transformers/examples/flax/language-modeling/run_mlm_flax.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
text file or a dataset.

Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=fill-mask
"""
import json
import logging
import math
import os
import sys
import time
import warnings
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain

# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
from pathlib import Path
from typing import Dict, List, Optional, Tuple

import flax
import jax
import jax.numpy as jnp
import numpy as np
import optax
from datasets import load_dataset
from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository, create_repo
from tqdm import tqdm

from transformers import (
    CONFIG_MAPPING,
    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoTokenizer,
    FlaxAutoModelForMaskedLM,
    HfArgumentParser,
    PreTrainedTokenizerBase,
    TensorType,
    is_tensorboard_available,
    set_seed,
)
from transformers.utils import send_example_telemetry


MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class TrainingArguments:
    output_dir: str = field(
        metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
    )
    overwrite_output_dir: bool = field(
        default=False,
        metadata={
            "help": (
                "Overwrite the content of the output directory. "
                "Use this to continue training if output_dir points to a checkpoint directory."
            )
        },
    )
    do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
    do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
    per_device_train_batch_size: int = field(
        default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
    )
    per_device_eval_batch_size: int = field(
        default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
    )
    learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
    weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
    adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
    adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
    adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
    adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
    num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
    warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
    logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
    save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
    eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
    seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
    push_to_hub: bool = field(
        default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
    )
    hub_model_id: str = field(
        default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
    )
    hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
    gradient_checkpointing: bool = field(
        default=False,
        metadata={
            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
        },
    )

    def __post_init__(self):
        if self.output_dir is not None:
            self.output_dir = os.path.expanduser(self.output_dir)

    def to_dict(self):
        """
        Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
        the token values by removing their value.
        """
        d = asdict(self)
        for k, v in d.items():
            if isinstance(v, Enum):
                d[k] = v.value
            if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
                d[k] = [x.value for x in v]
            if k.endswith("_token"):
                d[k] = f"<{k.upper()}>"
        return d


@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."
            )
        },
    )
    model_type: Optional[str] = field(
        default=None,
        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    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]`."
            )
        },
    )
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
                "execute code present on the Hub on your local machine."
            )
        },
    )


@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)."},
    )
    train_ref_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
    )
    validation_ref_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
    )
    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"
        },
    )
    max_seq_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated. Default to the max input length of the model."
            )
        },
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    mlm_probability: float = field(
        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to `max_seq_length`. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
            )
        },
    )
    line_by_line: bool = field(
        default=False,
        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
    )

    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."


@flax.struct.dataclass
class FlaxDataCollatorForLanguageModeling:
    """
    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
    are not all of the same length.

    Args:
        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
            The tokenizer used for encoding the data.
        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
            The probability with which to (randomly) mask tokens in the input.

    .. note::

        For best performance, this data collator should be used with a dataset having items that are dictionaries or
        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
        argument :obj:`return_special_tokens_mask=True`.
    """

    tokenizer: PreTrainedTokenizerBase
    mlm_probability: float = 0.15

    def __post_init__(self):
        if self.tokenizer.mask_token is None:
            raise ValueError(
                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
                "You should pass `mlm=False` to train on causal language modeling instead."
            )

    def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
        # Handle dict or lists with proper padding and conversion to tensor.
        batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)

        # If special token mask has been preprocessed, pop it from the dict.
        special_tokens_mask = batch.pop("special_tokens_mask", None)

        batch["input_ids"], batch["labels"] = self.mask_tokens(
            batch["input_ids"], special_tokens_mask=special_tokens_mask
        )
        return batch

    def mask_tokens(
        self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
        """
        labels = inputs.copy()
        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
        probability_matrix = np.full(labels.shape, self.mlm_probability)
        special_tokens_mask = special_tokens_mask.astype("bool")

        probability_matrix[special_tokens_mask] = 0.0
        masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
        labels[~masked_indices] = -100  # We only compute loss on masked tokens

        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
        indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)

        # 10% of the time, we replace masked input tokens with random word
        indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
        indices_random &= masked_indices & ~indices_replaced

        random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
        inputs[indices_random] = random_words[indices_random]

