[필수 과제] Multi-headed Attention Assignment
Multi-Headed Attention
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
%matplotlib inline
%config InlineBackend.figure_format='retina'
print ("PyTorch version:[%s]."%(torch.__version__))
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print ("device:[%s]."%(device))PyTorch version:[1.9.0+cu102].
device:[cuda:0].Scaled Dot-Product Attention (SDPA)
Scaled Dot Attention은 self(single)-attention이고, 이후에 multi attention이 나올 것임
class ScaledDotProductAttention(nn.Module):
def forward(self,Q,K,V,mask=None):
d_K = K.size()[-1] # key dimension
scores = Q.matmul(K.transpose(-2,-1)) / np.sqrt(d_K)
if mask is not None:
scores = scores.masked_fill(mask==0, -1e9)
attention = F.softmax(scores,dim=-1)
out = attention.matmul(V)
return out,attention2 : Q, K, V 벡터를 입력받는다. 정확히는 이것이 Batch가 되어서 들어오게 된다.
3 : Key dimension을 찾음. 왜? 점수를 square값으로 나눠야 하니까
4 : 점수를 계산
6 : softmax로 attention값 구하기
7 : Value벡터와 attention곱 구하기
# Demo run of scaled dot product attention
SPDA = ScaledDotProductAttention()
n_batch,d_K,d_V = 3,128,256 # d_K(=d_Q) does not necessarily be equal to d_V
n_Q,n_K,n_V = 30,50,50
Q = torch.rand(n_batch,n_Q,d_K)
K = torch.rand(n_batch,n_K,d_K)
V = torch.rand(n_batch,n_V,d_V)
out,attention = SPDA.forward(Q,K,V,mask=None)
def sh(x): return str(x.shape)[11:-1]
print ("SDPA: Q%s K%s V%s => out%s attention%s"%
(sh(Q),sh(K),sh(V),sh(out),sh(attention)))SDPA: Q[3, 30, 128] K[3, 50, 128] V[3, 50, 256] => out[3, 30, 256] attention[3, 30, 50]3
n_batch : 단어의 개수
d_K : K벡터의 차원
d_V : V벡터의 차원
V벡터는 K벡터와 차원이 달라도 된다.
4: 각각의 벡터의 총 개수
쿼리벡터의 개수와 키벡터의 개수가 달라도 된다. 개수가 달라도 서로의 interaction을 계산할 수 있다.
# It supports 'multi-headed' attention
n_batch,n_head,d_K,d_V = 3,5,128,256
n_Q,n_K,n_V = 30,50,50 # n_K and n_V should be the same
Q = torch.rand(n_batch,n_head,n_Q,d_K)
K = torch.rand(n_batch,n_head,n_K,d_K)
V = torch.rand(n_batch,n_head,n_V,d_V)
out,attention = SPDA.forward(Q,K,V,mask=None)
# out: [n_batch x n_head x n_Q x d_V]
# attention: [n_batch x n_head x n_Q x n_K]
def sh(x): return str(x.shape)[11:-1]
print ("(Multi-Headed) SDPA: Q%s K%s V%s => out%s attention%s"%
(sh(Q),sh(K),sh(V),sh(out),sh(attention)))(Multi-Headed) SDPA: Q[3, 5, 30, 128] K[3, 5, 50, 128] V[3, 5, 50, 256] => out[3, 5, 30, 256] attention[3, 5, 30, 50]멀티헤드에서는 각각의 벡터를 몇개할지가 2번째 인자자리에 추가되었다.
Multi-Headed Attention (MHA)
class MultiHeadedAttention(nn.Module):
def __init__(self,d_feat=128,n_head=5,actv=F.relu,USE_BIAS=True,dropout_p=0.1,device=None):
"""
:param d_feat: feature dimension
:param n_head: number of heads
:param actv: activation after each linear layer
:param USE_BIAS: whether to use bias
:param dropout_p: dropout rate
:device: which device to use (e.g., cuda:0)
"""
super(MultiHeadedAttention,self).__init__()
if (d_feat%n_head) != 0:
raise ValueError("d_feat(%d) should be divisible by b_head(%d)"%(d_feat,n_head))
self.d_feat = d_feat
self.n_head = n_head
self.d_head = self.d_feat // self.n_head
self.actv = actv
self.USE_BIAS = USE_BIAS
self.dropout_p = dropout_p # prob. of zeroed
self.lin_Q = nn.Linear(self.d_feat,self.d_feat,self.USE_BIAS)
self.lin_K = nn.Linear(self.d_feat,self.d_feat,self.USE_BIAS)
self.lin_V = nn.Linear(self.d_feat,self.d_feat,self.USE_BIAS)
self.lin_O = nn.Linear(self.d_feat,self.d_feat,self.USE_BIAS)
self.dropout = nn.Dropout(p=self.dropout_p)2 : dropout이 multihead 인자로 들어가게된다. 논문에는 설명이 안되어있는데 모든 코드에 쓴다
21-24 : Q, K ,V 벡터를 구하는 신경망을 구성하고 나오는 결과값을 가공해주는 Output도 정의해준다.
