Add example for fine tuning BERT language model (#1)

Adds an example for loading a pre-trained BERT model and fine tune it as a language model (masked tokens & nextSentence) on your target corpus.

examples/run_lm_finetuning.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 os
+import logging
+import argparse
+from tqdm import tqdm, trange
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, RandomSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.modeling import BertForPreTraining
+from pytorch_pretrained_bert.optimization import BertAdam
+
+from torch.utils.data import Dataset
+import random
+
+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 BERTDataset(Dataset):
+    def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True):
+        self.vocab = tokenizer.vocab
+        self.tokenizer = tokenizer
+        self.seq_len = seq_len
+        self.on_memory = on_memory
+        self.corpus_lines = corpus_lines  # number of non-empty lines in input corpus
+        self.corpus_path = corpus_path
+        self.encoding = encoding
+        self.current_doc = 0  # to avoid random sentence from same doc
+
+        # for loading samples directly from file
+        self.sample_counter = 0  # used to keep track of full epochs on file
+        self.line_buffer = None  # keep second sentence of a pair in memory and use as first sentence in next pair
+
+        # for loading samples in memory
+        self.current_random_doc = 0
+        self.num_docs = 0
+
+        # load samples into memory
+        if on_memory:
+            self.all_docs = []
+            doc = []
+            self.corpus_lines = 0
+            with open(corpus_path, "r", encoding=encoding) as f:
+                for line in tqdm(f, desc="Loading Dataset", total=corpus_lines):
+                    line = line.strip()
+                    if line == "":
+                        self.all_docs.append(doc)
+                        doc = []
+                    else:
+                        doc.append(line)
+                        self.corpus_lines =  self.corpus_lines + 1
+            # if last row in file is not empty
+            if self.all_docs[-1] != doc:
+                self.all_docs.append(doc)
+
+            self.num_docs = len(self.all_docs)
+
+        # load samples later lazily from disk
+        else:
+            if self.corpus_lines is None:
+                with open(corpus_path, "r", encoding=encoding) as f:
+                    self.corpus_lines = 0
+                    for line in tqdm(f, desc="Loading Dataset", total=corpus_lines):
+                        if line.strip() == "":
+                            self.num_docs += 1
+                        else:
+                            self.corpus_lines += 1
+
+                    # if doc does not end with empty line
+                    if line.strip() != "":
+                        self.num_docs += 1
+
+            self.file = open(corpus_path, "r", encoding=encoding)
+            self.random_file = open(corpus_path, "r", encoding=encoding)
+
+    def __len__(self):
+        # last line of doc won't be used, because there's no "nextSentence". Additionally, we start counting at 0.
+        return self.corpus_lines - self.num_docs - 1
+
+    def __getitem__(self, item):
+        cur_id = self.sample_counter
+        self.sample_counter += 1
+        if not self.on_memory:
+            # after one epoch we start again from beginning of file
+            if cur_id != 0 and (cur_id % len(self) == 0):
+                self.file.close()
+                self.file = open(self.corpus_path, "r", encoding=self.encoding)
+
+        t1, t2, is_next_label = self.random_sent(item)
+
+        # tokenize
+        tokens_a = self.tokenizer.tokenize(t1)
+        tokens_b = self.tokenizer.tokenize(t2)
+
+        # combine to one sample
+        cur_example = InputExample(guid=cur_id, tokens_a=tokens_a, tokens_b=tokens_b, is_next=is_next_label)
+
+        # transform sample to features
+        cur_features = convert_example_to_features(cur_example, self.seq_len, self.tokenizer)
+
+        cur_tensors = {"input_ids": torch.tensor(cur_features.input_ids),
+                       "input_mask": torch.tensor(cur_features.input_mask),
+                       "segment_ids": torch.tensor(cur_features.segment_ids),
+                       "lm_label_ids": torch.tensor(cur_features.lm_label_ids),
+                       "is_next": torch.tensor(cur_features.is_next)}
+
+        return cur_tensors
+
+    def random_sent(self, index):
+        """
+        Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences
+        from one doc. With 50% the second sentence will be a random one from another doc.
+        :param index: int, index of sample.
+        :return: (str, str, int), sentence 1, sentence 2, isNextSentence Label
+        """
+        t1, t2 = self.get_corpus_line(index)
+        if random.random() > 0.5:
+            label = 0
+        else:
+            t2 = self.get_random_line()
+            label = 1
+
+        assert len(t1) > 0
+        assert len(t2) > 0
+        return t1, t2, label
+
+    def get_corpus_line(self, item):
+        """
+        Get one sample from corpus consisting of a pair of two subsequent lines from the same doc.
+        :param item: int, index of sample.