        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
        return inputs, labels


def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:
    """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
    the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned."""
    num_samples = len(samples_idx)
    if drop_last:
        samples_to_remove = num_samples % batch_size
        if samples_to_remove != 0:
            samples_idx = samples_idx[:-samples_to_remove]
        sections_split = num_samples // batch_size
        samples_idx = samples_idx.reshape((sections_split, batch_size))
    else:
        sections_split = math.ceil(num_samples / batch_size)
        samples_idx = np.array_split(samples_idx, sections_split)
    return samples_idx


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 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 model_args.use_auth_token is not None:
        warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
        if model_args.token is not None:
            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
        model_args.token = model_args.use_auth_token

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_mlm", model_args, data_args, framework="flax")

    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."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        level=logging.INFO,
        datefmt="[%X]",
    )

    # Log on each process the small summary:
    logger = logging.getLogger(__name__)

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

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Handle the repository creation
    if training_args.push_to_hub:
        # Retrieve of infer repo_name
        repo_name = training_args.hub_model_id
        if repo_name is None:
            repo_name = Path(training_args.output_dir).absolute().name
        # Create repo and retrieve repo_id
        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
        # Clone repo locally
        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)

    # 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.
        datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )

        if "validation" not in datasets.keys():
            datasets["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,
                token=model_args.token,
            )
            datasets["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,
                token=model_args.token,
            )
    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"
        datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )

        if "validation" not in datasets.keys():
            datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )
            datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )
    # 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_name:
        config = AutoConfig.from_pretrained(
            model_args.config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        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,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            use_fast=model_args.use_fast_tokenizer,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    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."
        )

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

    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    if data_args.line_by_line:
        # When using line_by_line, we just tokenize each nonempty line.
        padding = "max_length" if data_args.pad_to_max_length else False

        def tokenize_function(examples):
            # Remove empty lines
            examples = [line for line in examples if len(line) > 0 and not line.isspace()]
            return tokenizer(
                examples,
                return_special_tokens_mask=True,
                padding=padding,
                truncation=True,
                max_length=max_seq_length,
            )

        tokenized_datasets = datasets.map(
            tokenize_function,
            input_columns=[text_column_name],
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    else:
        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
        # efficient when it receives the `special_tokens_mask`.
        def tokenize_function(examples):
            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)

        tokenized_datasets = datasets.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,
        )

        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
        # max_seq_length.
        def group_texts(examples):
            # Concatenate all texts.
            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
            total_length = len(concatenated_examples[list(examples.keys())[0]])
            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
            # customize this part to your needs.
            if total_length >= max_seq_length:
                total_length = (total_length // max_seq_length) * max_seq_length
            # Split by chunks of max_len.
            result = {
                k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
                for k, t in concatenated_examples.items()
            }
            return result

        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
        # might be slower to preprocess.
        #
        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
        # https://huggingface.co/docs/datasets/process#map
        tokenized_datasets = tokenized_datasets.map(
            group_texts,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # 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."
        )

    # Data collator
    # This one will take care of randomly masking the tokens.
    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)

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

    if model_args.model_name_or_path:
        model = FlaxAutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path,
            config=config,
            seed=training_args.seed,
            dtype=getattr(jnp, model_args.dtype),
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        model = FlaxAutoModelForMaskedLM.from_config(
            config,
            seed=training_args.seed,
            dtype=getattr(jnp, model_args.dtype),
            trust_remote_code=model_args.trust_remote_code,
        )

    if training_args.gradient_checkpointing:
        model.enable_gradient_checkpointing()

    # 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()
    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
    eval_batch_size = per_device_eval_batch_size * jax.device_count()

    num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs

    # Create learning rate schedule
    warmup_fn = optax.linear_schedule(
        init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
    )
    decay_fn = optax.linear_schedule(
        init_value=training_args.learning_rate,
        end_value=0,
        transition_steps=num_train_steps - training_args.warmup_steps,
    )
    linear_decay_lr_schedule_fn = optax.join_schedules(
        schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
    )

    # 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.
    def decay_mask_fn(params):
        flat_params = traverse_util.flatten_dict(params)
        # find out all LayerNorm parameters
        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
        layer_norm_named_params = {
            layer[-2:]
            for layer_norm_name in layer_norm_candidates
            for layer in flat_params.keys()
            if layer_norm_name in "".join(layer).lower()
        }
        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) 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 = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer)

