Exemple #1
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def load_and_cache_examples(args, tokenizer):
    # Load data features from cache or dataset file
    input_file = args.train_file
    if args.test is True:
        logger.info("Test")
        examples = read_squad_examples(input_file="data/korquad/sample.json",
                                       eda_type=args.eda_type,
                                       is_training=True,
                                       version_2_with_negative=False,
                                       args=args)
        features = convert_examples_to_features(
            examples=examples,
            tokenizer=tokenizer,
            max_seq_length=args.max_seq_length,
            doc_stride=args.doc_stride,
            max_query_length=args.max_query_length,
            is_training=True)

        # Convert to Tensors and build dataset
        all_input_ids = torch.tensor([f.input_ids for f in features],
                                     dtype=torch.long)
        all_input_mask = torch.tensor([f.input_mask for f in features],
                                      dtype=torch.long)
        all_segment_ids = torch.tensor([f.segment_ids for f in features],
                                       dtype=torch.long)
        all_start_positions = torch.tensor(
            [f.start_position for f in features], dtype=torch.long)
        all_end_positions = torch.tensor([f.end_position for f in features],
                                         dtype=torch.long)
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_start_positions, all_end_positions)
        return dataset
    logger.info("Creating features from dataset file at %s", input_file)
    examples = read_squad_examples(input_file=args.train_file,
                                   is_training=True,
                                   version_2_with_negative=False,
                                   args=args)
    features = convert_examples_to_features(
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=args.max_seq_length,
        doc_stride=args.doc_stride,
        max_query_length=args.max_query_length,
        is_training=True)

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features],
                                 dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in features],
                                  dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in features],
                                   dtype=torch.long)
    all_start_positions = torch.tensor([f.start_position for f in features],
                                       dtype=torch.long)
    all_end_positions = torch.tensor([f.end_position for f in features],
                                     dtype=torch.long)
    dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                            all_start_positions, all_end_positions)
    return dataset
Exemple #2
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def load_and_cache_examples(args, tokenizer):
    # Load data features from cache or dataset file
    input_file = args.train_file
    cached_features_file = os.path.join(os.path.dirname(input_file), '_cached_{}_{}_{}'.format('train',
                                                                                               str(args.max_seq_length),
                                                                                               args.doc_stride))
    if os.path.exists(cached_features_file):
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", input_file)
        examples = read_squad_examples(input_file=args.train_file, is_training=True, version_2_with_negative=False)
        features = convert_examples_to_features(examples=examples,
                                                tokenizer=tokenizer,
                                                max_seq_length=args.max_seq_length,
                                                doc_stride=args.doc_stride,
                                                max_query_length=args.max_query_length,
                                                is_training=True)

        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file, pickle_protocol=pickle.HIGHEST_PROTOCOL)

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
    all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
    all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
    dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions)
    return dataset
Exemple #3
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def load_and_cache_examples(args, tokenizer):
    # Load data features from cache or dataset file
    examples = read_squad_examples(input_file=args.predict_file,
                                   is_training=False,
                                   version_2_with_negative=False)
    features = convert_examples_to_features(
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=args.max_seq_length,
        doc_stride=args.doc_stride,
        max_query_length=args.max_query_length,
        is_training=False)
    return examples, features
Exemple #4
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def load_and_cache_examples(args, tokenizer):
    # Load data features from cache or dataset file
    eval_cache_file = "evaluate_cache_file"
    os.path.join(args.output_dir, eval_cache_file)
    if os.path.exists(eval_cache_file):
        examples_and_features = torch.load(eval_cache_file)
        return (examples_and_features["examples"],
                examples_and_features["features"])
    else:
        examples = read_squad_examples(input_file=args.predict_file,
                                       is_training=False,
                                       version_2_with_negative=False)
        features = convert_examples_to_features(
            examples=examples,
            tokenizer=tokenizer,
            max_seq_length=args.max_seq_length,
            doc_stride=args.doc_stride,
            max_query_length=args.max_query_length,
            is_training=False)
        torch.save({
            "examples": examples,
            "features": features
        }, eval_cache_file)
    return examples, features
def main():
    parser = argparse.ArgumentParser()
    # Required parameters

