You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi,
I have a doubt in your BitLinear.forward() implementation.
The BitNet paper says the output should be the form as ; y = binarized_weight(W) @ AbsMaxQuant(LN(x)) * bettagamma/Q_b
(LN is layer normalization as the paper describes).
However, in your implementation, the output looks implemented as ; y = AbsMaxQuant(binarized_weight(W) @ x)
Why do you drop LN(x) and switch the order of paper's implementation? And there isn't dequantization with rescaling with bettagamma/Q_b in your implementation.
Can I get some ideas behind your implementation? If I misunderstand your implementation, please correct me.
The text was updated successfully, but these errors were encountered:
Hi,
I have a doubt in your BitLinear.forward() implementation.
The BitNet paper says the output should be the form as ; y = binarized_weight(W) @ AbsMaxQuant(LN(x)) * bettagamma/Q_b
(LN is layer normalization as the paper describes).
However, in your implementation, the output looks implemented as ; y = AbsMaxQuant(binarized_weight(W) @ x)
Why do you drop LN(x) and switch the order of paper's implementation? And there isn't dequantization with rescaling with bettagamma/Q_b in your implementation.
Can I get some ideas behind your implementation? If I misunderstand your implementation, please correct me.
The text was updated successfully, but these errors were encountered: