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from typing import Union |
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import torch |
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from torch import nn, Tensor |
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class LearnedPositionEmbeddings(nn.Module): |
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def __init__(self, seq_len, model_dim, init=.02): |
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super().__init__() |
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self.emb = nn.Embedding(seq_len, model_dim) |
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self.emb.weight.data.normal_(mean=0.0, std=init) |
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def forward(self, x): |
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""" |
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Returns positional embeddings for index 0 up to the length of x |
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""" |
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sl = x.shape[1] |
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return self.emb(torch.arange(0, sl, device=x.device)) |
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def get_fixed_embedding(self, idx: 'Union[int, Tensor]'): |
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""" |
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Args: |
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idx: scalar int or an integer tensor of shape (T,) or (B, T) |
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Returns: |
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positional embeddings for given indices, shape (B, T, dim), ie (1, 1, dim) for int input |
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""" |
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device = self.emb.weight.device |
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idx = idx.to(device) if torch.is_tensor(idx) else torch.tensor(idx, device=device) |
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idx = torch.atleast_2d(idx) |
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assert idx.ndim == 2 |
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return self.emb(idx) |
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