def forward(self,Q,K,V,mask=None):
"""
:param Q: [n_batch, n_Q, d_feat]
:param K: [n_batch, n_K, d_feat]
:param V: [n_batch, n_V, d_feat] <= n_K and n_V must be the same
:param mask:
"""
n_batch = Q.shape[0]
Q_feat = self.lin_Q(Q)
K_feat = self.lin_K(K)
V_feat = self.lin_V(V)
# Q_feat: [n_batch, n_Q, d_feat]
# K_feat: [n_batch, n_K, d_feat]
# V_feat: [n_batch, n_V, d_feat]
# Multi-head split of Q, K, and V (d_feat = n_head*d_head)
Q_split = Q_feat.view(n_batch, -1, self.n_head, self.d_head).permute(0, 2, 1, 3)
K_split = K_feat.view(n_batch, -1, self.n_head, self.d_head).permute(0, 2, 1, 3)
V_split = V_feat.view(n_batch, -1, self.n_head, self.d_head).permute(0, 2, 1, 3)
# Q_split: [n_batch, n_head, n_Q, d_head]
# K_split: [n_batch, n_head, n_K, d_head]
# V_split: [n_batch, n_head, n_V, d_head]
# Multi-Headed Attention
d_K = K.size()[-1] # key dimension
scores = torch.matmul(Q_split, K_split.permute(0, 1, 3, 2)) / np.sqrt(d_K)
if mask is not None:
scores = scores.masked_fill(mask==0,-1e9)
attention = torch.softmax(scores,dim=-1)
x_raw = torch.matmul(self.dropout(attention),V_split) # dropout is NOT mentioned in the paper
# attention: [n_batch, n_head, n_Q, n_K]
# x_raw: [n_batch, n_head, n_Q, d_head]
# Reshape x
x_rsh1 = x_raw.permute(0,2,1,3).contiguous()
# x_rsh1: [n_batch, n_Q, n_head, d_head]
x_rsh2 = x_rsh1.view(n_batch,-1,self.d_feat)
# x_rsh2: [n_batch, n_Q, d_feat]
# Linear
x = self.lin_O(x_rsh2)
# x: [n_batch, n_Q, d_feat]
out = {'Q_feat':Q_feat,'K_feat':K_feat,'V_feat':V_feat,
'Q_split':Q_split,'K_split':K_split,'V_split':V_split,
'scores':scores,'attention':attention,
'x_raw':x_raw,'x_rsh1':x_rsh1,'x_rsh2':x_rsh2,'x':x}
return out9-11 : 각각의 벡터를 신경망에 넣는다
17-19 : 그리고 이 벡터를 조각조각 내준다.
# Self-Attention Layer
n_batch = 128
n_src = 32
d_feat = 200
n_head = 5
src = torch.rand(n_batch,n_src,d_feat)
self_attention = MultiHeadedAttention(
d_feat=d_feat,n_head=n_head,actv=F.relu,USE_BIAS=True,dropout_p=0.1,device=device)
out = self_attention.forward(src,src,src,mask=None)
Q_feat,K_feat,V_feat = out['Q_feat'],out['K_feat'],out['V_feat']
Q_split,K_split,V_split = out['Q_split'],out['K_split'],out['V_split']
scores,attention = out['scores'],out['attention']
x_raw,x_rsh1,x_rsh2,x = out['x_raw'],out['x_rsh1'],out['x_rsh2'],out['x']
# Print out shapes
def sh(_x): return str(_x.shape)[11:-1]
print ("Input src:\t%s \t= [n_batch, n_src, d_feat]"%(sh(src)))
print ()
print ("Q_feat: \t%s \t= [n_batch, n_src, d_feat]"%(sh(Q_feat)))
print ("K_feat: \t%s \t= [n_batch, n_src, d_feat]"%(sh(K_feat)))
print ("V_feat: \t%s \t= [n_batch, n_src, d_feat]"%(sh(V_feat)))
print ()
print ("Q_split: \t%s \t= [n_batch, n_head, n_src, d_head]"%(sh(Q_split)))
print ("K_split: \t%s \t= [n_batch, n_head, n_src, d_head]"%(sh(K_split)))
print ("V_split: \t%s \t= [n_batch, n_head, n_src, d_head]"%(sh(V_split)))
print ()
print ("scores: \t%s \t= [n_batch, n_head, n_src, n_src]"%(sh(scores)))
print ("attention:\t%s \t= [n_batch, n_head, n_src, n_src]"%(sh(attention)))
print ()
print ("x_raw: \t%s \t= [n_batch, n_head, n_src, d_head]"%(sh(x_raw)))
print ("x_rsh1: \t%s \t= [n_batch, n_src, n_head, d_head]"%(sh(x_rsh1)))
print ("x_rsh2: \t%s \t= [n_batch, n_src, d_feat]"%(sh(x_rsh2)))
print ()
print ("Output x: \t%s \t= [n_batch, n_src, d_feat]"%(sh(x)))Input src: [128, 32, 200] = [n_batch, n_src, d_feat]
Q_feat: [128, 32, 200] = [n_batch, n_src, d_feat]
K_feat: [128, 32, 200] = [n_batch, n_src, d_feat]
V_feat: [128, 32, 200] = [n_batch, n_src, d_feat]
Q_split: [128, 5, 32, 40] = [n_batch, n_head, n_src, d_head]
K_split: [128, 5, 32, 40] = [n_batch, n_head, n_src, d_head]
V_split: [128, 5, 32, 40] = [n_batch, n_head, n_src, d_head]
scores: [128, 5, 32, 32] = [n_batch, n_head, n_src, n_src]
attention: [128, 5, 32, 32] = [n_batch, n_head, n_src, n_src]
x_raw: [128, 5, 32, 40] = [n_batch, n_head, n_src, d_head]
x_rsh1: [128, 32, 5, 40] = [n_batch, n_src, n_head, d_head]
x_rsh2: [128, 32, 200] = [n_batch, n_src, d_feat]
Output x: [128, 32, 200] = [n_batch, n_src, d_feat]Last updated
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