train.py

'''
    Pre-training/Fine-tuning seq2seq models on autoencoding a dataset.

    TODO:
    - [ ] Add reg loss
        - [x] calculate MMD loss
        - [ ] schedule MMD loss weight
            - [ ] Add these params to the training arguments.

                reg_schedule_k (:obj:`float`, `optional`, defaults to 0.0025):
                    Multiplied by global_step in a sigmoid, more gradually increase regulariser loss weight.
                reg_schedule_b (:obj:`float`, `optional`, defaults to 6.25):
                    Added to global step in sigmoid, further delays increase in regulariser loss weight.
                use_extra_logs (:obj:`bool`, `optional`, defaults to False):
                    Store extra logs during each training inference.

            - [ ] Send the schedule time to the compute_loss method and calculate a coefficient based on that.
'''
import logging
import math
import os
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable, Optional

import datasets
from datasets import Dataset, load_dataset
from tqdm import tqdm

import jax
import jax.numpy as jnp
import optax
import transformers
from flax import jax_utils, traverse_util
from flax.jax_utils import unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from transformers import (
    AutoTokenizer,
    HfArgumentParser,
    TrainingArguments,
    is_tensorboard_available,
)
from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
from transformers.testing_utils import CaptureLogger

from t5_vae_flax.src.t5_vae import FlaxT5VaeForAutoencoding
from t5_vae_flax.src.config import T5VaeConfig


logger = logging.getLogger(__name__)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "The model checkpoint for weights initialization."
            "Don't set if you want to train a model from scratch."
        },
    )
    t5_model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "The T5 model checkpoint for weights initialization."
            "Needed when not starting from a T5-VAE model."
        },
    )
    n_latent_tokens: Optional[int] = field(
        default=6,
        metadata={
            "help": "Number of latent tokens (must be less than seq length)."
        },
    )
    latent_token_size: Optional[int] = field(
        default=32,
        metadata={
            "help": "Number of dimensions to use for each latent token."
        },
    )
    add_special_tokens: bool = field(
        default=False,
        metadata={"help": "Add these special tokens to the tokenizer: {'pad_token': '<PAD>', 'bos_token': '<BOS>', 'eos_token': '<EOS>'}"},
    )
    config_path: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config path"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    dtype: Optional[str] = field(
        default="float32",
        metadata={
            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    validation_split_percentage: Optional[int] = field(
        default=5,
        metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        },
    )
    block_size: Optional[int] = field(
        default=None,
        metadata={
            "help": "Optional input sequence length after tokenization. "
            "The training dataset will be truncated in block of this size for training. "
            "Default to the model max input length for single sentence inputs (take into account special tokens)."
        },
    )
    streaming: bool = field(
        default=False, metadata={"help": "Stream the dataset."}
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )

    def __post_init__(self):
        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
            raise ValueError("Need either a dataset name or a training/validation file.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


class TrainState(train_state.TrainState):
    dropout_rng: jnp.ndarray

    def replicate(self):
        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))


def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
    """
    Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
    Shuffle batches if `shuffle` is `True`.
    """
    steps_per_epoch = len(dataset) // batch_size

    if shuffle:
        batch_idx = jax.random.permutation(rng, len(dataset))
    else:
        batch_idx = jnp.arange(len(dataset))

    batch_idx = batch_idx[: steps_per_epoch * batch_size]  # Skip incomplete batch.
    batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))

    for idx in batch_idx:
        batch = dataset[idx]
        batch = {k: jnp.array(v) for k, v in batch.items()}

        batch = shard(batch)

        yield batch


def write_train_metric(summary_writer, train_metrics, train_time, step):
    summary_writer.scalar("train_time", train_time, step)

    train_metrics = get_metrics(train_metrics)
    for key, vals in train_metrics.items():
        tag = f"train_{key}"
        for i, val in enumerate(vals):
            summary_writer.scalar(tag, val, step - len(vals) + i + 1)


def write_eval_metric(summary_writer, eval_metrics, step):
    for metric_name, value in eval_metrics.items():
        summary_writer.scalar(f"eval_{metric_name}", value, step)


def create_learning_rate_fn(
    train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
    """Returns a linear warmup, linear_decay learning rate function."""
    steps_per_epoch = train_ds_size // train_batch_size
    num_train_steps = steps_per_epoch * num_train_epochs
    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
    decay_fn = optax.linear_schedule(
        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
    )
    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
    return schedule_fn


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty."
            "Use --overwrite_output_dir to overcome."
        )

    if data_args.block_size is None:
        raise Exception('Must set block_size so we know what length of sequence to autoencode.')

