Merge pull request #1 from erenup/run_multiple_choice

roberta, xlnet for multiple choice

examples/single_model_scripts/run_multiple_choice.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Finetuning the library models for multiple choice (Bert, XLM, XLNet)."""
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import glob
+import logging
+import os
+import random
+
+
+import numpy as np
+import torch
+from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
+                              TensorDataset)
+from torch.utils.data.distributed import DistributedSampler
+from tensorboardX import SummaryWriter
+from tqdm import tqdm, trange
+
+from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
+                                  BertForMultipleChoice, BertTokenizer,
+                                  XLNetConfig, XLNetForMultipleChoice,
+                                  XLNetTokenizer, RobertaConfig,
+                                  RobertaForMultipleChoice, RobertaTokenizer)
+
+from pytorch_transformers import AdamW, WarmupLinearSchedule
+
+from utils_multiple_choice import (convert_examples_to_features, processors)
+
+logger = logging.getLogger(__name__)
+
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig)), ())
+
+MODEL_CLASSES = {
+    'bert': (BertConfig, BertForMultipleChoice, BertTokenizer),
+    'xlnet': (XLNetConfig, XLNetForMultipleChoice, XLNetTokenizer),
+    'roberta': (RobertaConfig, RobertaForMultipleChoice, RobertaTokenizer)
+}
+
+def select_field(features, field):
+    return [
+        [
+            choice[field]
+            for choice in feature.choices_features
+        ]
+        for feature in features
+    ]
+
+
+def simple_accuracy(preds, labels):
+    return (preds == labels).mean()
+
+
+def set_seed(args):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
+def train(args, train_dataset, model, tokenizer):
+    """ Train the model """
+    if args.local_rank in [-1, 0]:
+        tb_writer = SummaryWriter()
+
+    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
+    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
+    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
+
+    if args.max_steps > 0:
+        t_total = args.max_steps
+        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
+    else:
+        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
+
+    # Prepare optimizer and schedule (linear warmup and decay)
+    no_decay = ['bias', 'LayerNorm.weight']
+    optimizer_grouped_parameters = [
+        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
+        {'params': [p for n, p in model.named_parameters() 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=args.warmup_steps, t_total=t_total)
+    if args.fp16:
+        try:
+            from apex import amp
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
+
+    # multi-gpu training (should be after apex fp16 initialization)
+    if args.n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    # Distributed training (should be after apex fp16 initialization)
+    if args.local_rank != -1:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
+                                                          output_device=args.local_rank,
+                                                          find_unused_parameters=True)
+
+    # Train!
+    logger.info("***** Running training *****")
+    logger.info("  Num examples = %d", len(train_dataset))
+    logger.info("  Num Epochs = %d", args.num_train_epochs)
+    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
+    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
+                   args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
+    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
+    logger.info("  Total optimization steps = %d", t_total)
+
+    global_step = 0
+    tr_loss, logging_loss = 0.0, 0.0
+    best_dev_acc, best_dev_loss = 0.0, 99999999999.0
+    best_steps = 0
+    model.zero_grad()
+    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
+    for _ in train_iterator:
+        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
+        for step, batch in enumerate(epoch_iterator):
+            model.train()
+            batch = tuple(t.to(args.device) for t in batch)
+            inputs = {'input_ids':      batch[0],
+                      'attention_mask': batch[1],
+                      'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM don't use segment_ids
+                      'labels':         batch[3]}
+            outputs = model(**inputs)
+            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
+
+            if args.n_gpu > 1:
+                loss = loss.mean() # mean() to average on multi-gpu parallel training
+            if args.gradient_accumulation_steps > 1:
+                loss = loss / args.gradient_accumulation_steps
+
+            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)
+
+            tr_loss += loss.item()
+            if (step + 1) % args.gradient_accumulation_steps == 0:
+                scheduler.step()  # Update learning rate schedule
+                optimizer.step()
+                model.zero_grad()
+                global_step += 1
+
+                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
+                    # Log metrics
+                    if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
+                        results = evaluate(args, model, tokenizer)
+                        for key, value in results.items():
