finetune_on_pregenerated.py

from argparse import ArgumentParser
from pathlib import Path
import torch
import logging
import json
import random
import numpy as np
from collections import namedtuple

from torch.utils.data import DataLoader, Dataset, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

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

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):
        # TODO Add an option to memmap and shuffle the training data if needed (see note in pregenerate_training_data)
        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']
        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")):
                example = json.loads(line.rstrip())
                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,
                        choices=["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("--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_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("--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)
    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
    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)
        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
                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
                pbar.update(1)
                mean_loss = tr_loss / nb_tr_steps
                pbar.set_postfix_str(f"Loss: {mean_loss:.5f}")
                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
    logging.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 = args.output_dir / "pytorch_model.bin"
    torch.save(model_to_save.state_dict(), str(output_model_file))




if __name__ == '__main__':
    main()