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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2021-2022 The HuggingFace & DALL·E Mini 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.
	"""
	Training DALL·E Mini.
	Script adapted from run_summarization_flax.py
	"""

	import io
	import logging
	import os
	import sys
	import tempfile
	import time
	from dataclasses import asdict, dataclass, field
	from functools import partial
	from pathlib import Path
	from typing import Any, Callable, NamedTuple, Optional

	import datasets
	import flax
	import jax
	import jax.numpy as jnp
	import jaxlib
	import numpy as np
	import optax
	import transformers
	import wandb
	from datasets import Dataset
	from flax import core, struct, traverse_util
	from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
	from flax.serialization import from_bytes, to_bytes
	from flax.training.common_utils import onehot
	from jax.experimental import PartitionSpec, maps
	from jax.experimental.compilation_cache import compilation_cache as cc
	from jax.experimental.pjit import pjit, with_sharding_constraint
	from scalable_shampoo.distributed_shampoo import GraftingType, distributed_shampoo
	from tqdm import tqdm
	from transformers import HfArgumentParser

	import dalle_mini
	from dalle_mini.data import Dataset
	from dalle_mini.model import (
	DalleBart,
	DalleBartConfig,
	DalleBartTokenizer,
	set_partitions,
	)

	try:
	from google.cloud import storage
	except:
	storage = None

	logger = logging.getLogger(__name__)

	cc.initialize_cache("jax_cache")


	@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. "
	"W&B artifact references are supported in addition to the sources supported by `PreTrainedModel`."
	},
	)
	config_name: Optional[str] = field(
	default=None,
	metadata={
	"help": "Pretrained config name or path if not the same as model_name_or_path"
	},
	)
	tokenizer_name: Optional[str] = field(
	default=None,
	metadata={
	"help": "Pretrained tokenizer name or path if not the same as model_name_or_path"
	},
	)
	dtype: Optional[str] = field(
	default="float32",
	metadata={
	"help": "Floating-point format in which the computations will be performed (not the model weights). Choose one of `[float32, float16, bfloat16]`."
	},
	)
	restore_state: Optional[bool] = field(
	default=False,
	metadata={
	"help": "Restore optimizer and training state. Can be True (will retrieve associated wandb artifact), a local directory or a Google bucket path."
	},
	)
	dropout: Optional[float] = field(
	default=None,
	metadata={"help": "Dropout rate. Overwrites config."},
	)
	activation_dropout: Optional[float] = field(
	default=None,
	metadata={"help": "Activation dropout rate. Overwrites config."},
	)
	attention_dropout: Optional[float] = field(
	default=None,
	metadata={"help": "Attention dropout rate. Overwrites config."},
	)

	def __post_init__(self):
	if self.tokenizer_name is None:
	self.tokenizer_name = self.model_name_or_path
	assert (
	self.tokenizer_name is not None
	), "Tokenizer name or model name/path needs to be specified"
	if self.restore_state:
	assert self.model_name_or_path is not None and (
	"/model-" in self.model_name_or_path
	), "Restoring state only available with W&B artifact reference"

	def get_metadata(self):
	if self.model_name_or_path is not None and ":" in self.model_name_or_path:
	if jax.process_index() == 0:
	artifact = wandb.run.use_artifact(self.model_name_or_path)
	else:
	artifact = wandb.Api().artifact(self.model_name_or_path)
	return artifact.metadata
	else:
	return dict()

	def get_opt_state(self):
	with tempfile.TemporaryDirectory() as tmp_dir: # avoid multiple artifact copies
	if self.restore_state is True:
	# wandb artifact
	state_artifact = self.model_name_or_path.replace(
	"/model-", "/state-", 1
	)
	if jax.process_index() == 0:
	artifact = wandb.run.use_artifact(state_artifact)
	else:
	artifact = wandb.Api().artifact(state_artifact)
	if artifact.metadata.get("bucket_path"):
	# we will read directly file contents
	self.restore_state = artifact.metadata["bucket_path"]
	else:
	artifact_dir = artifact.download(tmp_dir)
	self.restore_state = str(Path(artifact_dir) / "opt_state.msgpack")

	if self.restore_state.startswith("gs://"):
	bucket_path = Path(self.restore_state[5:]) / "opt_state.msgpack"
	bucket, blob_name = str(bucket_path).split("/", 1)
	assert (
	storage is not None
	), 'Could not find google.storage. Install with "pip install google-cloud-storage"'
	client = storage.Client()
	bucket = client.bucket(bucket)
	blob = bucket.blob(blob_name)
	return blob.download_as_bytes()

	with Path(self.restore_state).open("rb") as f:
	return f.read()


