Attention mechanism has been widely applied in natural langugage processing and computer vision tasks.

# Attention mechanism

## Background: what is WRONG with seq2seq?

**Encoder-decoder** architecture:

Encode a source sentence into a fixed-length vector from which a decoder generates a translation.^{[3]}

**Seq2seq**models**encode**an**input sentence of variable length**into a**fixed-length vector representation**$c$ (a.k.a sentence embedding, “thought” vector), by apply one LSTM to read the input sentence, one timestep at a time. The representation vector $c$ is expected to well capture the meaning of the source sentence.Then

**decode**the vector representation $v$ to the target sentence with another LSTM whose initial hidden state is the last hidden state of the encoder (i.e. the representation of the input sentence: $c$).^{[1]}

where $g$ is a RNN that outputs the probability of , and is the hidden state of the RNN. $c$ is the fixed-length context vector for the input sentence.

**Drawbacks**: The fixed-length context vector $c$ is incapable of remembering long sentences^{[2]}. It will forget the former part when processing the latter sequence. Sutskever et al.(2014)^{[1]} proposed a trick that only reversing the order of source sentences rather than target sentences could be of benefit for MT.

Basic encoder-decoder architecture **compresses all the necessary information of a source sentence into a fixed-length vector**. This may be **incapable of coping with long sentences**, especially those that are longer than the sentences in the training corpus ^{[3]}. The performance of basic encoder-decoder drops rapidly *as the length of an input sentence increases* ^{[4]}.

Thus attention mechanism is proposed to tackle this problem.

## Attention mechanism

### NMT by jointly learning to align and translate (EMNLP 2014)

#### Encoder

Bi-RNNs, obtain the annotation for each word by concatenating the forward and backward hidden states:

#### Decoder

The conditional probability is :

where $s_i$ is the RNN hidden state for time $i$:

Unlike the basic encoder-decoder architecture, the probability of each output word is conditioned on a distinct context vector for each target word .

where is an alignment model which scores how well the input at the position $j$ and the output at position $i$ match ^{[3]}. Here $\text{score}$ is a simple FF-NN layer:

where and are learnable.

## Attention zoo

^{[5]}

FF-FC attention(a.k.a additive attention)^{[9]}

## Transformer

### Background

End2end memory networks are based on a **recurrent attention mechanism** instead of **sequence-aligned recurrence**. However, sequence-aligned RNNs preclude parallelization.

### Model architecture

**Architecture**: stacked self-attention + point-wise FC layer (with residual connection + layer normalization)

#### Encoder

The transformer encoder applies one **multi-head attention** followed by one FC-FF layer, adopting the **residual connection** and **layer normalization** trick:

1 | class SublayerConnection(nn.Module): |

Layer Normalization:

where $\gamma$ and $\beta$ are learnable affine transform parameters.

1 | class LayerNorm(nn.Module): |

- N = 6 stack transformer layers
- Output dimension $d_{model}$ = 512

1 | def clones(module, N): |

### Decoder

Same as the encoder, N = 6 identical stacked layers

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29class Decoder(nn.Module):

""" N layer decoder with masking"""

def __init__(self, layer, N):

super(Decoder, self).__init__()

self.layers = clones(layer, N)

self.norm = nn.LayerNorm(layer.size)

def forward(self, x, memory, src_mask, tgt_mask):

for layer in self.layers:

x = layer(x, memory, src_mask, tgt_mask)

return self.norm(x)

class DecoderLayer(nn.Module):

""" decoder"""

def __init__(self, size, self_attn, src_attn, feed_forward, dropout):

super(DecoderLayer, self).__init__()

self.size = size

self.self_attn = self_attn

self.src_attn = src_attn

self.feed_forward = feed_forward

self.sublyer = utils.clones(SublayerConnection(size, dropout), 3)

def forward(self, x, memory, src_mask, tgt_mask):

m = memory

x = self.sublyer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))

x = self.sublyer[1](x, lambda x: self.src_attn(x, m, m, src_mask))

return self.sublyer[2](x, self.feed_forward)**Difference**

The*first*multi-head attention layer is masked to**prevent positions from attending to subsequent positions**, ensuring that the prediction output at position $i$ only depends on the known outputs at positions less than $i$,**regardless of the future**.

