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Atomo: Communication-efficient Learning via Atomic Sparsification
Hi! Thanks a lot for the ATOMO paper and sharing your code over here. I just had a look at your implementation of 1-bit QSGD compression, and was wondering where the Elias coding is implemented. I would be curious if you have any thoughts on doing this efficiently. I found utils.compress
, which I thought could be related, but I cannot find a use of this function in this repo.
hello, I‘m trying to test this code on imagenet, but I find that when the program runs to self.comm.Bcast([self.model_recv_buf.recv_buf[layer_idx], MPI.DOUBLE], root=0)
in function async_fetch_weights_bcast
in distributed_worker.py at the first step, it thrown an error that is MPI_ERR_TRUNCATE: message truncated
, but I check the memory size in Bcast and it works when the program ran on Cifar10/100, have u encountered this problem?
And another issue: then I replaced the Pytorch0.3.0 with Pytorch0.4/1.1, the proceeding time on decode of QSGD is significantly higher than 0.3.0, almost 10 times than it, have u tried this?
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