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""" |
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Various positional encodings for the transformer. |
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""" |
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import math |
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import torch |
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from torch import nn |
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|
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def PE1d_sincos(seq_length, dim): |
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""" |
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:param d_model: dimension of the model |
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:param length: length of positions |
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:return: length*d_model position matrix |
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""" |
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if dim % 2 != 0: |
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raise ValueError("Cannot use sin/cos positional encoding with " |
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"odd dim (got dim={:d})".format(dim)) |
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pe = torch.zeros(seq_length, dim) |
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position = torch.arange(0, seq_length).unsqueeze(1) |
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div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.float) * |
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-(math.log(10000.0) / dim))) |
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pe[:, 0::2] = torch.sin(position.float() * div_term) |
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pe[:, 1::2] = torch.cos(position.float() * div_term) |
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return pe.unsqueeze(1) |
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class PositionEmbedding(nn.Module): |
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""" |
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Absolute pos embedding (standard), learned. |
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""" |
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def __init__(self, seq_length, dim, dropout, grad=False): |
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super().__init__() |
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self.embed = nn.Parameter(data=PE1d_sincos(seq_length, dim), requires_grad=grad) |
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self.dropout = nn.Dropout(p=dropout) |
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def forward(self, x): |
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|
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l = x.shape[1] |
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x = x.permute(1, 0, 2) + self.embed[:l].expand(x.permute(1, 0, 2).shape) |
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x = self.dropout(x.permute(1, 0, 2)) |
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return x |
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