Attention Is All You Need

Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works.

1 Introduction

Recurrent neural networks, long short-term memory 13 and gated recurrent 7 neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation 35 2 5. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures 38 24 15.

Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_{t}$ , as a function of the previous hidden state $h_{t-1}$ and the input for position $t$ . This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.

Table 1

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참고 문헌

[36] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567, 2015.

[37] Vinyals & Kaiser, Koo, Petrov, Sutskever, and Hinton. Grammar as a foreign language. In Advances in Neural Information Processing Systems, 2015.

[38] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016.

[39] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with fast-forward connections for neural machine translation. CoRR, abs/1606.04199, 2016.

[40] Muhua Zhu, Yue Zhang, Wenliang Chen, Min Zhang, and Jingbo Zhu. Fast and accurate shift-reduce constituent parsing. In Proceedings of the 51st Annual Meeting of the ACL (Volume 1: Long Papers), pages 434–443. ACL, August 2013.

The fundamental constraint of sequential computation, however, remains.

In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output.

The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.

In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet.

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence.

To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.

3 Model Architecture

The Transformer - model architecture.

Here, the encoder maps an input sequence of symbol representations

(x_{1},...,x_{n})

to a sequence of continuous representations

\mathbf{z}=(z_{1},...,z_{n})

Given

\mathbf{z}

, the decoder then generates an output sequence

(y_{1},...,y_{m})

of symbols one element at a time.

3.1 Encoder and Decoder Stacks

The encoder is composed of a stack of

N=6

identical layers.

Each layer has two sub-layers.

The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.

That is, the output of each sub-layer is

\mathrm{LayerNorm}(x+\mathrm{Sublayer}(x))

, where

\mathrm{Sublayer}(x)

is the function implemented by the sub-layer itself.

To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension

d_{\text{model}}=512

The decoder is also composed of a stack of

N=6

identical layers.

In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.

Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.

We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.

This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position

i

can depend only on the known outputs at positions less than

i

An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.

The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.

3.2.1 Scaled Dot-Product Attention

The input consists of queries and keys of dimension

d_{k}

, and values of dimension

d_{v}

We compute the dot products of the query with all keys, divide each by

\sqrt{d_{k}}

, and apply a softmax function to obtain the weights on the values.

In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix

Q

The keys and values are also packed together into matrices

K

and

V

We compute the matrix of outputs as:

\mathrm{Attention}(Q,K,V)=\mathrm{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})V

Dot-product attention is identical to our algorithm, except for the scaling factor of

\frac{1}{\sqrt{d_{k}}}

Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.

While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.

We suspect that for large values of

d_{k}

, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients .

To counteract this effect, we scale the dot products by

\frac{1}{\sqrt{d_{k}}}

3.2.2 Multi-Head Attention

(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.

Instead of performing a single attention function with

d_{\text{model}}

-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values

h

times with different, learned linear projections to

d_{k}

d_{k}

and

d_{v}

dimensions, respectively.

On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding

d_{v}

-dimensional output values.

Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions.

With a single attention head, averaging inhibits this.

\begin{aligned} \displaystyle\mathrm{MultiHead}(Q,K,V) & \displaystyle=\mathrm{Concat}(\mathrm{head_{1}},...,\mathrm{head_{h}})W^{O} \\ \displaystyle\text{where}~\mathrm{head_{i}} & \displaystyle=\mathrm{Attention}(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i}) \end{aligned}

Where the projections are parameter matrices

W^{Q}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}

W^{K}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}

W^{V}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{v}}

and

W^{O}\in\mathbb{R}^{hd_{v}\times d_{\text{model}}}

In this work we employ

h=8

parallel attention layers, or heads.

For each of these we use

d_{k}=d_{v}=d_{\text{model}}/h=64

Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.

3.2.3 Applications of Attention in our Model

The Transformer uses multi-head attention in three different ways:

In "encoder-decoder attention" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.

This allows every position in the decoder to attend over all positions in the input sequence.

The encoder contains self-attention layers.

In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.

Each position in the encoder can attend to all positions in the previous layer of the encoder.

Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.

We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.

We implement this inside of scaled dot-product attention by masking out (setting to

-\infty

) all values in the input of the softmax which correspond to illegal connections.

3.3 Position-wise Feed-Forward Networks

In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.

This consists of two linear transformations with a ReLU activation in between.

\mathrm{FFN}(x)=\max(0,xW_{1}+b_{1})W_{2}+b_{2}

While the linear transformations are the same across different positions, they use different parameters from layer to layer.

Another way of describing this is as two convolutions with kernel size 1.

