C3_finetune.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."""

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

import csv
import os
import logging
import argparse
import random
import pickle
from tqdm import tqdm

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

from tokenizations import official_tokenization as tokenization
from models.google_albert_pytorch_modeling import AlbertConfig, AlbertForMultipleChoice
from models.pytorch_modeling import BertConfig, BertForMultipleChoice, ALBertConfig, ALBertForMultipleChoice
from optimizations.pytorch_optimization import get_optimization, warmup_linear

import json

n_class = 4
reverse_order = False
sa_step = False

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

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


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self):
        """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_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


class c3Processor(DataProcessor):
    def __init__(self, data_dir):
        self.D = [[], [], []]
        self.data_dir = data_dir

        for sid in range(3):
            data = []
            for subtask in ["d", "m"]:
                with open(self.data_dir + "/c3-" + subtask + "-" + ["train.json", "dev.json", "test.json"][sid], "r",
                          encoding="utf8") as f:
                    data += json.load(f)
            if sid == 0:
                random.shuffle(data)
            for i in range(len(data)):
                for j in range(len(data[i][1])):
                    d = ['\n'.join(data[i][0]).lower(), data[i][1][j]["question"].lower()]
                    for k in range(len(data[i][1][j]["choice"])):
                        d += [data[i][1][j]["choice"][k].lower()]
                    for k in range(len(data[i][1][j]["choice"]), 4):
                        d += ['']
                    d += [data[i][1][j]["answer"].lower()]
                    self.D[sid] += [d]

    def get_train_examples(self):
        """See base class."""
        return self._create_examples(self.D[0], "train")

    def get_test_examples(self):
        """See base class."""
        return self._create_examples(self.D[2], "test")

    def get_dev_examples(self):
        """See base class."""
        return self._create_examples(self.D[1], "dev")

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

    def _create_examples(self, data, set_type):
        """Creates examples for the training and dev sets."""
        cache_dir = os.path.join(self.data_dir, set_type + '_examples.pkl')
        if os.path.exists(cache_dir):
            examples = pickle.load(open(cache_dir, 'rb'))
        else:
            examples = []
            for (i, d) in enumerate(data):
                answer = -1
                # 这里data[i]有6个元素，0是context，1是问题，2~5是choice，6是答案
                for k in range(4):
                    if data[i][2 + k] == data[i][6]:
                        answer = str(k)

                label = tokenization.convert_to_unicode(answer)

                for k in range(4):
                    guid = "%s-%s-%s" % (set_type, i, k)
                    text_a = tokenization.convert_to_unicode(data[i][0])
                    text_b = tokenization.convert_to_unicode(data[i][k + 2])
                    text_c = tokenization.convert_to_unicode(data[i][1])
                    examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, text_c=text_c))

            with open(cache_dir, 'wb') as w:
                pickle.dump(examples, w)

        return examples


def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
    """Loads a data file into a list of `InputBatch`s."""

    print("#examples", len(examples))

    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i

    features = [[]]
    for (ex_index, example) in enumerate(tqdm(examples)):
        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = tokenizer.tokenize(example.text_b)

        tokens_c = tokenizer.tokenize(example.text_c)

        _truncate_seq_tuple(tokens_a, tokens_b, tokens_c, max_seq_length - 4)
        tokens_b = tokens_c + ["[SEP]"] + tokens_b

        tokens = []
        segment_ids = []
        tokens.append("[CLS]")
        segment_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            segment_ids.append(0)
        tokens.append("[SEP]")
        segment_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                segment_ids.append(1)
            tokens.append("[SEP]")
            segment_ids.append(1)

        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] * len(input_ids)