+                            tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
+                        if results["eval_acc"] < best_dev_acc:
+                            best_dev_acc = results["eval_acc"]
+                            best_dev_loss = results["eval_loss"]
+                            best_steps = global_step
+                            if args.do_test:
+                                results_test = evaluate(args, model, tokenizer, test=True)
+                                for key, value in results_test.items():
+                                    tb_writer.add_scalar('test_{}'.format(key), value, global_step)
+                                logger.info("test acc: %s, loss: %s, global steps: %s", str(results_test['eval_acc']), str(results_test['eval_loss']), str(global_step))
+                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
+                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
+                    logger.info("Average loss: %s at global step: %s", str((tr_loss - logging_loss)/args.logging_steps), str(global_step))
+                    logging_loss = tr_loss
+
+                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
+                    # Save model checkpoint
+                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
+                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+                    model_to_save.save_pretrained(output_dir)
+                    tokenizer.save_vocabulary(output_dir)
+                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
+                    logger.info("Saving model checkpoint to %s", output_dir)
+
+            if args.max_steps > 0 and global_step > args.max_steps:
+                epoch_iterator.close()
+                break
+        if args.max_steps > 0 and global_step > args.max_steps:
+            train_iterator.close()
+            break
+
+    if args.local_rank in [-1, 0]:
+        tb_writer.close()
+
+    return global_step, tr_loss / global_step, best_steps
+
+
+def evaluate(args, model, tokenizer, prefix="", test=False):
+    # Loop to handle MNLI double evaluation (matched, mis-matched)
+    eval_task_names = (args.task_name,)
+    eval_outputs_dirs = (args.output_dir,)
+
+    results = {}
+    for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
+        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=not test, test=test)
+
+        if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(eval_output_dir)
+
+        args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
+        # Note that DistributedSampler samples randomly
+        eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
+        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
+
+        # Eval!
+        logger.info("***** Running evaluation {} *****".format(prefix))
+        logger.info("  Num examples = %d", len(eval_dataset))
+        logger.info("  Batch size = %d", args.eval_batch_size)
+        eval_loss = 0.0
+        nb_eval_steps = 0
+        preds = None
+        out_label_ids = None
+        for batch in tqdm(eval_dataloader, desc="Evaluating"):
+            model.eval()
+            batch = tuple(t.to(args.device) for t in batch)
+
+            with torch.no_grad():
+                inputs = {'input_ids':      batch[0],
+                          'attention_mask': batch[1],
+                          'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM don't use segment_ids
+                          'labels':         batch[3]}
+                outputs = model(**inputs)
+                tmp_eval_loss, logits = outputs[:2]
+
+                eval_loss += tmp_eval_loss.mean().item()
+            nb_eval_steps += 1
+            if preds is None:
+                preds = logits.detach().cpu().numpy()
+                out_label_ids = inputs['labels'].detach().cpu().numpy()
+            else:
+                preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
+                out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)
+
+        eval_loss = eval_loss / nb_eval_steps
+        preds = np.argmax(preds, axis=1)
+        acc = simple_accuracy(preds, out_label_ids)
+        result = {"eval_acc": acc, "eval_loss": eval_loss}
+        results.update(result)
+
+        output_eval_file = os.path.join(eval_output_dir, "is_test_" + str(test) + "_eval_results.txt")
+
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results {} *****".format(str(prefix) + " is test:" + str(test)))
+            writer.write("model           =%s\n" % str(args.model_name_or_path))
+            writer.write("total batch size=%d\n" % (args.per_gpu_train_batch_size * args.gradient_accumulation_steps *
+                         (torch.distributed.get_world_size() if args.local_rank != -1 else 1)))
+            writer.write("train num epochs=%d\n" % args.num_train_epochs)
+            writer.write("fp16            =%s\n" % args.fp16)
+            writer.write("max seq length  =%d\n" % args.max_seq_length)
+            for key in sorted(result.keys()):
+                logger.info("  %s = %s", key, str(result[key]))
+                writer.write("%s = %s\n" % (key, str(result[key])))
+    return results
+
+
+def load_and_cache_examples(args, task, tokenizer, evaluate=False, test=False):
+    if args.local_rank not in [-1, 0]:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+
+    processor = processors[task]()
+    # Load data features from cache or dataset file
+    if evaluate:
+        cached_mode = 'dev'
+    elif test:
+        cached_mode = 'test'