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

	text_column: Optional[str] = field(
	default="caption",
	metadata={
	"help": "The name of the column in the datasets containing the full texts (for summarization)."
	},
	)
	encoding_column: Optional[str] = field(
	default="encoding",
	metadata={
	"help": "The name of the column in the datasets containing the image encodings."
	},
	)
	dataset_repo_or_path: str = field(
	default=None,
	metadata={"help": "The dataset repository containing encoded files."},
	)
	train_file: Optional[str] = field(
	default=None,
	metadata={
	"help": "The input training data file (glob & braceexpand acceptable)."
	},
	)
	validation_file: Optional[str] = field(
	default=None,
	metadata={
	"help": "An optional input evaluation data file (glob & braceexpand acceptable)."
	},
	)
	# data loading should not be a bottleneck so we use "streaming" mode by default
	streaming: Optional[bool] = field(
	default=True,
	metadata={"help": "Whether to stream the dataset."},
	)
	use_auth_token: Optional[bool] = field(
	default=False,
	metadata={
	"help": "Whether to use the authentication token for private datasets."
	},
	)
	shard_by_host: Optional[bool] = field(
	default=False,
	metadata={
	"help": "Whether to shard data files by host in multi-host environments."
	},
	)
	blank_caption_prob: Optional[float] = field(
	default=0.0,
	metadata={
	"help": "Probability of removing some captions for classifier-free guidance."
	},
	)
	clip_score_column: Optional[str] = field(
	default="clip_score",
	metadata={"help": "Column that containts clip score for filtering."},
	)
	min_clip_score: Optional[float] = field(
	default=None,
	metadata={"help": "Minimum clip score required."},
	)
	max_clip_score: Optional[float] = field(
	default=None,
	metadata={"help": "Maximum clip score required."},
	)
	filter_column: Optional[str] = field(
	default=None,
	metadata={"help": "Column that containts classes to be filtered."},
	)
	filter_value: Optional[str] = field(
	default=None,
	metadata={"help": "Class value to be kept during filtering."},
	)
	multi_eval_ds: Optional[bool] = field(
	default=False,
	metadata={
	"help": "Whether to look for multiple validation datasets (local support only)."
	},
	)
	max_train_samples: Optional[int] = field(
	default=None,
	metadata={
	"help": "For debugging purposes or quicker training, truncate the number of training examples."
	},
	)
	max_eval_samples: Optional[int] = field(
	default=None,
	metadata={
	"help": "For debugging purposes or quicker training, truncate the number of evaluation examples."
	},
	)
	preprocessing_num_workers: Optional[int] = field(
	default=None,
	metadata={
	"help": "The number of processes to use for the preprocessing. Not used in streaming mode."
	},
	)
	overwrite_cache: bool = field(
	default=False,
	metadata={
	"help": "Overwrite the cached training and evaluation sets. Not used in streaming mode."
	},
	)
	# default seed of None ensures we don't repeat the same items if script was interrupted during an epoch
	seed_dataset: int = field(
	default=None,
	metadata={
	"help": "Random seed for the dataset that will be set at the beginning of training."
	},
	)

	def __post_init__(self):
	if self.dataset_repo_or_path is None:
	raise ValueError("Need a dataset repository or path.")


	@dataclass
	class TrainingArguments:
	"""
	Arguments pertaining to training parameters.
	"""

