add base model code

.gitignore

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+.vscode
+venv
+*.pyc

README.md

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+---
+language: en
+tags: vae
+license: apache-2.0
+---
+
+# T5-VAE-Wiki (flax)
+
+A Transformer-VAE made using flax.
+
+Try the [demo] (TODO)!
+
+It has been trained to interpolate on sentences form wikipedia.
+
+Done as part of Huggingface community training ([see forum post](https://discuss.huggingface.co/t/train-a-vae-to-interpolate-on-english-sentences/7548)).
+
+Builds on T5, using an autoencoder to convert it into an MMD-VAE ([more info](http://fras.uk/ml/large%20prior-free%20models/transformer-vae/2020/08/13/Transformers-as-Variational-Autoencoders.html)).
+
+## How to use from the 🤗/transformers library
+
+Add model repo as a submodule:
+```bash
+git submodule add https://github.com/Fraser-Greenlee/t5-vae-flax.git t5_vae_flax
+```
+
+```python
+from transformers import AutoTokenizer
+from t5_vae_flax.src.t5_vae import FlaxT5VaeForAutoencoding
+
+tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+model = FlaxT5VaeForAutoencoding.from_pretrained("flax-community/t5-vae-python")
+```
+
+## Setup
+
+Run `setup_tpu_vm_venv.sh` to setup a virtual enviroment on a TPU VM for training.

check_install.py

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+from transformers import FlaxRobertaModel, RobertaTokenizerFast
+from datasets import load_dataset
+import jax
+
+dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
+
+dummy_input = next(iter(dataset))["text"]
+
+tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
+input_ids = tokenizer(dummy_input, return_tensors="np").input_ids[:, :10]
+
+model = FlaxRobertaModel.from_pretrained("julien-c/dummy-unknown")
+
+# run a forward pass, should return an object `FlaxBaseModelOutputWithPooling`
+z = model(input_ids)

setup_tpu_vm_venv.sh

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+# setup training on a TPU VM
+rm -fr venv
+python3 -m venv venv
+source venv/bin/activate
+pip install -U pip
+pip install -U wheel
+pip install requests
+pip install "jax[tpu]>=0.2.16" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
+
+cd ..
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+pip install -e ".[flax]"
+cd ..
+
+git clone https://github.com/huggingface/datasets.git
+cd datasets
+pip install -e ".[streaming]"
+cd ..

train.py

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+'''
+    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()