Merge pull request #96 from rodgzilla/multiple-choice-code

BertForMultipleChoice and Swag dataset example.

README.md

@@ -52,8 +52,9 @@ This package comprises the following classes that can be imported in Python and
   - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L752) - BERT Transformer with the pre-trained next sentence prediction classifier on top  (**fully pre-trained**),
   - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L620) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**),
   - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L814) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
-  - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
-  - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L946) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
+  - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
+  - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L949) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
+  - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1015) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
 
 - Three tokenizers (in the [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) file):
   - `BasicTokenizer` - basic tokenization (punctuation splitting, lower casing, etc.),
@@ -68,10 +69,11 @@ This package comprises the following classes that can be imported in Python and
 
 The repository further comprises:
 
-- Three examples on how to use Bert (in the [`examples` folder](./examples)):
+- Four examples on how to use Bert (in the [`examples` folder](./examples)):
   - [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`,
   - [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task,
   - [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task.
+  - [`run_swag.py`](./examples/run_swag.py) - Show how to fine-tune an instance of `BertForMultipleChoice` on Swag task.
 
   These examples are detailed in the [Examples](#examples) section of this readme.
 
@@ -278,13 +280,23 @@ The sequence-level classifier is a linear layer that takes as input the last hid
 
 An example on how to use this class is given in the [`run_classifier.py`](./examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task.
 
-#### 6. `BertForTokenClassification`
+#### 6. `BertForMultipleChoice`
+
+`BertForMultipleChoice` is a fine-tuning model that includes `BertModel` and a linear layer on top of the `BertModel`.
+
+The linear layer outputs a single value for each choice of a multiple choice problem, then all the outputs corresponding to an instance are passed through a softmax to get the model choice.
+
+This implementation is largely inspired by the work of OpenAI in [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) and the answer of Jacob Devlin in the following [issue](https://github.com/google-research/bert/issues/38).
+
+An example on how to use this class is given in the [`run_swag.py`](./examples/run_swag.py) script which can be used to fine-tune a multiple choice classifier using BERT, for example for the Swag task.
+
+#### 7. `BertForTokenClassification`
 
 `BertForTokenClassification` is a fine-tuning model that includes `BertModel` and a token-level classifier on top of the `BertModel`.
 
 The token-level classifier is a linear layer that takes as input the last hidden state of the sequence.
 
-#### 7. `BertForQuestionAnswering`
+#### 8. `BertForQuestionAnswering`
 
 `BertForQuestionAnswering` is a fine-tuning model that includes `BertModel` with a token-level classifiers on top of the full sequence of last hidden states.
 
