pooler.py

import torch
import torch.nn as nn

from .linear import Linear


class Pooler(nn.Module):
    """Pooler layer.

    Pool hidden states of a specific token (for example start of the
    sequence) and add a linear transformation followed by a tanh.

    Arguments:
        hidden_size: hidden size
        init_method: weight initialization method for the linear layer.
            bias is set to zero.
    """

    def __init__(self, hidden_size):
        super(Pooler, self).__init__()
        self.dense = Linear(hidden_size, hidden_size)

    def forward(self, hidden_states, sequence_index=0):
        # hidden_states: [b, s, h]
        # sequence_index: index of the token to pool.
        pooled = hidden_states[:, sequence_index, :]
        pooled = self.dense(pooled)
        pooled = torch.tanh(pooled)
        return pooled