torchtext.nn

MultiheadAttentionContainer

class torchtext.nn.MultiheadAttentionContainer(nhead, in_proj_container, attention_layer, out_proj, batch_first=False)

__init__(nhead, in_proj_container, attention_layer, out_proj, batch_first=False)

A multi-head attention container

Parameters

• nhead – the number of heads in the multiheadattention model

• in_proj_container – A container of multi-head in-projection linear layers (a.k.a nn.Linear).

• attention_layer – The custom attention layer. The input sent from MHA container to the attention layer is in the shape of (…, L, N * H, E / H) for query and (…, S, N * H, E / H) for key/value while the output shape of the attention layer is expected to be (…, L, N * H, E / H). The attention_layer needs to support broadcast if users want the overall MultiheadAttentionContainer with broadcast.

• out_proj – The multi-head out-projection layer (a.k.a nn.Linear).

• batch_first – If True, then the input and output tensors are provided as (…, N, L, E). Default: False

Examples::

>>> import torch
>>> from torchtext.nn import MultiheadAttentionContainer, InProjContainer, ScaledDotProduct
>>> embed_dim, num_heads, bsz = 10, 5, 64
>>> in_proj_container = InProjContainer(torch.nn.Linear(embed_dim, embed_dim),
                                        torch.nn.Linear(embed_dim, embed_dim),
                                        torch.nn.Linear(embed_dim, embed_dim))
>>> MHA = MultiheadAttentionContainer(num_heads,
                                      in_proj_container,
                                      ScaledDotProduct(),
                                      torch.nn.Linear(embed_dim, embed_dim))
>>> query = torch.rand((21, bsz, embed_dim))
>>> key = value = torch.rand((16, bsz, embed_dim))
>>> attn_output, attn_weights = MHA(query, key, value)
>>> print(attn_output.shape)
>>> torch.Size([21, 64, 10])

forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, bias_k: Optional[torch.Tensor] = None, bias_v: Optional[torch.Tensor] = None) → Tuple[torch.Tensor, torch.Tensor]

Parameters

• query (Tensor) – The query of the attention function. See “Attention Is All You Need” for more details.

• key (Tensor) – The keys of the attention function. See “Attention Is All You Need” for more details.

• value (Tensor) – The values of the attention function. See “Attention Is All You Need” for more details.

• attn_mask (BoolTensor, optional) – 3D mask that prevents attention to certain positions.

• bias_k (Tensor, optional) – one more key and value sequence to be added to keys at sequence dim (dim=-3). Those are used for incremental decoding. Users should provide bias_v.

• bias_v (Tensor, optional) – one more key and value sequence to be added to values at sequence dim (dim=-3). Those are used for incremental decoding. Users should also provide bias_k.

Shape:

• Inputs:

  • query: \((..., L, N, E)\)

  • key: \((..., S, N, E)\)

  • value: \((..., S, N, E)\)

  • attn_mask, bias_k and bias_v: same with the shape of the corresponding args in attention layer.

• Outputs:

  • attn_output: \((..., L, N, E)\)

  • attn_output_weights: \((N * H, L, S)\)

Note: It’s optional to have the query/key/value inputs with more than three dimensions (for broadcast purpose). The MultiheadAttentionContainer module will operate on the last three dimensions.

where where L is the target length, S is the sequence length, H is the number of attention heads, N is the batch size, and E is the embedding dimension.

InProjContainer

class torchtext.nn.InProjContainer(query_proj, key_proj, value_proj)

__init__(query_proj, key_proj, value_proj)

A in-proj container to project query/key/value in MultiheadAttention. This module happens before reshaping the projected query/key/value into multiple heads. See the linear layers (bottom) of Multi-head Attention in Fig 2 of Attention Is All You Need paper. Also check the usage example in torchtext.nn.MultiheadAttentionContainer.

Parameters

• query_proj – a proj layer for query. A typical projection layer is torch.nn.Linear.

• key_proj – a proj layer for key. A typical projection layer is torch.nn.Linear.

• value_proj – a proj layer for value. A typical projection layer is torch.nn.Linear.

forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Projects the input sequences using in-proj layers. query/key/value are simply passed to the forward func of query/key/value_proj, respectively.

Parameters

• query (Tensor) – The query to be projected.

• key (Tensor) – The keys to be projected.

• value (Tensor) – The values to be projected.

Examples::

>>> import torch
>>> from torchtext.nn import InProjContainer
>>> embed_dim, bsz = 10, 64
>>> in_proj_container = InProjContainer(torch.nn.Linear(embed_dim, embed_dim),
                                        torch.nn.Linear(embed_dim, embed_dim),
                                        torch.nn.Linear(embed_dim, embed_dim))
>>> q = torch.rand((5, bsz, embed_dim))
>>> k = v = torch.rand((6, bsz, embed_dim))
>>> q, k, v = in_proj_container(q, k, v)

ScaledDotProduct

class torchtext.nn.ScaledDotProduct(dropout=0.0, batch_first=False)

__init__(dropout=0.0, batch_first=False)

Processes a projected query and key-value pair to apply scaled dot product attention.

Parameters

• dropout (float) – probability of dropping an attention weight.

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

Examples::

>>> import torch, torchtext
>>> SDP = torchtext.nn.ScaledDotProduct(dropout=0.1)
>>> q = torch.randn(21, 256, 3)
>>> k = v = torch.randn(21, 256, 3)
>>> attn_output, attn_weights = SDP(q, k, v)
>>> print(attn_output.shape, attn_weights.shape)
torch.Size([21, 256, 3]) torch.Size([256, 21, 21])

forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, bias_k: Optional[torch.Tensor] = None, bias_v: Optional[torch.Tensor] = None) → Tuple[torch.Tensor, torch.Tensor]

Uses a scaled dot product with the projected key-value pair to update the projected query.

Parameters

• query (Tensor) – Projected query

• key (Tensor) – Projected key

• value (Tensor) – Projected value

• attn_mask (BoolTensor, optional) – 3D mask that prevents attention to certain positions.

• attn_mask – 3D mask that prevents attention to certain positions.

• bias_k (Tensor, optional) – one more key and value sequence to be added to keys at sequence dim (dim=-3). Those are used for incremental decoding. Users should provide bias_v.

• bias_v (Tensor, optional) – one more key and value sequence to be added to values at sequence dim (dim=-3). Those are used for incremental decoding. Users should also provide bias_k.

Shape:

• query: \((..., L, N * H, E / H)\)

• key: \((..., S, N * H, E / H)\)

• value: \((..., S, N * H, E / H)\)

• attn_mask: \((N * H, L, S)\), positions with True are not allowed to attend

  while False values will be unchanged.

• bias_k and bias_v:bias: \((1, N * H, E / H)\)

• Output: \((..., L, N * H, E / H)\), \((N * H, L, S)\)

Note: It’s optional to have the query/key/value inputs with more than three dimensions (for broadcast purpose).

The ScaledDotProduct module will operate on the last three dimensions.

where L is the target length, S is the source length, H is the number of attention heads, N is the batch size, and E is the embedding dimension.