run_multiway_att.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import csv
import os
import logging
import argparse
import random
from tqdm import tqdm, trange

import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler

from tokenization import BertTokenizer
from optimization import BertAdam
from file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from modeling import BertMultiwayMatch

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__)

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2, 3"


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 InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, 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.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputExampleWithListFourFields(object):
    """A single training/test example for simple multiple choice classification."""

    def __init__(self, guid, text_a, text_b, text_c, text_d, label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: list. A list containing untokenized text
            text_b: list. containing untokenized text associated of the same size as text_A
            text_c: list. containing untokenized text associated of the same size as text_A
            text_d: list. containing untokenized text associated of the same size as text_A
            Only must be specified for multiple choice options.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        assert isinstance(text_a, list)
        assert isinstance(text_b, list)
        assert text_c is None or isinstance(text_c, list)
        assert text_d is None or isinstance(text_d, list)
        assert len(text_a) == len(text_b)
        if text_c is not None:
            assert len(text_c) == len(text_a)
        if text_d is not None:
            assert len(text_d) == len(text_a)

        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.text_c = text_c
        self.text_d = text_d
        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,
                'doc_len': doc_len,
                'ques_len': ques_len,
                'option_len': option_len
            }
            for _, input_ids, input_mask, segment_ids, doc_len, ques_len, option_len 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_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_csv(cls, input_file):
        """Reads a csv file."""
        lines = []
        with open(input_file, 'r') as csv_file:
            reader = csv.DictReader(csv_file)
            for row in reader:
                lines.append(row)
        return lines

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding='utf-8') as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


class CommonsenseQaProcessor(DataProcessor):
    """Processor for the ANLI data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        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."""
        return self._create_examples(
            self._read_csv(os.path.join(data_dir, "valid.csv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_csv(os.path.join(data_dir, "test.csv")), "dev")

    def get_examples_from_file(self, input_file):
        return self._create_examples(
            self._read_csv(input_file), "to-pred")

    def get_labels(self):
        """See base class."""
        return ["0", "1", "2", "3"]

    def _create_examples(self, records, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, record) in enumerate(records):
            guid = record["id"]
            context = record["context"]
            question = record["question"]
            answer1 = record["answer0"]
            answer2 = record["answer1"]
            answer3 = record["answer2"]
            answer4 = record["answer3"]
            label = record["label"]

            examples.append(
                SwagExample(swag_id=guid,
                 context_sentence=context,
                 start_ending=question,
                 ending_0=answer1,
                 ending_1=answer2,
                 ending_2=answer3,
                 ending_3=answer4,
                 label=int(label))
            )
        return examples

    def label_field(self):
        return "AnswerRightEnding"


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 = tokenizer.tokenize(ending)
            option_len = len(ending_tokens)
            ques_len = len(start_ending_tokens)
            ending_tokens = start_ending_tokens + ending_tokens

            # 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"
            # ending_tokens = start_ending_tokens + ending_tokens
            _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
            doc_len = len(context_tokens_choice)

            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
            assert (doc_len + ques_len + option_len) <= max_seq_length

            choices_features.append((tokens, input_ids, input_mask, segment_ids,
                                     doc_len, ques_len, option_len))

        label = int(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, doc_len,
                             ques_len, option_len) 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 _truncate_sequences(max_length, inputs):
    idx = 0
    for ta, tb in zip(inputs[0], inputs[1]):
        _truncate_seq_pair(ta, tb, max_length)


def accuracy(out, labels):
    outputs = np.argmax(out, axis=1)
    return np.sum(outputs == labels)


def warmup_linear(x, warmup=0.002):
    if x < warmup:
        return x / warmup
    return 1.0 - x


def select_field(features, field):
    return [
        [
            choice[field]
            for choice in feature.choices_features
        ]
        for feature in features
    ]


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("--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-large-cased, bert-base-multilingual-uncased, "
                             "bert-base-multilingual-cased, bert-base-chinese.")
    parser.add_argument("--task_name",
                        default=None,
                        type=str,
                        required=True,
                        help="The name of the task to train.")
    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("--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",
                        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_lower_case",
                        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",
                        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('--fp16',
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale',
                        type=float, default=0,
                        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                             "0 (default value): dynamic loss scaling.\n"
                             "Positive power of 2: static loss scaling value.\n")
    parser.add_argument('--cuda',
                        type=str,
                        default="",
                        help="cuda index")

    args = parser.parse_args()

    processors = {
        "commonsenseqa": CommonsenseQaProcessor,
    }

    num_labels_task = {
        "commonsenseqa":4,
    }

    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:
        torch.cuda.set_device(args.local_rank)
        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')

    logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
        device, n_gpu, bool(args.local_rank != -1), args.fp16))
    print("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
        device, n_gpu, bool(args.local_rank != -1), args.fp16))

    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) and args.do_train:
        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
    os.makedirs(args.output_dir, exist_ok=True)

    task_name = args.task_name.lower()

    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    print("current task is " + str(task_name))

    processor = processors[task_name]()
    num_labels = num_labels_task[task_name]
    label_list = processor.get_labels()

    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 = processor.get_train_examples(args.data_dir)
        num_train_steps = int(
            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)

