matsubara integration

We present torchami, an advanced implementation of algorithmic Matsubara integration (AMI) that utilizes pytorch as a backend to provide easy parallelization and GPU support. AMI is a tool for analytically resolving the sequence of nested Matsubara integrals that arise in virtually all Feynman perturbative expansions. In this implementation we present a new AMI algorithm that creates a more natural symbolic representation of the Feynman integrands. In addition, we include peripheral tools that allow for import and labeling of simple graph structures and conversion to torchami input. The code is written in c++ with python bindings provided.我们推出了torchami，一款以PyTorch为后端的算法型松原积分（algorithmic Matsubara integration, AMI）高级实现框架，可实现便捷的并行计算与GPU加速支持。AMI是一款用于解析求解几乎所有费曼微扰展开（Feynman perturbative expansions）中嵌套松原积分序列的工具。在本次实现中，我们提出了一种全新的AMI算法，可为费曼被积函数（Feynman integrands）生成更自然的符号表示形式。此外，该框架还附带了配套工具，支持简单图结构（graph structures）的导入、标注，以及格式转换为torchami的输入格式。本框架采用C++编写，并提供了Python绑定（python bindings）接口。