@@ -420,6 +432,32 @@ Training with the previous hyper-parameters gave us the following results:
 {"f1": 88.52381567990474, "exact_match": 81.22043519394512}
 ```
 
+The data for Swag can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf)
+
+```shell
+export SWAG_DIR=/path/to/SWAG
+
+python run_swag.py \
+  --bert_model bert-base-uncased \
+  --do_train \
+  --do_eval \
+  --data_dir $SWAG_DIR/data
+  --train_batch_size 16 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3.0 \
+  --max_seq_length 80 \
+  --output_dir /tmp/swag_output/
+  --gradient_accumulation_steps 4
+```
+
+Training with the previous hyper-parameters gave us the following results:
+```
+eval_accuracy = 0.8062081375587323
+eval_loss = 0.5966546792367169
+global_step = 13788
+loss = 0.06423990014260186
+```
+
 ## Fine-tuning BERT-large on GPUs
 
 The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation.

examples/run_swag.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+#
+# 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 finetuning runner."""
+
+import logging
+import os
+import argparse
+import random
+from tqdm import tqdm, trange
+import csv
+
+import numpy as np
+import torch
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.modeling import BertForMultipleChoice
+from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+
+logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                    datefmt = '%m/%d/%Y %H:%M:%S',
+                    level = logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class SwagExample(object):
+    """A single training/test example for the SWAG dataset."""
+    def __init__(self,
+                 swag_id,
+                 context_sentence,
+                 start_ending,
+                 ending_0,
+                 ending_1,
+                 ending_2,
+                 ending_3,
+                 label = None):
+        self.swag_id = swag_id
+        self.context_sentence = context_sentence
+        self.start_ending = start_ending
+        self.endings = [
+            ending_0,
+            ending_1,
+            ending_2,
+            ending_3,
+        ]
+        self.label = label
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        l = [
+            f"swag_id: {self.swag_id}",
+            f"context_sentence: {self.context_sentence}",
+            f"start_ending: {self.start_ending}",
+            f"ending_0: {self.endings[0]}",
+            f"ending_1: {self.endings[1]}",
+            f"ending_2: {self.endings[2]}",
+            f"ending_3: {self.endings[3]}",
+        ]
+
+        if self.label is not None:
+            l.append(f"label: {self.label}")
+
+        return ", ".join(l)
+
+
+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
+
+
+def read_swag_examples(input_file, is_training):
+    with open(input_file, 'r') as f:
+        reader = csv.reader(f)
+        lines = list(reader)
+
+    if is_training and lines[0][-1] != 'label':
+        raise ValueError(
+            "For training, the input file must contain a label column."
+        )
+
+    examples = [
+        SwagExample(
+            swag_id = line[2],
+            context_sentence = line[4],
+            start_ending = line[5], # in the swag dataset, the
+                                         # common beginning of each
+                                         # choice is stored in "sent2".
+            ending_0 = line[7],
+            ending_1 = line[8],
+            ending_2 = line[9],
+            ending_3 = line[10],
+            label = int(line[11]) if is_training else None
+        ) for line in lines[1:] # we skip the line with the column names
+    ]
+
+    return examples
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+                                 is_training):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    # Swag is a multiple choice task. To perform this task using Bert,
+    # we will use the formatting proposed in "Improving Language
+    # Understanding by Generative Pre-Training" and suggested by
+    # @jacobdevlin-google in this issue
+    # https://github.com/google-research/bert/issues/38.
+    #
+    # Each choice will correspond to a sample on which we run the
+    # inference. For a given Swag example, we will create the 4
+    # following inputs:
+    # - [CLS] context [SEP] choice_1 [SEP]
+    # - [CLS] context [SEP] choice_2 [SEP]
+    # - [CLS] context [SEP] choice_3 [SEP]
+    # - [CLS] context [SEP] choice_4 [SEP]
+    # The model will output a single value for each input. To get the
+    # final decision of the model, we will run a softmax over these 4
+    # outputs.
+    features = []
+    for example_index, example in enumerate(examples):
+        context_tokens = tokenizer.tokenize(example.context_sentence)
+        start_ending_tokens = tokenizer.tokenize(example.start_ending)
+
+        choices_features = []
+        for ending_index, ending in enumerate(example.endings):
+            # We create a copy of the context tokens in order to be
+            # able to shrink it according to ending_tokens
+            context_tokens_choice = context_tokens[:]
+            ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
+            # Modifies `context_tokens_choice` and `ending_tokens` in
+            # place so that the total length is less than the
+            # specified length.  Account for [CLS], [SEP], [SEP] with
+            # "- 3"
+            _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
+
+            tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
+            segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+            input_mask = [1] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            padding = [0] * (max_seq_length - len(input_ids))
+            input_ids += padding
+            input_mask += padding
+            segment_ids += padding
+
+            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 = example.label
+        if example_index < 5:
+            logger.info("*** Example ***")
+            logger.info(f"swag_id: {example.swag_id}")
+            for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
+                logger.info(f"choice: {choice_idx}")
+                logger.info(f"tokens: {' '.join(tokens)}")
+                logger.info(f"input_ids: {' '.join(map(str, input_ids))}")
+                logger.info(f"input_mask: {' '.join(map(str, input_mask))}")
+                logger.info(f"segment_ids: {' '.join(map(str, segment_ids))}")
+            if is_training:
+                logger.info(f"label: {label}")
+
+        features.append(
+            InputFeatures(
+                example_id = example.swag_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()
+
+def accuracy(out, labels):
+    outputs = np.argmax(out, axis=1)
+    return np.sum(outputs == labels)
+
+def select_field(features, field):
+    return [
+        [
+            choice[field]
+            for choice in feature.choices_features
+        ]
+        for feature in features
+    ]
+
+def copy_optimizer_params_to_model(named_params_model, named_params_optimizer):
+    """ Utility function for optimize_on_cpu and 16-bits training.
+        Copy the parameters optimized on CPU/RAM back to the model on GPU
