Open ghost opened 4 years ago
Yes, it should work without any problem. You can just follow the steps of wrapping the transformer into the Masking
class and it should work just fine. What is happening in the background is that all weights in the module (and all its sub-modules) are multiplied with a binary mask before each forward pass.
If you apply this to transformers you should make sure though that you keep the layer norm parameters dense. You can achieve this by using the remove_type(torch.nn.LayerNorm)
method fo the Masking
class.
Let me know if you run into any problems.
Yes, it should work without any problem. You can just follow the steps of wrapping the transformer into the
Masking
class and it should work just fine. What is happening in the background is that all weights in the module (and all its sub-modules) are multiplied with a binary mask before each forward pass.If you apply this to transformers you should make sure though that you keep the layer norm parameters dense. You can achieve this by using the
remove_type(torch.nn.LayerNorm)
method fo theMasking
class.Let me know if you run into any problems.
It's 2022 now, do you get any positive results?
Hi, I am interested in this work. I want to try this algorithm to accelerate trainning procedure of NLP models. So I want to know if I can directly use this library on NLP models? Thanks!