(self, lq, lk)
| 231 | self.embedding = nn.Embedding(num_buckets, num_heads) |
| 232 | |
| 233 | def forward(self, lq, lk): |
| 234 | device = self.embedding.weight.device |
| 235 | # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \ |
| 236 | # torch.arange(lq).unsqueeze(1).to(device) |
| 237 | rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \ |
| 238 | torch.arange(lq, device=device).unsqueeze(1) |
| 239 | rel_pos = self._relative_position_bucket(rel_pos) |
| 240 | rel_pos_embeds = self.embedding(rel_pos) |
| 241 | rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze( |
| 242 | 0) # [1, N, Lq, Lk] |
| 243 | return rel_pos_embeds.contiguous() |
| 244 | |
| 245 | def _relative_position_bucket(self, rel_pos): |
| 246 | # preprocess |
nothing calls this directly
no test coverage detected