	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 validation set."}
	)

	per_device_train_batch_size: int = field(
	default=8,
	metadata={"help": "Batch size per data parallel device for training."},
	)
	per_device_eval_batch_size: Optional[int] = field(
	default=None,
	metadata={
	"help": "Batch size per data parallel device for evaluation. Same as training batch size if not set."
	},
	)

	gradient_accumulation_steps: int = field(
	default=1,
	metadata={
	"help": "Number of updates steps to accumulate before performing an update pass."
	},
	)
	gradient_checkpointing: bool = field(
	default=False, metadata={"help": "Use gradient checkpointing."}
	)

	learning_rate: float = field(
	default=5e-5, metadata={"help": "The initial learning rate."}
	)
	optim: str = field(
	default="distributed_shampoo",
	metadata={
	"help": 'The optimizer to use. Can be "distributed_shampoo" (default), "adam" or "adafactor"'
	},
	)
	weight_decay: float = field(
	default=0.0, metadata={"help": "Weight decay applied to parameters."}
	)
	beta1: float = field(
	default=0.9,
	metadata={"help": "Beta1 for Adam & Distributed Shampoo."},
	)
	beta2: float = field(
	default=0.999,
	metadata={"help": "Beta2 for for Adam & Distributed Shampoo."},
	)
	adam_epsilon: float = field(
	default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}
	)
	max_grad_norm: float = field(
	default=1.0, metadata={"help": "Max gradient norm for Adafactor."}
	)
	block_size: int = field(
	default=1024,
	metadata={"help": "Chunked size for large layers with Distributed Shampoo."},
	)
	preconditioning_compute_steps: int = field(
	default=10, metadata={"help": "Number of steps to update preconditioner."}
	)
	skip_preconditioning_dim_size_gt: int = field(
	default=4096,
	metadata={"help": "Max size for preconditioning with Distributed Shampoo."},
	)
	graft_type: str = field(
	default="rmsprop_normalized",
	metadata={
	"help": "The type of grafting to use. Can be 'rmsprop_normalized' (default), 'rmsprop', 'adagrad', 'adagrad_normalized', 'sgd' or 'sqrt_n'"
	},
	)
	nesterov: bool = field(
	default=False,
	metadata={"help": "Use Nesterov momentum for Distributed Shampoo."},
	)
	optim_quantized: bool = field(
	default=False,
	metadata={
	"help": "Whether to quantize optimizer (only supported with Distributed Shampoo)."
	},
	)
	shard_shampoo_across: str = field(
	default="dp",
	metadata={
	"help": "Whether to shard the optimizer across data devices (dp), model devices (mp) or both (2d)."
	},
	)

	num_train_epochs: int = field(
	default=3, metadata={"help": "Total number of training epochs to perform."}
	)

	warmup_steps: int = field(
	default=0, metadata={"help": "Linear warmup over warmup_steps."}
	)
	lr_decay: str = field(
	default=None,
	metadata={
	"help": "Decay to be used in the learning rate scheduler. Can be None (default), linear or exponential."
	},
	)
	lr_transition_steps: int = field(
	default=None,
	metadata={
	"help": "Number of transition steps associated with learning rate decay when using exponential decay."
	},
	)
	lr_decay_rate: float = field(
	default=None,
	metadata={
	"help": "Decay rate associated with learning rate when using exponential decay."
	},
	)
	lr_staircase: bool = field(
	default=False,
	metadata={
	"help": "Whether to use staircase or continuous learning rate when using exponential decay."
	},
	)
	lr_offset: int = field(
	default=0,
	metadata={"help": "Number of steps to offset learning rate and keep it at 0."},
	)
	logging_steps: int = field(
	default=40, metadata={"help": "Log every X updates steps."}
	)
	eval_steps: int = field(
	default=400, metadata={"help": "Run an evaluation every X steps."}
	)
	save_steps: int = field(
	default=4000, metadata={"help": "Save checkpoint every X updates steps."}
	)
	log_model: bool = field(
	default=False,
	metadata={"help": "Log model to wandb at `save_steps` frequency."},
	)
	log_norm_steps: int = field(
	default=True,
	metadata={"help": "Log parameters and gradients norm at this frequency."},
	)
	log_histogram_steps: int = field(
	default=False,
	metadata={
	"help": "Log parameters and gradients histograms at this frequency. Slows down training."
	},
	)

	seed_model: int = field(
	default=42,
	metadata={
	"help": "Random seed for the model that will be set at the beginning of training."
	},
	)

	embeddings_only: bool = field(
	default=False, metadata={"help": "Train only embedding layers."}
	)
	init_embeddings: bool = field(
	default=False,
	metadata={"help": "When training embedding layers, initialize them."},
	)