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    # Setup logging, we only want one process per machine to log things on the screen.
    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
    if jax.process_index() == 0:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # Set the verbosity to info of the Transformers logger (on main process only):
    logger.info(f"Training/evaluation parameters {training_args}")

    #  Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        dataset = load_dataset(
            data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, streaming=data_args.streaming, keep_in_memory=False
        )

        if "validation" not in dataset.keys():
            dataset["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
            )
            dataset["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
            )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = data_args.train_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
        dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer

    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.config_path:
        config = T5VaeConfig.from_pretrained(
            model_args.config_path, cache_dir=model_args.cache_dir
        )
    elif model_args.model_name_or_path:
        config = T5VaeConfig.from_pretrained(
            model_args.model_name_or_path, cache_dir=model_args.cache_dir
        )
    else:
        config = T5VaeConfig(**model_args.__dict__)
        logger.warning("You are instantiating a new config instance from scratch.")

    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
        )
    elif model_args.t5_model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.t5_model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if model_args.model_name_or_path:
        model = FlaxT5VaeForAutoencoding.from_pretrained(
            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
        )
        assert model.params['t5']['shared']['embedding'].shape[0] == len(tokenizer), "T5 Tokenizer doesn't match T5Vae embedding size."
    else:
        vocab_size = len(tokenizer)
        config.t5.vocab_size = vocab_size
        config.vocab_size = vocab_size
        logger.info("Training new model from scratch.")
        model = FlaxT5VaeForAutoencoding(
            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
        )

    if model_args.add_special_tokens:
        special_tokens_dict = {'pad_token': '<PAD>', 'bos_token': '<BOS>', 'eos_token': '<EOS>'}
        num_added_tokens = tokenizer.add_special_tokens(special_tokens_dict)
        print('We have added', num_added_tokens, 'tokens to GPT2')
        model.resize_token_embeddings(len(tokenizer))
        assert tokenizer.pad_token == '<PAD>'

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        column_names = dataset["train"].column_names
    else:
        column_names = dataset["validation"].column_names
    text_column_name = "text" if "text" in column_names else column_names[0]

    # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
    tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")

    def tokenize_function(examples):
        with CaptureLogger(tok_logger) as cl:
            output = tokenizer(examples[text_column_name])
        # clm input could be much much longer than block_size
        if "Token indices sequence length is longer than the" in cl.out:
            tok_logger.warning(
                "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
            )
        return output

    # remove dataset tasks
    for k in dataset.keys():
        dataset[k].info.task_templates = []

    tokenized_datasets = dataset.map(
        tokenize_function,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        remove_columns=column_names,
        load_from_cache_file=not data_args.overwrite_cache,
    )

    if data_args.block_size > tokenizer.model_max_length:
        logger.warning(
            f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
            f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
        )
    block_size = min(data_args.block_size, tokenizer.model_max_length)

    pad_token_id, start_token_id = tokenizer.pad_token_id, config.decoder_start_token_id

    def clip_texts(examples):
        examples["labels"] = examples["input_ids"].copy()

        for i, input_ids in enumerate(examples["input_ids"]):
            if len(input_ids) > block_size:
                for k in examples.keys():
                    examples[k][i] = examples[k][i][:block_size]
            elif len(input_ids) < block_size:
                delta = block_size - len(input_ids)
                examples['input_ids'][i] = examples['input_ids'][i] + [pad_token_id] * delta
                examples['attention_mask'][i] = examples['attention_mask'][i] + [0] * delta
                examples['labels'][i] = examples['labels'][i] + [-100] * delta

        return examples

    logger.info('clip_texts...')
    clipped_lm_datasets = tokenized_datasets.map(
        clip_texts,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        load_from_cache_file=not data_args.overwrite_cache,
    )

