Construct an EncoderLayer object.
(
self,
in_size,
size,
self_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
stochastic_depth_rate=0.0,
)
| 344 | |
| 345 | class EncoderLayerSANM(nn.Module): |
| 346 | def __init__( |
| 347 | self, |
| 348 | in_size, |
| 349 | size, |
| 350 | self_attn, |
| 351 | feed_forward, |
| 352 | dropout_rate, |
| 353 | normalize_before=True, |
| 354 | concat_after=False, |
| 355 | stochastic_depth_rate=0.0, |
| 356 | ): |
| 357 | """Construct an EncoderLayer object.""" |
| 358 | super(EncoderLayerSANM, self).__init__() |
| 359 | self.self_attn = self_attn |
| 360 | self.feed_forward = feed_forward |
| 361 | self.norm1 = LayerNorm(in_size) |
| 362 | self.norm2 = LayerNorm(size) |
| 363 | self.dropout = nn.Dropout(dropout_rate) |
| 364 | self.in_size = in_size |
| 365 | self.size = size |
| 366 | self.normalize_before = normalize_before |
| 367 | self.concat_after = concat_after |
| 368 | if self.concat_after: |
| 369 | self.concat_linear = nn.Linear(size + size, size) |
| 370 | self.stochastic_depth_rate = stochastic_depth_rate |
| 371 | self.dropout_rate = dropout_rate |
| 372 | |
| 373 | def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None): |
| 374 | """Compute encoded features. |