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

        def loss_fn(params):
            labels = batch.pop("labels")

            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]

            # compute loss, ignore padded input tokens
            label_mask = jnp.where(labels > 0, 1.0, 0.0)
            loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask

            # take average
            loss = loss.sum()
            num_labels = label_mask.sum()

            return loss, num_labels

        grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
        (loss, num_labels), grad = grad_fn(state.params)
        num_labels = jax.lax.psum(num_labels, "batch")

        # true loss = total loss / total samples
        loss = jax.lax.psum(loss, "batch")
        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)

        # true grad = total grad / total samples
        grad = jax.lax.psum(grad, "batch")
        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
        new_state = state.apply_gradients(grads=grad)

        metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}

        return new_state, metrics, new_dropout_rng

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

    # Define eval fn
    def eval_step(params, batch):
        labels = batch.pop("labels")

        logits = model(**batch, params=params, train=False)[0]

        # compute loss, ignore padded input tokens
        label_mask = jnp.where(labels > 0, 1.0, 0.0)
        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask

        # compute accuracy
        accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask

        # summarize metrics
        metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
        metrics = jax.lax.psum(metrics, axis_name="batch")

        return metrics

    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))

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

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

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

        # Generate an epoch by shuffling sampling indices from the train dataset
        num_train_samples = len(tokenized_datasets["train"])
        # Avoid using jax.numpy here in case of TPU training
        train_samples_idx = np.random.permutation(np.arange(num_train_samples))
        train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)

        # Gather the indexes for creating the batch and do a training step
        for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
            samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
            model_inputs = data_collator(samples, pad_to_multiple_of=16)

            # Model forward
            model_inputs = shard(model_inputs.data)
            state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
            train_metrics.append(train_metric)

            cur_step = epoch * (num_train_samples // train_batch_size) + step

            if cur_step % training_args.logging_steps == 0 and cur_step > 0:
                # Save metrics
                train_metric = jax_utils.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']}, Learning Rate:"
                    f" {train_metric['learning_rate']})"
                )

                train_metrics = []

            if cur_step % training_args.eval_steps == 0 and cur_step > 0:
                # ======================== Evaluating ==============================
                num_eval_samples = len(tokenized_datasets["validation"])
                # Avoid using jax.numpy here in case of TPU training
                eval_samples_idx = np.arange(num_eval_samples)
                eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)

                eval_metrics = []
                for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
                    samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
                    model_inputs = data_collator(samples, pad_to_multiple_of=16)

                    # Model forward
                    metrics = pad_shard_unpad(p_eval_step, static_return=True)(
                        state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
                    )
                    eval_metrics.append(metrics)

                # normalize eval metrics
                eval_metrics = get_metrics(eval_metrics)
                eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
                eval_normalizer = eval_metrics.pop("normalizer")
                eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)

                # Update progress bar
                epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"

                # Save metrics
                if has_tensorboard and jax.process_index() == 0:
                    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(jax.tree_util.tree_map(lambda x: x[0], state.params))
                    model.save_pretrained(training_args.output_dir, params=params)
                    tokenizer.save_pretrained(training_args.output_dir)
                    if training_args.push_to_hub:
                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)

    # Eval after training
    if training_args.do_eval:
        num_eval_samples = len(tokenized_datasets["validation"])
        # Avoid using jax.numpy here in case of TPU training
        eval_samples_idx = np.arange(num_eval_samples)
        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)

        eval_metrics = []
        for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
            model_inputs = data_collator(samples, pad_to_multiple_of=16)

            # Model forward
            metrics = pad_shard_unpad(p_eval_step, static_return=True)(
                state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
            )
            eval_metrics.append(metrics)

        # normalize eval metrics
        eval_metrics = get_metrics(eval_metrics)
        eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
        eval_normalizer = eval_metrics.pop("normalizer")
        eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)

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

        if jax.process_index() == 0:
            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
            path = os.path.join(training_args.output_dir, "eval_results.json")
            with open(path, "w") as f:
                json.dump(eval_metrics, f, indent=4, sort_keys=True)


if __name__ == "__main__":
    main()