I’m working through a tutorial on transformers (Tutorial 6: Transformers and Multi-Head Attention — UvA DL Notebooks v1.0 documentation) and I came across this block of code about positional encoding.
def __init__(self, d_model, max_len=5000): """ Inputs d_model - Hidden dimensionality of the input. max_len - Maximum length of a sequence to expect. """ super().__init__() # Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) # register_buffer => Tensor which is not a parameter, but should be part of the modules state. # Used for tensors that need to be on the same device as the module. # persistent=False tells PyTorch to not add the buffer to the state dict (e.g. when we save the model) self.register_buffer('pe', pe, persistent=False) def forward(self, x): x = x + self.pe[:, :x.size(1)] return x
I haven’t understood what the
register_buffer does, would someone be able to explain it in an easier way? I get that it’s saved alongside model parameters but isn’t included in gradient calculations - so what’s the difference between this and just setting the
requires_grad to be