The dimensionality of input and output is

d_{\text{model}}=512

, and the inner-layer has dimensionality

d_{ff}=2048

3.4 Embeddings and Softmax

Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension

d_{\text{model}}

We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.

In the embedding layers, we multiply those weights by

\sqrt{d_{\text{model}}}

3.5 Positional Encoding

Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.

To this end, we add "positional encodings" to the input embeddings at the bottoms of the encoder and decoder stacks.

The positional encodings have the same dimension

d_{\text{model}}

as the embeddings, so that the two can be summed.

In this work, we use sine and cosine functions of different frequencies:

\begin{aligned} \displaystyle PE_{(pos,2i)}=sin(pos/10000^{2i/d_{\text{model}}}) \\ \displaystyle PE_{(pos,2i+1)}=cos(pos/10000^{2i/d_{\text{model}}}) \end{aligned}

where

pos

is the position and

i

is the dimension.

That is, each dimension of the positional encoding corresponds to a sinusoid.

The wavelengths form a geometric progression from

2\pi

10000\cdot 2\pi

We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset

k

PE_{pos+k}

can be represented as a linear function of

PE_{pos}

We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.

4 Why Self-Attention

In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations

(x_{1},...,x_{n})

to another sequence of equal length

(z_{1},...,z_{n})

, with

x_{i},z_{i}\in\mathbb{R}^{d}

, such as a hidden layer in a typical sequence transduction encoder or decoder.

Motivating our use of self-attention we consider three desiderata.

One is the total computational complexity per layer.

Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.

The third is the path length between long-range dependencies in the network.

Learning long-range dependencies is a key challenge in many sequence transduction tasks.

One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network.

Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.

Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. $n$ is the sequence length, $d$ is the representation dimension, $k$ is the kernel size of convolutions and $r$ the size of the neighborhood in restricted self-attention.

Layer Type	Complexity per Layer	Sequential	Maximum Path Length
		Operations
Self-Attention	$O(n^{2}\cdot d)$	$O(1)$	$O(1)$
Recurrent	$O(n\cdot d^{2})$	$O(n)$	$O(n)$
Convolutional	$O(k\cdot n\cdot d^{2})$	$O(1)$	$O ($
Self-Attention (restricted)	$O(r\cdot n\cdot d)$	$O(1)$	$O(n/r)$

To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size

r

in the input sequence centered around the respective output position.

This would increase the maximum path length to

O(n/r)

We plan to investigate this approach further in future work.

A single convolutional layer with kernel width

k<n

does not connect all pairs of input and output positions.

Convolutional layers are generally more expensive than recurrent layers, by a factor of

k

Even with

k=n

, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.

As side benefit, self-attention could yield more interpretable models.

We inspect attention distributions from our models and present and discuss examples in the appendix.

Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.

This section describes the training regime for our models.

5.1 Training Data and Batching

We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs.

Sentence pairs were batched together by approximate sequence length.

Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.

5.2 Hardware and Schedule

We trained our models on one machine with 8 NVIDIA P100 GPUs.

For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds.

We trained the base models for a total of 100,000 steps or 12 hours.

The big models were trained for 300,000 steps (3.5 days).

We varied the learning rate over the course of training, according to the formula:

lrate=d_{\text{model}}^{-0.5}\cdot\min({step\_num}^{-0.5},{step\_num}\cdot{warmup\_steps}^{-1.5})

This corresponds to increasing the learning rate linearly for the first

warmup\_steps

training steps, and decreasing it thereafter proportionally to the inverse square root of the step number.

We used

warmup\_steps=4000

We employ three types of regularization during training:

In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks.

For the base model, we use a rate of

P_{drop}=0.1

This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.

6.1 Machine Translation

The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.

Training took

3.5

days on

8

P100 GPUs.

Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.

On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of

41.0

, outperforming all of the previously published single models, at less than

1/4

the training cost of the previous state-of-the-art model.

The Transformer (big) model trained for English-to-French used dropout rate

P_{drop}=0.1

, instead of

0.3

For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals.

For the big models, we averaged the last 20 checkpoints.

These hyperparameters were chosen after experimentation on the development set.

We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU .

6.2 Model Variations

Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.


