Method forward

bert/modeling_utils.py:869–902

Args: One of ``start_states``, ``start_positions`` should be not None. If both are set, ``start_positions`` overrides ``start_states``. **start_states**: ``torch.LongTensor`` of shape identical to hidden_states hidden states of the first tokens f

(self, hidden_states, start_states=None, start_positions=None, p_mask=None)

        self.dense_1 = nn.Linear(config.hidden_size, 1)

    def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):
        """ Args:
            One of ``start_states``, ``start_positions`` should be not None.
            If both are set, ``start_positions`` overrides ``start_states``.

            **start_states**: ``torch.LongTensor`` of shape identical to hidden_states
                hidden states of the first tokens for the labeled span.
            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
                position of the first token for the labeled span:
            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
                Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
                1.0 means token should be masked.
        """
        assert (
            start_states is not None or start_positions is not None
        ), "One of start_states, start_positions should be not None"
        if start_positions is not None:
            slen, hsz = hidden_states.shape[-2:]
            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
            start_states = hidden_states.gather(-2, start_positions)  # shape (bsz, 1, hsz)
            start_states = start_states.expand(-1, slen, -1)  # shape (bsz, slen, hsz)

        x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
        x = self.activation(x)
        x = self.LayerNorm(x)
        x = self.dense_1(x).squeeze(-1)

        if p_mask is not None:
            if next(self.parameters()).dtype == torch.float16:
                x = x * (1 - p_mask) - 65500 * p_mask
            else:
                x = x * (1 - p_mask) - 1e30 * p_mask

        return x


class PoolerAnswerClass(nn.Module):