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shibing624 avatar shibing624 commented on July 27, 2024

参数设置需要为:CUDA_VISIBLE_DEVICES=0,1 torchrun --nnodes 1 --nproc_per_node 2 pretraining.py torchrun模式下,是每张卡加载全部模型参数,数据并行训练,如果显存不足,可以开启cpu_offload

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shibing624 avatar shibing624 commented on July 27, 2024

还可以这样:CUDA_VISIBLE_DEVICES=0,1 python pretraining.py 使用device_map="auto"可以自动分配多个卡加载模型。

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zzzhaoguziji avatar zzzhaoguziji commented on July 27, 2024

谢谢大佬,我再试试

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boxter007 avatar boxter007 commented on July 27, 2024

还可以这样:CUDA_VISIBLE_DEVICES=0,1 python pretraining.py 使用device_map="auto"可以自动分配多个卡加载模型。

针对glm6b2我试过了,还是不行。报同样的错误。但是glm6b就能用。

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Alfer-Feng avatar Alfer-Feng commented on July 27, 2024

glm和glm2模型参数是不一样的,转换时要修改,我现在也在入手这个,有结果后再来评论,插个眼

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archerbj avatar archerbj commented on July 27, 2024

参数设置需要为:CUDA_VISIBLE_DEVICES=0,1 torchrun --nnodes 1 --nproc_per_node 2 pretraining.py torchrun模式下,是每张卡加载全部模型参数,数据并行训练,如果显存不足,可以开启cpu_offload

请问下 cpu_offload 怎么开启?

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chloefresh avatar chloefresh commented on July 27, 2024

请问最后解决了吗?怎么解决的可以分享一下吗 @zzzhaoguziji @boxter007

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chloefresh avatar chloefresh commented on July 27, 2024

glm和glm2模型参数是不一样的,转换时要修改,我现在也在入手这个,有结果后再来评论,插个眼

有结果了吗?

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