+    else:
+        cached_mode = 'train'
+    assert (evaluate == True and test == True) == False
+    cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format(
+        cached_mode,
+        list(filter(None, args.model_name_or_path.split('/'))).pop(),
+        str(args.max_seq_length),
+        str(task)))
+    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", args.data_dir)
+        label_list = processor.get_labels()
+        if evaluate:
+            examples = processor.get_dev_examples(args.data_dir)
+        elif test:
+            examples = processor.get_test_examples(args.data_dir)
+        else:
+            examples = processor.get_train_examples(args.data_dir)
+        logger.info("Training number: %s", str(len(examples)))
+        features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer,
+            cls_token_at_end=bool(args.model_type in ['xlnet']),            # xlnet has a cls token at the end
+            cls_token=tokenizer.cls_token,
+            sep_token=tokenizer.sep_token,
+            sep_token_extra=bool(args.model_type in ['roberta']),
+            cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0,
+            pad_on_left=bool(args.model_type in ['xlnet']),                 # pad on the left for xlnet
+            pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0)
+        if args.local_rank in [-1, 0]:
+            logger.info("Saving features into cached file %s", cached_features_file)
+            torch.save(features, cached_features_file)
+
+    if args.local_rank == 0:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+
+    # Convert to Tensors and build dataset
+    all_input_ids = torch.tensor(select_field(features, 'input_ids'), dtype=torch.long)
+    all_input_mask = torch.tensor(select_field(features, 'input_mask'), dtype=torch.long)
+    all_segment_ids = torch.tensor(select_field(features, 'segment_ids'), dtype=torch.long)
+    all_label_ids = torch.tensor([f.label for f in features], dtype=torch.long)
+
+    dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
+    return dataset
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    ## Required parameters
+    parser.add_argument("--data_dir", default=None, type=str, required=True,
+                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
+    parser.add_argument("--model_type", default=None, type=str, required=True,
+                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
+    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
+                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
+    parser.add_argument("--task_name", default=None, type=str, required=True,
+                        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
+    parser.add_argument("--output_dir", default=None, type=str, required=True,
+                        help="The output directory where the model predictions and checkpoints will be written.")
+
+    ## Other parameters
+    parser.add_argument("--config_name", default="", type=str,
+                        help="Pretrained config name or path if not the same as model_name")
+    parser.add_argument("--tokenizer_name", default="", type=str,
+                        help="Pretrained tokenizer name or path if not the same as model_name")
+    parser.add_argument("--cache_dir", default="", type=str,
+                        help="Where do you want to store the pre-trained models downloaded from s3")
+    parser.add_argument("--max_seq_length", default=128, type=int,
+                        help="The maximum total input sequence length after tokenization. Sequences longer "
+                             "than this will be truncated, sequences shorter will be padded.")
+    parser.add_argument("--do_train", action='store_true',
+                        help="Whether to run training.")
+    parser.add_argument("--do_eval", action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--do_test", action='store_true', help='Whether to run test on the test set')
+    parser.add_argument("--evaluate_during_training", action='store_true',
+                        help="Rul evaluation during training at each logging step.")
+    parser.add_argument("--do_lower_case", action='store_true',
+                        help="Set this flag if you are using an uncased model.")
+
+    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
+                        help="Batch size per GPU/CPU for training.")
+    parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
+                        help="Batch size per GPU/CPU for evaluation.")
+    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
+                        help="Number of updates steps to accumulate before performing a backward/update pass.")
+    parser.add_argument("--learning_rate", default=5e-5, type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--weight_decay", default=0.0, type=float,
+                        help="Weight deay if we apply some.")
+    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
+                        help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float,
+                        help="Max gradient norm.")
+    parser.add_argument("--num_train_epochs", default=3.0, type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument("--max_steps", default=-1, type=int,
+                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
+    parser.add_argument("--warmup_steps", default=0, type=int,
+                        help="Linear warmup over warmup_steps.")
