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