import torch import time from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from multiprocessing import cpu_count from transformers import ( AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, squad_convert_examples_to_features ) from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample from transformers.data.metrics.squad_metrics import compute_predictions_logits def run_prediction(question_texts, context_text, model_path, n_best_size=1): ### Setting hyperparameters max_seq_length = 512 doc_stride = 256 n_best_size = n_best_size max_query_length = 64 max_answer_length = 512 do_lower_case = False null_score_diff_threshold = 0.0 def to_list(tensor): return tensor.detach().cpu().tolist() config_class, model_class, tokenizer_class = (AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer) config = config_class.from_pretrained(model_path) tokenizer = tokenizer_class.from_pretrained(model_path, do_lower_case=True, use_fast=False) model = model_class.from_pretrained(model_path, config=config) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) processor = SquadV2Processor() examples = [] timer = time.time() for i, question_text in enumerate(question_texts): example = SquadExample( qas_id=str(i), question_text=question_text, context_text=context_text, answer_text=None, start_position_character=None, title="Predict", answers=None, ) examples.append(example) print(f'Created Squad Examples in {time.time()-timer} seconds') print(f'Number of CPUs: {cpu_count()}') timer = time.time() features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=doc_stride, max_query_length=max_query_length, is_training=False, return_dataset="pt", threads=cpu_count(), ) print(f'Converted Examples to Features in {time.time()-timer} seconds') eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10) all_results = [] timer = time.time() for batch in eval_dataloader: model.eval() batch = tuple(t.to(device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2], } example_indices = batch[3] outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs.to_tuple()] start_logits, end_logits = output result = SquadResult(unique_id, start_logits, end_logits) all_results.append(result) print(f'Model predictions completed in {time.time()-timer} seconds') print(all_results) output_nbest_file = None if n_best_size > 1: output_nbest_file = "nbest.json" timer = time.time() final_predictions = compute_predictions_logits( all_examples=examples, all_features=features, all_results=all_results, n_best_size=n_best_size, max_answer_length=max_answer_length, do_lower_case=do_lower_case, output_prediction_file=None, output_nbest_file=output_nbest_file, output_null_log_odds_file=None, verbose_logging=False, version_2_with_negative=True, null_score_diff_threshold=null_score_diff_threshold, tokenizer=tokenizer ) print(f'Logits converted to predictions in {time.time()-timer} seconds') return final_predictions