+    """
+    for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model):
+        if name_opti != name_model:
+            logger.error("name_opti != name_model: {} {}".format(name_opti, name_model))
+            raise ValueError
+        param_model.data.copy_(param_opti.data)
+
+def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False):
+    """ Utility function for optimize_on_cpu and 16-bits training.
+        Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model
+    """
+    is_nan = False
+    for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model):
+        if name_opti != name_model:
+            logger.error("name_opti != name_model: {} {}".format(name_opti, name_model))
+            raise ValueError
+        if param_model.grad is not None:
+            if test_nan and torch.isnan(param_model.grad).sum() > 0:
+                is_nan = True
+            if param_opti.grad is None:
+                param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size()))
+            param_opti.grad.data.copy_(param_model.grad.data)
+        else:
+            param_opti.grad = None
+    return is_nan
+
+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 .csv files (or other data files) for the task.")
+    parser.add_argument("--bert_model", default=None, type=str, required=True,
+                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
+    parser.add_argument("--output_dir",
+                        default=None,
+                        type=str,
+                        required=True,
+                        help="The output directory where the model checkpoints will be written.")
+
+    ## Other parameters
+    parser.add_argument("--max_seq_length",
+                        default=128,
+                        type=int,
+                        help="The maximum total input sequence length after WordPiece tokenization. \n"
+                             "Sequences longer than this will be truncated, and sequences shorter \n"
+                             "than this will be padded.")
+    parser.add_argument("--do_train",
+                        default=False,
+                        action='store_true',
+                        help="Whether to run training.")
+    parser.add_argument("--do_eval",
+                        default=False,
+                        action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--do_lower_case",
+                        default=False,
+                        action='store_true',
+                        help="Set this flag if you are using an uncased model.")
+    parser.add_argument("--train_batch_size",
+                        default=32,
+                        type=int,
+                        help="Total batch size for training.")
+    parser.add_argument("--eval_batch_size",
+                        default=8,
+                        type=int,
+                        help="Total batch size for eval.")
+    parser.add_argument("--learning_rate",
+                        default=5e-5,
+                        type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs",
+                        default=3.0,
+                        type=float,
+                        help="Total number of training epochs to perform.")
+    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("--no_cuda",
+                        default=False,
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument("--local_rank",
+                        type=int,
+                        default=-1,
+                        help="local_rank for distributed training on gpus")
+    parser.add_argument('--seed',
+                        type=int,
+                        default=42,
+                        help="random seed for initialization")
+    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('--optimize_on_cpu',
+                        default=False,
+                        action='store_true',
+                        help="Whether to perform optimization and keep the optimizer averages on CPU")
+    parser.add_argument('--fp16',
+                        default=False,
+                        action='store_true',
+                        help="Whether to use 16-bit float precision instead of 32-bit")
+    parser.add_argument('--loss_scale',
+                        type=float, default=128,
+                        help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
+
+    args = parser.parse_args()
+
+    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")
+        n_gpu = torch.cuda.device_count()
+    else:
+        device = torch.device("cuda", args.local_rank)
+        n_gpu = 1
+        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.distributed.init_process_group(backend='nccl')
+        if args.fp16:
+            logger.info("16-bits training currently not supported in distributed training")
+            args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
+    logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1))
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+                            args.gradient_accumulation_steps))
+
+    args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
+
+    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 args.do_train and not args.do_eval:
+        raise ValueError("At least one of `do_train` or `do_eval` must be True.")
+
+    if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
+
+    train_examples = None
+    num_train_steps = None
+    if args.do_train:
+        train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
+        num_train_steps = int(
+            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
+
+    # Prepare model
+    model = BertForMultipleChoice.from_pretrained(args.bert_model,
+        cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
+        num_choices = 4
+    )
+    if args.fp16:
+        model.half()
+    model.to(device)
+    if args.local_rank != -1:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
+                                                          output_device=args.local_rank)
+    elif n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    # Prepare optimizer
+    if args.fp16:
+        param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \
+                            for n, param in model.named_parameters()]
+    elif args.optimize_on_cpu:
+        param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \
+                            for n, param in model.named_parameters()]
+    else:
+        param_optimizer = list(model.named_parameters())
+    no_decay = ['bias', 'gamma', 'beta']
+    optimizer_grouped_parameters = [
+        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
+        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
+        ]
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
+    optimizer = BertAdam(optimizer_grouped_parameters,
+                         lr=args.learning_rate,
+                         warmup=args.warmup_proportion,
+                         t_total=t_total)
+
+    global_step = 0
+    if args.do_train:
+        train_features = convert_examples_to_features(
+            train_examples, tokenizer, args.max_seq_length, True)
