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Understanding and Improving Transformer
From a Multi-Particle Dynamic System
Point of View
1
• https://arxiv.org/abs/1906.02762
• Microsoft Research Asia
•
• Transformer
• Transformer
• Transformer Lie-Trotter
• Lie-Trotter Strang-Marchuk
Macaron
• GLUE
2
• Transformer (eg. ) , = (eg. )
• Transformer SA/Residual→FFN/Residual
Lie-Trotter
• Self-Attention(SA)
• PositionWise—FFN (PW-FFN)
•
• Lie-Trotter 2 3
Strang-Marchuk Transformer
Macaron
• Lie-Trotter Strang-
Marchuk
• NLP Transformer Macaron
Macaron
3
Residual
• 1
• eg. : t :
•
• x(t)
→ Residual
x(t) f(x, t)
4
x(t = tl) ·= xl, (l ∈ [0,L − 1]) tl = t0 + γl, γ ·= Δt
Δx
Δt
=
x2 − x1
t2 − t1
≈ f(xl, tl)
ODE Lie-Trotter
• t i-th
• 2
• F t
•
• G t
•
• Lie-Trotter
•
• Lie-Trotter
5
t + γ
˜x(t)
Transformer
• Encoder (Figure1 ) Decoder (Figure1 )
• Encoder
• Residual
• Self-Attention
• Position-Wise Feed Forword Network
• Decoder
•
6
Multihead(Q, K, V) = concat(head1, ⋯, headH)WO
headk = Attention(QWQ
k
, KWK
k , VWV
k )
Attention(Q, K, V) = softmax(QKT
/ dmodel) ⋅ V
dmodel
dK
dV
WQ
k
∈ ℝdmodel×dK
WK
k ∈ ℝdmodel×dK
WV
k ∈ ℝdmodel×dV
WO
∈ ℝHdV×dmodel
Transformer Lie-Trotter
• Self-Attention
• : l-th i-th
• l t i-th
head
concat
• PW-FFN
• PW-FFN Residual l+1
xl,i
˜xl,i xl,i
˜xl,i
7
e(k)
i,j
= (xl,iWQ,l
k
) ⋅ (xl,jWK,l
k
)T
/ dmodel
Transformer Lie-Trotter
• Self-Attention PW-FFN Transformer 1
• Lie-Trotter
• Transformer 2 Lie-Trotter
F G 1γ
8
Strang-Marchuk
•
• Transformer Lie-Trotter
• Lie-Trotter
• F G
→ 2 :
• Lie-Trotter
→ Strang-Marchuk
• Lie-Trotter F→G Strang-Marchuk
G F
• Strang-Marchuk 3 : Lie-Trotter
𝒪(γ2
)
𝒪(γ3
)
9
Strang-Marchuk Transformer
• Transformer Strang-Marchuk
→ Macaron
10
•
• IWSLT14 De→En
• WMT14 En→De
• Transformer
• 6 encoders + 6 decoders
• 3 (base, big, small)
• base:512 8 big:1024 16 small:512 4
• Transformer Macaron
• GLUE
• BERT base 12 768 12
•
• FFN Macaron
11
• Macaron
•
• GLUE
12
• Transformer
• Transformer Lie-Trotter
• Lie-Trotter Strang-Marchuk
Macaron
• GLUE
13

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