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TransformerDecoderLayer#

classtorch.nn.TransformerDecoderLayer(d_model,nhead,dim_feedforward=2048,dropout=0.1,activation=<functionrelu>,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 TransformerDecoderLayer implements the original architecture describedin theAttention Is All You Need paper. Theintent of this layer is as a reference implementation for foundational understandingand thus it contains only limited features relative to newer Transformer architectures.Given the fast pace of innovation in transformer-like architectures, we recommendexploring thistutorialto build efficient layers from building blocks in core or using higherlevel libraries from thePyTorch Ecosystem.

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) – IfTrue, then the input and output tensors are providedas (batch, seq, feature). Default:False (seq, batch, feature).

  • norm_first (bool) – ifTrue, layer norm is done prior to self attention, multiheadattention and feedforward operations, respectively. Otherwise it’s done after.Default:False (after).

  • bias (bool) – If set toFalse,Linear andLayerNorm layers will not learn an additivebias. 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, whenbatch_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 astgtmask.Default:False.Warning:tgt_is_causal provides a hint thattgt_mask isthe causal mask. Providing incorrect hints can result inincorrect execution, including forward and backwardcompatibility.

  • memory_is_causal (bool) – If specified, applies a causal mask asmemorymask.Default:False.Warning:memory_is_causal provides a hint thatmemory_mask is the causal mask. Providing incorrecthints can result in incorrect execution, includingforward and backward compatibility.

Return type

Tensor

Shape:

see the docs inTransformer.