Transformer-Explainability/BERT_explainability/modules/BERT/ExplanationGenerator.py

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]