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This is low-hanging fruit that got overlooked until now. It turns out there is a little overhead with
nn.Dropout
even whenp=0.0
.To avoid that overhead, this PR implements our own subclass of
nn.Dropout
that just overrides the.forward()
method to return the input as-is whenself.p == 0.0
.With a tiny model on A100 GPUs, this slightly increases throughput from ~50,622 tokens/sec to ~50,722 tokens/sec. I expect we'll see a greater gain for larger models.