The document discusses Transformers and sequence-to-sequence learning. It provides an overview of: - The encoder-decoder architecture of sequence-to-sequence models including self-attention. - Key components of the Transformer model including multi-head self-attention, positional encodings, and residual connections. - Training procedures like label smoothing and techniques used to improve Transformer performance like adding feedforward layers and stacking multiple Transformer blocks.

1. Transformers and sequence-
to-sequence learning
CS 685, Fall 2021
Mohit Iyyer
College of Information and Computer Sciences
University of Massachusetts Amherst
some slides from Emma Strubell

2. Attention is great
• Attention significantly improves NMT performance
• It’s very useful to allow decoder to focus on certain parts of the source
• Attention solves the bottleneck problem
• Attention allows decoder to look directly at source; bypass bottleneck
• Attention helps with vanishing gradient problem
• Provides shortcut to faraway states
• Attention provides some interpretability
• By inspecting attention distribution, we can see
what the decoder was focusing on
• We get alignment for free!
• This is cool because we never explicitly trained
an alignment system
• The network just learned alignment by itself
Alignments: hardest
The
poor
don’t
have
any
money
Les
pauvres
sont
démunis
The
poor
don t
have
any
money
Les
pauvres
sont
démunis
From last time
• Project proposals now due 10/1!
• Quiz 2 due Friday
• Can TAs record zoom office hours? Maybe
• How did we get this grid in the previous lecture?
Will explain in today’s class.
• Final proj. reports due Dec. 16th

3. iPad

4. sequence-to-sequence learning
• we’ll use French (f) to English (e) as a running
example
• goal: given French sentence f with tokens f1, f2,
… fn produce English translation e with tokens
e1, e2, … em
arg max
e
p(e| f )
• real goal: compute
Used when inputs and outputs are
both sequences of words (e.g.,
machine translation, summarization)

5. This is an instance of
conditional language modeling
p(e| f ) = p(e1, e2, …, em | f )
= p(e1 | f ) ⋅ p(e2 |e1, f ) ⋅ p(e3 |e2, e1, f ) ⋅ …
=
m
∏
i=1
p(ei |e1, …, ei−1, f )
Just like we’ve seen before, except we
additionally condition our prediction of
the next word on some other input
(here, the French sentence)

6. seq2seq models
• use two different neural networks to model
• first we have the encoder, which encodes the
French sentence f
• then, we have the decoder, which produces
the English sentence e
6
L
∏
i=1
p(ei |e1, …, ei−1, f )

7. 7
Encoder
RNN
Neural Machine Translation (NMT)
2/15/18
23
<START>
Source sentence (input)
les pauvres sont démunis
The sequence-to-sequence model
Target sentence (output)
Decoder
RNN
Encoder RNN produces
an encoding of the
source sentence.
Encoding of the source sentence.
Provides initial hidden state
for Decoder RNN.
Decoder RNN is a Language Model that generates
target sentence conditioned on encoding.
the
argmax
the
argmax
poor
poor
argmax
don’t
Note: This diagram shows test time behavior:
decoder output is fed in as next step’s input
have any money <END>
don’t have any money
argmax
argmax
argmax
argmax

8. 8
Encoder
RNN
Neural Machine Translation (NMT)
2/15/18
23
<START>
Source sentence (input)
les pauvres sont démunis
The sequence-to-sequence model
Target sentence (output)
Decoder
RNN
Encoder RNN produces
an encoding of the
source sentence.
Encoding of the source sentence.
Provides initial hidden state
for Decoder RNN.
Decoder RNN is a Language Model that generates
target sentence conditioned on encoding.
the
argmax
the
argmax
poor
poor
argmax
don’t
Note: This diagram shows test time behavior:
decoder output is fed in as next step’s input
have any money <END>
don’t have any money
argmax
argmax
argmax
argmax

9. 9
Training a Neural Machine Translation system
2/15/18
25
Encoder
RNN
Source sentence (from corpus)
<START> the poor don’t have any money
les pauvres sont démunis
Target sentence (from corpus)
Seq2seq is optimized as a single system.
Backpropagation operates “end to end”.
Decoder
RNN
!
"# !
"$ !
"% !
"& !
"' !
"( !
")
*# *$ *% *& *' *( *)
= negative log
prob of “the”
* =
1
-
.
/0#
1
*/ = + + + + + +
= negative log
prob of <END>
= negative log
prob of “have”

10. We’ll talk much more about
machine translation / other seq2seq
problems later… but for now, let’s
go back to the Transformer

12. encoder

13. encoder
decoder

14. encoder
decoder
So far we’ve just talked
about self-attention… what
is all this other stuff?

15. Self-attention (in encoder)
15
committee awards Strickland advanced optics
who
Layer p
Q
K
V
[Vaswani et al. 2017]
Nobel
Slides by Emma Strubell!

