NLP Attention

Reason is the light and the light of life.

Jerry Su Mar 28, 2020 3 mins

Context attention - LSTM

Encoder and Decoder

class Attention(nn.Module):
    def __init__(self, method, hidden_size):
        super(Attention, self).__init__()
        self.method = method
        self.hidden_size = hidden_size

        if self.method == 'general':
            self.attention = nn.Linear(self.hidden_size, self.hidden_size)
        elif self.method == 'concat':
            self.attention = nn.Linear(self.hidden_size*3, self.hidden_size)
            self.v = nn.Parameter(nn.init.normal_(torch.empty(self.hidden_size)))

    def forward(self, hidden, encoder_outputs):
        attention_energies = self.score(hidden, encoder_outputs)
        attention_energies = attention_energies.t()
        return F.softmax(attention_energies, dim=1).unsqueeze(1)

    def score(self, hidden, encoder_output):
        if self.method == 'dot':
            energy =
            return energy
        elif self.method == 'general':
            energy = self.attention(encoder_output)
            energy =
            return energy
        elif self.method == 'concat':
            encoder_output = encoder_output.transpose(0, 1)
            energy = self.attention(, -1, -1),
                                               encoder_output), 2)).tanh()
            return torch.sum(self.v * energy, dim=2)

# attention by jerry
self.attention = Attention(attn_model, hidden_size)

# Calculate attention weights from the current RNN last hidden output
attn_weights = self.attention(last_hidden.unsqueeze(0), encoder_outputs)
# Multiply attention weights to encoder outputs to get new "weighted sum" context vector
context = attn_weights.bmm(encoder_outputs)
# Concatenate weighted context vector and GRU output using Luong eq. 5
last_hidden = last_hidden.squeeze(0)
context = context.squeeze(1)
concat_input =, context), 1)
concat_output = torch.tanh(self.concat(concat_input))

Self attention

Encoder or Decoder

Masked self attention


padding masked & sequence masked


Self Attention Cross Attention - Transformer

from torch import nn, Tensor
from typing import Dict, List, Optional, Tuple

class Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        encoder_decoder_attention=False,  # otherwise self_attention
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
        self.scaling = self.head_dim ** -0.5 

        self.encoder_decoder_attention = encoder_decoder_attention
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"

    def _shape(self, tensor, seq_len, bsz):
        return tensor.contiguous().view(seq_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)

    def forward(
        key: Tensor,
        key_padding_mask: Optional[Tensor] = None,
        layer_state: Optional[Dict[str, Tensor]] = None,
        attn_mask: Optional[Tensor] = None,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        """Input shape: Time(SeqLen) x Batch x Channel"""
        static_kv: bool = self.encoder_decoder_attention
        tgt_len, bsz, embed_dim = query.size()
        # get here for encoder decoder cause of static_kv
        if layer_state is not None:  # reuse k,v and encoder_padding_mask
            saved_state = layer_state.get(self.cache_key, {})
            if "prev_key" in saved_state and static_kv:
                # previous time steps are cached - no need to recompute key and value if they are static
                key = None
            # this branch is hit by encoder
            saved_state = None

        # Scale q to prevent the dot product between q and k from growing too large.
        q = self.q_proj(query) * self.scaling
        if static_kv and key is None:  # cross-attention with cache
            k = v = None
        elif static_kv and key is not None:  # cross-attention no prev_key found in cache
            k = self.k_proj(key)
            v = self.v_proj(key)
        else:  # self-attention
            k = self.k_proj(query)
            v = self.v_proj(query)

        q = self._shape(q, tgt_len, bsz)
        if k is not None:
            k = self._shape(k, -1, bsz)
        if v is not None:
            v = self._shape(v, -1, bsz)

        if saved_state:
            k, v = self._concat_saved_state(k, v, saved_state, static_kv, bsz)

        # Update cache
        if isinstance(layer_state, dict):
            cached_shape = (bsz, self.num_heads, -1, self.head_dim)  # bsz must be first for reorder_cache
            layer_state[self.cache_key] = dict(prev_key=k.view(*cached_shape), prev_value=v.view(*cached_shape))

        src_len = k.size(1)
        assert key_padding_mask is None or key_padding_mask.shape == (bsz, src_len)
        # 注意力矩阵,即query与key的求出的矩阵系数
        attn_weights = torch.bmm(q, k.transpose(1, 2))
        assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)

        if attn_mask is not None:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        # Note: deleted workaround to get around fork/join parallelism not supporting Optional types. on 2020/10/15

        if key_padding_mask is not None:  # don't attend to padding symbols
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
            attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        attn_weights = F.softmax(attn_weights, dim=-1)
        attn_probs = F.dropout(attn_weights, p=self.dropout,

        assert v is not None
        attn_output = torch.bmm(attn_probs, v)
        assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        attn_output = self.out_proj(attn_output)
        if output_attentions:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = None
        return attn_output, attn_weights

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