+
+    parser.add_argument('--logging_steps', type=int, default=50,
+                        help="Log every X updates steps.")
+    parser.add_argument('--save_steps', type=int, default=50,
+                        help="Save checkpoint every X updates steps.")
+    parser.add_argument("--eval_all_checkpoints", action='store_true',
+                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
+    parser.add_argument("--no_cuda", action='store_true',
+                        help="Avoid using CUDA when available")
+    parser.add_argument('--overwrite_output_dir', action='store_true',
+                        help="Overwrite the content of the output directory")
+    parser.add_argument('--overwrite_cache', action='store_true',
+                        help="Overwrite the cached training and evaluation sets")
+    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 (mixed) precision (through NVIDIA apex) instead of 32-bit")
+    parser.add_argument('--fp16_opt_level', type=str, default='O1',
+                        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("--local_rank", type=int, default=-1,
+                        help="For distributed training: local_rank")
+    parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
+    parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
+    args = parser.parse_args()
+
+    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
+        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
+
+    # Setup distant debugging if needed
+    if args.server_ip and args.server_port:
+        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
+        import ptvsd
+        print("Waiting for debugger attach")
+        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
+        ptvsd.wait_for_attach()
+
+    # Setup CUDA, GPU & distributed training
+    if args.local_rank == -1 or args.no_cuda:
+        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+        args.n_gpu = torch.cuda.device_count()
+    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.cuda.set_device(args.local_rank)
+        device = torch.device("cuda", args.local_rank)
+        torch.distributed.init_process_group(backend='nccl')
+        args.n_gpu = 1
+    args.device = device
+
+    # Setup logging
+    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                        datefmt = '%m/%d/%Y %H:%M:%S',
+                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
+    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
+                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
+
+    # Set seed
+    set_seed(args)
+
+    # Prepare GLUE task
+    args.task_name = args.task_name.lower()
+    if args.task_name not in processors:
+        raise ValueError("Task not found: %s" % (args.task_name))
+    processor = processors[args.task_name]()
+    label_list = processor.get_labels()
+    num_labels = len(label_list)
+
+    # Load pretrained model and tokenizer
+    if args.local_rank not in [-1, 0]:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+
+    args.model_type = args.model_type.lower()
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
+    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
+    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
+
+    if args.local_rank == 0:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+
+    model.to(args.device)
+
+    logger.info("Training/evaluation parameters %s", args)
+    best_steps = 0
+
+    # Training
+    if args.do_train:
+        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
+        global_step, tr_loss, best_steps = train(args, train_dataset, model, tokenizer)
+        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
+
+
+    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
+    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
+        logger.info("Saving model checkpoint to %s", args.output_dir)
+        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
+        # They can then be reloaded using `from_pretrained()`
+        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+        model_to_save.save_pretrained(args.output_dir)
+        tokenizer.save_pretrained(args.output_dir)
+
+        # Good practice: save your training arguments together with the trained model
+        torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
+
+        # Load a trained model and vocabulary that you have fine-tuned
+        model = model_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
+        model.to(args.device)
+
+
+    # Evaluation
+    results = {}
+    if args.do_eval and args.local_rank in [-1, 0]:
+        if not args.do_train:
+            args.output_dir = args.model_name_or_path
+        checkpoints = [args.output_dir]
+        if args.eval_all_checkpoints:
+            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
+            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+        logger.info("Evaluate the following checkpoints: %s", checkpoints)
+        for checkpoint in checkpoints:
+            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
+            model = model_class.from_pretrained(checkpoint)
+            model.to(args.device)
+            result = evaluate(args, model, tokenizer, prefix=global_step)
+            result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
+            results.update(result)
+
+    if args.do_test and args.local_rank in [-1, 0]:
+        if not args.do_train:
+            args.output_dir = args.model_name_or_path
+        checkpoints = [args.output_dir]
+        if args.eval_all_checkpoints: #can not use this to do test!! just for different paras
+            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
+            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+        logger.info("Evaluate the following checkpoints: %s", checkpoints)
+        for checkpoint in checkpoints:
+            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
+            model = model_class.from_pretrained(checkpoint)
+            model.to(args.device)
+            result = evaluate(args, model, tokenizer, prefix=global_step, test=True)
+            result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
+            results.update(result)
+    if best_steps:
+        logger.info("best steps of eval acc is the following checkpoints: %s", best_steps)
+    return results
+
+
+if __name__ == "__main__":
+    main()

examples/single_model_scripts/utils_multiple_choice.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" BERT classification fine-tuning: utilities to work with GLUE tasks """
+
+from __future__ import absolute_import, division, print_function
+
+
+import logging
+import os
+import sys
+from io import open
+import json
+import csv
+import glob
+import tqdm
+
+
+logger = logging.getLogger(__name__)
+
+
+class InputExample(object):
+    """A single training/test example for multiple choice"""
+
+    def __init__(self, example_id, question,  contexts, endings, label=None):
+        """Constructs a InputExample.
