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]