1 | def subsequent_mask(size): |

### Multi-head attention

Transformer regarded encoded representation of input sequences as a set of **key-value** pairs (K,V), with dimension of input sequence length $n$. In MT context, encoder hidden states serve as (K, V) pairs. In the decoder the previous output is a query (with dimension $m$)

#### Scaled dot-product attention

where is the dimension of the key.

Q: **Why** dividing in dot-product operation?

- For small values of , the additive attention and dopt product attention perform similarly.
- For large values of , additive attention outperforms dot product attention
**without scaling**. **Interpretation**: The dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients. Assume $q$ and $k$ are independent random variables with mean 0 and variance 1. Their product has mean 0 and variance . Thus, scales the dot products by .

In conventional attention view:

Dot-product is faster and more space-efficient compared with additive attention (one FF layer) in practice.^{[6]}

More precisely, for the input sequence , dot-product attention outputs the new sequence of the same length,

1 | def attention(query, key, value, mask=None, dropout=None): |

#### Multi-head attention

**Multi-head**: “linear project the $Q$, $K$ and $V$ $h$ times with different, learned linear projections to , and dimensions, respectively.”^{[6]} Then concat all ($h$) the and use a linear layer to project to the final representation values.

Multi-head allows to “jointly attend to information from **different representation subspaces at different positions**“:

where , , ,

1 | class MultiHeadedAttention(nn.Module): |

Transformer attention:

- Mimic the conventional encoder-decoder attention mechanisms: $Q$ comes from previous decoder, $K$, $V$ come from the decoder output. This allows every position in the decoder to attend over all positions in the
**input sequence**(as figure above). - Encoder: K=V=Q, i.e. the output of previous layer. Each position in the encoder can attend to all positions in the previous layer of the encoder.
- Decoder: allow each position in the decoder to attend to all positions in the decoder
**up to and including that position**.

### Point-wise feed-forward nets

1 | class PositionwiseFeedForward(nn.Module): |

### Positional encoding

**Drawbacks**: self-attention cannot capture the order information of input sequences.

Positional embeddings can be learned or pre-fixed ^{[8]}.

RNNs solution: inherently model the sequential information, but preclude parallelization.

- Residual connections help propagate position information to higher layers.

#### Absolute Positional Encoding

Transformer solution: use sinusoidal timing signal as **positional encoding** (PE).

where $\text{pos}$ is the position in the sentence and $i$ is the order along the embedding vector dimension. Assume this allows to learn to attend by relative positions, since for and fixed offset $k$, can be represented as the linear function of ^{[6]}

1 | # positional encoding layer in PyTorch |

**Usage**: before stacked encoder/decoder, take the **sum of PE and input embeddings** (as figure below).

1 | class Embeddings(nn.Module): |

#### Relative Positional Representation(RPR)

**Relation-aware self-attn**

Consider the pairwise relationships between input elements, which can be seen as a labeled, directed fully-connected graph. Let represent the edge between input elements and .

Then add the pairwise information to the sublayer output:

- Clip RPR

$k$ denotes the maximum relative position. The relative position information beyond $k$ will be clipped to the maximum value, which generalizes to the unseen sequence lengths during training.^{[14]}In other words, RPR only considers context in a fixed window size $2k+1$, indicating $k$ elements on the l.h.s, and $k$ elements on the r.h.s, as well as itself.

where rpr and are learnable.

Trainable param number:

- MADPA:
MADPA with RPR:

My PyTorch implementation

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102class MultiHeadedAttention_RPR(nn.Module):

""" @ author: Yekun CHAI """

def __init__(self, d_model, h, max_relative_position, dropout=.0):

"""

multi-head attention

:param h: nhead

:param d_model: d_model

:param dropout: float

"""

super(MultiHeadedAttention_RPR, self).__init__()

assert d_model % h == 0

# assume d_v always equals d_k

self.d_k = d_model // h

self.h = h

self.linears = utils.clones(nn.Linear(d_model, d_model), 4)

self.dropout = nn.Dropout(p=dropout)

self.max_relative_position = max_relative_position

self.vocab_size = max_relative_position * 2 + 1

self.embed_K = nn.Embedding(self.vocab_size, self.d_k)

self.embed_V = nn.Embedding(self.vocab_size, self.d_k)

def forward(self, query, key, value, mask=None):

"""

---------------------------

L : target sequence length

S : source sequence length:

N : batch size

E : embedding dim

---------------------------

:param query: (N,L,E)

