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| 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 forward(self, input_ids, attention_mask): | |
| return self.model(input_ids, attention_mask) | |
| def _build_one_hot(self, output, index): | |
| 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).to(output.device) | |
| one_hot = torch.sum(one_hot * output) | |
| return one_hot, one_hot_vector | |
| 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} | |
| one_hot, one_hot_vector = self._build_one_hot(output, index) | |
| 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 = _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] | |
| kwargs = {"alpha": 1} | |
| one_hot, one_hot_vector = self._build_one_hot(output, index) | |
| self.model.zero_grad() | |
| one_hot.backward(retain_graph=True) | |
| self.model.relprop(torch.tensor(one_hot_vector).to(input_ids.device), **kwargs) | |
| 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] | |
| kwargs = {"alpha": 1} | |
| one_hot, one_hot_vector = self._build_one_hot(output, index) | |
| 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 = _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 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, one_hot_vector = self._build_one_hot(output, index) | |
| self.model.zero_grad() | |
| one_hot.backward(retain_graph=True) | |
| self.model.relprop(torch.tensor(one_hot_vector).to(input_ids.device), **kwargs) | |
| 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] | |