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import torch
import numpy as np
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class PGDAttacker():
def __init__(self, radius, steps, step_size, random_start, norm_type, ascending=True):
self.radius = radius # attack radius
self.steps = steps # how many step to conduct pgd
self.step_size = step_size # coefficient of PGD
self.random_start = random_start
self.norm_type = norm_type # which norm of your noise
self.ascending = ascending # perform gradient ascending, i.e, to maximum the loss
def output(self, x, model, tokens_lens, text_token):
x = x + model.positional_embedding.type(model.dtype)
x = x.permute(1, 0, 2) # NLD -> LND
x, weight = model.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
x = model.ln_final(x).type(model.dtype)
x = x[torch.arange(x.shape[0]), text_token.argmax(dim=-1)] @ model.text_projection
attention_weights_all = []
for i in range(len(tokens_lens)):
attention_weights = weight[-1][i][min(76, tokens_lens[i])][:1+min(75, max(tokens_lens))][1:][:-1]
attention_weights_all.append(attention_weights)
attention_weights = torch.stack(attention_weights_all, dim=0)
return x, attention_weights
def perturb(self, device, m_tokens_len, bs, criterion, x, y,a_indices,encoder, tokens_lens=None, model=None, text_token=None):
if self.steps==0 or self.radius==0:
return x.clone()
adv_x = x.clone()
if self.random_start:
if self.norm_type == 'l-infty':
adv_x += 2 * (torch.rand_like(x) - 0.5) * self.radius
else:
adv_x += 2 * (torch.rand_like(x) - 0.5) * self.radius / self.steps
self._clip_(adv_x, x)
''' temporarily shutdown autograd of model to improve pgd efficiency '''
# adv_x, attention_weights = self.output(adv_x, model, tokens_lens, text_token)
# model.eval()
encoder.eval()
for pp in encoder.parameters():
pp.requires_grad = False
for step in range(self.steps):
adv_x_o = adv_x.clone()
adv_x.requires_grad_()
_y = encoder(a_indices,adv_x)
loss = criterion(y.to(device), _y, m_tokens_len, bs)
grad = torch.autograd.grad(loss, [adv_x])[0]
with torch.no_grad():
if not self.ascending: grad.mul_(-1)
if self.norm_type == 'l-infty':
adv_x.add_(torch.sign(grad), alpha=self.step_size)
else:
if self.norm_type == 'l2':
grad_norm = (grad.reshape(grad.shape[0],-1)**2).sum(dim=1).sqrt()
elif self.norm_type == 'l1':
grad_norm = grad.reshape(grad.shape[0],-1).abs().sum(dim=1)
grad_norm = grad_norm.reshape( -1, *( [1] * (len(x.shape)-1) ) )
scaled_grad = grad / (grad_norm + 1e-10)
adv_x.add_(scaled_grad, alpha=self.step_size)
self._clip_(adv_x, adv_x_o)
''' reopen autograd of model after pgd '''
# decoder.trian()
for pp in encoder.parameters():
pp.requires_grad = True
return adv_x # , attention_weights
def perturb_random(self, criterion, x, data, decoder,y,target_model,encoder=None):
if self.steps==0 or self.radius==0:
return x.clone()
adv_x = x.clone()
if self.norm_type == 'l-infty':
adv_x += 2 * (torch.rand_like(x) - 0.5) * self.radius
else:
adv_x += 2 * (torch.rand_like(x) - 0.5) * self.radius / self.steps
self._clip_(adv_x, x)
return adv_x.data
def perturb_iat(self, criterion, x, data, decoder,y,target_model,encoder=None):
if self.steps==0 or self.radius==0:
return x.clone()
B = x.shape[0]
L = x.shape[1]
H = x.shape[2]
nb_num = 8
alpha = torch.rand(B,L,nb_num,1).to(device)
A_1 = x.unsqueeze(2).expand(B,L,nb_num,H)
A_2 = x.unsqueeze(1).expand(B,L,L,H)
rand_idx = []
for i in range(L):
rand_idx.append(np.random.choice(L,nb_num,replace=False))
rand_idx = np.array(rand_idx)
rand_idx = torch.tensor(rand_idx).long().reshape(1,L,1,nb_num).expand(B,L,H,nb_num).to(device)
# A_2 = A_2[:,np.arange(0,L), rand_idx,:]
A_2 = torch.gather(A_2.reshape(B,L,H,L),-1,rand_idx).reshape(B,L,nb_num, H)
A_e = A_1 - A_2
# A_e
# adv_x = (A_e * alpha).sum(dim=-1) + x.clone()
adv_x = x.clone()
if self.random_start:
if self.norm_type == 'l-infty':
adv_x += 2 * (torch.rand_like(x) - 0.5) * self.radius
else:
adv_x += 2 * (torch.rand_like(x) - 0.5) * self.radius / self.steps
self._clip_(adv_x, x)
# assert adv_x.shape[0] == 8
''' temporarily shutdown autograd of model to improve pgd efficiency '''
# model.eval()
decoder.eval()
for pp in decoder.parameters():
pp.requires_grad = False
adv_x = x.clone()
alpha.requires_grad_()
for step in range(self.steps):
alpha.requires_grad_()
dot_Ae_alpha = (A_e * alpha).sum(dim=-2)
# print("dot_Ae_alpha:", dot_Ae_alpha.shape)
adv_x.add_(torch.sign(dot_Ae_alpha), alpha=self.step_size)
self._clip_(adv_x, x)
if encoder is None:
adv_x_input = adv_x.squeeze(-1)
else:
adv_x_input = adv_x
_y = target_model(adv_x_input, data,decoder,encoder)
loss = criterion(y.to(device), _y)
grad = torch.autograd.grad(loss, [alpha],retain_graph=True)[0]
# with torch.no_grad():
with torch.no_grad():
if not self.ascending: grad.mul_(-1)
assert self.norm_type == 'l-infty'
alpha = alpha.detach()+ grad * 0.01
''' reopen autograd of model after pgd '''
# decoder.trian()
for pp in decoder.parameters():
pp.requires_grad = True
return adv_x.data
def _clip_(self, adv_x, x):
adv_x -= x
if self.norm_type == 'l-infty':
adv_x.clamp_(-self.radius, self.radius)
else:
if self.norm_type == 'l2':
norm = (adv_x.reshape(adv_x.shape[0],-1)**2).sum(dim=1).sqrt()
elif self.norm_type == 'l1':
norm = adv_x.reshape(adv_x.shape[0],-1).abs().sum(dim=1)
norm = norm.reshape( -1, *( [1] * (len(x.shape)-1) ) )
adv_x /= (norm + 1e-10)
adv_x *= norm.clamp(max=self.radius)
adv_x += x
adv_x.clamp_(0, 1)
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