+
+        Args:
+            guid: Unique id for the example.
+            text_a: string. The untokenized text of the first sequence. For single
+            sequence tasks, only this sequence must be specified.
+            text_b: (Optional) string. The untokenized text of the second sequence.
+            Only must be specified for sequence pair tasks.
+            label: (Optional) string. The label of the example. This should be
+            specified for train and dev examples, but not for test examples.
+        """
+        self.example_id = example_id
+        self.question = question
+        self.contexts = contexts
+        self.endings = endings
+        self.label = label
+
+
+class InputFeatures(object):
+    def __init__(self,
+                 example_id,
+                 choices_features,
+                 label
+
+    ):
+        self.example_id = example_id
+        self.choices_features = [
+            {
+                'input_ids': input_ids,
+                'input_mask': input_mask,
+                'segment_ids': segment_ids
+            }
+            for _, input_ids, input_mask, segment_ids in choices_features
+        ]
+        self.label = label
+
+
+class DataProcessor(object):
+    """Base class for data converters for sequence classification data sets."""
+
+    def get_train_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the train set."""
+        raise NotImplementedError()
+
+    def get_dev_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the dev set."""
+        raise NotImplementedError()
+
+    def get_test_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the dev set."""
+        raise NotImplementedError()
+
+    def get_labels(self):
+        """Gets the list of labels for this data set."""
+        raise NotImplementedError()
+
+
+class RaceProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} train".format(data_dir))
+        high = os.path.join(data_dir, 'train/high')
+        middle = os.path.join(data_dir, 'train/middle')
+        high = self._read_txt(high)
+        middle = self._read_txt(middle)
+        return self._create_examples(high + middle, 'train')
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} dev".format(data_dir))
+        high = os.path.join(data_dir, 'dev/high')
+        middle = os.path.join(data_dir, 'dev/middle')
+        high = self._read_txt(high)
+        middle = self._read_txt(middle)
+        return self._create_examples(high + middle, 'dev')
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} test".format(data_dir))
+        high = os.path.join(data_dir, 'test/high')
+        middle = os.path.join(data_dir, 'test/middle')
+        high = self._read_txt(high)
+        middle = self._read_txt(middle)
+        return self._create_examples(high + middle, 'test')
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1", "2", "3"]
+
+    def _read_txt(self, input_dir):
+        lines = []
+        files = glob.glob(input_dir + "/*txt")
+        for file in tqdm.tqdm(files, desc="read files"):
+            with open(file, 'r', encoding='utf-8') as fin:
+                data_raw = json.load(fin)
+                data_raw["race_id"] = file
+                lines.append(data_raw)
+        return lines
+
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (_, data_raw) in enumerate(lines):
+            race_id = "%s-%s" % (set_type, data_raw["race_id"])
+            article = data_raw["article"]
+            for i in range(len(data_raw["answers"])):
+                truth = str(ord(data_raw['answers'][i]) - ord('A'))
+                question = data_raw['questions'][i]
+                options = data_raw['options'][i]
+
+                examples.append(
+                    InputExample(
+                        example_id=race_id,
+                        question=question,
+                        contexts=[article, article, article, article],
+                        endings=[options[0], options[1], options[2], options[3]],
+                        label=truth))
+        return examples
+
+class SwagProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} train".format(data_dir))
+        return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} dev".format(data_dir))
+        return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} test".format(data_dir))
+        return self._create_examples(self._read_csv(os.path.join(data_dir, "test.csv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1", "2", "3"]
+
+    def _read_csv(self, input_file):
+        with open(input_file, 'r', encoding='utf-8') as f:
+            reader = csv.reader(f)
+            lines = []
+            for line in reader:
+                if sys.version_info[0] == 2:
+                    line = list(unicode(cell, 'utf-8') for cell in line)
+                lines.append(line)
+            return lines
+
+
+    def _create_examples(self, lines, type):
+        """Creates examples for the training and dev sets."""
