"Hi, I am reading the GPT-2 paper and encountering a problem with the following phrase related to implementation:
'A modified initialization method is used to account for the accumulation on the residual path with model depth. We scale the weights of residual layers at initialization by a factor of 1/√N, where N is the number of residual layers.'
My problem is that we normalize after accumulation (addition then normalization). So, why do we need to scale weights? Aren't we doing this to reduce the impact of accumulation?"
"Hi, I am reading the GPT-2 paper and encountering a problem with the following phrase related to implementation:
'A modified initialization method is used to account for the accumulation on the residual path with model depth. We scale the weights of residual layers at initialization by a factor of 1/√N, where N is the number of residual layers.'
My problem is that we normalize after accumulation (addition then normalization). So, why do we need to scale weights? Aren't we doing this to reduce the impact of accumulation?"