+        logger.info("***** Running training *****")
+        logger.info("  Num examples = %d", len(train_examples))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_steps)
+        all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
+        all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
+        all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
+        all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
+        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
+        if args.local_rank == -1:
+            train_sampler = RandomSampler(train_data)
+        else:
+            train_sampler = DistributedSampler(train_data)
+        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
+
+        model.train()
+        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
+            tr_loss = 0
+            nb_tr_examples, nb_tr_steps = 0, 0
+            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
+                batch = tuple(t.to(device) for t in batch)
+                input_ids, input_mask, segment_ids, label_ids = batch
+                loss = model(input_ids, segment_ids, input_mask, label_ids)
+                if n_gpu > 1:
+                    loss = loss.mean() # mean() to average on multi-gpu.
+                if args.fp16 and args.loss_scale != 1.0:
+                    # rescale loss for fp16 training
+                    # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
+                    loss = loss * args.loss_scale
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+                loss.backward()
+                tr_loss += loss.item()
+                nb_tr_examples += input_ids.size(0)
+                nb_tr_steps += 1
+                if (step + 1) % args.gradient_accumulation_steps == 0:
+                    if args.fp16 or args.optimize_on_cpu:
+                        if args.fp16 and args.loss_scale != 1.0:
+                            # scale down gradients for fp16 training
+                            for param in model.parameters():
+                                if param.grad is not None:
+                                    param.grad.data = param.grad.data / args.loss_scale
+                        is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True)
+                        if is_nan:
+                            logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling")
+                            args.loss_scale = args.loss_scale / 2
+                            model.zero_grad()
+                            continue
+                        optimizer.step()
+                        copy_optimizer_params_to_model(model.named_parameters(), param_optimizer)
+                    else:
+                        optimizer.step()
+                    model.zero_grad()
+                    global_step += 1
+
+    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
+        eval_features = convert_examples_to_features(
+            eval_examples, tokenizer, args.max_seq_length, True)
+        logger.info("***** Running evaluation *****")
+        logger.info("  Num examples = %d", len(eval_examples))
+        logger.info("  Batch size = %d", args.eval_batch_size)
+        all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
+        all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
+        all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
+        all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
+        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
+        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
+
+        model.eval()
+        eval_loss, eval_accuracy = 0, 0
+        nb_eval_steps, nb_eval_examples = 0, 0
+        for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
+            input_ids = input_ids.to(device)
+            input_mask = input_mask.to(device)
+            segment_ids = segment_ids.to(device)
+            label_ids = label_ids.to(device)
+
+            with torch.no_grad():
+                tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
+                logits = model(input_ids, segment_ids, input_mask)
+
+            logits = logits.detach().cpu().numpy()
+            label_ids = label_ids.to('cpu').numpy()
+            tmp_eval_accuracy = accuracy(logits, label_ids)
+
+            eval_loss += tmp_eval_loss.mean().item()
+            eval_accuracy += tmp_eval_accuracy
+
+            nb_eval_examples += input_ids.size(0)
+            nb_eval_steps += 1
+
+        eval_loss = eval_loss / nb_eval_steps
+        eval_accuracy = eval_accuracy / nb_eval_examples
+
+        result = {'eval_loss': eval_loss,
+                  'eval_accuracy': eval_accuracy,
+                  'global_step': global_step,
+                  'loss': tr_loss/nb_tr_steps}
+
+        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results *****")
+            for key in sorted(result.keys()):
+                logger.info("  %s = %s", key, str(result[key]))
+                writer.write("%s = %s\n" % (key, str(result[key])))
+
+
+if __name__ == "__main__":
+    main()

pytorch_pretrained_bert/__init__.py

 from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer
 from .modeling import (BertConfig, BertModel, BertForPreTraining,
                        BertForMaskedLM, BertForNextSentencePrediction,
-                       BertForSequenceClassification, BertForTokenClassification,
-                       BertForQuestionAnswering)
+                       BertForSequenceClassification, BertForMultipleChoice,
+                       BertForTokenClassification, BertForQuestionAnswering)
 from .optimization import BertAdam
 from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE

pytorch_pretrained_bert/modeling.py

@@ -877,6 +877,75 @@ class BertForSequenceClassification(PreTrainedBertModel):
             return logits
 
 
+class BertForMultipleChoice(PreTrainedBertModel):
+    """BERT model for multiple choice tasks.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_choices`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
+            and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_choices].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
+    input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
+    token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_choices = 2
+
+    model = BertForMultipleChoice(config, num_choices)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_choices=2):
+        super(BertForMultipleChoice, self).__init__(config)
+        self.num_choices = num_choices
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+        self.apply(self.init_bert_weights)
+
+    def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
+        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))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
+        _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, self.num_choices)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            return loss
+        else:
+            return reshaped_logits
+
+
 class BertForTokenClassification(PreTrainedBertModel):
     """BERT model for token-level classification.
     This module is composed of the BERT model with a linear layer on top of