run_t5_mlm_flax.py · birgermoell/t5-base-swedish at main

t5-base-swedish / run_t5_mlm_flax.py

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	#!/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.
	"""
	Pretraining the library models for T5-like span-masked language modeling on a text file or a dataset.

	Here is the full list of checkpoints on the hub that can be pretrained by this script:
	https://huggingface.co/models?filter=t5
	"""
	# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
	import logging
	import os
	import sys
	import time
	from dataclasses import dataclass, field
	from pathlib import Path
	from typing import Dict, List, Optional

	import numpy as np
	from datasets import load_dataset
	from tqdm import tqdm

	import flax
	import jax
	import jax.numpy as jnp
	import optax
	from flax import jax_utils, traverse_util
	from flax.training import train_state
	from flax.training.common_utils import get_metrics, onehot, shard
	from transformers import (
	CONFIG_MAPPING,
	FLAX_MODEL_FOR_MASKED_LM_MAPPING,
	AutoTokenizer,
	BatchEncoding,
	FlaxT5ForConditionalGeneration,
	HfArgumentParser,
	PreTrainedTokenizerBase,
	T5Config,
	TrainingArguments,
	is_tensorboard_available,
	set_seed,
	)
	from transformers.models.t5.modeling_flax_t5 import shift_tokens_right


	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 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]`."
	},
	)


	@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 and masking. 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 span masked language modeling loss"}
	)
	mean_noise_span_length: float = field(
	default=3.0,
	metadata={"help": "Mean span length of masked tokens"},
	)

	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."


	def compute_input_and_target_lengths(inputs_length, noise_density, mean_noise_span_length):
	"""This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2466>`__ .

	Training parameters to avoid padding with random_spans_noise_mask.
	When training a model with random_spans_noise_mask, we would like to set the other
	training hyperparmeters in a way that avoids padding.
	This function helps us compute these hyperparameters.
	We assume that each noise span in the input is replaced by extra_tokens_per_span_inputs sentinel tokens,
	and each non-noise span in the targets is replaced by extra_tokens_per_span_targets sentinel tokens.
	This function tells us the required number of tokens in the raw example (for split_tokens())
	as well as the length of the encoded targets. Note that this function assumes
	the inputs and targets will have EOS appended and includes that in the reported length.

	Args:
	inputs_length: an integer - desired length of the tokenized inputs sequence
	noise_density: a float
	mean_noise_span_length: a float
	Returns:
	tokens_length: length of original text in tokens
	targets_length: an integer - length in tokens of encoded targets sequence
	"""

	def _tokens_length_to_inputs_length_targets_length(tokens_length):
	num_noise_tokens = int(round(tokens_length * noise_density))
	num_nonnoise_tokens = tokens_length - num_noise_tokens
	num_noise_spans = int(round(num_noise_tokens / mean_noise_span_length))
	# inputs contain all nonnoise tokens, sentinels for all noise spans
	# and one EOS token.
	_input_length = num_nonnoise_tokens + num_noise_spans + 1
	_output_length = num_noise_tokens + num_noise_spans + 1
	return _input_length, _output_length

	tokens_length = inputs_length

	while _tokens_length_to_inputs_length_targets_length(tokens_length + 1)[0] <= inputs_length:
	tokens_length += 1

	inputs_length, targets_length = _tokens_length_to_inputs_length_targets_length(tokens_length)

	# minor hack to get the targets length to be equal to inputs length
	# which is more likely to have been set to a nice round number.
	if noise_density == 0.5 and targets_length > inputs_length:
	tokens_length -= 1
	targets_length -= 1
	return tokens_length, targets_length


	@flax.struct.dataclass
	class FlaxDataCollatorForT5MLM:
	"""
	Data collator used for T5 span-masked language modeling.
	It is made sure that after masking the inputs are of length `data_args.max_seq_length` and targets are also of fixed length.
	For more information on how T5 span-masked language modeling works, one can take a look
	at the `official paper <https://arxiv.org/pdf/1910.10683.pdf>`__
	or the `official code for preprocessing <https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py>`__ .

