pyro-ppl/pyro

[FR] Support Automatic Mixed Precision training

Open

#3,316 创建于 2024年1月31日

在 GitHub 查看
 (7 评论) (0 反应) (0 负责人)Python (8,211 star) (981 fork)batch import
enhancementhelp wanted

描述

Issue Description

Better support for mixed precision training would be extremely helpful, at least for SVI. I can manually cast data into float16 or bfloat16 but I am unable to leverage PyTorch's automatic mixed precision training. This is because it requires the use of the GradScaler class during the optimization loop to properly scale gradients in a mixed-precision-aware manner. See the documentation for more info: https://pytorch.org/docs/stable/amp.html

It would be nice to have support for using this class within pyro optimizers to allow for amp support.

贡献者指南

技术栈
pythonpytorch
领域
machine learningperformance
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
active
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
PyTorchPyro SVIAMP basics
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
40
研究方向
Examine Pyro's optimizer classes in pyro/optim/ to understand gradient application. Review issue comments for proposed approaches. Implement GradScaler integration into the optimizer's step method, following PyTorch's AMP guidelines. Consider a flag to enable AMP in SVI.
[FR] Support Automatic Mixed Precision training · pyro-ppl/pyro#3316 | Good First Issue