16. 16
Layer p
Q
K
V
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
Slides by Emma Strubell!
Self-attention (in encoder)

17. 17
Layer p
Q
K
V
optics
advanced
who
Strickland
awards
committee
Nobel
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
Slides by Emma Strubell!
Self-attention (in encoder)

18. optics
advanced
who
Strickland
awards
committee
Nobel
18
Layer p
Q
K
V
A
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
Slides by Emma Strubell!
Self-attention (in encoder)

19. 19
Layer p
Q
K
V
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
optics
advanced
who
Strickland
awards
committee
Nobel
A
Slides by Emma Strubell!
Self-attention (in encoder)

20. 20
Layer p
Q
K
V
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
optics
advanced
who
Strickland
awards
committee
Nobel
A
Slides by Emma Strubell!
Self-attention (in encoder)

21. 21
Layer p
Q
K
V
M
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
optics
advanced
who
Strickland
awards
committee
Nobel
A
Slides by Emma Strubell!
Self-attention (in encoder)

22. 22
Layer p
Q
K
V
M
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
optics
advanced
who
Strickland
awards
committee
Nobel
A
Slides by Emma Strubell!
Self-attention (in encoder)

23. Multi-head self-attention
23
Layer p
Q
K
V
M
M1
MH
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
optics
advanced
who
Strickland
awards
committee
Nobel
A
Slides by Emma Strubell!

24. 24
Layer p
Q
K
V
MH
M1
[Vaswani et al. 2017]
committee awards Strickland advanced optics
who
Nobel
optics
advanced
who
Strickland
awards
committee
Nobel
A
Multi-head self-attention
Slides by Emma Strubell!

25. 25
Layer p
Q
K
V
MH
M1
Layer
p+1
Feed
Forward
Feed
Forward
Feed
Forward
Feed
Forward
Feed
Forward
Feed
Forward
Feed
Forward
committee awards Strickland advanced optics
who
Nobel
[Vaswani et al. 2017]
optics
advanced
who
Strickland
awards
committee
Nobel
A
Multi-head self-attention
Slides by Emma Strubell!

26. 26
committee awards Strickland advanced optics
who
Nobel
[Vaswani et al. 2017]
p+1
Multi-head self-attention
Slides by Emma Strubell!

27. Multi-head self-attention + feed forward
Multi-head self-attention + feed forward
27
Layer 1
Layer p
Multi-head self-attention + feed forward
Layer J
committee awards Strickland advanced optics
who
Nobel
[Vaswani et al. 2017]
Multi-head self-attention
Slides by Emma Strubell!

28. Position embeddings are added to each
word embedding. Otherwise, since we
have no recurrence, our model is unaware
of the position of a word in the sequence!

29. Residual connections, which mean that we
add the input to a particular block to its
output, help improve gradient flow

30. A feed-forward layer on top of the attention-
weighted averaged value vectors allows us
to add more parameters / nonlinearity

31. We stack as many of these
Transformer blocks on top of each
other as we can (bigger models are
generally better given enough data!)

32. Moving onto the decoder, which
takes in English sequences that
have been shifted to the right
(e.g., <START> schools opened
their)

33. We first have an instance of
masked self attention. Since
the decoder is responsible
for predicting the English
words, we need to apply
masking as we saw before.

34. We first have an instance of
masked self attention. Since
the decoder is responsible
for predicting the English
words, we need to apply
masking as we saw before.
Why don’t we do
masked self-attention
in the encoder?

35. Now, we have cross attention,
which connects the decoder to
the encoder by enabling it to
attend over the encoder’s final
hidden states.

36. After stacking a bunch of
these decoder blocks, we
finally have our familiar
Softmax layer to predict
the next English word

37. Positional encoding

38. Creating positional encodings?
• We could just concatenate a fixed value to each time step
(e.g., 1, 2, 3, … 1000) that corresponds to its position,
but then what happens if we get a sequence with 5000
words at test time?
• We want something that can generalize to arbitrary
sequence lengths. We also may want to make attending
to relative positions (e.g., tokens in a local window to the
current token) easier.
• Distance between two positions should be consistent
with variable-length inputs

39. Intuitive example
https://kazemnejad.com/blog/transformer_architecture_positional_encoding/

40. Transformer positional encoding
Positional encoding is a 512d vector
i = a particular dimension of this vector
pos = dimension of the word
d_model = 512

41. What does this look like?
(each row is the pos. emb. of a 50-word sentence)
https://kazemnejad.com/blog/transformer_architecture_positional_encoding/

42. Despite the intuitive flaws, many models
these days use learned positional
embeddings (i.e., they cannot generalize to
longer sequences, but this isn’t a big deal for
their use cases)

43. Hacks to make
Transformers work

45. I went to class and took ___
0 0 1 0 0
cats TV notes took sofa
0.025 0.025 0.9 0.025 0.025
with label smoothing

46. Get penalized for
overconfidence!
Loss
Target word confidence

47. Why these decisions?
Unsatisfying answer: they empirically worked well.
Neural architecture search finds even better Transformer variants:
Primer: Searching for efficient Transformer architectures… So et al., Sep. 2021

48. OpenAI’s Transformer LMs
• GPT (Jun 2018): 117 million parameters, trained on
13GB of data (~1 billion tokens)
• GPT2 (Feb 2019): 1.5 billion parameters, trained on
40GB of data
• GPT3 (July 2020): 175 billion parameters, ~500GB
data (300 billion tokens)

49. Coming up!
• Transfer learning via Transformer models like BERT
• Tokenization (word vs subword vs character/byte)
• Prompt-based learning
• Efficient / long-range Transformers
• Downstream tasks