run_lm_hstuning.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. Example taken and modified from Huggingface's BERT Pytorch repository."""

from __future__ import absolute_import, division, print_function, unicode_literals

import argparse
import logging
import os
import random
from io import open

import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange

from pytorch_pretrained_bert.modeling import BertForPreTraining
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear

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 
        self.corpus_path = corpus_path
        self.encoding = encoding
        self.current_doc = 0 

        self.sample_counter = 0
        self.line_buffer = None

        self.current_random_doc = 0
        self.num_docs = 0
        self.sample_to_doc = []

        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 = []
                        self.sample_to_doc.pop()
                    else:
                        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 self.all_docs[-1] != doc:
                self.all_docs.append(doc)
                self.sample_to_doc.pop()

            self.num_docs = len(self.all_docs)

        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 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):
        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:
            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)

        tokens_a = self.tokenizer.tokenize(t1)
        tokens_b = self.tokenizer.tokenize(t2)

        cur_example = InputExample(guid=cur_id, tokens_a=tokens_a, tokens_b=tokens_b, is_next=is_next_label)

        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]
            self.current_doc = sample["doc_id"]
            return t1, t2
        else:
            if self.line_buffer is None:
                while t1 == "" :
                    t1 = next(self.file).strip()
                    t2 = next(self.file).strip()
            else:
                t1 = self.line_buffer
                t2 = next(self.file).strip()
                while t2 == "" or t1 == "":
                    t1 = next(self.file).strip()
                    t2 = next(self.file).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
        """
        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)
                for _ in range(rand_index):
                    line = self.get_next_line()
            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 = next(self.random_file).strip()
            if line == "":
                self.current_random_doc = self.current_random_doc + 1
                line = next(self.random_file).strip()
        except StopIteration:
            self.random_file.close()
            self.random_file = open(self.corpus_path, "r", encoding=self.encoding)
            line = next(self.random_file).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 
        self.lm_labels = lm_labels 


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()
        if prob < 0.15:
            prob /= 0.15

            if prob < 0.8:
                tokens[i] = "[MASK]"

            elif prob < 0.9:
                tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]

            try:
                output_label.append(tokenizer.vocab[token])
            except KeyError:
                output_label.append(tokenizer.vocab["[UNK]"])
                logger.warning("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token))
        else:
            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
    _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)
    lm_label_ids = ([-1] + t1_label + [-1] + t2_label + [-1])

    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)

    input_mask = [1] * len(input_ids)

    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()

    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.")

    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
        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 not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

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

    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()

    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)

    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:
            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()
                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:
                        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

        logger.info("** ** * Saving fine - tuned model ** ** * ")
        model_to_save = model.module if hasattr(model, 'module') else model 
        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."""
    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()