    # Prepare model
    model = BertMultiwayMatch.from_pretrained(args.bert_model,
                                              cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
                                                  args.local_rank),
                                              num_choices=num_labels)
    if args.fp16:
        model.half()
    model.to(device)
    if args.local_rank != -1:
        try:
            from apex.parallel import DistributedDataParallel as DDP
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")

        model = DDP(model)
    elif n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Prepare optimizer
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.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}
    ]
    t_total = num_train_steps
    if args.local_rank != -1:
        t_total = t_total // torch.distributed.get_world_size()
    if args.fp16:
        try:
            from apex.optimizers import FP16_Optimizer
            from apex.optimizers import FusedAdam
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")

        optimizer = FusedAdam(optimizer_grouped_parameters,
                              lr=args.learning_rate,
                              bias_correction=False,
                              max_grad_norm=1.0)
        if args.loss_scale == 0:
            optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
        else:
            optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)

    else:
        optimizer = BertAdam(optimizer_grouped_parameters,
                             lr=args.learning_rate,
                             warmup=args.warmup_proportion,
                             t_total=t_total)

    global_step = 0
    nb_tr_steps = 0
    tr_loss = 0

    best_eval_accuracy = 0.0

    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_doc_len = torch.tensor(select_field(train_features, 'doc_len'), dtype=torch.long)
        all_ques_len = torch.tensor(select_field(train_features, 'ques_len'), dtype=torch.long)
        all_option_len = torch.tensor(select_field(train_features, 'option_len'), 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,
                                   all_doc_len, all_ques_len, all_option_len)
        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()

        # Save a trained model
        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
        output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")

        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, doc_len, ques_len, option_len = batch
                loss, logits = model(input_ids, segment_ids, input_mask, doc_len,
                                     ques_len, option_len, label_ids)
                if n_gpu > 1:
                    loss = loss.mean()  # mean() to average on multi-gpu.
                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps

                if args.fp16:
                    optimizer.backward(loss)
                else:
                    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:
                    # modify learning rate with special warm up BERT uses
                    lr_this_step = args.learning_rate * warmup_linear(global_step / t_total,
                                                                      args.warmup_proportion)
                    for param_group in optimizer.param_groups:
                        param_group['lr'] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    global_step += 1

            if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
                eval_examples = processor.get_dev_examples(args.data_dir)
                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_doc_len = torch.tensor(select_field(eval_features, 'doc_len'), dtype=torch.long)
                all_ques_len = torch.tensor(select_field(eval_features, 'ques_len'), dtype=torch.long)
                all_option_len = torch.tensor(select_field(eval_features, 'option_len'), 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,
                                          all_doc_len, all_ques_len, all_option_len)
                # 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, doc_len, ques_len, option_len \
                        in tqdm(eval_dataloader, desc="Evaluating"):
                    input_ids = input_ids.to(device)
                    input_mask = input_mask.to(device)
                    segment_ids = segment_ids.to(device)
                    label_ids = label_ids.to(device)
                    doc_len = doc_len.to(device)
                    ques_len = ques_len.to(device)
                    option_len = option_len.to(device)

                    with torch.no_grad():
                        tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask,
                                                      doc_len, ques_len, option_len, label_ids)

                    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_accuracy = eval_accuracy / nb_eval_examples
                print("the current eval accuracy is: " + str(eval_accuracy))
                if eval_accuracy > best_eval_accuracy:
                    best_eval_accuracy = eval_accuracy

                    if args.do_train:
                        torch.save(model_to_save.state_dict(), output_model_file)

                model.train()

    # Load a trained model that you have fine-tuned
    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
    model_state_dict = torch.load(output_model_file)
    model = BertMultiwayMatch.from_pretrained(args.bert_model,
                                              state_dict=model_state_dict,
                                              num_choices=num_labels)
    if args.fp16:
        model.half()
    model.to(device)

    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        eval_examples = processor.get_dev_examples(args.data_dir)
        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_doc_len = torch.tensor(select_field(eval_features, 'doc_len'), dtype=torch.long)
        all_ques_len = torch.tensor(select_field(eval_features, 'ques_len'), dtype=torch.long)
        all_option_len = torch.tensor(select_field(eval_features, 'option_len'), 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,
                                  all_doc_len, all_ques_len, all_option_len)
        # 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

        all_pred_labels = []
        all_anno_labels = []
        all_logits = []

        for input_ids, input_mask, segment_ids, label_ids, doc_len, ques_len, option_len \
                in tqdm(eval_dataloader, desc="Evaluating"):
            input_ids = input_ids.to(device)
            input_mask = input_mask.to(device)
            segment_ids = segment_ids.to(device)
            label_ids = label_ids.to(device)
            doc_len = doc_len.to(device)
            ques_len = ques_len.to(device)
            option_len = option_len.to(device)

            with torch.no_grad():
                tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, doc_len,
                                              ques_len, option_len, label_ids)

            logits = logits.detach().cpu().numpy()
            label_ids = label_ids.to('cpu').numpy()

            output_labels = np.argmax(logits, axis=1)
            all_pred_labels.extend(output_labels.tolist())
            all_logits.extend(list(logits))
            all_anno_labels.extend(list(label_ids))

            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
        loss = tr_loss / nb_tr_steps if args.do_train else None
        result = {'eval_loss': eval_loss,
                  'eval_accuracy': eval_accuracy,
                  'best_eval_accuracy': best_eval_accuracy,
                  'global_step': global_step,
                  'loss': loss}

        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])))
            for i in range(len(all_pred_labels)):
                writer.write(str(i) + "\t" + str(all_anno_labels[i]) + "\t" +
                             str(all_pred_labels[i]) + "\t" + str(all_logits[i]) + "\n")


if __name__ == "__main__":
    main()