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[Auto Parallel] handle partial status propagate for `add_n` by waliwali777 · Pull Request #69439 · PaddlePaddle/Paddle · GitHub
[Auto Parallel] handle partial status propagate for
[Auto Parallel] handle partial status propagate for
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你的PR提交成功，感谢你对开源项目的贡献! |
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| st_losses = self.run_llama(to_static=1) | ||
| assert len(dy_losses) == len(st_losses) | ||
| for idx in range(len(dy_losses)): | ||
| np.testing.assert_allclose( |
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why not use md5 to compare dy-auto and static-auto ?
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动半反向中插入的 GradNodeAccumulation 也会和静半引发精度误差。正向中，同个 tensor 分别被两个 matmul 进行计算，反向会对两个 matmul_grad 的结果绑定 hook，插入 GradNodeAccumulation 算子。在反向计算时，设备间会先进行通信同步，再进行累加。因此，同样会与静半先求和再通信的算子执行顺序产生差异，造成精度误差，故不使用 md5 进行对比
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LGTM
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PR Category
Auto Parallel
PR Types
Performance
Description
为
add_n算子添加partial状态的切分推导：当所有输入都有相同的partial状态，则输出也包含 相同的partial状态，并为其增加切分推导的单测动手与静半：这会影响到部分
reshard插入位置，可能导致部分 动手 与 静半 单测的算子执行顺序产生差异，进而造成合理的精度误差：all_reduce，reduce_scatter等)再执行add_nadd_n再执行通信算子动半与静半：动半 反向中插入的
GradNodeAccumulation也会引发精度误差，在设备间是会先进行通信同步，再进行累积。因此，同样会与静半 单测的算子执行顺序产生差异，造成精度误差。因此，增加对部分相关单测
baseline的修改PCard-88057