MultiheadAttention

class torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None)

Allows the model to jointly attend to information from different representation subspaces. See Attention Is All You Need

$$\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O$$

where $head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)$.

Parameters

• embed_dim – total dimension of the model.

• num_heads – parallel attention heads.

• dropout – a Dropout layer on attn_output_weights. Default: 0.0.

• bias – add bias as module parameter. Default: True.

• add_bias_kv – add bias to the key and value sequences at dim=0.

• add_zero_attn – add a new batch of zeros to the key and value sequences at dim=1.

• kdim – total number of features in key. Default: None.

• vdim – total number of features in value. Default: None.

• batch_first – If True, then the input and output tensors are provided as (batch, seq, feature). Default: False (seq, batch, feature).

Note that if kdim and vdim are None, they will be set to embed_dim such that query, key, and value have the same number of features.

Examples:

>>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
>>> attn_output, attn_output_weights = multihead_attn(query, key, value)

forward(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None)

Parameters

• key, value (query,) – map a query and a set of key-value pairs to an output. See “Attention Is All You Need” for more details.

• key_padding_mask – if provided, specified padding elements in the key will be ignored by the attention. When given a binary mask and a value is True, the corresponding value on the attention layer will be ignored. When given a byte mask and a value is non-zero, the corresponding value on the attention layer will be ignored

• need_weights – output attn_output_weights.

• attn_mask – 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch.

Shapes for inputs:

• query: $(L, N, E)$ where L is the target sequence length, N is the batch size, E is the embedding dimension. $(N, L, E)$ if batch_first is True.

• key: $(S, N, E)$, where S is the source sequence length, N is the batch size, E is the embedding dimension. $(N, S, E)$ if batch_first is True.

• value: $(S, N, E)$ where S is the source sequence length, N is the batch size, E is the embedding dimension. $(N, S, E)$ if batch_first is True.

• key_padding_mask: $(N, S)$ where N is the batch size, S is the source sequence length. If a ByteTensor is provided, the non-zero positions will be ignored while the position with 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.

• attn_mask: if a 2D mask: $(L, S)$ where L is the target sequence length, S is the source sequence length.

  If a 3D mask: $(N\cdot\text{num\_heads}, L, S)$ where N is the batch size, L is the target sequence length, S is the source sequence length. attn_mask ensure 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 is not allowed to attend while False values will be unchanged. If a FloatTensor is provided, it will be added to the attention weight.

Shapes for outputs:

• attn_output: $(L, N, E)$ where L is the target sequence length, N is the batch size, E is the embedding dimension. $(N, L, E)$ if batch_first is True.

• attn_output_weights: $(N, L, S)$ where N is the batch size, L is the target sequence length, S is the source sequence length.