Comments (6)
怀疑是因为 clip grads 的时候某些 expert 没有梯度, 导致不同 worker 之间 allreduce 的数据量不同
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怀疑是因为 clip grads 的时候某些 expert 没有梯度, 导致不同 worker 之间 allreduce 的数据量不同
是的,这是一个存在问题的地方。所以,我修改了clip_gradients.py文件,注释掉其中的所有all_reduce操作。这样的话,训练可以进行数十到数百step,然后仍然hang。所以,应该还有其它地方存在通信不一致的问题。
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我注意到您开了
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
可以把这俩都改成 1 试试能不能跑吗?
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可以把这俩都改成 1 试试能不能跑吗?
抱歉,上面的配置贴错了,正确的如下:
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 1 \
所以,只是使用了模型并行。
当上面两个配置均为1时,即只使用数据并行+MoE,单机多卡配置下,可以正常运行。
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看起来这个问题依然悬而未决. 我这里没有能复现这个问题. @sandyhouse 您这边有什么新的进展吗?
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Closing due to lack of further response. Feel free to re-open if you have any progress.
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