    def add_decoder_input_ids(examples):
        arr_input_ids = jnp.array(examples["input_ids"])
        pad = pad_token_id * jnp.ones((arr_input_ids.shape[0], 1), dtype=jnp.int32)
        arr_pad_input_ids = jnp.concatenate((arr_input_ids, pad), axis=1)
        examples['decoder_input_ids'] = shift_tokens_right(arr_pad_input_ids, pad_token_id, start_token_id)

        arr_attention_mask = jnp.array(examples['attention_mask'])
        ones = jnp.ones((arr_attention_mask.shape[0], 1), dtype=jnp.int32)
        examples['decoder_attention_mask'] = jnp.concatenate((ones, arr_attention_mask), axis=1)

        for k in ['decoder_input_ids', 'decoder_attention_mask']:
            examples[k] = examples[k].tolist()

        return examples

    logger.info('add_decoder_input_ids...')
    lm_datasets = clipped_lm_datasets.map(
        add_decoder_input_ids,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        load_from_cache_file=not data_args.overwrite_cache,
    )

    if training_args.do_train:
        if "train" not in tokenized_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = lm_datasets["train"]
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(data_args.max_train_samples))

    if training_args.do_eval:
        if "validation" not in tokenized_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = lm_datasets["validation"]
        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))

    # Enable tensorboard only on the master node
    has_tensorboard = is_tensorboard_available()
    if has_tensorboard and jax.process_index() == 0:
        try:
            from flax.metrics.tensorboard import SummaryWriter

            summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
        except ImportError as ie:
            has_tensorboard = False
            logger.warning(
                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
            )
    else:
        logger.warning(
            "Unable to display metrics through TensorBoard because the package is not installed: "
            "Please run pip install tensorboard to enable."
        )

    # Initialize our training
    rng = jax.random.PRNGKey(training_args.seed)
    rng, dropout_rng = jax.random.split(rng)

    # Store some constant
    num_epochs = int(training_args.num_train_epochs)
    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
    steps_per_epoch = len(train_dataset) // train_batch_size
    total_train_steps = steps_per_epoch * num_epochs

    # Create learning rate schedule
    linear_decay_lr_schedule_fn = create_learning_rate_fn(
        len(train_dataset),
        train_batch_size,
        training_args.num_train_epochs,
        training_args.warmup_steps,
        training_args.learning_rate,
    )

    # We use Optax's "masking" functionality to not apply weight decay
    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
    # mask boolean with the same structure as the parameters.
    # The mask is True for parameters that should be decayed.
    # Note that this mask is specifically adapted for FlaxGPT2.
    # For other models, one should correct the layer norm parameter naming
    # accordingly.
    def decay_mask_fn(params):
        flat_params = traverse_util.flatten_dict(params)
        flat_mask = {
            path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
            for path in flat_params
        }
        return traverse_util.unflatten_dict(flat_mask)

    # create adam optimizer
    if training_args.adafactor:
        # We use the default parameters here to initialize adafactor,
        # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
        optimizer = optax.adafactor(
            learning_rate=linear_decay_lr_schedule_fn,
        )
    else:
        optimizer = optax.adamw(
            learning_rate=linear_decay_lr_schedule_fn,
            b1=training_args.adam_beta1,
            b2=training_args.adam_beta2,
            eps=training_args.adam_epsilon,
            weight_decay=training_args.weight_decay,
            mask=decay_mask_fn,
        )

    # Setup train state
    state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)

    def compute_kernel(x, y):
        x_size = x.shape[0]
        y_size = y.shape[0]
        dim = x.shape[1]
        tiled_x = jnp.repeat(jnp.reshape(x, (x_size, 1, dim)), y_size, axis=1)
        tiled_y = jnp.repeat(jnp.reshape(y, (1, y_size, dim)), x_size, axis=0)
        return jnp.exp(-jnp.mean((tiled_x - tiled_y) ** 2, axis=2) / dim * 1.0)

    def compute_mmd(x, y):
        x_kernel = compute_kernel(x, x)
        y_kernel = compute_kernel(y, y)
        xy_kernel = compute_kernel(x, y)
        return jnp.mean(x_kernel) + jnp.mean(y_kernel) - 2 * jnp.mean(xy_kernel)

    def regulariser_loss(latent_codes, rng):
        true_samples = jax.random.normal(rng, latent_codes.shape)
        # return jax.vmap(compute_mmd)(true_samples, latent_codes)
        return compute_mmd(true_samples, latent_codes)

    def loss_fn(logits, labels, latent_codes, regulariser_rng):
        shift_logits = logits[..., :-1, :]
        loss = optax.softmax_cross_entropy(shift_logits, onehot(labels, logits.shape[-1]))
        reg_loss = regulariser_loss(latent_codes.reshape(-1, latent_codes.shape[-1]), regulariser_rng)
        return loss.mean() + reg_loss.mean()

