finetune_on_pregenerated.py

import json
import logging
import random
from argparse import ArgumentParser
from collections import namedtuple
from pathlib import Path
from tempfile import TemporaryDirectory

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

from pytorch_transformers.modeling_bert import BertForPreTraining
from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule
from pytorch_transformers.tokenization_bert import BertTokenizer

InputFeatures = namedtuple("InputFeatures", "input_ids input_mask segment_ids lm_label_ids is_next")

log_format = '%(asctime)-10s: %(message)s'
logging.basicConfig(level=logging.INFO, format=log_format)


def convert_example_to_features(example, tokenizer, max_seq_length):
    tokens = example["tokens"]
    segment_ids = example["segment_ids"]
    is_random_next = example["is_random_next"]
    masked_lm_positions = example["masked_lm_positions"]
    masked_lm_labels = example["masked_lm_labels"]

    assert len(tokens) == len(segment_ids) <= max_seq_length  # The preprocessed data should be already truncated
    input_ids = tokenizer.convert_tokens_to_ids(tokens)
    masked_label_ids = tokenizer.convert_tokens_to_ids(masked_lm_labels)

    input_array = np.zeros(max_seq_length, dtype=np.int)
    input_array[:len(input_ids)] = input_ids

    mask_array = np.zeros(max_seq_length, dtype=np.bool)
    mask_array[:len(input_ids)] = 1

    segment_array = np.zeros(max_seq_length, dtype=np.bool)
    segment_array[:len(segment_ids)] = segment_ids

    lm_label_array = np.full(max_seq_length, dtype=np.int, fill_value=-1)
    lm_label_array[masked_lm_positions] = masked_label_ids

    features = InputFeatures(input_ids=input_array,
                             input_mask=mask_array,
                             segment_ids=segment_array,
                             lm_label_ids=lm_label_array,
                             is_next=is_random_next)
    return features


class PregeneratedDataset(Dataset):
    def __init__(self, training_path, epoch, tokenizer, num_data_epochs, reduce_memory=False):
        self.vocab = tokenizer.vocab
        self.tokenizer = tokenizer
        self.epoch = epoch
        self.data_epoch = epoch % num_data_epochs
        data_file = training_path / f"epoch_{self.data_epoch}.json"
        metrics_file = training_path / f"epoch_{self.data_epoch}_metrics.json"
        assert data_file.is_file() and metrics_file.is_file()
        metrics = json.loads(metrics_file.read_text())
        num_samples = metrics['num_training_examples']
        seq_len = metrics['max_seq_len']
        self.temp_dir = None
        self.working_dir = None
        if reduce_memory:
            self.temp_dir = TemporaryDirectory()
            self.working_dir = Path(self.temp_dir.name)
            input_ids = np.memmap(filename=self.working_dir / 'input_ids.memmap',
                                  mode='w+', dtype=np.int32, shape=(num_samples, seq_len))
            input_masks = np.memmap(filename=self.working_dir / 'input_masks.memmap',
                                    shape=(num_samples, seq_len), mode='w+', dtype=np.bool)
            segment_ids = np.memmap(filename=self.working_dir / 'segment_ids.memmap',
                                    shape=(num_samples, seq_len), mode='w+', dtype=np.bool)
            lm_label_ids = np.memmap(filename=self.working_dir / 'lm_label_ids.memmap',
                                     shape=(num_samples, seq_len), mode='w+', dtype=np.int32)
            lm_label_ids[:] = -1
            is_nexts = np.memmap(filename=self.working_dir / 'is_nexts.memmap',
                                 shape=(num_samples,), mode='w+', dtype=np.bool)
        else:
            input_ids = np.zeros(shape=(num_samples, seq_len), dtype=np.int32)
            input_masks = np.zeros(shape=(num_samples, seq_len), dtype=np.bool)
            segment_ids = np.zeros(shape=(num_samples, seq_len), dtype=np.bool)
            lm_label_ids = np.full(shape=(num_samples, seq_len), dtype=np.int32, fill_value=-1)
            is_nexts = np.zeros(shape=(num_samples,), dtype=np.bool)
        logging.info(f"Loading training examples for epoch {epoch}")
        with data_file.open() as f:
            for i, line in enumerate(tqdm(f, total=num_samples, desc="Training examples")):
                line = line.strip()
                example = json.loads(line)
                features = convert_example_to_features(example, tokenizer, seq_len)
                input_ids[i] = features.input_ids
                segment_ids[i] = features.segment_ids
                input_masks[i] = features.input_mask
                lm_label_ids[i] = features.lm_label_ids
                is_nexts[i] = features.is_next
        assert i == num_samples - 1  # Assert that the sample count metric was true
        logging.info("Loading complete!")
        self.num_samples = num_samples
        self.seq_len = seq_len
        self.input_ids = input_ids
        self.input_masks = input_masks
        self.segment_ids = segment_ids
        self.lm_label_ids = lm_label_ids
        self.is_nexts = is_nexts

    def __len__(self):
        return self.num_samples

    def __getitem__(self, item):
        return (torch.tensor(self.input_ids[item].astype(np.int64)),
                torch.tensor(self.input_masks[item].astype(np.int64)),
                torch.tensor(self.segment_ids[item].astype(np.int64)),
                torch.tensor(self.lm_label_ids[item].astype(np.int64)),
                torch.tensor(self.is_nexts[item].astype(np.int64)))


def main():
    parser = ArgumentParser()
    parser.add_argument('--pregenerated_data', type=Path, required=True)
    parser.add_argument('--output_dir', type=Path, required=True)
    parser.add_argument("--bert_model", 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("--do_lower_case", action="store_true")
    parser.add_argument("--reduce_memory", action="store_true",
                        help="Store training data as on-disc memmaps to massively reduce memory usage")

