There are existing sinusoidal position encoding modules out there, but the ones that I confronted were mostly assuming the position to be incrementing from 0 to the size of sequence. For example, when a token embedding sequence with shape of (B, L, D_token) is given then the sinusoidal position encoding module will take this tensor as input and manually create a tensor (B,L) where the values for each row is (0,1,2,3, …., L-1) and then apply sinusoidal encoding on this.

But I wanted a sinusoidal position encoding module that can handle when position values are not incremental. For example, when I have a token embedding tensor (B,L,D_token), I also have a position array shape (B,L) where the position values are not incremental (e.g. (0,1,2,3,0,1,2,0,1,0,1,2,3,4,5,…) ).

To handle such cases, I coded my own sinusoidal position encoding module in pytorch where the input will be a tensor containing position integer values.

class SinusoidalPositionEncoding(torch.nn.Module): def __init__(self, dim, max_period=5000): assert dim % 2 == 0 self.dim = dim self.max_period = max_period super().__init__() w_arr = torch.arange(0, self.dim // 2) w_arr = 1 / (max_period) ** (w_arr * 2 / dim) self.register_buffer("w_arr", w_arr) def forward(self, x): """ assume x has shape (B,T) where B=batch size, T=token size(or sequence length) and values of x are integers >=0. """ _x = torch.unsqueeze(x, -1) # (B,T,1) v = _x * self.w_arr # (B,T,dim//2) sin = torch.sin(v) sin = torch.unsqueeze(sin, -1) # (B,T,m,1) cos = torch.cos(v) cos = torch.unsqueeze(cos, -1) # (B,T,m,1) m = torch.cat([sin, cos], -1) # (B,T,m,2) b, t, _, _ = m.shape y = m.reshape(b, t, -1) # (B,T,dim) where 2m=`dim` return y

If you are just using implicit incremental positions, then you don’t have to use this and just use one from any framework that goes with that approach.

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