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, bias=True, 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 (int) – the number of expected features in the input (required).
nhead (int) – the number of heads in the multiheadattention models (required).
dim_feedforward (int) – the dimension of the feedforward network model (default=2048).
dropout (float) – the dropout value (default=0.1).
activation (Union[str, Callable[[Tensor], Tensor]]) – the activation function of the intermediate layer, can be a string (“relu” or “gelu”) or a unary callable. Default: relu
layer_norm_eps (float) – the eps value in layer normalization components (default=1e-5).
batch_first (bool) – If
True
, then the input and output tensors are provided as (batch, seq, feature). Default:False
(seq, batch, feature).norm_first (bool) – if
True
, layer norm is done prior to self attention, multihead attention and feedforward operations, respectively. Otherwise it’s done after. Default:False
(after).bias (bool) – If set to
False
,Linear
andLayerNorm
layers will not learn an additive bias. Default:True
.
- 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, tgt_is_causal=False, memory_is_causal=False)[source]¶
Pass the inputs (and mask) through the decoder layer.
- Parameters
tgt (Tensor) – the sequence to the decoder layer (required).
memory (Tensor) – the sequence from the last layer of the encoder (required).
tgt_mask (Optional[Tensor]) – the mask for the tgt sequence (optional).
memory_mask (Optional[Tensor]) – the mask for the memory sequence (optional).
tgt_key_padding_mask (Optional[Tensor]) – the mask for the tgt keys per batch (optional).
memory_key_padding_mask (Optional[Tensor]) – the mask for the memory keys per batch (optional).
tgt_is_causal (bool) – If specified, applies a causal mask as
tgt mask
. Default:False
. Warning:tgt_is_causal
provides a hint thattgt_mask
is the causal mask. Providing incorrect hints can result in incorrect execution, including forward and backward compatibility.memory_is_causal (bool) – If specified, applies a causal mask as
memory mask
. Default:False
. Warning:memory_is_causal
provides a hint thatmemory_mask
is the causal mask. Providing incorrect hints can result in incorrect execution, including forward and backward compatibility.
- Return type
- Shape:
see the docs in Transformer class.