    parser.add_argument("--epochs", type=int, default=3, help="Number of epochs to train for")
    parser.add_argument("--local_rank",
                        type=int,
                        default=-1,
                        help="local_rank for distributed training on gpus")
    parser.add_argument("--no_cuda",
                        action='store_true',
                        help="Whether not to use CUDA when available")
    parser.add_argument('--gradient_accumulation_steps',
                        type=int,
                        default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument("--train_batch_size",
                        default=32,
                        type=int,
                        help="Total batch size for training.")
    parser.add_argument('--fp16',
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale',
                        type=float, default=0,
                        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                             "0 (default value): dynamic loss scaling.\n"
                             "Positive power of 2: static loss scaling value.\n")
    parser.add_argument("--warmup_steps",
                        default=0,
                        type=int,
                        help="Linear warmup over warmup_steps.")
    parser.add_argument("--adam_epsilon",
                        default=1e-8,
                        type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--learning_rate",
                        default=3e-5,
                        type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument('--seed',
                        type=int,
                        default=42,
                        help="random seed for initialization")
    args = parser.parse_args()

    assert args.pregenerated_data.is_dir(), \
        "--pregenerated_data should point to the folder of files made by pregenerate_training_data.py!"

    samples_per_epoch = []
    for i in range(args.epochs):
        epoch_file = args.pregenerated_data / f"epoch_{i}.json"
        metrics_file = args.pregenerated_data / f"epoch_{i}_metrics.json"
        if epoch_file.is_file() and metrics_file.is_file():
            metrics = json.loads(metrics_file.read_text())
            samples_per_epoch.append(metrics['num_training_examples'])
        else:
            if i == 0:
                exit("No training data was found!")
            print(f"Warning! There are fewer epochs of pregenerated data ({i}) than training epochs ({args.epochs}).")
            print("This script will loop over the available data, but training diversity may be negatively impacted.")
            num_data_epochs = i
            break
    else:
        num_data_epochs = args.epochs

    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')
    logging.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 args.output_dir.is_dir() and list(args.output_dir.iterdir()):
        logging.warning(f"Output directory ({args.output_dir}) already exists and is not empty!")
    args.output_dir.mkdir(parents=True, exist_ok=True)

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

    total_train_examples = 0
    for i in range(args.epochs):
        # The modulo takes into account the fact that we may loop over limited epochs of data
        total_train_examples += samples_per_epoch[i % len(samples_per_epoch)]

    num_train_optimization_steps = int(
        total_train_examples / args.train_batch_size / args.gradient_accumulation_steps)
    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)
    # We don't need to manually call model.half() following Apex's recommend
    # 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}
    ]

    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps,
                                     t_total=num_train_optimization_steps)

    if args.fp16:
        try:
            # from apex.optimizers import FP16_Optimizer
            # from apex.optimizers import FusedAdam
            from apex import amp
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")

        # This below line of code is the main upgrade of Apex Fp16 implementation. I chose opt_leve="01"
        # because it's recommended for typical use by Apex. We can make it configured
        model, optimizer = amp.initialize(model, optimizer, opt_level="O1")

    # We don't need to use FP16_Optimizer wrapping over FusedAdam as well. Now Apex supports all Pytorch Optimizer

    #     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 = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    # scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=num_train_optimization_steps)

    global_step = 0
    logging.info("***** Running training *****")
    logging.info(f"  Num examples = {total_train_examples}")
    logging.info("  Batch size = %d", args.train_batch_size)
    logging.info("  Num steps = %d", num_train_optimization_steps)
    model.train()
    for epoch in range(args.epochs):
        epoch_dataset = PregeneratedDataset(epoch=epoch, training_path=args.pregenerated_data, tokenizer=tokenizer,
                                            num_data_epochs=num_data_epochs, reduce_memory=args.reduce_memory)
        if args.local_rank == -1:
            train_sampler = RandomSampler(epoch_dataset)
        else:
            train_sampler = DistributedSampler(epoch_dataset)
        train_dataloader = DataLoader(epoch_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
        tr_loss = 0
        nb_tr_examples, nb_tr_steps = 0, 0
        with tqdm(total=len(train_dataloader), desc=f"Epoch {epoch}") as pbar:
            for step, batch in enumerate(train_dataloader):
                batch = tuple(t.to(device) for t in batch)
                input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
                outputs = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
                loss = outputs[0]
                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:
                    # I depricate FP16_Optimizer's backward func and replace as Apex document
                    # optimizer.backward(loss)
                    with amp.scale_loss(loss, optimizer) as scaled_loss:
                        scaled_loss.backward()
                else:
                    loss.backward()
                tr_loss += loss.item()
                nb_tr_examples += input_ids.size(0)
                nb_tr_steps += 1
                pbar.update(1)
                mean_loss = tr_loss * args.gradient_accumulation_steps / nb_tr_steps
                pbar.set_postfix_str(f"Loss: {mean_loss:.5f}")
                if (step + 1) % args.gradient_accumulation_steps == 0:
                    optimizer.step()
                    scheduler.step()  # Update learning rate schedule
                    optimizer.zero_grad()
                    global_step += 1

    # Save a trained model
    if args.local_rank == -1 or torch.distributed.get_rank() == 0:
        logging.info("** ** * Saving fine-tuned model ** ** * ")
        model_to_save = model.module if hasattr(model,
                                                'module') else model  # Take care of distributed/parallel training
        model_to_save.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)


if __name__ == '__main__':
    main()