Source code for textattack.search_methods.greedy_word_swap_wir

"""
Greedy Word Swap with Word Importance Ranking
===================================================


When WIR method is set to ``unk``, this is a reimplementation of the search
method from the paper: Is BERT Really Robust?

A Strong Baseline for Natural Language Attack on Text Classification and
Entailment by Jin et. al, 2019. See https://arxiv.org/abs/1907.11932 and
https://github.com/jind11/TextFooler.
"""

import numpy as np
import torch
from torch.nn.functional import softmax

from textattack.goal_function_results import GoalFunctionResultStatus
from textattack.search_methods import SearchMethod
from textattack.shared.validators import (
    transformation_consists_of_word_swaps_and_deletions,
)


[docs]class GreedyWordSwapWIR(SearchMethod): """An attack that greedily chooses from a list of possible perturbations in order of index, after ranking indices by importance. Args: wir_method: method for ranking most important words model_wrapper: model wrapper used for gradient-based ranking """ def __init__(self, wir_method="unk", unk_token="[UNK]"): self.wir_method = wir_method self.unk_token = unk_token def _get_index_order(self, initial_text): """Returns word indices of ``initial_text`` in descending order of importance.""" len_text, indices_to_order = self.get_indices_to_order(initial_text) if self.wir_method == "unk": leave_one_texts = [ initial_text.replace_word_at_index(i, self.unk_token) for i in indices_to_order ] leave_one_results, search_over = self.get_goal_results(leave_one_texts) index_scores = np.array([result.score for result in leave_one_results]) elif self.wir_method == "weighted-saliency": # first, compute word saliency leave_one_texts = [ initial_text.replace_word_at_index(i, self.unk_token) for i in indices_to_order ] leave_one_results, search_over = self.get_goal_results(leave_one_texts) saliency_scores = np.array([result.score for result in leave_one_results]) softmax_saliency_scores = softmax( torch.Tensor(saliency_scores), dim=0 ).numpy() # compute the largest change in score we can find by swapping each word delta_ps = [] for idx in indices_to_order: # Exit Loop when search_over is True - but we need to make sure delta_ps # is the same size as softmax_saliency_scores if search_over: delta_ps = delta_ps + [0.0] * ( len(softmax_saliency_scores) - len(delta_ps) ) break transformed_text_candidates = self.get_transformations( initial_text, original_text=initial_text, indices_to_modify=[idx], ) if not transformed_text_candidates: # no valid synonym substitutions for this word delta_ps.append(0.0) continue swap_results, search_over = self.get_goal_results( transformed_text_candidates ) score_change = [result.score for result in swap_results] if not score_change: delta_ps.append(0.0) continue max_score_change = np.max(score_change) delta_ps.append(max_score_change) index_scores = softmax_saliency_scores * np.array(delta_ps) elif self.wir_method == "delete": leave_one_texts = [ initial_text.delete_word_at_index(i) for i in indices_to_order ] leave_one_results, search_over = self.get_goal_results(leave_one_texts) index_scores = np.array([result.score for result in leave_one_results]) elif self.wir_method == "gradient": victim_model = self.get_victim_model() index_scores = np.zeros(len_text) grad_output = victim_model.get_grad(initial_text.tokenizer_input) gradient = grad_output["gradient"] word2token_mapping = initial_text.align_with_model_tokens(victim_model) for i, index in enumerate(indices_to_order): matched_tokens = word2token_mapping[index] if not matched_tokens: index_scores[i] = 0.0 else: agg_grad = np.mean(gradient[matched_tokens], axis=0) index_scores[i] = np.linalg.norm(agg_grad, ord=1) search_over = False elif self.wir_method == "random": index_order = indices_to_order np.random.shuffle(index_order) search_over = False else: raise ValueError(f"Unsupported WIR method {self.wir_method}") if self.wir_method != "random": index_order = np.array(indices_to_order)[(-index_scores).argsort()] return index_order, search_over
[docs] def check_transformation_compatibility(self, transformation): """Since it ranks words by their importance, GreedyWordSwapWIR is limited to word swap and deletion transformations.""" return transformation_consists_of_word_swaps_and_deletions(transformation)
@property def is_black_box(self): if self.wir_method == "gradient": return False else: return True
[docs] def extra_repr_keys(self): return ["wir_method"]