puffy310/yandset · Datasets at Hugging Face

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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, P Epos+k can be represented as a linear function of
P Epos.
We also experimented with using learned positional embeddings [9] instead, and found that the two
versions produced nearly identical results (see Table 3 row (E)). 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
(x1, ..., xn) to another sequence of equal length (z1, ..., zn), with xi
, zi ∈ 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. The shorter these paths between any combination of positions in the input
and output sequences, the easier it is to learn long-range dependencies [12]. Hence we also compare
the maximum path length between any two input and output positions in networks composed of the
different layer types.
As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially
executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of
computational complexity, self-attention layers are faster than recurrent layers when the sequence
length n is smaller than the representation dimensionality d, which is most often the case with
sentence representations used by state-of-the-art models in machine translations, such as word-piece
[38] and byte-pair [31] representations. To improve computational performance for tasks involving
very long sequences, self-attention could be restricted to considering only a neighborhood of size r in
6
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. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels,
or O(logk(n)) in the case of dilated convolutions [18], increasing the length of the longest paths
between any two positions in the network. Convolutional layers are generally more expensive than
recurrent layers, by a factor of k. Separable convolutions [6], however, decrease the complexity
considerably, to O(k · n · d + n · d
2
). 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.
5 Training
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. Sentences were encoded using byte-pair encoding [3], which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT
2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece
vocabulary [38]. 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. For our big models,(described on the
bottom line of table 3), step time was 1.0 seconds. The big models were trained for 300,000 steps
(3.5 days).
5.3 Optimizer
We used the Adam optimizer [20] with β1 = 0.9, β2 = 0.98 and = 10−9
. We varied the learning
rate over the course of training, according to the formula:
lrate = d
−0.5
model · min(step_num−0.5
, step_num · warmup_steps−1.5
) (3)
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.
5.4 Regularization
We employ three types of regularization during training:
Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the
sub-layer input and normalized. 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
Pdrop = 0.1.
7
Table 2: 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.
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 · 1020
GNMT + RL [38] 24.6 39.92 2.3 · 1019 1.4 · 1020
ConvS2S [9] 25.16 40.46 9.6 · 1018 1.5 · 1020
MoE [32] 26.03 40.56 2.0 · 1019 1.2 · 1020
Deep-Att + PosUnk Ensemble [39] 40.4 8.0 · 1020
GNMT + RL Ensemble [38] 26.30 41.16 1.8 · 1020 1.1 · 1021
ConvS2S Ensemble [9] 26.36 41.29 7.7 · 1019 1.2 · 1021
Transformer (base model) 27.3 38.1 3.3 · 1018
Transformer (big) 28.4 41.8 2.3 · 1019
Label Smoothing During training, we employed label smoothing of value ls = 0.1 [36]. This
hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.
6 Results

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, P Epos+k can be represented as a linear function of

P Epos.

We also experimented with using learned positional embeddings [9] instead, and found that the two

versions produced nearly identical results (see Table 3 row (E)). 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

(x1, ..., xn) to another sequence of equal length (z1, ..., zn), with xi

, zi ∈ 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. The shorter these paths between any combination of positions in the input

and output sequences, the easier it is to learn long-range dependencies [12]. Hence we also compare

the maximum path length between any two input and output positions in networks composed of the

different layer types.

As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially

executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of

computational complexity, self-attention layers are faster than recurrent layers when the sequence

length n is smaller than the representation dimensionality d, which is most often the case with

sentence representations used by state-of-the-art models in machine translations, such as word-piece

[38] and byte-pair [31] representations. To improve computational performance for tasks involving

very long sequences, self-attention could be restricted to considering only a neighborhood of size r in

6

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. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels,

or O(logk(n)) in the case of dilated convolutions [18], increasing the length of the longest paths

between any two positions in the network. Convolutional layers are generally more expensive than

recurrent layers, by a factor of k. Separable convolutions [6], however, decrease the complexity

considerably, to O(k · n · d + n · d

2

). 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.

5 Training

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. Sentences were encoded using byte-pair encoding [3], which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT

2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece

vocabulary [38]. 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. For our big models,(described on the

bottom line of table 3), step time was 1.0 seconds. The big models were trained for 300,000 steps

(3.5 days).

5.3 Optimizer

We used the Adam optimizer [20] with β1 = 0.9, β2 = 0.98 and = 10−9

. We varied the learning

rate over the course of training, according to the formula:

lrate = d

−0.5

model · min(step_num−0.5

, step_num · warmup_steps−1.5

) (3)

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.

5.4 Regularization

We employ three types of regularization during training:

Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the

sub-layer input and normalized. 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

Pdrop = 0.1.

7

Table 2: 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.

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 · 1020

GNMT + RL [38] 24.6 39.92 2.3 · 1019 1.4 · 1020

ConvS2S [9] 25.16 40.46 9.6 · 1018 1.5 · 1020

MoE [32] 26.03 40.56 2.0 · 1019 1.2 · 1020

Deep-Att + PosUnk Ensemble [39] 40.4 8.0 · 1020

GNMT + RL Ensemble [38] 26.30 41.16 1.8 · 1020 1.1 · 1021

ConvS2S Ensemble [9] 26.36 41.29 7.7 · 1019 1.2 · 1021

Transformer (base model) 27.3 38.1 3.3 · 1018

Transformer (big) 28.4 41.8 2.3 · 1019

Label Smoothing During training, we employed label smoothing of value ls = 0.1 [36]. This

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

6 Results