+        if type == "train" and lines[0][-1] != 'label':
+            raise ValueError(
+                "For training, the input file must contain a label column."
+            )
+
+        examples = [
+            InputExample(
+                example_id=line[2],
+                question=line[5],  # in the swag dataset, the
+                # common beginning of each
+                # choice is stored in "sent2".
+                contexts = [line[4], line[4], line[4], line[4]],
+                endings = [line[7], line[8], line[9], line[10]],
+                label=line[11]
+            ) for line in lines[1:]  # we skip the line with the column names
+        ]
+
+        return examples
+
+
+class ArcProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} train".format(data_dir))
+        return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {} dev".format(data_dir))
+        return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")
+
+    def get_test_examples(self, data_dir):
+        logger.info("LOOKING AT {} test".format(data_dir))
+        return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1", "2", "3"]
+
+    def _read_json(self, input_file):
+        with open(input_file, 'r', encoding='utf-8') as fin:
+            lines = fin.readlines()
+            return lines
+
+
+    def _create_examples(self, lines, type):
+        """Creates examples for the training and dev sets."""
+
+        def normalize(truth):
+            if truth in "ABCD":
+                return ord(truth) - ord("A")
+            elif truth in "1234":
+                return int(truth) - 1
+            else:
+                logger.info("truth ERROR! %s", str(truth))
+                return None
+
+        examples = []
+        three_choice = 0
+        four_choice = 0
+        five_choice = 0
+        other_choices = 0
+        for line in tqdm.tqdm(lines, desc="read arc data"):
+            data_raw = json.loads(line.strip("\n"))
+            if len(data_raw["question"]["choices"]) == 3:
+                three_choice += 1
+                continue
+            elif len(data_raw["question"]["choices"]) == 5:
+                five_choice += 1
+                continue
+            elif len(data_raw["question"]["choices"]) != 4:
+                other_choices += 1
+                continue
+            four_choice += 1
+            truth = str(normalize(data_raw["answerKey"]))
+            assert truth != "None"
+            question_choices = data_raw["question"]
+            question = question_choices["stem"]
+            id = data_raw["id"]
+            options = question_choices["choices"]
+
+            if len(options) == 4:
+                examples.append(
+                    InputExample(
+                        example_id = id,
+                        question=question,
+                        contexts=[options[0]["para"].replace("_", ""), options[1]["para"].replace("_", ""),
+                                  options[2]["para"].replace("_", ""), options[3]["para"].replace("_", "")],
+                        endings=[options[0]["text"], options[1]["text"], options[2]["text"], options[3]["text"]],
+                        label=truth))
+
+        if type == "train":
+            assert len(examples) > 1
+            assert examples[0].label is not None
+        logger.info("len examples: %s}", str(len(examples)))
+        logger.info("Three choices: %s", str(three_choice))
+        logger.info("Five choices: %s", str(five_choice))
+        logger.info("Other choices: %s", str(other_choices))
+        logger.info("four choices: %s", str(four_choice))
+
+        return examples
+
+
+def convert_examples_to_features(examples, label_list, max_seq_length,
+                                 tokenizer,
+                                 cls_token_at_end=False,
+                                 cls_token='[CLS]',
+                                 cls_token_segment_id=1,
+                                 sep_token='[SEP]',
+                                 sequence_a_segment_id=0,
+                                 sequence_b_segment_id=1,
+                                 sep_token_extra=False,
+                                 pad_token_segment_id=0,
+                                 pad_on_left=False,
+                                 pad_token=0,
+                                 mask_padding_with_zero=True):
+    """ Loads a data file into a list of `InputBatch`s
+        `cls_token_at_end` define the location of the CLS token:
+            - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
+            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
+        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
+    """
+
+    label_map = {label : i for i, label in enumerate(label_list)}
+
+    features = []
+    for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
+        if ex_index % 10000 == 0:
+            logger.info("Writing example %d of %d" % (ex_index, len(examples)))
