33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
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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# Initializing this way is standard for GPT-2
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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) # (B, T, dim)
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