        # Zero-pad up to the sequence length.
        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        label_id = label_map[example.label]
        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("tokens: %s" % " ".join(
                [tokenization.printable_text(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            logger.info(
                "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label_id))

        features[-1].append(
            InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=label_id))
        if len(features[-1]) == n_class:
            features.append([])

    if len(features[-1]) == 0:
        features = features[:-1]
    print('#features', len(features))
    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_seq_tuple(tokens_a, tokens_b, tokens_c, max_length):
    """Truncates a sequence tuple 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) + len(tokens_c)
        if total_length <= max_length:
            break
        if len(tokens_a) >= len(tokens_b) and len(tokens_a) >= len(tokens_c):
            tokens_a.pop()
        elif len(tokens_b) >= len(tokens_a) and len(tokens_b) >= len(tokens_c):
            tokens_b.pop()
        else:
            tokens_c.pop()


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


def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--gpu_ids",
                        default='0,1,2,3',
                        type=str)
    parser.add_argument("--data_dir",
                        default='origin_data/C3',
                        type=str)
    parser.add_argument("--task_name",
                        default='c3',
                        type=str)
    parser.add_argument("--bert_config_file",  # albert_xxlarge_google_zh_v1121 # roberta_wwm_ext_large
                        default='check_points/pretrain_models/albert_xxlarge_google_zh_v1121/bert_config.json',
                        type=str)
    parser.add_argument("--vocab_file",
                        default='check_points/pretrain_models/google_bert_base/vocab.txt',
                        type=str)
    parser.add_argument("--output_dir",
                        default='check_points/c3/albert_xxlarge_google_zh_v1121',
                        type=str)

    ## Other parameters
    parser.add_argument("--init_checkpoint",
                        default='check_points/pretrain_models/albert_xxlarge_google_zh_v1121/pytorch_model.pth',
                        type=str,
                        help="Initial checkpoint (usually from a pre-trained BERT model).")
    parser.add_argument("--do_lower_case",
                        default=True,
                        action='store_true',
                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
    parser.add_argument("--max_seq_length",
                        default=512,
                        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=True,
                        action='store_true',
                        help="Whether to run training.")
    parser.add_argument("--do_eval",
                        default=True,
                        action='store_true',
                        help="Whether to run eval on the dev set.")
    parser.add_argument("--train_batch_size",
                        default=16,
                        type=int,
                        help="Total batch size for training.")
    parser.add_argument("--eval_batch_size",
                        default=16,
                        type=int,
                        help="Total batch size for eval.")
    parser.add_argument("--learning_rate",
                        default=2e-5,
                        type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--schedule",
                        default='warmup_linear',
                        type=str,
                        help='schedule')
    parser.add_argument("--weight_decay_rate",
                        default=0.01,
                        type=float,
                        help='weight_decay_rate')
    parser.add_argument('--clip_norm',
                        type=float,
                        default=1.0)
    parser.add_argument("--num_train_epochs",
                        default=8.0,
                        type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--warmup_proportion",
                        default=0.05,
                        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('--float16',
                        type=bool,
                        default=True)
    parser.add_argument("--local_rank",
                        type=int,
                        default=-1,
                        help="local_rank for distributed training on gpus")
    parser.add_argument('--seed',
                        type=int,
                        default=345,
                        help="random seed for initialization")
    parser.add_argument('--gradient_accumulation_steps',
                        type=int,
                        default=4,
                        help="Number of updates steps to accumualte before performing a backward/update pass.")
    parser.add_argument('--setting_file', type=str, default='setting.txt')
    parser.add_argument('--log_file', type=str, default='log.txt')

    args = parser.parse_args()
    args.setting_file = os.path.join(args.output_dir, args.setting_file)
    args.log_file = os.path.join(args.output_dir, args.log_file)
    os.makedirs(args.output_dir, exist_ok=True)
    with open(args.setting_file, 'wt') as opt_file:
        opt_file.write('------------ Options -------------\n')
        print('------------ Options -------------')
        for k in args.__dict__:
            v = args.__dict__[k]
            opt_file.write('%s: %s\n' % (str(k), str(v)))
            print('%s: %s' % (str(k), str(v)))
        opt_file.write('-------------- End ----------------\n')
        print('------------ End -------------')
    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_ids

    if os.path.exists(args.log_file):
        os.remove(args.log_file)

