# Adapt code from https://github.com/yuh-zha/AlignScore/tree/main import sys sys.path.append("..") import nltk nltk.download('punkt') from nltk.tokenize import sent_tokenize import torch from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification import torch.nn as nn from tqdm import tqdm import torch.nn.functional as F import os def sent_tokenize_with_newlines(text): blocks = text.split('\n') tokenized_blocks = [sent_tokenize(block) for block in blocks] tokenized_text = [] for block in tokenized_blocks: tokenized_text.extend(block) tokenized_text.append('\n') return tokenized_text[:-1] class Inferencer(): def __init__(self, path, chunk_size, max_input_length, batch_size) -> None: self.path = path self.device = "cuda:0" if torch.cuda.is_available() else "cpu" self.model = AutoModelForSeq2SeqLM.from_pretrained(path).to(self.device) self.tokenizer = AutoTokenizer.from_pretrained(path) self.chunk_size=500 if chunk_size is None else chunk_size self.max_input_length=2048 if max_input_length is None else max_input_length self.max_output_length = 256 self.model.eval() self.batch_size = batch_size self.softmax = nn.Softmax(dim=-1) def inference_example_batch(self, doc: list, claim: list): """ inference a example, doc: list claim: list using self.inference to batch the process """ assert len(doc) == len(claim), "doc must has the same length with claimthesis!" max_support_probs = [] used_chunks = [] support_prob_per_chunk = [] for one_doc, one_claim in tqdm(zip(doc, claim), desc="Evaluating", total=len(doc)): output = self.inference_per_example(one_doc, one_claim) max_support_probs.append(output['max_support_prob']) used_chunks.append(output['used_chunks']) support_prob_per_chunk.append(output['support_prob_per_chunk']) return { 'max_support_probs': max_support_probs, 'used_chunks': used_chunks, 'support_prob_per_chunk': support_prob_per_chunk } def inference_per_example(self, doc:str, claim: str): """ inference a example, doc: string claim: string using self.inference to batch the process """ def chunks(lst, n): """Yield successive chunks from lst with each having approximately n tokens. For flan-t5, we split using the white space; For roberta and deberta, we split using the tokenization. """ current_chunk = [] current_word_count = 0 for sentence in lst: sentence_word_count = len(sentence.split()) if current_word_count + sentence_word_count > n: yield ' '.join(current_chunk) current_chunk = [sentence] current_word_count = sentence_word_count else: current_chunk.append(sentence) current_word_count += sentence_word_count if current_chunk: yield ' '.join(current_chunk) doc_sents = sent_tokenize_with_newlines(doc) doc_sents = doc_sents or [''] doc_chunks = [chunk.replace(" \n ", '\n').strip() for chunk in chunks(doc_sents, self.chunk_size)] ''' [chunk_1, chunk_2, chunk_3, chunk_4, ...] [claim] ''' claim_repeat = [claim] * len(doc_chunks) output = self.inference(doc_chunks, claim_repeat) return output def inference(self, doc, claim): """ inference a list of doc and claim Standard aggregation (max) over chunks of doc Note: We do not have any post-processing steps for 'claim' and directly check 'doc' against 'claim'. If there are multiple sentences in 'claim'. Sentences are not splitted and are checked as a single piece of text. If there are multiple sentences in 'claim', we suggest users to split 'claim' into sentences beforehand and prepares data like (doc, claim_1), (doc, claim_2), ... for a multi-sentence 'claim'. **We leave the user to decide how to aggregate the results from multiple sentences.** Note: AggreFact-CNN is the only dataset that contains three-sentence summaries and have annotations on the whole summaries, so we do not split the sentences in each 'claim' during prediciotn for simplicity. Therefore, for this dataset, our result is based on treating the whole summary as a single piece of text (one 'claim'). In general, sentence-level prediciton performance is better than that on the full-response-level. """ if isinstance(doc, str) and isinstance(claim, str): doc = [doc] claim = [claim] batch_input, _, batch_org_chunks = self.batch_tokenize(doc, claim) label_probs_list = [] used_chunks = [] for mini_batch_input, batch_org_chunk in zip(batch_input, batch_org_chunks): mini_batch_input = {k: v.to(self.device) for k, v in mini_batch_input.items()} with torch.no_grad(): decoder_input_ids = torch.zeros((mini_batch_input['input_ids'].size(0), 1), dtype=torch.long).to(self.device) outputs = self.model(input_ids=mini_batch_input['input_ids'], attention_mask=mini_batch_input['attention_mask'], decoder_input_ids=decoder_input_ids) logits = outputs.logits.squeeze(1) # 3 for no support and 209 for support label_logits = logits[:, torch.tensor([3, 209])].cpu() label_probs = torch.nn.functional.softmax(label_logits, dim=-1) label_probs_list.append(label_probs) used_chunks.extend(batch_org_chunk) label_probs = torch.cat(label_probs_list) support_prob_per_chunk = label_probs[:, 1].cpu().numpy() max_support_prob = label_probs[:, 1].max().item() return { 'max_support_prob': max_support_prob, 'used_chunks': used_chunks, 'support_prob_per_chunk': support_prob_per_chunk } def batch_tokenize(self, doc, claim): """ input doc and claims are lists """ assert isinstance(doc, list) and isinstance(claim, list) assert len(doc) == len(claim), "doc and claim should be in the same length." original_text = [self.tokenizer.eos_token.join([one_doc, one_claim]) for one_doc, one_claim in zip(doc, claim)] batch_input = [] batch_concat_text = [] batch_org_chunks = [] for mini_batch in self.chunks(original_text, self.batch_size): model_inputs = self.tokenizer( ['predict: ' + text for text in mini_batch], max_length=self.max_input_length, truncation=True, padding=True, return_tensors="pt" ) batch_input.append(model_inputs) batch_concat_text.append(mini_batch) batch_org_chunks.append([item[:item.find('')] for item in mini_batch]) return batch_input, batch_concat_text, batch_org_chunks def chunks(self, lst, n): """Yield successive n-sized chunks from lst.""" for i in range(0, len(lst), n): yield lst[i:i + n] def fact_check(self, doc, claim): outputs = self.inference_example_batch(doc, claim) return outputs['max_support_probs'], outputs['used_chunks'], outputs['support_prob_per_chunk']