:param key: (N,S,E)

:param value: (N,S,E)

:param mask:

"""

nbatches = query.size(0) # batch size

seq_len = query.size(1)

# 1) split embedding dim to h heads : from d_model => h * d_k

# dim: (nbatch, h, seq_length, d_model//h)

query, key, value = \

[l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)

for l, x in zip(self.linears, (query, key, value))]

# 2) rpr

relation_keys = self.generate_relative_positions_embeddings(seq_len, seq_len, self.embed_K)

relation_values = self.generate_relative_positions_embeddings(seq_len, seq_len, self.embed_V)

logits = self._relative_attn_inner(query, key, relation_keys, True)

weights = self.dropout(F.softmax(logits, -1))

x = self._relative_attn_inner(weights, value, relation_values, False)

# 3) "Concat" using a view and apply a final linear.

# dim: (nbatch, h, d_model)

x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)

return self.linears[-1](x)

def _generate_relative_positions_matrix(self, len_q, len_k):

"""

genetate rpr matrix

---------------------------

:param len_q: seq_len

:param len_k: seq_len

:return: rpr matrix, dim: (len_q, len_q)

"""

assert len_q == len_k

range_vec_q = range_vec_k = torch.arange(len_q)

distance_mat = range_vec_k.unsqueeze(0) - range_vec_q.unsqueeze(-1)

disntance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)

return disntance_mat_clipped + self.max_relative_position

def generate_relative_positions_embeddings(self, len_q, len_k, embedding_table):

"""

generate relative position embedding

----------------------

:param len_q:

:param len_k:

:return: rpr embedding, dim: (len_q, len_q, d_k)

"""

relative_position_matrix = self._generate_relative_positions_matrix(len_q, len_k)

return embedding_table(relative_position_matrix)

def _relative_attn_inner(self, x, y, z, transpose):

"""

efficient implementation

------------------------

:param x:

:param y:

:param z:

:param transpose:

:return:

"""

nbatches = x.size(0)

heads = x.size(1)

seq_len = x.size(2)

# (N, h, s, s)

xy_matmul = torch.matmul(x, y.transpose(-1, -2) if transpose else y)

# (s, N, h, d) => (s, N*h, d)

x_t_v = x.permute(2, 0, 1, 3).contiguous().view(seq_len, nbatches * heads, -1)

# (s, N*h, d) @ (s, d, s) => (s, N*h, s)

x_tz_matmul = torch.matmul(x_t_v, z.transpose(-1, -2) if transpose else z)

# (N, h, s, s)

x_tz_matmul_v_t = x_tz_matmul.view(seq_len, nbatches, heads, -1).permute(1, 2, 0, 3)

return xy_matmul + x_tz_matmul_v_tTensorflow implementation:

^{[16]}**Thought**: current attention mechanism is one round, and one dimension (at sequence dimension)

# Citation

1 | @misc{chai2019attn-summary, |

# References

- 1.Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112). ↩
- 2.Weng L. (2018, Jun 24). Attention? Attention! [Blog post]. Retrieved from https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html ↩
- 3.Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. CoRR, abs/1409.0473. ↩
- 4.Cho, K., Merrienboer, B.V., Bahdanau, D., & Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. SSST@EMNLP. ↩
- 5.Luong, T., Pham, H., & Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. EMNLP. ↩
- 6.Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. NIPS. ↩
- 7.Cheng, J., Dong, L., & Lapata, M. (2016). Long Short-Term Memory-Networks for Machine Reading. EMNLP. ↩
- 8.LeCun, Y., Bottou, L., & Bengio, Y. (2006). PROC OF THE IEEE NOVEMBER Gradient Based Learning Applied to Document Recognition. ↩
- 9.Raffel, C., & Ellis, D.P. (2015). Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems. CoRR, abs/1512.08756. ↩
- 10.Towards Data Science: How to code the transformer in PyTorch ↩
- 11.Harvard nlp: the annotated Transformer ↩
- 12.Illustrated Transformer ↩
- 13.Medium: How Self-Attention with Relative Position Representations works ↩
- 14.Shaw, P., Uszkoreit, J., & Vaswani, A. (2018). Self-attention with relative position representations. arXiv preprint arXiv:1803.02155. ↩
- 15.RPR blog (in Chinese) ↩
- 16.Tensor2Tensor tensorflow code ↩
- 17.Attn: Illustrated Attention ↩