    # Define gradient update step fn
    def train_step(state, batch):
        dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
        new_dropout_rng, regulariser_rng = jax.random.split(new_dropout_rng)

        def compute_loss(params):
            labels = batch.pop("labels")
            outputs = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)
            loss = loss_fn(outputs[0], labels, outputs[1], regulariser_rng)
            return loss

        grad_fn = jax.value_and_grad(compute_loss)
        loss, grad = grad_fn(state.params)
        grad = jax.lax.pmean(grad, "batch")

        new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)

        metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
        metrics = jax.lax.pmean(metrics, axis_name="batch")

        return new_state, metrics

    # Define eval fn
    def eval_step(params, rng, batch):
        labels = batch.pop("labels")
        logits, latent_codes = model(**batch, params=params, train=False)[:2]
        loss = loss_fn(logits, labels, latent_codes, rng)

        # summarize metrics
        metrics = {"loss": loss}
        metrics = jax.lax.pmean(metrics, axis_name="batch")
        return metrics

    # Create parallel version of the train and eval step
    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
    p_eval_step = jax.pmap(eval_step, "batch")

    # Replicate the train state on each device
    state = state.replicate()

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {num_epochs}")
    logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel & distributed) = {train_batch_size}")
    logger.info(f"  Total optimization steps = {total_train_steps}")

    train_time = 0
    train_metrics = []
    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
    for epoch in epochs:
        # ======================== Training ================================
        train_start = time.time()

        # Create sampling rng
        rng, input_rng = jax.random.split(rng)

        # Generate an epoch by shuffling sampling indices from the train dataset
        train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
        steps_per_epoch = len(train_dataset) // train_batch_size
        # train
        for step in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
            batch = next(train_loader)
            state, train_metric = p_train_step(state, batch)
            train_metrics.append(train_metric)

            cur_step = epoch * (len(train_dataset) // train_batch_size) + step

            if cur_step % training_args.logging_steps == 0 and cur_step > 0:
                # Save metrics
                train_metric = unreplicate(train_metric)
                train_time += time.time() - train_start
                if has_tensorboard and jax.process_index() == 0:
                    write_train_metric(summary_writer, train_metrics, train_time, cur_step)

                epochs.write(
                    f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
                )

                train_metrics = []

            if cur_step % training_args.eval_steps == 0 and cur_step > 0:
                # ======================== Evaluating ==============================
                eval_metrics = []
                eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
                eval_steps = len(eval_dataset) // eval_batch_size
                for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
                    # Model forward
                    batch = next(eval_loader)
                    metrics = p_eval_step(state.params, state.dropout_rng, batch)
                    eval_metrics.append(metrics)

                # normalize eval metrics
                eval_metrics = get_metrics(eval_metrics)
                eval_metrics = jax.tree_map(jnp.mean, eval_metrics)

                try:
                    eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
                except OverflowError:
                    eval_metrics["perplexity"] = float("inf")

                # Print metrics and update progress bar
                desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
                epochs.write(desc)
                epochs.desc = desc

                # Save metrics
                if has_tensorboard and jax.process_index() == 0:
                    cur_step = epoch * (len(train_dataset) // train_batch_size)
                    write_eval_metric(summary_writer, eval_metrics, cur_step)

            if cur_step % training_args.save_steps == 0 and cur_step > 0:
                # save checkpoint after each epoch and push checkpoint to the hub
                if jax.process_index() == 0:
                    params = jax.device_get(unreplicate(state.params))
                    model.save_pretrained(
                        training_args.output_dir,
                        params=params,
                        push_to_hub=training_args.push_to_hub,
                        commit_message=f"Saving weights and logs of step {cur_step}",
                    )


if __name__ == "__main__":
    main()