	wandb_entity: Optional[str] = field(
	default=None,
	metadata={"help": "The wandb entity to use (for teams)."},
	)
	wandb_project: str = field(
	default="dalle-mini",
	metadata={"help": "The name of the wandb project."},
	)
	wandb_job_type: str = field(
	default="Seq2Seq",
	metadata={"help": "The name of the wandb job type."},
	)

	assert_TPU_available: bool = field(
	default=False,
	metadata={"help": "Verify that TPU is not in use."},
	)

	use_vmap_trick: bool = field(
	default=True,
	metadata={"help": "Verify that TPU is not in use."},
	)

	mp_devices: Optional[int] = field(
	default=1,
	metadata={
	"help": "Number of devices required for model parallelism. The other dimension of available devices is used for data parallelism."
	},
	)

	dp_devices: int = field(init=False)

	def __post_init__(self):
	if self.assert_TPU_available:
	assert (
	jax.local_device_count() == 8
	), "TPUs in use, please check running processes"
	if self.output_dir.startswith("gs://"):
	assert (
	storage is not None
	), 'Could not find google.storage. Install with "pip install google-cloud-storage"'
	assert self.optim in [
	"distributed_shampoo",
	"adam",
	"adafactor",
	], f"Selected optimizer not supported: {self.optim}"
	if self.optim == "adafactor" and self.weight_decay == 0:
	self.weight_decay = None
	assert self.graft_type in [
	"rmsprop_normalized",
	"rmsprop",
	"adagrad",
	"adagrad_normalized",
	"sgd",
	"sqrt_n",
	], f"Selected graft type not supported: {self.graft_type}"
	assert self.lr_decay in [
	None,
	"linear",
	"exponential",
	], f"Selected learning rate decay not supported: {self.lr_decay}"
	if self.per_device_eval_batch_size is None:
	self.per_device_eval_batch_size = self.per_device_train_batch_size
	if self.log_norm_steps is True:
	self.log_norm_steps = self.logging_steps
	if not self.do_train:
	self.num_train_epochs = 1
	if (
	os.path.exists(self.output_dir)
	and os.listdir(self.output_dir)
	and self.do_train
	and not self.overwrite_output_dir
	):
	raise ValueError(
	f"Output directory ({self.output_dir}) already exists and is not empty."
	"Use --overwrite_output_dir to overcome."
	)
	assert self.shard_shampoo_across in [
	"dp",
	"mp",
	"2d",
	], f"Shard shampoo across {self.shard_shampoo_across} not supported."
	assert (
	self.mp_devices > 0
	), f"Number of devices for model parallelism must be > 0"
	assert (
	jax.device_count() % self.mp_devices == 0
	), f"Number of available devices ({jax.device_count()} must be divisible by number of devices used for model parallelism ({self.mp_devices})."
	self.dp_devices = jax.device_count() // self.mp_devices


	def split_params(data):
	"""Split params between scanned and non-scanned"""
	flat = traverse_util.flatten_dict(unfreeze(data))
	split = {"standard": {}, "scanned_encoder": {}, "scanned_decoder": {}}
	for k, v in flat.items():
	if "FlaxBartEncoderLayers" in k:
	split["scanned_encoder"][k] = v
	elif "FlaxBartDecoderLayers" in k:
	split["scanned_decoder"][k] = v
	else:
	split["standard"][k] = v
	# remove empty keys
	split = {k: v for k, v in split.items() if v}
	for k, v in split.items():
	split[k] = freeze(traverse_util.unflatten_dict(v))
	return split


	def unsplit_params(data):
	flat = {}
	for k in ["standard", "scanned_encoder", "scanned_decoder"]:
	if k in data:
	flat.update(traverse_util.flatten_dict(unfreeze(data[k])))
	return freeze(traverse_util.unflatten_dict(flat))


	def trainable_params(data, embeddings_only):
	"""Keep only trainable parameters"""

	if not embeddings_only:
	return data

	data = unfreeze(data)
	trainable = {
	"lm_head": data["lm_head"],
	"model": {
	"decoder": {
	layer: data["model"]["decoder"][layer]
	for layer in [
	"embed_positions",
	"embed_tokens",
	"final_ln",
	"layernorm_embedding",
	]
	}
	},
	}
	return freeze(trainable)


	def init_embeddings(model, params):
	"""Reinitialize trainable embeddings"""
	# Must match params in trainable_params() above
	trainable_keypaths = [
	"lm_head.kernel",
	"model.decoder.embed_positions.embedding",
	"model.decoder.embed_tokens.embedding",
	"model.decoder.final_ln.bias",
	"model.decoder.layernorm_embedding.bias",
	"model.decoder.layernorm_embedding.scale",
	]

