transformer

CLASS torch.nn.Transformer(d_model=512, nhead=8, num_encoder_layers=6,
 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, 
 activation=<function relu>, custom_encoder=None, custom_decoder=None,
 layer_norm_eps=1e-05, batch_first=False, norm_first=False, device=None,
 dtype=None)
1
2
3
4
5

Parameters

• d_model – the number of expected features in the encoder/decoder inputs (default=512).
• nhead – the number of heads in the multiheadattention models (default=8).
• 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, can be a string (“relu” or “gelu”) or a unary callable. Default: relu
• custom_encoder – custom encoder (default=None).
• custom_decoder – custom decoder (default=None).
• layer_norm_eps – the eps value in layer normalization components (default=1e-5).
• batch_first – If True, then the input and output tensors are provided as (batch, seq, feature). Default: False (seq, batch, feature).
• norm_first – if True, encoder and decoder layers will perform LayerNorms before other attention and feedforward operations, otherwise after. Default: False (after).

forward(src, tgt, src_mask=None, tgt_mask=None, memory_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)
1

Parameters

• src – the sequence to the encoder (required).
• tgt – the sequence to the decoder (required).
• src_mask – the additive mask for the src sequence (optional).
• tgt_mask – the additive mask for the tgt sequence (optional).
• memory_mask – the additive mask for the encoder output (optional).
• src_key_padding_mask – the ByteTensor mask for src keys per batch (optional).
• tgt_key_padding_mask – the ByteTensor mask for tgt keys per batch (optional).
• memory_key_padding_mask – the ByteTensor mask for memory keys per batch (optional).

Shape

• src: (S,E) for unbatched input, (S,N,E) if batch_first=False or (N,S,E) if batch_first=True.
• tgt:(T,E) for unbatched input, (T,N,E) if batch_first=False or (N,T,E) if batch_first=True.
• src_mask: (S,S) or (N⋅num_heads,S,S).
• tgt_mask: (T,T) or (N⋅num_heads,T,T).
• memory_mask: (T,S).
• src_key_padding_mask: (S) for unbatched input otherwise (N,S).
• tgt_key_padding_mask: (T) for unbatched input otherwise (N,T).
• memory_key_padding_mask:(S) for unbatched input otherwise (N,S).
• output: (T,E) for unbatched input, (T,N,E) if batch_first=False or (N,T,E) if batch_first=True.