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import argparse
import glob
import numpy as np
import torch
# compute rollout between attention layers
def compute_rollout_attention(all_layer_matrices, start_layer=0):
# adding residual consideration- code adapted from https://github.com/samiraabnar/attention_flow
num_tokens = all_layer_matrices[0].shape[1]
batch_size = all_layer_matrices[0].shape[0]
eye = (
torch.eye(num_tokens)
.expand(batch_size, num_tokens, num_tokens)
.to(all_layer_matrices[0].device)
)
all_layer_matrices = [
all_layer_matrices[i] + eye for i in range(len(all_layer_matrices))
]
matrices_aug = [
all_layer_matrices[i] / all_layer_matrices[i].sum(dim=-1, keepdim=True)
for i in range(len(all_layer_matrices))
]
joint_attention = matrices_aug[start_layer]
for i in range(start_layer + 1, len(matrices_aug)):
joint_attention = matrices_aug[i].bmm(joint_attention)
return joint_attention
class Generator:
def __init__(self, model):
self.model = model
self.model.eval()
def forward(self, input_ids, attention_mask):
return self.model(input_ids, attention_mask)
def generate_LRP(self, input_ids, attention_mask, index=None, start_layer=11):
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
kwargs = {"alpha": 1}
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0, index] = 1
one_hot_vector = one_hot
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
one_hot = torch.sum(one_hot.cuda() * output)
self.model.zero_grad()
one_hot.backward(retain_graph=True)
self.model.relprop(torch.tensor(one_hot_vector).to(input_ids.device), **kwargs)
cams = []
blocks = self.model.bert.encoder.layer
for blk in blocks:
grad = blk.attention.self.get_attn_gradients()
cam = blk.attention.self.get_attn_cam()
cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
cam = grad * cam
cam = cam.clamp(min=0).mean(dim=0)
cams.append(cam.unsqueeze(0))
rollout = compute_rollout_attention(cams, start_layer=start_layer)
rollout[:, 0, 0] = rollout[:, 0].min()
return rollout[:, 0]
def generate_LRP_last_layer(self, input_ids, attention_mask, index=None):
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
kwargs = {"alpha": 1}
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0, index] = 1
one_hot_vector = one_hot
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
one_hot = torch.sum(one_hot.cuda() * output)
self.model.zero_grad()
one_hot.backward(retain_graph=True)
self.model.relprop(torch.tensor(one_hot_vector).to(input_ids.device), **kwargs)
cam = self.model.bert.encoder.layer[-1].attention.self.get_attn_cam()[0]
cam = cam.clamp(min=0).mean(dim=0).unsqueeze(0)
cam[:, 0, 0] = 0
return cam[:, 0]
def generate_full_lrp(self, input_ids, attention_mask, index=None):
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
kwargs = {"alpha": 1}
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0, index] = 1
one_hot_vector = one_hot
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
one_hot = torch.sum(one_hot.cuda() * output)
self.model.zero_grad()
one_hot.backward(retain_graph=True)
cam = self.model.relprop(
torch.tensor(one_hot_vector).to(input_ids.device), **kwargs
)
cam = cam.sum(dim=2)
cam[:, 0] = 0
return cam
def generate_attn_last_layer(self, input_ids, attention_mask, index=None):
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
cam = self.model.bert.encoder.layer[-1].attention.self.get_attn()[0]
cam = cam.mean(dim=0).unsqueeze(0)
cam[:, 0, 0] = 0
return cam[:, 0]
def generate_rollout(self, input_ids, attention_mask, start_layer=0, index=None):
self.model.zero_grad()
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
blocks = self.model.bert.encoder.layer
all_layer_attentions = []
for blk in blocks:
attn_heads = blk.attention.self.get_attn()
avg_heads = (attn_heads.sum(dim=1) / attn_heads.shape[1]).detach()
all_layer_attentions.append(avg_heads)
rollout = compute_rollout_attention(
all_layer_attentions, start_layer=start_layer
)
rollout[:, 0, 0] = 0
return rollout[:, 0]
def generate_attn_gradcam(self, input_ids, attention_mask, index=None):
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
kwargs = {"alpha": 1}
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0, index] = 1
one_hot_vector = one_hot
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
one_hot = torch.sum(one_hot.cuda() * output)
self.model.zero_grad()
one_hot.backward(retain_graph=True)
self.model.relprop(torch.tensor(one_hot_vector).to(input_ids.device), **kwargs)
cam = self.model.bert.encoder.layer[-1].attention.self.get_attn()
grad = self.model.bert.encoder.layer[-1].attention.self.get_attn_gradients()
cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
grad = grad.mean(dim=[1, 2], keepdim=True)
cam = (cam * grad).mean(0).clamp(min=0).unsqueeze(0)
cam = (cam - cam.min()) / (cam.max() - cam.min())
cam[:, 0, 0] = 0
return cam[:, 0]
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