GPU Parallel problems

autograd
1

I want to implement a graphic ram efficient trainning programs,but get threading lock problems . here is the steps .
1 replicate my modules to 4 GPUS .
2 caculate forward and loss in this 4 GPUS.
3 caculate backward in 4 GPUS , here comes the problem the backward step cannot be parallel .
According debug ,i find when run loss.backward() in GPU1 , the modules in gpu1 which copy from default model ,it parameter’s grad is None ,and default model get the grad that gpu1 actually caculated

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models[1] is copy from model

2

Why won’t DataParallel not work in your use case?

3

yeah, I first implement it use dataparallel ,but my category is more than 20000。 when pytorch gather all output to gpu0 ，GPU 0 have more graphics ram consume than others ，it limits the batch_size value，my dataset nearly 400gb large， it a very long time to wait