    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')
    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.")

    processor = c3Processor(args.data_dir)
    label_list = processor.get_labels()

    tokenizer = tokenization.BertTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)

    train_examples = None
    num_train_steps = None
    if args.do_train:
        train_examples = processor.get_train_examples()
        num_train_steps = int(len(train_examples) / n_class / args.train_batch_size /
                              args.gradient_accumulation_steps * args.num_train_epochs)

    if 'albert' in args.bert_config_file:
        if 'google' in args.bert_config_file:
            bert_config = AlbertConfig.from_json_file(args.bert_config_file)
            model = AlbertForMultipleChoice(bert_config, num_choices=n_class)
        else:
            bert_config = ALBertConfig.from_json_file(args.bert_config_file)
            model = ALBertForMultipleChoice(bert_config, num_choices=n_class)
    else:
        bert_config = BertConfig.from_json_file(args.bert_config_file)
        model = BertForMultipleChoice(bert_config, num_choices=n_class)

    if args.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError(
            "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format(
                args.max_seq_length, bert_config.max_position_embeddings))

    if args.init_checkpoint is not None:
        utils.torch_show_all_params(model)
        utils.torch_init_model(model, args.init_checkpoint)
    if args.float16:
        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)

    optimizer = get_optimization(model=model,
                                 float16=args.float16,
                                 learning_rate=args.learning_rate,
                                 total_steps=num_train_steps,
                                 schedule=args.schedule,
                                 warmup_rate=args.warmup_proportion,
                                 max_grad_norm=args.clip_norm,
                                 weight_decay_rate=args.weight_decay_rate,
                                 opt_pooler=True)  # multi_choice must update pooler

    global_step = 0
    eval_dataloader = None
    if args.do_eval:
        eval_examples = processor.get_dev_examples()
        feature_dir = os.path.join(args.data_dir, 'dev_features{}.pkl'.format(args.max_seq_length))
        if os.path.exists(feature_dir):
            eval_features = pickle.load(open(feature_dir, 'rb'))
        else:
            eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer)
            with open(feature_dir, 'wb') as w:
                pickle.dump(eval_features, w)

        input_ids = []
        input_mask = []
        segment_ids = []
        label_id = []

        for f in eval_features:
            input_ids.append([])
            input_mask.append([])
            segment_ids.append([])
            for i in range(n_class):
                input_ids[-1].append(f[i].input_ids)
                input_mask[-1].append(f[i].input_mask)
                segment_ids[-1].append(f[i].segment_ids)
            label_id.append(f[0].label_id)

        all_input_ids = torch.tensor(input_ids, dtype=torch.long)
        all_input_mask = torch.tensor(input_mask, dtype=torch.long)
        all_segment_ids = torch.tensor(segment_ids, dtype=torch.long)
        all_label_ids = torch.tensor(label_id, dtype=torch.long)

        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
        if args.local_rank == -1:
            eval_sampler = SequentialSampler(eval_data)
        else:
            eval_sampler = DistributedSampler(eval_data)
        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)

    if args.do_train:
        best_accuracy = 0

        feature_dir = os.path.join(args.data_dir, 'train_features{}.pkl'.format(args.max_seq_length))
        if os.path.exists(feature_dir):
            train_features = pickle.load(open(feature_dir, 'rb'))
        else:
            train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer)
            with open(feature_dir, 'wb') as w:
                pickle.dump(train_features, w)