+        :return: (str, str), two subsequent sentences from corpus
+        """
+        t1 = ""
+        t2 = ""
+        assert item < self.corpus_lines
+        if self.on_memory:
+            # get the right doc
+            doc_id = 0
+            doc_start = 0
+            doc_end = len(self.all_docs[doc_id]) - 2
+            while item > doc_end:
+                doc_id += 1
+                doc_start = doc_end + 1
+                doc_end += len(self.all_docs[doc_id]) - 1
+            # get the right line within doc
+            line_in_doc = item - doc_start
+            t1 = self.all_docs[doc_id][line_in_doc]
+            t2 = self.all_docs[doc_id][line_in_doc + 1]
+            # used later to avoid random nextSentence from same doc
+            self.current_doc = doc_id
+            return t1, t2
+        else:
+            if self.line_buffer is None:
+                # read first non-empty line of file
+                while t1 == "" :
+                    t1 = self.file.__next__().strip()
+                    t2 = self.file.__next__().strip()
+            else:
+                # use t2 from previous iteration as new t1
+                t1 = self.line_buffer
+                t2 = self.file.__next__().strip()
+                # skip empty rows that are used for separating documents and keep track of current doc id
+                while t2 == "" or t1 == "":
+                    t1 = self.file.__next__().strip()
+                    t2 = self.file.__next__().strip()
+                    self.current_doc = self.current_doc+1
+            self.line_buffer = t2
+
+        assert t1 != ""
+        assert t2 != ""
+        return t1, t2
+
+    def get_random_line(self):
+        """
+        Get random line from another document for nextSentence task.
+        :return: str, content of one line
+        """
+        # Similar to original tf repo: This outer loop should rarely go for more than one iteration for large
+        # corpora. However, just to be careful, we try to make sure that
+        # the random document is not the same as the document we're processing.
+        for _ in range(10):
+            if self.on_memory:
+                rand_doc_idx = random.randint(0, len(self.all_docs)-1)
+                rand_doc = self.all_docs[rand_doc_idx]
+                line = rand_doc[random.randrange(len(rand_doc))]
+            else:
+                rand_index = random.randint(1, self.corpus_lines if self.corpus_lines < 1000 else 1000)
+                #pick random line
+                for _ in range(rand_index):
+                    line = self.get_next_line()
+            #check if our picked random line is really from another doc like we want it to be
+            if self.current_random_doc != self.current_doc:
+                break
+        return line
+
+    def get_next_line(self):
+        """ Gets next line of random_file and starts over when reaching end of file"""
+        try:
+            line = self.random_file.__next__().strip()
+            #keep track of which document we are currently looking at to later avoid having the same doc as t1
+            if line == "":
+                self.current_random_doc = self.current_random_doc + 1
+                line = self.random_file.__next__().strip()
+        except StopIteration:
+            self.random_file.close()
+            self.random_file = open(self.corpus_path, "r", encoding=self.encoding)
+            line = self.random_file.__next__().strip()
+        return line
+
+
+class InputExample(object):
+    """A single training/test example for the language model."""
+
+    def __init__(self, guid, tokens_a, tokens_b=None, is_next=None, lm_labels=None):
+        """Constructs a InputExample.
+
+        Args:
+            guid: Unique id for the example.
+            tokens_a: string. The untokenized text of the first sequence. For single
+            sequence tasks, only this sequence must be specified.
+            tokens_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.tokens_a = tokens_a
+        self.tokens_b = tokens_b
+        self.is_next = is_next  # nextSentence
+        self.lm_labels = lm_labels  # masked words for language model
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self, input_ids, input_mask, segment_ids, is_next, lm_label_ids):
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.is_next = is_next
+        self.lm_label_ids = lm_label_ids
+
+
+def random_word(tokens, tokenizer):
+    """
+    Masking some random tokens for Language Model task with probabilities as in the original BERT paper.
+    :param tokens: list of str, tokenized sentence.
+    :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
+    :return: (list of str, list of int), masked tokens and related labels for LM prediction
+    """
+    output_label = []
+
+    for i, token in enumerate(tokens):
+        prob = random.random()
+        # mask token with 15% probability
+        if prob < 0.15:
+            prob /= 0.15
+
+            # 80% randomly change token to mask token
+            if prob < 0.8:
+                tokens[i] = "[MASK]"
+
+            # 10% randomly change token to random token
+            elif prob < 0.9:
+                tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]
+
+            # -> rest 10% randomly keep current token
+
+            # append current token to output (we will predict these later)
+            try:
+                output_label.append(tokenizer.vocab[token])
+            except KeyError:
+                # For unknown words (should not occur with BPE vocab)
+                output_label.append(tokenizer.vocab["[UNK]"])
+        else:
+            # no masking token (will be ignored by loss function later)
+            output_label.append(-1)
+
+    return tokens, output_label
+
+
+def convert_example_to_features(example, max_seq_length, tokenizer):
+    """
+    Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
+    IDs, LM labels, input_mask, CLS and SEP tokens etc.