+        choices_features = []
+        for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):
+            tokens_a = tokenizer.tokenize(context)
+            tokens_b = None
+            if example.question.find("_") != -1:
+                tokens_b = tokenizer.tokenize(example.question.replace("_", ending))
+            else:
+                tokens_b = tokenizer.tokenize(example.question)
+                tokens_b += [sep_token]
+                if sep_token_extra:
+                    tokens_b += [sep_token]
+                tokens_b += tokenizer.tokenize(ending)
+
+            special_tokens_count = 4 if sep_token_extra else 3
+            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count)
+
+            # The convention in BERT is:
+            # (a) For sequence pairs:
+            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+            #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
+            # (b) For single sequences:
+            #  tokens:   [CLS] the dog is hairy . [SEP]
+            #  type_ids:   0   0   0   0  0     0   0
+            #
+            # Where "type_ids" are used to indicate whether this is the first
+            # sequence or the second sequence. The embedding vectors for `type=0` and
+            # `type=1` were learned during pre-training and are added to the wordpiece
+            # embedding vector (and position vector). This is not *strictly* necessary
+            # since the [SEP] token unambiguously separates the sequences, but it makes
+            # it easier for the model to learn the concept of sequences.
+            #
+            # For classification tasks, the first vector (corresponding to [CLS]) is
+            # used as as the "sentence vector". Note that this only makes sense because
+            # the entire model is fine-tuned.
+            tokens = tokens_a + [sep_token]
+            if sep_token_extra:
+                # roberta uses an extra separator b/w pairs of sentences
+                tokens += [sep_token]
+
+            segment_ids = [sequence_a_segment_id] * len(tokens)
+
+            if tokens_b:
+                tokens += tokens_b + [sep_token]
+                segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
+
+            if cls_token_at_end:
+                tokens = tokens + [cls_token]
+                segment_ids = segment_ids + [cls_token_segment_id]
+            else:
+                tokens = [cls_token] + tokens
+                segment_ids = [cls_token_segment_id] + segment_ids
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+            # The mask has 1 for real tokens and 0 for padding tokens. Only real
+            # tokens are attended to.
+            input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            padding_length = max_seq_length - len(input_ids)
+            if pad_on_left:
+                input_ids = ([pad_token] * padding_length) + input_ids
+                input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
+                segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
+            else:
+                input_ids = input_ids + ([pad_token] * padding_length)
+                input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
+                segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
+
+            assert len(input_ids) == max_seq_length
+            assert len(input_mask) == max_seq_length
+            assert len(segment_ids) == max_seq_length
+            choices_features.append((tokens, input_ids, input_mask, segment_ids))
+        label = label_map[example.label]
+
+        if ex_index < 2:
+            logger.info("*** Example ***")
+            logger.info("race_id: {}".format(example.example_id))
+            for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
+                logger.info("choice: {}".format(choice_idx))
+                logger.info("tokens: {}".format(' '.join(tokens)))
+                logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
+                logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
+                logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
+                logger.info("label: {}".format(label))
+
+        features.append(
+            InputFeatures(
+                example_id = example.example_id,
+                choices_features = choices_features,
+                label = label
+            )
+        )
+
+    return features
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+    """Truncates a sequence pair in place to the maximum length."""
+
+    # This is a simple heuristic which will always truncate the longer sequence
+    # one token at a time. This makes more sense than truncating an equal percent
+    # of tokens from each, since if one sequence is very short then each token
+    # that's truncated likely contains more information than a longer sequence.
