run_lm_finetuning.py

# python -m run_lm_finetuning \ --bert_model bert-base-uncased \ --do_lower_case \ --do_train \ --train_file ./samples/sample_text.txt \ --output_dir ./samples/samples_out \ --num_train_epochs 5.0 \ --learning_rate 3e-5 \ --train_batch_size 16 \ --max_seq_length 128
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__)

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

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
        self.sample_to_doc = [] # map sample index to doc and line

        # 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 = []
                        #remove last added sample because there won't be a subsequent line anymore in the doc
                        self.sample_to_doc.pop()
                    else:
                        #store as one sample
                        sample = {"doc_id": len(self.all_docs),
                                  "line": len(doc)}
                        self.sample_to_doc.append(sample)
                        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.sample_to_doc.pop()

            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 = (torch.tensor(cur_features.input_ids),
                       torch.tensor(cur_features.input_mask),
                       torch.tensor(cur_features.segment_ids),
                       torch.tensor(cur_features.lm_label_ids),
                       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:
            sample = self.sample_to_doc[item]
            t1 = self.all_docs[sample["doc_id"]][sample["line"]]
            t2 = self.all_docs[sample["doc_id"]][sample["line"]+1]
            # used later to avoid random nextSentence from same doc
            self.current_doc = sample["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]"])
                logger.warning("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token))
        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)

    tokens_a, t1_label = random_word(tokens_a, tokenizer)
    tokens_b, 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",
                        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("--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",
                        action='store_true',
                        help="Whether not to use CUDA when available")
    parser.add_argument("--on_memory",
                        action='store_true',
                        help="Whether to load train samples into memory or use disk")
    parser.add_argument("--do_lower_case",
                        action='store_true',
                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
    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('--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")

    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:
        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))

    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 = 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:
        raise ValueError("Training is currently the only implemented execution option. Please set `do_train`.")

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

    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)

    #train_examples = None
    num_train_optimization_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_optimization_steps = int(
            len(train_dataset) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
        if args.local_rank != -1:
            num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()

    # Prepare model
    model = BertForPreTraining.from_pretrained(args.bert_model)
    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}
        ]

    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=num_train_optimization_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_optimization_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)
                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.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:
                    if args.fp16:
                        # modify learning rate with special warm up BERT uses
                        # if args.fp16 is False, BertAdam is used that handles this automatically
                        lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
                        for param_group in optimizer.param_groups:
                            param_group['lr'] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    global_step += 1

        # Save a trained model
        logger.info("** ** * Saving fine - tuned 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")
        if args.do_train:
            torch.save(model_to_save.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)


if __name__ == "__main__":
    main()
    # python -m run_lm_finetuning \
    # --bert_model bert-base-uncased \ --do_lower_case \ --do_train \
    # --train_file ./samples/sample_text.txt \
    # --output_dir ./samples/samples_out \
    # --max_seq_length 128
    # --train_batch_size 16 \
    # --learning_rate 3e-5 \
    # --num_train_epochs 5.0 \