	Args:
	tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
	The tokenizer used for encoding the data.
	noise_density (:obj:`float`):
	The probability with which to (randomly) mask tokens in the input.
	mean_noise_span_length (:obj:`float`):
	The average span length of the masked tokens.
	input_length (:obj:`int`):
	The expected input length after masking.
	target_length (:obj:`int`):
	The expected target length after masking.
	pad_token_id: (:obj:`int`):
	The pad token id of the model
	decoder_start_token_id: (:obj:`int):
	The decoder start token id of the model
	"""

	tokenizer: PreTrainedTokenizerBase
	noise_density: float
	mean_noise_span_length: float
	input_length: int
	target_length: int
	pad_token_id: int
	decoder_start_token_id: int

	def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:

	# convert list to dict and tensorize input
	batch = BatchEncoding(
	{k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
	)

	input_ids = batch["input_ids"]
	batch_size, expandend_input_length = input_ids.shape

	mask_indices = np.asarray([self.random_spans_noise_mask(expandend_input_length) for i in range(batch_size)])
	labels_mask = ~mask_indices

	input_ids_sentinel = self.create_sentinel_ids(mask_indices.astype(np.int8))
	labels_sentinel = self.create_sentinel_ids(labels_mask.astype(np.int8))

	batch["input_ids"] = self.filter_input_ids(input_ids, input_ids_sentinel)
	batch["labels"] = self.filter_input_ids(input_ids, labels_sentinel)

	if batch["input_ids"].shape[-1] != self.input_length:
	raise ValueError(
	f"`input_ids` are incorrectly preprocessed. `input_ids` length is {batch['input_ids'].shape[-1]}, but should be {self.target_length}."
	)

	if batch["labels"].shape[-1] != self.target_length:
	raise ValueError(
	f"`labels` are incorrectly preprocessed. `labels` length is {batch['labels'].shape[-1]}, but should be {self.target_length}."
	)

	# to check that tokens are correctly proprocessed, one can run `self.tokenizer.batch_decode(input_ids)` and `self.tokenizer.batch_decode(labels)` here...
	batch["decoder_input_ids"] = shift_tokens_right(
	batch["labels"], self.pad_token_id, self.decoder_start_token_id
	)

	return batch

	def create_sentinel_ids(self, mask_indices):
	"""
	Sentinel ids creation given the indices that should be masked.
	The start indices of each mask are replaced by the sentinel ids in increasing
	order. Consecutive mask indices to be deleted are replaced with `-1`.
	"""
	start_indices = mask_indices - np.roll(mask_indices, 1, axis=-1) * mask_indices
	start_indices[:, 0] = mask_indices[:, 0]

	sentinel_ids = np.where(start_indices != 0, np.cumsum(start_indices, axis=-1), start_indices)
	sentinel_ids = np.where(sentinel_ids != 0, (sentinel_ids + self.tokenizer.vocab_size - 1), 0)
	sentinel_ids -= mask_indices - start_indices

	return sentinel_ids

	def filter_input_ids(self, input_ids, sentinel_ids):
	"""
	Puts sentinel mask on `input_ids` and fuse consecutive mask tokens into a single mask token by deleting.
	This will reduce the sequence length from `expanded_inputs_length` to `input_length`.
	"""
	batch_size = input_ids.shape[0]

	input_ids_full = np.where(sentinel_ids != 0, sentinel_ids, input_ids)
	input_ids = input_ids_full[input_ids_full > 0].reshape((batch_size, -1))
	input_ids = np.concatenate(
	[input_ids, np.full((batch_size, 1), self.tokenizer.eos_token_id, dtype=np.int32)], axis=-1
	)
	return input_ids

	def random_spans_noise_mask(self, length):

	"""This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .

	Noise mask consisting of random spans of noise tokens.
	The number of noise tokens and the number of noise spans and non-noise spans
	are determined deterministically as follows:
	num_noise_tokens = round(length * noise_density)
	num_nonnoise_spans = num_noise_spans = round(num_noise_tokens / mean_noise_span_length)
	Spans alternate between non-noise and noise, beginning with non-noise.
	Subject to the above restrictions, all masks are equally likely.

	Args:
	length: an int32 scalar (length of the incoming token sequence)
	noise_density: a float - approximate density of output mask
	mean_noise_span_length: a number

	Returns:
	a boolean tensor with shape [length]
	"""

	orig_length = length

	num_noise_tokens = int(np.round(length * self.noise_density))
	# avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
	num_noise_tokens = min(max(num_noise_tokens, 1), length - 1)
	num_noise_spans = int(np.round(num_noise_tokens / self.mean_noise_span_length))

	# avoid degeneracy by ensuring positive number of noise spans
	num_noise_spans = max(num_noise_spans, 1)
	num_nonnoise_tokens = length - num_noise_tokens

