TransformerDecoderLayer¶
-
class
torch.nn.
TransformerDecoderLayer
(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=<function relu>, layer_norm_eps=1e-05, batch_first=False, norm_first=False, device=None, dtype=None)[source]¶ TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application.
- Parameters
d_model – the number of expected features in the input (required).
nhead – the number of heads in the multiheadattention models (required).
dim_feedforward – the dimension of the feedforward network model (default=2048).
dropout – the dropout value (default=0.1).
activation – the activation function of the intermediate layer, can be a string (“relu” or “gelu”) or a unary callable. Default: relu
layer_norm_eps – the eps value in layer normalization components (default=1e-5).
batch_first – If
True
, then the input and output tensors are provided as (batch, seq, feature). Default:False
.norm_first – if
True
, layer norm is done prior to self attention, multihead attention and feedforward operations, respectivaly. Otherwise it’s done after. Default:False
(after).
- Examples::
>>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = decoder_layer(tgt, memory)
- Alternatively, when
batch_first
isTrue
: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=True) >>> memory = torch.rand(32, 10, 512) >>> tgt = torch.rand(32, 20, 512) >>> out = decoder_layer(tgt, memory)
-
forward
(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)[source]¶ Pass the inputs (and mask) through the decoder layer.
- Parameters
tgt – the sequence to the decoder layer (required).
memory – the sequence from the last layer of the encoder (required).
tgt_mask – the mask for the tgt sequence (optional).
memory_mask – the mask for the memory sequence (optional).
tgt_key_padding_mask – the mask for the tgt keys per batch (optional).
memory_key_padding_mask – the mask for the memory keys per batch (optional).
- Shape:
see the docs in Transformer class.