+    while True:
+        total_length = len(tokens_a) + len(tokens_b)
+        if total_length <= max_length:
+            break
+        # if len(tokens_a) > len(tokens_b):
+        #     tokens_a.pop()
+        # else:
+        #     tokens_b.pop()
+        tokens_a.pop()
+
+
+processors = {
+    "race": RaceProcessor,
+    "swag": SwagProcessor,
+    "arc": ArcProcessor
+}
+
+
+GLUE_TASKS_NUM_LABELS = {
+    "race", 4,
+    "swag", 4,
+    "arc", 4
+}

pytorch_transformers/__init__.py

@@ -31,7 +31,7 @@ from .modeling_gpt2 import (GPT2Config, GPT2PreTrainedModel, GPT2Model,
                             GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
 from .modeling_xlnet import (XLNetConfig,
                              XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
-                             XLNetForSequenceClassification, XLNetForQuestionAnswering,
+                             XLNetForSequenceClassification, XLNetForQuestionAnswering, XLNetForMultipleChoice,
                              load_tf_weights_in_xlnet, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
                              XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
 from .modeling_xlm import (XLMConfig, XLMPreTrainedModel , XLMModel,
@@ -39,6 +39,7 @@ from .modeling_xlm import (XLMConfig, XLMPreTrainedModel , XLMModel,
                            XLMForQuestionAnswering, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
                            XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
 from .modeling_roberta import (RobertaConfig, RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
+                               RobertaForMultipleChoice,
                                ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
 from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
                           PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)

pytorch_transformers/modeling_roberta.py

@@ -329,6 +329,46 @@ class RobertaForSequenceClassification(BertPreTrainedModel):
         return outputs  # (loss), logits, (hidden_states), (attentions)
 
 
+class RobertaForMultipleChoice(BertPreTrainedModel):
+    config_class = RobertaConfig
+    pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+    base_model_prefix = "roberta"
+
+    def __init__(self, config):
+        super(RobertaForMultipleChoice, self).__init__(config)
+
+        self.roberta = RobertaModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        self.apply(self.init_weights)
+
+    def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
+                position_ids=None, head_mask=None):
+        num_choices = input_ids.shape[1]
+
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        outputs = self.roberta(flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids,
+                            attention_mask=flat_attention_mask, head_mask=head_mask)
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        outputs = (reshaped_logits,) + outputs[2:]  # add hidden states and attention if they are here
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            outputs = (loss,) + outputs
+
+        return outputs  # (loss), reshaped_logits, (hidden_states), (attentions)
+
+
 
 class RobertaClassificationHead(nn.Module):
     """Head for sentence-level classification tasks."""

pytorch_transformers/modeling_xlnet.py

@@ -1143,6 +1143,50 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
         return outputs  # return (loss), logits, mems, (hidden states), (attentions)
 
 
+class XLNetForMultipleChoice(XLNetPreTrainedModel):
+    r"""
+
+    """
+    def __init__(self, config):
+        super(XLNetForMultipleChoice, self).__init__(config)
+
+        self.transformer = XLNetModel(config)
+        self.sequence_summary = SequenceSummary(config)
+        self.logits_proj = nn.Linear(config.d_model, 1)
+
+        self.apply(self.init_weights)
+
+    def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
+                mems=None, perm_mask=None, target_mapping=None,
+                labels=None, head_mask=None):
+        num_choices = input_ids.shape[1]
+
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        flat_input_mask = input_mask.view(-1, input_mask.size(-1)) if input_mask is not None else None
+
+        transformer_outputs = self.transformer(flat_input_ids, token_type_ids=flat_token_type_ids,
+                                               input_mask=flat_input_mask, attention_mask=flat_attention_mask,
+                                               mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
+                                               head_mask=head_mask)
+
+
+        output = transformer_outputs[0]
+
+        output = self.sequence_summary(output)
+        logits = self.logits_proj(output)
+        reshaped_logits = logits.view(-1, num_choices)
+        outputs = (reshaped_logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels.view(-1))
+            outputs = (loss,) + outputs
+
+        return outputs  # return (loss), logits, mems, (hidden states), (attentions)
+
+
 @add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
     the hidden-states output to compute `span start logits` and `span end logits`). """,
     XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)