+    :param example: InputExample, containing sentence input as strings and is_next label
+    :param max_seq_length: int, maximum length of sequence.
+    :param tokenizer: Tokenizer
+    :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
+    """
+    tokens_a = example.tokens_a
+    tokens_b = example.tokens_b
+    # Modifies `tokens_a` and `tokens_b` in place so that the total
+    # length is less than the specified length.
+    # Account for [CLS], [SEP], [SEP] with "- 3"
+    _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+
+    t1_random, t1_label = random_word(tokens_a, tokenizer)
+    t2_random, t2_label = random_word(tokens_b, tokenizer)
+    # concatenate lm labels and account for CLS, SEP, SEP
+    lm_label_ids = ([-1] + t1_label + [-1] + t2_label + [-1])
+
+    # The convention in BERT is:
+    # (a) For sequence pairs:
+    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+    #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
+    # (b) For single sequences:
+    #  tokens:   [CLS] the dog is hairy . [SEP]
+    #  type_ids: 0   0   0   0  0     0 0
+    #
+    # Where "type_ids" are used to indicate whether this is the first
+    # sequence or the second sequence. The embedding vectors for `type=0` and
+    # `type=1` were learned during pre-training and are added to the wordpiece
+    # embedding vector (and position vector). This is not *strictly* necessary
+    # since the [SEP] token unambigiously separates the sequences, but it makes
+    # it easier for the model to learn the concept of sequences.
+    #
+    # For classification tasks, the first vector (corresponding to [CLS]) is
+    # used as as the "sentence vector". Note that this only makes sense because
+    # the entire model is fine-tuned.
+    tokens = []
+    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)
+
+    assert len(tokens_b) > 0
+    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)
+        lm_label_ids.append(-1)
+
+    assert len(input_ids) == max_seq_length
+    assert len(input_mask) == max_seq_length
+    assert len(segment_ids) == max_seq_length
+    assert len(lm_label_ids) == max_seq_length
+
+    if example.guid < 5:
+        logger.info("*** Example ***")
+        logger.info("guid: %s" % (example.guid))
+        logger.info("tokens: %s" % " ".join(
+                [str(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("LM label: %s " % (lm_label_ids))
+        logger.info("Is next sentence label: %s " % (example.is_next))
+
+    features = InputFeatures(input_ids=input_ids,
+                             input_mask=input_mask,
+                             segment_ids=segment_ids,
+                             lm_label_ids=lm_label_ids,
+                             is_next=example.is_next)
+    return features
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    ## Required parameters
+    parser.add_argument("--train_file",
+                        default=None,
+                        type=str,
+                        required=True,
+                        help="The input train corpus.")
+    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("--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=3e-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("--on_memory",
+                        default=False,
+                        action='store_true',
+                        help="Whether to load train samples into memory or use disk")
+    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 accumualte 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)
+
+    #train_examples = None
+    num_train_steps = None
+    if args.do_train:
+        print("Loading Train Dataset", args.train_file)
+        train_dataset = BERTDataset(args.train_file, tokenizer, seq_len=args.max_seq_length,
+                                    corpus_lines=None, on_memory=args.on_memory)
+        num_train_steps = int(
+            len(train_dataset) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
+
+    # Prepare model
+    model = BertForPreTraining.from_pretrained(args.bert_model)
+    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 n not in no_decay], 'weight_decay_rate': 0.01},
+        {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0}
+        ]
+    optimizer = BertAdam(optimizer_grouped_parameters,
+                         lr=args.learning_rate,
+                         warmup=args.warmup_proportion,
+                         t_total=num_train_steps)
+
+    global_step = 0
+    if args.do_train:
+        logger.info("***** Running training *****")
+        logger.info("  Num examples = %d", len(train_dataset))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_steps)
+
+        if args.local_rank == -1:
+            train_sampler = RandomSampler(train_dataset)
+        else:
+            #TODO: check if this works with current data generator from disk that relies on file.__next__
+            # (it doesn't return item back by index)
+            train_sampler = DistributedSampler(train_dataset)
+        train_dataloader = DataLoader(train_dataset, 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.values())
+                input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
+                loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
+                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():
+                                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
+
+        logger.info("** ** * Saving fine - tuned model ** ** * ")
+        output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+        if n_gpu > 1:
+            torch.save(model.module.bert.state_dict(), output_model_file)
+        else:
+            torch.save(model.bert.state_dict(), output_model_file)
+
+
+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 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
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file