        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)

        input_ids = []
        input_mask = []
        segment_ids = []
        label_id = []
        for f in train_features:
            input_ids.append([])
            input_mask.append([])
            segment_ids.append([])
            for i in range(n_class):
                input_ids[-1].append(f[i].input_ids)
                input_mask[-1].append(f[i].input_mask)
                segment_ids[-1].append(f[i].segment_ids)
            label_id.append(f[0].label_id)

        all_input_ids = torch.tensor(input_ids, dtype=torch.long)
        all_input_mask = torch.tensor(input_mask, dtype=torch.long)
        all_segment_ids = torch.tensor(segment_ids, dtype=torch.long)
        all_label_ids = torch.tensor(label_id, dtype=torch.long)

        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
        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,
                                      drop_last=True)
        steps_per_epoch = int(num_train_steps / args.num_train_epochs)

        for ie in range(int(args.num_train_epochs)):
            model.train()
            tr_loss = 0
            nb_tr_examples, nb_tr_steps = 0, 0
            with tqdm(total=int(steps_per_epoch), desc='Epoch %d' % (ie + 1)) as pbar:
                for step, batch in enumerate(train_dataloader):
                    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.gradient_accumulation_steps > 1:
                        loss = loss / args.gradient_accumulation_steps
                    tr_loss += loss.item()

                    if args.float16:
                        optimizer.backward(loss)
                        # modify learning rate with special warm up BERT uses
                        # if args.fp16 is False, BertAdam is used and handles this automatically
                        lr_this_step = args.learning_rate * warmup_linear(global_step / num_train_steps,
                                                                          args.warmup_proportion)
                        for param_group in optimizer.param_groups:
                            param_group['lr'] = lr_this_step
                    else:
                        loss.backward()

                    nb_tr_examples += input_ids.size(0)
                    if (step + 1) % args.gradient_accumulation_steps == 0:
                        optimizer.step()  # We have accumulated enought gradients
                        model.zero_grad()
                        global_step += 1
                        nb_tr_steps += 1
                        pbar.set_postfix({'loss': '{0:1.5f}'.format(tr_loss / (nb_tr_steps + 1e-5))})
                        pbar.update(1)

            if args.do_eval:
                model.eval()
                eval_loss, eval_accuracy = 0, 0
                nb_eval_steps, nb_eval_examples = 0, 0
                logits_all = []
                for input_ids, input_mask, segment_ids, label_ids in tqdm(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, logits = model(input_ids, segment_ids, input_mask, label_ids, return_logits=True)

                    logits = logits.detach().cpu().numpy()
                    label_ids = label_ids.cpu().numpy()
                    for i in range(len(logits)):
                        logits_all += [logits[i]]

                    tmp_eval_accuracy = accuracy(logits, label_ids.reshape(-1))

                    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

                if args.do_train:
                    result = {'eval_loss': eval_loss,
                              'eval_accuracy': eval_accuracy,
                              'global_step': global_step,
                              'loss': tr_loss / nb_tr_steps}
                else:
                    result = {'eval_loss': eval_loss,
                              'eval_accuracy': eval_accuracy}

                logger.info("***** Eval results *****")
                for key in sorted(result.keys()):
                    logger.info("  %s = %s", key, str(result[key]))

                with open(args.log_file, 'a') as aw:
                    aw.write("-------------------global steps:{}-------------------\n".format(global_step))
                    aw.write(str(json.dumps(result, indent=2)) + '\n')

                if eval_accuracy >= best_accuracy:
                    torch.save(model.state_dict(), os.path.join(args.output_dir, "model_best.pt"))
                    best_accuracy = eval_accuracy

        model.load_state_dict(torch.load(os.path.join(args.output_dir, "model_best.pt")))
        torch.save(model.state_dict(), os.path.join(args.output_dir, "model.pt"))

    model.load_state_dict(torch.load(os.path.join(args.output_dir, "model.pt")))

    if args.do_eval:
        logger.info("***** Running evaluation *****")
        logger.info("  Num examples = %d", len(eval_examples))
        logger.info("  Batch size = %d", args.eval_batch_size)

        model.eval()
        eval_loss, eval_accuracy = 0, 0
        nb_eval_steps, nb_eval_examples = 0, 0
        logits_all = []
        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, logits = model(input_ids, segment_ids, input_mask, label_ids, return_logits=True)

            logits = logits.detach().cpu().numpy()
            label_ids = label_ids.cpu().numpy()
            for i in range(len(logits)):
                logits_all += [logits[i]]

            tmp_eval_accuracy = accuracy(logits, label_ids.reshape(-1))

