pretraining/fairseq/modules/adaptive_input.py [16:75]:
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class AdaptiveInput(nn.Module):

    def __init__(
            self,
            vocab_size: int,
            padding_idx: int,
            initial_dim: int,
            factor: float,
            output_dim: int,
            cutoff: List[int],
    ):
        super().__init__()

        if vocab_size > cutoff[-1]:
            cutoff = cutoff + [vocab_size]
        else:
            assert vocab_size == cutoff[
                -1], 'cannot specify cutoff larger than vocab size'

        self.cutoff = cutoff
        self.embedding_dim = output_dim
        self.padding_idx = padding_idx

        self.embeddings = nn.ModuleList()
        for i in range(len(self.cutoff)):
            prev = self.cutoff[i - 1] if i > 0 else 0
            size = self.cutoff[i] - prev
            dim = int(initial_dim // (factor ** i))
            seq = nn.Sequential(
                nn.Embedding(size, dim, padding_idx),
                nn.Linear(dim, output_dim, bias=False)
            )
            self.embeddings.append(seq)

        def init_weights(m):
            if isinstance(m, nn.Embedding):
                nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5)
                nn.init.constant_(m.weight[padding_idx], 0)
            elif hasattr(m, 'weight'):
                nn.init.xavier_uniform_(m.weight)

        self.apply(init_weights)

        self.register_buffer('_float_tensor', torch.FloatTensor(1))

    def weights_for_band(self, band: int):
        return self.embeddings[band][0].weight, self.embeddings[band][1].weight

    def forward(self, input: torch.Tensor):
        result = self._float_tensor.new(input.shape + (self.embedding_dim,))
        for i in range(len(self.cutoff)):
            mask = input.lt(self.cutoff[i])
            if i > 0:
                mask.mul_(input.ge(self.cutoff[i - 1]))
                chunk_input = input[mask] - self.cutoff[i - 1]
            else:
                chunk_input = input[mask]
            if mask.any():
                result[mask] = self.embeddings[i](chunk_input)
        return result
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pretraining/fairseq/modules/adaptive_inputs.py [16:75]:
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class AdaptiveInput(nn.Module):

    def __init__(
            self,
            vocab_size: int,
            padding_idx: int,
            initial_dim: int,
            factor: float,
            output_dim: int,
            cutoff: List[int],
    ):
        super().__init__()

        if vocab_size > cutoff[-1]:
            cutoff = cutoff + [vocab_size]
        else:
            assert vocab_size == cutoff[
                -1], 'cannot specify cutoff larger than vocab size'

        self.cutoff = cutoff
        self.embedding_dim = output_dim
        self.padding_idx = padding_idx

        self.embeddings = nn.ModuleList()
        for i in range(len(self.cutoff)):
            prev = self.cutoff[i - 1] if i > 0 else 0
            size = self.cutoff[i] - prev
            dim = int(initial_dim // (factor ** i))
            seq = nn.Sequential(
                nn.Embedding(size, dim, padding_idx),
                nn.Linear(dim, output_dim, bias=False)
            )
            self.embeddings.append(seq)

        def init_weights(m):
            if isinstance(m, nn.Embedding):
                nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5)
                nn.init.constant_(m.weight[padding_idx], 0)
            elif hasattr(m, 'weight'):
                nn.init.xavier_uniform_(m.weight)

        self.apply(init_weights)

        self.register_buffer('_float_tensor', torch.FloatTensor(1))

    def weights_for_band(self, band: int):
        return self.embeddings[band][0].weight, self.embeddings[band][1].weight

    def forward(self, input: torch.Tensor):
        result = self._float_tensor.new(input.shape + (self.embedding_dim,))
        for i in range(len(self.cutoff)):
            mask = input.lt(self.cutoff[i])
            if i > 0:
                mask.mul_(input.ge(self.cutoff[i - 1]))
                chunk_input = input[mask] - self.cutoff[i - 1]
            else:
                chunk_input = input[mask]
            if mask.any():
                result[mask] = self.embeddings[i](chunk_input)
        return result
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