TransformerDecoderLayer

class torch.nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='relu') [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 intermediate layer, relu or gelu (default=relu).

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)

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.