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)

TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.

This TransformerDecoderLayer implements the original architecture described in the Attention Is All You Need paper. The intent of this layer is as a reference implementation for foundational understanding and thus it contains only limited features relative to newer Transformer architectures. Given the fast pace of innovation in transformer-like architectures, we recommend exploring this tutorial to build efficient layers from building blocks in core or using higher level libraries from the PyTorch 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) – 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 and LayerNorm 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 is True:

>>> 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)

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 that tgt_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 that memory_mask is the causal mask. Providing incorrect hints can result in incorrect execution, including forward and backward compatibility.

Return type

Tensor

Shape:

see the docs in Transformer.