            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}

        output_eval_file = os.path.join(args.output_dir, "results_dev.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])))
        output_eval_file = os.path.join(args.output_dir, "logits_dev.txt")
        with open(output_eval_file, "w") as f:
            for i in range(len(logits_all)):
                for j in range(len(logits_all[i])):
                    f.write(str(logits_all[i][j]))
                    if j == len(logits_all[i]) - 1:
                        f.write("\n")
                    else:
                        f.write(" ")

        test_examples = processor.get_test_examples()
        feature_dir = os.path.join(args.data_dir, 'test_features{}.pkl'.format(args.max_seq_length))
        if os.path.exists(feature_dir):
            test_features = pickle.load(open(feature_dir, 'rb'))
        else:
            test_features = convert_examples_to_features(test_examples, label_list, args.max_seq_length, tokenizer)
            with open(feature_dir, 'wb') as w:
                pickle.dump(test_features, w)

        logger.info("***** Running testing *****")
        logger.info("  Num examples = %d", len(test_examples))
        logger.info("  Batch size = %d", args.eval_batch_size)

        input_ids = []
        input_mask = []
        segment_ids = []
        label_id = []

        for f in test_features:
            input_ids.append([])
            input_mask.append([])
            segment_ids.append([])
            for i in range(n_class):
                input_ids[-1].append(f[i].input_ids)
                input_mask[-1].append(f[i].input_mask)
                segment_ids[-1].append(f[i].segment_ids)
            label_id.append(f[0].label_id)

        all_input_ids = torch.tensor(input_ids, dtype=torch.long)
        all_input_mask = torch.tensor(input_mask, dtype=torch.long)
        all_segment_ids = torch.tensor(segment_ids, dtype=torch.long)
        all_label_ids = torch.tensor(label_id, dtype=torch.long)

        test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
        if args.local_rank == -1:
            test_sampler = SequentialSampler(test_data)
        else:
            test_sampler = DistributedSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=args.eval_batch_size)

        model.eval()
        test_loss, test_accuracy = 0, 0
        nb_test_steps, nb_test_examples = 0, 0
        logits_all = []
        for input_ids, input_mask, segment_ids, label_ids in test_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_test_loss, logits = model(input_ids, segment_ids, input_mask, label_ids, return_logits=True)

            logits = logits.detach().cpu().numpy()
            label_ids = label_ids.to('cpu').numpy()
            for i in range(len(logits)):
                logits_all += [logits[i]]

            tmp_test_accuracy = accuracy(logits, label_ids.reshape(-1))

            test_loss += tmp_test_loss.mean().item()
            test_accuracy += tmp_test_accuracy

            nb_test_examples += input_ids.size(0)
            nb_test_steps += 1

        test_loss = test_loss / nb_test_steps
        test_accuracy = test_accuracy / nb_test_examples

        result = {'test_loss': test_loss,
                  'test_accuracy': test_accuracy}

        output_test_file = os.path.join(args.output_dir, "results_test.txt")
        with open(output_test_file, "w") as writer:
            logger.info("***** Test results *****")
            for key in sorted(result.keys()):
                logger.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))
        output_test_file = os.path.join(args.output_dir, "logits_test.txt")
        with open(output_test_file, "w") as f:
            for i in range(len(logits_all)):
                for j in range(len(logits_all[i])):
                    f.write(str(logits_all[i][j]))
                    if j == len(logits_all[i]) - 1:
                        f.write("\n")
                    else:
                        f.write(" ")


if __name__ == "__main__":
    main()