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update doc for modifying checkpoint and performance principles #72
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9. 测试1x1,2x8 | ||
10. 补充case文档,模型文档 | ||
11. 对照PR提交规范,提交PR | ||
2. 从头开始训练,保存ckpt(可选), 验证原始仓库精度达标 |
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是否需要说明一下:对于具有backbone的模型,仅需从头开始训练非backbone的部分
# 不太清楚咱们对于大模型是否有标准,是在一个较广泛使用的小数据集上完成finetune的全部训练吗
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具有backbone的模型,backbone可以使用pretrained weights
## 1. 厂商适配Case的代码和配置文件目录结构说明 | ||
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厂商基于标准case做硬件上的适配,适配原则: | ||
1. 优先默认使用标准case实现,不做上层接口变动,理想情况下,只有底层算子的区别,对用户不感知; | ||
2. 接受对模型做合理优化以此来提升模型的性能表现,如bs调整等,建议底层优化,暂不接受torch接口层优化, 具体可case by case讨论。 | ||
3. 对于标准case中厂商不支持的算子,可有合理替代方案,具体可讨论。 | ||
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标准Case实现路径在training/benchmarks/<model>/<framework>/下,厂商可以通过扩展模型实现的接口来适配自己的芯片。代码放在training/<vendor>/下,主要包括以下几部分(以Nvidia, glm, pytorch为例): | ||
标准Case实现路径在training/benchmarks/<model>/<framework>/下,厂商可以通过扩展模型实现的接口来适配自己的芯片。厂商修改的代码放在training/<vendor>/glm-pytorch下,主要包括以下几部分(以kunlunxin, glm, pytorch为例): |
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还有一点Markdown格式上的小问题,< > 需要转义一下。方式1:< /> 首页readme是这样处理的 方式2:用HTML的语法 < 和 > 推荐方式1,更简单些。
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