    parser.add_argument("--output_dir", default='output', type=str,
                        help="The output directory where the model checkpoints and predictions will be written.")
    parser.add_argument("--checkpoint", default='pretrain_ckpt/bert_small_ckpt.bin',
                        type=str,
                        help="checkpoint")
    parser.add_argument("--model_config", default='data/bert_small.json',
                        type=str)
    # Other parameters
    parser.add_argument("--train_file", default='data/KorQuAD_v1.0_train.json', type=str,
                        help="SQuAD json for training. E.g., train-v1.1.json")
    parser.add_argument("--max_seq_length", default=512, type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
                             "longer than this will be truncated, and sequences shorter than this will be padded.")
    parser.add_argument("--doc_stride", default=128, type=int,
                        help="When splitting up a long document into chunks, how much stride to take between chunks.")
    parser.add_argument("--max_query_length", default=96, type=int,
                        help="The maximum number of tokens for the question. Questions longer than this will "
                             "be truncated to this length.")
    parser.add_argument("--train_batch_size", default=16, type=int, help="Total batch size for training.")
    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
    parser.add_argument("--num_train_epochs", default=4.0, type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
    parser.add_argument("--adam_epsilon", default=1e-6, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--warmup_proportion", default=0.1, type=float,
                        help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
                             "of training.")
    parser.add_argument("--n_best_size", default=20, type=int,
                        help="The total number of n-best predictions to generate in the nbest_predictions.json "
                             "output file.")
    parser.add_argument("--max_answer_length", default=30, type=int,
                        help="The maximum length of an answer that can be generated. This is needed because the start "
                             "and end predictions are not conditioned on one another.")
    parser.add_argument("--verbose_logging", action='store_true',
                        help="If true, all of the warnings related to data processing will be printed. "
                             "A number of warnings are expected for a normal SQuAD evaluation.")
    parser.add_argument("--no_cuda",
                        action='store_true',
                        help="Whether not to use CUDA when available")
    parser.add_argument('--seed',
                        type=int,
                        default=42,
                        help="random seed for initialization")
    parser.add_argument('--fp16',
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--fp16_opt_level', type=str, default='O2',
                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                             "See details at https://nvidia.github.io/apex/amp.html")
    parser.add_argument('--null_score_diff_threshold',
                        type=float, default=0.0,
                        help="If null_score - best_non_null is greater than the threshold predict null.")

    args = parser.parse_args()

    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    n_gpu = torch.cuda.device_count()
    logger.info("device: {} n_gpu: {}, 16-bits training: {}".format(
        device, n_gpu, args.fp16))

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    tokenizer = BertTokenizer('ko_vocab_32k.txt',
                              max_len=args.max_seq_length,
                              do_basic_tokenize=True)
    # Prepare model
    config = Config.from_json_file(args.model_config)
    model = QuestionAnswering(config)
    model.bert.load_state_dict(torch.load(args.checkpoint))
    num_params = count_parameters(model)
    logger.info("Total Parameter: %d" % num_params)
    model.to(device)

    cached_train_features_file = args.train_file + '_{0}_{1}_{2}'.format(str(args.max_seq_length), str(args.doc_stride),
                                                                         str(args.max_query_length))
    train_examples = read_squad_examples(input_file=args.train_file, is_training=True, version_2_with_negative=False)
    try:
        with open(cached_train_features_file, "rb") as reader:
            train_features = pickle.load(reader)
    except:
        train_features = convert_examples_to_features(
            examples=train_examples,
            tokenizer=tokenizer,
            max_seq_length=args.max_seq_length,
            doc_stride=args.doc_stride,
            max_query_length=args.max_query_length,
            is_training=True)
        logger.info("  Saving train features into cached file %s", cached_train_features_file)
        with open(cached_train_features_file, "wb") as writer:
            pickle.dump(train_features, writer)

    num_train_optimization_steps = int(len(train_features) / args.train_batch_size) * args.num_train_epochs

    # Prepare optimizer
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
         'weight_decay': 0.01},
        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
    ]

    optimizer = AdamW(optimizer_grouped_parameters,
                      lr=args.learning_rate,
                      eps=args.adam_epsilon)
    scheduler = WarmupLinearSchedule(optimizer,
                                     warmup_steps=num_train_optimization_steps*0.1,
                                     t_total=num_train_optimization_steps)