	# pick the lengths of the noise spans and the non-noise spans
	def _random_segmentation(num_items, num_segments):
	"""Partition a sequence of items randomly into non-empty segments.
	Args:
	num_items: an integer scalar > 0
	num_segments: an integer scalar in [1, num_items]
	Returns:
	a Tensor with shape [num_segments] containing positive integers that add
	up to num_items
	"""
	mask_indices = np.arange(num_items - 1) < (num_segments - 1)
	np.random.shuffle(mask_indices)
	first_in_segment = np.pad(mask_indices, [[1, 0]])
	segment_id = np.cumsum(first_in_segment)
	segment_length = np.asarray(jax.ops.segment_sum(np.ones_like(segment_id), segment_id))
	return segment_length

	noise_span_lengths = _random_segmentation(num_noise_tokens, num_noise_spans)
	nonnoise_span_lengths = _random_segmentation(num_nonnoise_tokens, num_noise_spans)

	interleaved_span_lengths = np.reshape(
	np.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2]
	)
	span_starts = np.cumsum(interleaved_span_lengths)[:-1]
	span_start_indicator = np.zeros((length,), dtype=np.int8)
	span_start_indicator[span_starts] = True
	span_num = np.cumsum(span_start_indicator)
	is_noise = np.equal(span_num % 2, 1)

	return is_noise[:orig_length]


	def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
	num_samples = len(samples_idx)
	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
	batch_idx = np.split(samples_idx, sections_split)
	return batch_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)


	if __name__ == "__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."
	)

	# Setup logging
	logging.basicConfig(
	format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
	level="NOTSET",
	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)

	# 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).
	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)

	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,
	)
	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,
	)
	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)

	# 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

	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.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
	)
	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.config_name:
	config = T5Config.from_pretrained(
	model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
	)
	elif model_args.model_name_or_path:
	config = T5Config.from_pretrained(
	model_args.model_name_or_path, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
	)
	else:
	config = CONFIG_MAPPING[model_args.model_type]()
	logger.warning("You are instantiating a new config instance from scratch.")

	# 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)

	# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
	# Since we make sure that all sequences are of the same length, no attention_mask is needed.
	def tokenize_function(examples):
	return tokenizer(examples[text_column_name], return_attention_mask=False)

	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,
	)

	# T5-like span masked language modeling will fuse consecutively masked tokens to a single sentinel token.
	# To ensure that the input length is `max_seq_length`, we need to increase the maximum length
	# according to `mlm_probability` and `mean_noise_span_length`. We can also define the label length accordingly.
	expanded_inputs_length, targets_length = compute_input_and_target_lengths(
	inputs_length=max_seq_length,
	noise_density=data_args.mlm_probability,
	mean_noise_span_length=data_args.mean_noise_span_length,
	)

	# Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
	def group_texts(examples):
	# Concatenate all texts.
	concatenated_examples = {k: sum(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 >= expanded_inputs_length:
	total_length = (total_length // expanded_inputs_length) * expanded_inputs_length
	# Split by chunks of max_len.
	result = {
	k: [t[i : i + expanded_inputs_length] for i in range(0, total_length, expanded_inputs_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/package_reference/main_classes.html#datasets.Dataset.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."
	)

	# 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 = FlaxT5ForConditionalGeneration.from_pretrained(
	model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
	)
	else:
	model = FlaxT5ForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))

	# Data collator
	# This one will take care of randomly masking the tokens.
	data_collator = FlaxDataCollatorForT5MLM(
	tokenizer=tokenizer,
	noise_density=data_args.mlm_probability,
	mean_noise_span_length=data_args.mean_noise_span_length,
	input_length=max_seq_length,
	target_length=targets_length,
	pad_token_id=model.config.pad_token_id,
	decoder_start_token_id=model.config.decoder_start_token_id,
	)

	# 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()

	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)
	flat_mask = {
	path: (path[-1] != "bias" and path[-2:] not in [("layer_norm", "scale"), ("final_layer_norm", "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,
	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
	loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()

	return loss

	grad_fn = jax.value_and_grad(loss_fn)
	loss, grad = grad_fn(state.params)
	grad = jax.lax.pmean(grad, "batch")
	new_state = state.apply_gradients(grads=grad)

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

	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
	loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))

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

	# summarize metrics
	metrics = {"loss": loss.mean(), "accuracy": accuracy.mean()}
	metrics = jax.lax.pmean(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"])
	train_samples_idx = jax.random.permutation(input_rng, jnp.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)

	# 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'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
	)

	train_metrics = []

	if cur_step % training_args.eval_steps == 0 and cur_step > 0:
	# ======================== Evaluating ==============================
	num_eval_samples = len(tokenized_datasets["validation"])
	eval_samples_idx = jnp.arange(num_eval_samples)
	eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)

	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)

	# Model forward
	model_inputs = shard(model_inputs.data)
	metrics = p_eval_step(state.params, model_inputs)
	eval_metrics.append(metrics)

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

	# Update progress bar
	epochs.write(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_map(lambda x: x[0], 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}",
	)