I am implementing “Deconstructing lottery ticket” paper.
I am training Alexnet for several times.
In the first epoch, it has exactly the same weight of Alexnet.
In the second Epoch, I selected 20% of the weights(4M indexes in index_dict) and keep them as zero while training. To do this,
I defined hooks for all layers, and grad_clone[some weights] =0:
grad_clone = grad.clone()
for i in range(len(index_dict4d[“conv1”])):
a,b,c,d = index_dict4d[“conv1”][i]
grad_clone[a,b,c,d] = 0
and then register hooks for all layers, before training started for the second epoch.
- This process makes the training very slow. (4M operation in the above for loop). Is there a way to speed up this process?