File size: 7,195 Bytes
2e1a3f8 4f67e27 2e1a3f8 4f67e27 2e1a3f8 4f67e27 2e1a3f8 4f67e27 2e1a3f8 4f67e27 2e1a3f8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
import argparse
import numpy as np
import torch
import glob
from captum._utils.common import _get_module_from_name
# 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, key="bert.encoder.layer"):
self.model = model
self.key = key
self.model.eval()
def tokens_from_ids(self, ids):
return list(map(lambda s: s[1:] if s[0] == "Ġ" else s, self.tokenizer.convert_ids_to_tokens(ids)))
def _calculate_gradients(self, output, index, do_relprop=True):
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
one_hot_vector = (torch.nn.functional
.one_hot(
# one_hot requires ints
torch.tensor(index, dtype=torch.int64),
num_classes=output.size(-1)
)
# but requires_grad_ needs floats
.to(torch.float)
).to(output.device)
hot_output = torch.sum(one_hot_vector.clone().requires_grad_(True) * output)
self.model.zero_grad()
hot_output.backward(retain_graph=True)
if do_relprop:
return self.model.relprop(one_hot_vector, alpha=1)
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]
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
self._calculate_gradients(output, index)
cams = []
blocks = _get_module_from_name(self.model, self.key)
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]
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
self._calculate_gradients(output, index)
cam = _get_module_from_name(self.model, self.key)[-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]
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
cam = self._calculate_gradients(output, index)
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 = _get_module_from_name(self.model, self.key)[-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 = _get_module_from_name(self.model, self.key)
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 output, 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]
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
self._calculate_gradients(output, index)
cam = _get_module_from_name(self.model, self.key)[-1].attention.self.get_attn()
grad = _get_module_from_name(self.model, self.key)[-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]
def generate_rollout_attn_gradcam(self, input_ids, attention_mask, index=None, start_layer=0):
# rule 5 from paper
def avg_heads(cam, grad):
return (grad * cam).clamp(min=0).mean(dim=-3)
# rule 6 from paper
def apply_self_attention_rules(R_ss, cam_ss):
return torch.matmul(cam_ss, R_ss)
output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
self._calculate_gradients(output, index, do_relprop=False)
num_tokens = input_ids.size(-1)
R = torch.eye(num_tokens).expand(output.size(0), -1, -1).clone().to(output.device)
blocks = _get_module_from_name(self.model, self.key)
for i, blk in enumerate(blocks):
if i < start_layer:
continue
grad = blk.attention.self.get_attn_gradients().detach()
cam = blk.attention.self.get_attn().detach()
cam = avg_heads(cam, grad)
joint = apply_self_attention_rules(R, cam)
R += joint
# 0 because we look at the influence *on* the CLS token
# 1:-1 because we don't want the influence *from* the CLS/SEP tokens
return output, R[:, 0, 1:-1]
|