	$N$	$d_{\text{model}}$	$d_{\text{ff}}$	$h$	$d_{k}$	$d_{v}$	$P_{drop}$	$\epsilon_{ls}$	train	PPL	BLEU	params
									steps	(dev)	(dev)	$\times 10^{6}$
base	6	512	2048	8	64	64	0.1	0.1	100K	4.92	25.8	65
(A)				1	512	512				5.29	24.9
				4	128	128				5.00	25.5
				16	32	32				4.91	25.8
				32	16	16				5.01	25.4
(B)					16					5.16	25.1	58
					32					5.01	25.4	60
(C)	2									6.11	23.7	36
	4									5.19	25.3	50
	8									4.88	25.5	80
		256			32	32				5.75	24.5	28
		1024			128	128				4.66	26.0	168
			1024							5.12	25.4	53
			4096							4.75	26.2	90
(D)							0.0			5.77	24.6
							0.2			4.95	25.5
								0.0		4.67	25.3
								0.2		5.47	25.7
(E)		positional embedding instead of sinusoids								4.92	25.7
big	6	1024	4096	16			0.3		300K	4.33	26.4	213

To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the development set, newstest2013.

We used beam search as described in the previous section, but no checkpoint averaging.

While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.

This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial.

We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting.

6.3 English Constituency Parsing

The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)

To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing.

This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input.

We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.

During inference, we increased the maximum output length to input length +

300

We used a beam size of

21

and

\alpha=0.3

for both WSJ only and the semi-supervised setting.

In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.

For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers.

On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art.

In the former task our best model outperforms even all previously reported ensembles.

We are excited about the future of attention-based models and plan to apply them to other tasks.

We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.

Making generation less sequential is another research goals of ours.

We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.

Attention Visualizations

An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb ‘making’, completing the phrase ‘making…more difficult’. Attentions here shown only for the word ‘making’. Different colors represent different heads. Best viewed in color.

Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word ‘its’ for attention heads 5 and 6. Note that the attentions are very sharp for this word.

Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.

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Recent work has achieved significant improvements in computational efficiency through factorization tricks 21 and conditional computation 32, while also improving model performance in case of the latter.

Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences 2 19.

In all but a few cases 27, however, such attention mechanisms are used in conjunction with a recurrent network.

The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU 16, ByteNet 18 and ConvS2S 9, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions.

This makes it more difficult to learn dependencies between distant positions 12.

In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.

Model	BLEU		Training Cost (FLOPs)
	EN-DE	EN-FR	EN-DE	EN-FR
ByteNet 18	23.75
Deep-Att + PosUnk 39		39.2		$1.0\cdot 10^{20}$
GNMT + RL 38	24.6	39.92	$2.3\cdot 10^{19}$	$1.4\cdot 10^{20}$
ConvS2S 9	25.16	40.46	$9.6\cdot 10^{18}$	$1.5\cdot 10^{20}$
MoE 32	26.03	40.56	$2.0\cdot 10^{19}$	$1.2\cdot 10^{20}$
Deep-Att + PosUnk Ensemble 39		40.4		$8.0\cdot 10^{20}$
GNMT + RL Ensemble 38	26.30	41.16	$1.8\cdot 10^{20}$	$1.1\cdot 10^{21}$
ConvS2S Ensemble 9	26.36	41.29	$7.7\cdot 10^{19}$	$1.2\cdot 10^{21}$
Transformer (base model)	27.3	38.1	$3.3\cdot 10^{18}$
Transformer (big)	28.4	41.8	$2.3\cdot 10^{19}$


Parser	Training	WSJ 23 F1
Vinyals & Kaiser el al. (2014) 37	WSJ only, discriminative	88.3
Petrov et al. (2006) 29	WSJ only, discriminative	90.4
Zhu et al. (2013) 40	WSJ only, discriminative	90.4
Dyer et al. (2016) 8	WSJ only, discriminative	91.7
Transformer (4 layers)	WSJ only, discriminative	91.3
Zhu et al. (2013) 40	semi-supervised	91.3
Huang & Harper (2009) 14	semi-supervised	91.3
McClosky et al. (2006) 26	semi-supervised	92.1
Vinyals & Kaiser el al. (2014) 37	semi-supervised	92.1
Transformer (4 layers)	semi-supervised	92.7
Luong et al. (2015) 23	multi-task	93.0
Dyer et al. (2016) 8	generative	93.3

Attention Is All You Need

초록

1 Introduction

참고 문헌

2 Background

3 Model Architecture

3.1 Encoder and Decoder Stacks

Encoder:

Decoder:

3.2 Attention

3.2.1 Scaled Dot-Product Attention

3.2.2 Multi-Head Attention

3.2.3 Applications of Attention in our Model

3.3 Position-wise Feed-Forward Networks

3.4 Embeddings and Softmax

3.5 Positional Encoding

4 Why Self-Attention

5 Training

5.1 Training Data and Batching

5.2 Hardware and Schedule

5.3 Optimizer

5.4 Regularization

Residual Dropout

Label Smoothing

6 Results

6.1 Machine Translation

6.2 Model Variations

6.3 English Constituency Parsing

7 Conclusion

Acknowledgements

Attention Visualizations