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# TransformerDecoder

class torch.nn.TransformerDecoder(decoder_layer, num_layers, norm=None)

  TransformerDecoder is a stack of N decoder layers

  Parameters:

    - decoder_layer – an instance of the TransformerDecoderLayer() class (required).

    - num_layers – the number of sub-decoder-layers in the decoder (required).

    - norm – the layer normalization component (optional).

  Examples::

        >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
        >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
        >>> memory = torch.rand(10, 32, 512)
        >>> tgt = torch.rand(20, 32, 512)
        >>> out = transformer_decoder(tgt, memory)

  forward(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None, tgt_is_causal=None, memory_is_causal=False)

    Pass the inputs (and mask) through the decoder layer in turn.

    Parameters:

      - tgt (Tensor) – the sequence to the decoder (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 (Optional[bool]) – If specified, applies a causal mask as `tgt mask`. Default: `None`; try to detect a causal mask. 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 class.

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