# Transformer¶

class torch.nn.Transformer(d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6, num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1, activation: str = 'relu', custom_encoder: Optional[Any] = None, custom_decoder: Optional[Any] = None)[source]

A transformer model. User is able to modify the attributes as needed. The architecture 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 can build the BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.

Parameters
• d_model – the number of expected features in the encoder/decoder inputs (default=512).

• num_encoder_layers – the number of sub-encoder-layers in the encoder (default=6).

• num_decoder_layers – the number of sub-decoder-layers in the decoder (default=6).

• dim_feedforward – the dimension of the feedforward network model (default=2048).

• dropout – the dropout value (default=0.1).

• activation – the activation function of encoder/decoder intermediate layer, relu or gelu (default=relu).

• custom_encoder – custom encoder (default=None).

• custom_decoder – custom decoder (default=None).

Examples::
>>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
>>> src = torch.rand((10, 32, 512))
>>> tgt = torch.rand((20, 32, 512))
>>> out = transformer_model(src, tgt)


Note: A full example to apply nn.Transformer module for the word language model is available in https://github.com/pytorch/examples/tree/master/word_language_model

forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: Optional[torch.Tensor] = None, tgt_mask: Optional[torch.Tensor] = None, memory_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, tgt_key_padding_mask: Optional[torch.Tensor] = None, memory_key_padding_mask: Optional[torch.Tensor] = None) → torch.Tensor[source]

Take in and process masked source/target sequences.

Parameters
• src – the sequence to the encoder (required).

• tgt – the sequence to the decoder (required).

Shape:
• src: $(S, N, E)$ .

• tgt: $(T, N, E)$ .

• src_mask: $(S, S)$ .

• tgt_mask: $(T, T)$ .

• memory_mask: $(T, S)$ .

• src_key_padding_mask: $(N, S)$ .

• tgt_key_padding_mask: $(N, T)$ .

• memory_key_padding_mask: $(N, S)$ .

Note: [src/tgt/memory]_mask ensures that position i is allowed to attend the unmasked positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend while the zero positions will be unchanged. If a BoolTensor is provided, positions with True are not allowed to attend while False values will be unchanged. If a FloatTensor is provided, it will be added to the attention weight. [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by the attention. If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions will be unchanged. If a BoolTensor is provided, the positions with the value of True will be ignored while the position with the value of False will be unchanged.

• output: $(T, N, E)$ .

Note: Due to the multi-head attention architecture in the transformer model, the output sequence length of a transformer is same as the input sequence (i.e. target) length of the decode.

where S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number

Examples

>>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)

generate_square_subsequent_mask(sz: int) → torch.Tensor[source]

Generate a square mask for the sequence. The masked positions are filled with float(‘-inf’). Unmasked positions are filled with float(0.0).