    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)

    logger.info("***** Running training *****")
    logger.info("  Num orig examples = %d", len(train_examples))
    logger.info("  Num split examples = %d", len(train_features))
    logger.info("  Batch size = %d", args.train_batch_size)
    logger.info("  Num steps = %d", num_train_optimization_steps)
    num_train_step = num_train_optimization_steps

    all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
    all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
    all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
    train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                               all_start_positions, all_end_positions)

    train_sampler = RandomSampler(train_data)
    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)

    model.train()
    global_step = 0
    epoch = 0
    for _ in trange(int(args.num_train_epochs), desc="Epoch"):
        iter_bar = tqdm(train_dataloader, desc="Train(XX Epoch) Step(XX/XX) (Mean loss=X.X) (loss=X.X)")
        tr_step, total_loss, mean_loss = 0, 0., 0.
        for step, batch in enumerate(iter_bar):
            if n_gpu == 1:
                batch = tuple(t.to(device) for t in batch)  # multi-gpu does scattering it-self
            input_ids, input_mask, segment_ids, start_positions, end_positions = batch
            loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
            if n_gpu > 1:
                loss = loss.mean()  # mean() to average on multi-gpu.
            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
            else:
                loss.backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

            scheduler.step()
            optimizer.step()
            optimizer.zero_grad()
            global_step += 1
            tr_step += 1
            total_loss += loss
            mean_loss = total_loss / tr_step
            iter_bar.set_description("Train Step(%d / %d) (Mean loss=%5.5f) (loss=%5.5f)" %
                                     (global_step, num_train_step, mean_loss, loss.item()))

        logger.info("** ** * Saving file * ** **")
        model_checkpoint = "korquad_%d.bin" % (epoch)
        logger.info(model_checkpoint)
        output_model_file = os.path.join(args.output_dir,model_checkpoint)
        if n_gpu > 1:
            torch.save(model.module.state_dict(), output_model_file)
        else:
            torch.save(model.state_dict(), output_model_file)
        epoch += 1
Exemple #6
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def main(input, output):
    max_seq_length = 512
    doc_stride = 64
    max_query_length = 64
    batch_size = 16
    n_best_size = 20
    max_answer_length = 30
    seed = 42
    fp16 = False

    device = torch.device("cpu")
    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    n_gpu = torch.cuda.device_count()
    logger.info("device: {} n_gpu: {}".format(device, n_gpu))

    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(seed)

    tokenizer = BertTokenizer(vocab_file=VOCAB_PATH,
                              max_len=max_seq_length,
                              do_basic_tokenize=True)
    eval_examples = read_squad_examples(input_file=input,
                                        is_training=False,
                                        version_2_with_negative=False)
    eval_features = convert_examples_to_features(
        examples=eval_examples,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=doc_stride,
        max_query_length=max_query_length,
        is_training=False)

    # Prepare model
    config = Config.from_json_file(CONFIG_PATH)
    model = QuestionAnswering(config)
    if fp16 is True:
        model.half()
    model.load_state_dict(torch.load(CHK_PATH, map_location=device))
    model.to(device)
    logger.info("***** Running training *****")
    logger.info("  Num orig examples = %d", len(eval_examples))
    logger.info("  Num split examples = %d", len(eval_features))
    logger.info("  Batch size = %d", batch_size)

    all_input_ids = torch.tensor([f.input_ids for f in eval_features],
                                 dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in eval_features],
                                  dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
                                   dtype=torch.long)
    all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
    eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                              all_example_index)

    eval_sampler = SequentialSampler(eval_data)
    eval_dataloader = DataLoader(eval_data,
                                 sampler=eval_sampler,
                                 batch_size=batch_size)

    model.eval()
    all_results = []
    logger.info("Start evaluating")
    for input_ids, input_mask, segment_ids, example_indices in tqdm(
            eval_dataloader, desc="Evaluating"):
        input_ids = input_ids.to(device)
        input_mask = input_mask.to(device)
        segment_ids = segment_ids.to(device)
        with torch.no_grad():
            batch_start_logits, batch_end_logits = model(
                input_ids, segment_ids, input_mask)
        for i, example_index in enumerate(example_indices):
            start_logits = batch_start_logits[i].detach().cpu().tolist()
            end_logits = batch_end_logits[i].detach().cpu().tolist()
            eval_feature = eval_features[example_index.item()]
            unique_id = int(eval_feature.unique_id)
            all_results.append(
                RawResult(unique_id=unique_id,
                          start_logits=start_logits,
                          end_logits=end_logits))
    output_nbest_file = os.path.join("nbest_predictions.json")
    write_predictions(eval_examples, eval_features, all_results, n_best_size,
                      max_answer_length, False, output, output_nbest_file,
                      None, False, False, 0.0)