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.

Method described in the paper: Attention Is All You Need.

Multi-Head Attention is defined as:

$$\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1,\dots,\text{head}_h)W^O$$

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

nn.MultiheadAttention will use the optimized implementations of scaled_dot_product_attention() when possible.

In addition to support for the new scaled_dot_product_attention() function, for speeding up Inference, MHA will use fastpath inference with support for Nested Tensors, iff:

• self attention is being computed (i.e., query, key, and value are the same tensor).

• inputs are batched (3D) with batch_first==True

• Either autograd is disabled (using torch.inference_mode or torch.no_grad) or no tensor argument requires_grad

• training is disabled (using .eval())

• add_bias_kv is False

• add_zero_attn is False

• kdim and vdim are equal to embed_dim

• if a NestedTensor is passed, neither key_padding_mask nor attn_mask is passed

• autocast is disabled

If the optimized inference fastpath implementation is in use, a NestedTensor can be passed for query/key/value to represent padding more efficiently than using a padding mask. In this case, a NestedTensor will be returned, and an additional speedup proportional to the fraction of the input that is padding can be expected.

Parameters

• embed_dim – Total dimension of the model.

• num_heads – Number of parallel attention heads. Note that embed_dim will be split across num_heads (i.e. each head will have dimension embed_dim // num_heads).

• dropout – Dropout probability on attn_output_weights. Default: 0.0 (no dropout).

• bias – If specified, adds bias to input / output projection layers. Default: True.

• add_bias_kv – If specified, adds bias to the key and value sequences at dim=0. Default: False.

• add_zero_attn – If specified, adds a new batch of zeros to the key and value sequences at dim=1. Default: False.

• kdim – Total number of features for keys. Default: None (uses kdim=embed_dim).

• vdim – Total number of features for values. Default: None (uses vdim=embed_dim).

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

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, average_attn_weights=True, is_causal=False)

Compute attention outputs using query, key, and value embeddings.

Supports optional parameters for padding, masks and attention weights.

Parameters

• query (Tensor) – Query embeddings of shape $(L, E_q)$ for unbatched input, $(L, N, E_q)$ when batch_first=False or $(N, L, E_q)$ when batch_first=True, where $L$ is the target sequence length, $N$ is the batch size, and $E_q$ is the query embedding dimension embed_dim. Queries are compared against key-value pairs to produce the output. See “Attention Is All You Need” for more details.

• key (Tensor) – Key embeddings of shape $(S, E_k)$ for unbatched input, $(S, N, E_k)$ when batch_first=False or $(N, S, E_k)$ when batch_first=True, where $S$ is the source sequence length, $N$ is the batch size, and $E_k$ is the key embedding dimension kdim. See “Attention Is All You Need” for more details.

• value (Tensor) – Value embeddings of shape $(S, E_v)$ for unbatched input, $(S, N, E_v)$ when batch_first=False or $(N, S, E_v)$ when batch_first=True, where $S$ is the source sequence length, $N$ is the batch size, and $E_v$ is the value embedding dimension vdim. See “Attention Is All You Need” for more details.

• key_padding_mask (Optional[Tensor]) – If specified, a mask of shape $(N, S)$ indicating which elements within key to ignore for the purpose of attention (i.e. treat as “padding”). For unbatched query, shape should be $(S)$. Binary and float masks are supported. For a binary mask, a True value indicates that the corresponding key value will be ignored for the purpose of attention. For a float mask, it will be directly added to the corresponding key value.

• need_weights (bool) – If specified, returns attn_output_weights in addition to attn_outputs. Set need_weights=False to use the optimized scaled_dot_product_attention and achieve the best performance for MHA. Default: True.

• attn_mask (Optional[Tensor]) – If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape $(L, S)$ or $(N\cdot\text{num\_heads}, L, S)$, where $N$ is the batch size, $L$ is the target sequence length, and $S$ is the source sequence length. A 2D mask will be broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch. Binary and float masks are supported. For a binary mask, a True value indicates that the corresponding position is not allowed to attend. For a float mask, the mask values will be added to the attention weight. If both attn_mask and key_padding_mask are supplied, their types should match.

• average_attn_weights (bool) – If true, indicates that the returned attn_weights should be averaged across heads. Otherwise, attn_weights are provided separately per head. Note that this flag only has an effect when need_weights=True. Default: True (i.e. average weights across heads)

• is_causal (bool) – If specified, applies a causal mask as attention mask. Default: False. Warning: is_causal provides a hint that attn_mask is the causal mask. Providing incorrect hints can result in incorrect execution, including forward and backward compatibility.

Return type

Tuple[Tensor, Optional[Tensor]]

Outputs:

• attn_output - Attention outputs of shape $(L, E)$ when input is unbatched, $(L, N, E)$ when batch_first=False or $(N, L, E)$ when batch_first=True, where $L$ is the target sequence length, $N$ is the batch size, and $E$ is the embedding dimension embed_dim.

• attn_output_weights - Only returned when need_weights=True. If average_attn_weights=True, returns attention weights averaged across heads of shape $(L, S)$ when input is unbatched or $(N, L, S)$, where $N$ is the batch size, $L$ is the target sequence length, and $S$ is the source sequence length. If average_attn_weights=False, returns attention weights per head of shape $(\text{num\_heads}, L, S)$ when input is unbatched or $(N, \text{num\_heads}, L, S)$.

Note

batch_first argument is ignored for unbatched inputs.

merge_masks(attn_mask, key_padding_mask, query)

Determine mask type and combine masks if necessary.

If only one mask is provided, that mask and the corresponding mask type will be returned. If both masks are provided, they will be both expanded to shape (batch_size, num_heads, seq_len, seq_len), combined with logical or and mask type 2 will be returned :param attn_mask: attention mask of shape (seq_len, seq_len), mask type 0 :param key_padding_mask: padding mask of shape (batch_size, seq_len), mask type 1 :param query: query embeddings of shape (batch_size, seq_len, embed_dim)

Returns

merged mask mask_type: merged mask type (0, 1, or 2)

Return type

merged_mask