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| """ | |
| This script provides an example to use prompt for classification inference. | |
| """ | |
| import sys | |
| import os | |
| tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) | |
| sys.path.append(tencentpretrain_dir) | |
| from tencentpretrain.model_loader import load_model | |
| from tencentpretrain.opts import infer_opts, tokenizer_opts | |
| from finetune.run_classifier_prompt import * | |
| def read_dataset(args, path): | |
| dataset, columns = [], {} | |
| with open(path, mode="r", encoding="utf-8") as f: | |
| for line_id, line in enumerate(f): | |
| if line_id == 0: | |
| for i, column_name in enumerate(line.rstrip("\r\n").split("\t")): | |
| columns[column_name] = i | |
| continue | |
| line = line.rstrip("\r\n").split("\t") | |
| mask_position = -1 | |
| tgt_token_id = [1] | |
| src = [args.tokenizer.vocab.get(CLS_TOKEN)] | |
| if "text_b" not in columns: # Sentence classification. | |
| text_a = line[columns["text_a"]] | |
| text_a_token_id = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_a)) | |
| max_length = args.seq_length - args.template_length - 2 | |
| text_a_token_id = text_a_token_id[:max_length] | |
| for prompt_token in args.prompt_template: | |
| if prompt_token == "[TEXT_A]": | |
| src += text_a_token_id | |
| elif prompt_token == "[ANS]": | |
| src += [args.tokenizer.vocab.get(MASK_TOKEN)] | |
| mask_position = len(src) - 1 | |
| else: | |
| src += prompt_token | |
| else: # Sentence-pair classification. | |
| text_a, text_b = line[columns["text_a"]], line[columns["text_b"]] | |
| text_a_token_id = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_a)) | |
| text_b_token_id = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b)) | |
| max_length = args.seq_length - args.template_length - len(text_a_token_id) - 3 | |
| text_b_token_id = text_b_token_id[:max_length] | |
| for prompt_token in args.prompt_template: | |
| if prompt_token == "[TEXT_A]": | |
| src += text_a_token_id | |
| src += [args.tokenizer.vocab.get(SEP_TOKEN)] | |
| elif prompt_token == "[ANS]": | |
| src += [args.tokenizer.vocab.get(MASK_TOKEN)] | |
| mask_position = len(src) - 1 | |
| elif prompt_token == "[TEXT_B]": | |
| src += text_b_token_id | |
| else: | |
| src += prompt_token | |
| src += [args.tokenizer.vocab.get(SEP_TOKEN)] | |
| seg = [1] * len(src) | |
| PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0] | |
| while len(src) < args.seq_length: | |
| src.append(PAD_ID) | |
| seg.append(0) | |
| tgt = [0] * len(src) | |
| tgt[mask_position] = tgt_token_id[0] | |
| dataset.append((src, tgt, seg)) | |
| return dataset | |
| def main(): | |
| parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| infer_opts(parser) | |
| tokenizer_opts(parser) | |
| parser.add_argument("--output_logits", action="store_true", help="Write logits to output file.") | |
| parser.add_argument("--output_prob", action="store_true", help="Write probabilities to output file.") | |
| parser.add_argument("--prompt_id", type=str, default="chnsenticorp_char") | |
| parser.add_argument("--prompt_path", type=str, default="models/prompts.json") | |
| args = parser.parse_args() | |
| # Load the hyperparameters from the config file. | |
| args = load_hyperparam(args) | |
| # Build tokenizer. | |
| args.tokenizer = str2tokenizer[args.tokenizer](args) | |
| process_prompt_template(args) | |
| answer_position = [0] * len(args.tokenizer.vocab) | |
| for answer in args.answer_word_dict_inv: | |
| answer_position[int(args.tokenizer.vocab[answer])] = 1 | |
| args.answer_position = torch.LongTensor(answer_position) | |
| args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Build classification model and load parameters. | |
| model = ClozeTest(args) | |
| model = load_model(model, args.load_model_path) | |
| # For simplicity, we use DataParallel wrapper to use multiple GPUs. | |
| model = model.to(args.device) | |
| if torch.cuda.device_count() > 1: | |
| print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count())) | |
| model = torch.nn.DataParallel(model) | |
| dataset = read_dataset(args, args.test_path) | |
| src = torch.LongTensor([sample[0] for sample in dataset]) | |
| tgt = torch.LongTensor([sample[1] for sample in dataset]) | |
| seg = torch.LongTensor([sample[2] for sample in dataset]) | |
| batch_size = args.batch_size | |
| instances_num = src.size()[0] | |
| print("The number of prediction instances: ", instances_num) | |
| model.eval() | |
| with open(args.prediction_path, mode="w", encoding="utf-8") as f: | |
| f.write("label") | |
| if args.output_logits: | |
| f.write("\t" + "logits") | |
| if args.output_prob: | |
| f.write("\t" + "prob") | |
| f.write("\n") | |
| for _, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)): | |
| src_batch = src_batch.to(args.device) | |
| tgt_batch = tgt_batch.to(args.device) | |
| seg_batch = seg_batch.to(args.device) | |
| with torch.no_grad(): | |
| _, pred, logits = model(src_batch, tgt_batch, seg_batch) | |
| logits = logits[:, args.answer_position > 0] | |
| prob = nn.Softmax(dim=1)(logits) | |
| logits = logits.cpu().numpy().tolist() | |
| prob = prob.cpu().numpy().tolist() | |
| for j in range(len(pred)): | |
| f.write(str(pred[j])) | |
| if args.output_logits: | |
| f.write("\t" + " ".join([str(v) for v in logits[j]])) | |
| if args.output_prob: | |
| f.write("\t" + " ".join([str(v) for v in prob[j]])) | |
| f.write("\n") | |
| if __name__ == "__main__": | |
| main() | |