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import torch |
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from transformers import AutoModelForSequenceClassification |
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from transformers import AutoTokenizer |
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from captum.attr import LayerIntegratedGradients |
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from captum.attr import visualization |
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from roberta2 import RobertaForSequenceClassification |
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from ExplanationGenerator import Generator |
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from util import visualize_text |
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classifications = ["NEGATIVE", "POSITIVE"] |
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class IntegratedGradientsExplainer: |
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def __init__(self, model, tokenizer): |
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self.model = model |
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self.device = model.device |
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self.tokenizer = tokenizer |
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self.baseline_map = { |
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'Unknown': self.tokenizer.unk_token_id, |
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'Padding': self.tokenizer.pad_token_id, |
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} |
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def tokens_from_ids(self, ids): |
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return list(map(lambda s: s[1:] if s[0] == "Ġ" else s, self.tokenizer.convert_ids_to_tokens(ids))) |
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def custom_forward(self, inputs, attention_mask=None, pos=0): |
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result = self.model(inputs, attention_mask=attention_mask, return_dict=True) |
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preds = result.logits |
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return preds |
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@staticmethod |
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def summarize_attributions(attributions): |
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attributions = attributions.sum(dim=-1).squeeze(0) |
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attributions = attributions / torch.norm(attributions) |
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return attributions |
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def run_attribution_model(self, input_ids, attention_mask, baseline=None, index=None, layer=None, steps=20): |
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if baseline is None: |
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baseline = self.tokenizer.unk_token_id |
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else: |
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baseline = self.baseline_map[baseline] |
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try: |
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output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0] |
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ablator = LayerIntegratedGradients(self.custom_forward, layer) |
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input_tensor = input_ids |
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attention_mask = attention_mask |
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attributions = ablator.attribute( |
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inputs=input_ids, |
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baselines=baseline, |
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additional_forward_args=(attention_mask), |
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target=1, |
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n_steps=steps, |
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) |
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return self.summarize_attributions(attributions).unsqueeze_(0), output, index |
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finally: |
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pass |
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def build_visualization(self, input_ids, attention_mask, **kwargs): |
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vis_data_records = [] |
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attributions, output, index = self.run_attribution_model(input_ids, attention_mask, **kwargs) |
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for record in range(input_ids.size(0)): |
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classification = output[record].argmax(dim=-1).item() |
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class_name = classifications[classification] |
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attr = attributions[record] |
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tokens = self.tokens_from_ids(input_ids[record].flatten())[ |
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1 : 0 - ((attention_mask[record] == 0).sum().item() + 1) |
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] |
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vis_data_records.append( |
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visualization.VisualizationDataRecord( |
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attr, |
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output[record][classification], |
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classification, |
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classification, |
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index, |
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1, |
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tokens, |
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1, |
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) |
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) |
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return visualize_text(vis_data_records) |
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def __call__(self, input_text, layer, baseline): |
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text_batch = [input_text] |
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encoding = self.tokenizer(text_batch, return_tensors="pt") |
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input_ids = encoding["input_ids"].to(self.device) |
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attention_mask = encoding["attention_mask"].to(self.device) |
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layer = int(layer) |
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if layer == 0: |
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layer = self.model.roberta.embeddings |
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else: |
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layer = getattr(self.model.roberta.encoder.layer, str(layer-1)) |
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return self.build_visualization(input_ids, attention_mask, layer=layer, baseline=baseline) |
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