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import math |
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import torch |
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from torch import nn |
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class TokenEmbedding(nn.Module): |
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def __init__( |
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self, |
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embedding_dim: int, |
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vocab_size: int, |
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dropout: float = 0.0, |
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): |
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super().__init__() |
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self.vocab_size = vocab_size |
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self.embedding_dim = embedding_dim |
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self.dropout = torch.nn.Dropout(p=dropout) |
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self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim) |
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@property |
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def weight(self) -> torch.Tensor: |
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return self.word_embeddings.weight |
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def embedding(self, index: int) -> torch.Tensor: |
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return self.word_embeddings.weight[index : index + 1] |
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def forward(self, x: torch.Tensor): |
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x = self.word_embeddings(x) |
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x = self.dropout(x) |
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return x |
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class SinePositionalEmbedding(nn.Module): |
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def __init__( |
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self, |
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embedding_dim: int, |
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dropout: float = 0.0, |
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scale: bool = False, |
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alpha: bool = False, |
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): |
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super().__init__() |
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self.embedding_dim = embedding_dim |
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self.x_scale = math.sqrt(embedding_dim) if scale else 1.0 |
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self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) |
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self.dropout = torch.nn.Dropout(p=dropout) |
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self.reverse = False |
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self.pe = None |
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self.extend_pe(torch.tensor(0.0).expand(1, 4000)) |
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def extend_pe(self, x): |
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"""Reset the positional encodings.""" |
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if self.pe is not None: |
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if self.pe.size(1) >= x.size(1): |
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if self.pe.dtype != x.dtype or self.pe.device != x.device: |
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self.pe = self.pe.to(dtype=x.dtype, device=x.device) |
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return |
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pe = torch.zeros(x.size(1), self.embedding_dim) |
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if self.reverse: |
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position = torch.arange( |
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x.size(1) - 1, -1, -1.0, dtype=torch.float32 |
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).unsqueeze(1) |
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else: |
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) |
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div_term = torch.exp( |
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torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) |
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* -(math.log(10000.0) / self.embedding_dim) |
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) |
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pe[:, 0::2] = torch.sin(position * div_term) |
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pe[:, 1::2] = torch.cos(position * div_term) |
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pe = pe.unsqueeze(0) |
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self.pe = pe.to(device=x.device, dtype=x.dtype).detach() |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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self.extend_pe(x) |
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output = x.unsqueeze(-1) if x.ndim == 2 else x |
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output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)] |
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return self.dropout(output) |
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