def forward()

in models.py [0:0]

• 21 lines of code
• 5 McCabe index (conditional complexity)

    def forward(self, query, key, value, key_pe):
        # query size = B x M x H
        # key, value sizes = B x (M+L) x H

        if self.adapt_span_enabled:
            # [optional] trim out memory to reduce unnecessary computation
            key, value, key_pe = self.adaptive_span.trim_memory(
                query, key, value, key_pe)

        # compute attention from context
        # B x M (dest) x (M+L) (src)
        attn_cont = torch.matmul(query, key.transpose(-1, -2))
        attn_cont = _unskew(attn_cont)  # B x M x L

        # compute the effect of position embedding
        attn_pos = torch.matmul(query, key_pe)  # B x M x L_pos
        attn = attn_cont + attn_pos

        if self.persistent_memory is not None:
            attn, pers_mem_out = self.persistent_memory(query, attn)
        else:
            attn = attn / math.sqrt(self.hidden_size)  # B x M X L_pos
            attn = F.softmax(attn, dim=-1)

            if self.adapt_span_enabled:
                # trim attention lengths according to the learned span
                attn = self.adaptive_span(attn)

        attn = self.dropout(attn)  # B x M X L_pos

        attn_cont = _skew(attn, 0)  # B x M X (L+M)
        out = torch.matmul(attn_cont, value)  # B x M x H

        if self.persistent_memory is not None:
            out = out + pers_mem_out

        return out