(self,
vocab,
dim,
dim_attn,
dim_ffn,
num_heads,
num_layers,
num_buckets,
shared_pos=True,
dropout=0.1)
| 320 | class T5Decoder(nn.Module): |
| 321 | |
| 322 | def __init__(self, |
| 323 | vocab, |
| 324 | dim, |
| 325 | dim_attn, |
| 326 | dim_ffn, |
| 327 | num_heads, |
| 328 | num_layers, |
| 329 | num_buckets, |
| 330 | shared_pos=True, |
| 331 | dropout=0.1): |
| 332 | super(T5Decoder, self).__init__() |
| 333 | self.dim = dim |
| 334 | self.dim_attn = dim_attn |
| 335 | self.dim_ffn = dim_ffn |
| 336 | self.num_heads = num_heads |
| 337 | self.num_layers = num_layers |
| 338 | self.num_buckets = num_buckets |
| 339 | self.shared_pos = shared_pos |
| 340 | |
| 341 | # layers |
| 342 | self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ |
| 343 | else nn.Embedding(vocab, dim) |
| 344 | self.pos_embedding = T5RelativeEmbedding( |
| 345 | num_buckets, num_heads, bidirectional=False) if shared_pos else None |
| 346 | self.dropout = nn.Dropout(dropout) |
| 347 | self.blocks = nn.ModuleList([ |
| 348 | T5CrossAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, |
| 349 | shared_pos, dropout) for _ in range(num_layers) |
| 350 | ]) |
| 351 | self.norm = T5LayerNorm(dim) |
| 352 | |
| 353 | # initialize weights |
| 354 | self.apply(init_weights) |
| 355 | |
| 356 | def forward(self, ids, mask=None, encoder_states=None, encoder_mask=None): |
| 357 | b, s = ids.size() |
nothing calls this directly
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