	# Note: using private _missing_keys
	init_keys = {tuple(k.split(".")) for k in trainable_keypaths}
	model._missing_keys = init_keys
	return model.init_weights(model.key, model.input_shape, params=params)


	def main():
	# See all possible arguments by passing the --help flag to this script.
	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()

	# check arguments
	if training_args.mp_devices > jax.local_device_count():
	assert (
	data_args.seed_dataset is not None
	), "Seed dataset must be provided when model is split over multiple hosts"

	# 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}")

	# Load dataset
	dataset = Dataset(
	**asdict(data_args),
	do_train=training_args.do_train,
	do_eval=training_args.do_eval,
	)

	logger.info(f"Local TPUs: {jax.local_device_count()}")
	logger.info(f"Global TPUs: {jax.device_count()}")

	# Set up wandb run
	if jax.process_index() == 0:
	wandb.init(
	entity=training_args.wandb_entity,
	project=training_args.wandb_project,
	job_type=training_args.wandb_job_type,
	config=parser.parse_args(),
	)

	# Set up our new model config
	config_args = {
	k: getattr(model_args, k)
	for k in ["dropout", "activation_dropout", "attention_dropout"]
	if getattr(model_args, k) is not None
	}
	config_args["gradient_checkpointing"] = training_args.gradient_checkpointing
	if model_args.config_name:
	config = DalleBartConfig.from_pretrained(model_args.config_name)
	else:
	config = None

	# Load or create new model
	if model_args.model_name_or_path:
	model, params = DalleBart.from_pretrained(
	model_args.model_name_or_path,
	config=config,
	seed=training_args.seed_model,
	dtype=getattr(jnp, model_args.dtype),
	_do_init=False,
	)
	if training_args.embeddings_only and training_args.init_embeddings:
	params = init_embeddings(model, params)
	else:
	model = DalleBart(
	config,
	seed=training_args.seed_model,
	dtype=getattr(jnp, model_args.dtype),
	_do_init=False,
	)
	params = None
	for k, v in config_args.items():
	setattr(model.config, k, v)
	params_shape = model.params_shape_tree

	# get model metadata
	model_metadata = model_args.get_metadata()

	# get PartitionSpec for model params (required to be a dict)
	param_spec = set_partitions(params_shape, model.config.use_scan)
	params_shape = freeze(params_shape)
	if params is not None:
	params = freeze(params)

	# Load tokenizer
	tokenizer = DalleBartTokenizer.from_pretrained(
	model_args.tokenizer_name, use_fast=True
	)

	# Preprocessing the datasets.
	# We need to normalize and tokenize inputs and targets.
	dataset.preprocess(tokenizer=tokenizer, config=model.config)

	# Initialize our training
	dropout_rng = jax.random.PRNGKey(training_args.seed_model)

	# Store some constant
	num_epochs = training_args.num_train_epochs
	# batch size
	batch_size_per_node_per_grad_step = (
	training_args.per_device_train_batch_size
	* jax.local_device_count()
	// training_args.mp_devices
	)
	batch_size_per_node = (
	batch_size_per_node_per_grad_step * training_args.gradient_accumulation_steps
	)
	batch_size_per_step = batch_size_per_node * jax.process_count()
	eval_batch_size_per_node = (
	training_args.per_device_eval_batch_size
	* jax.local_device_count()
	// training_args.mp_devices
	)
	eval_batch_size_per_step = eval_batch_size_per_node * jax.process_count()
	len_train_dataset, len_eval_dataset = dataset.length
	steps_per_epoch = (
	len_train_dataset // batch_size_per_node
	if len_train_dataset is not None
	else None
	)
	num_train_steps = (
	steps_per_epoch * num_epochs if steps_per_epoch is not None else None
	)
	num_params = model.num_params(params_shape)

	logger.info("*** Running training ***")
	logger.info(f" Num examples = {len_train_dataset}")
	logger.info(f" Num Epochs = {num_epochs}")
	logger.info(
	f" Batch size per dp device = {training_args.per_device_train_batch_size}"
	)
	logger.info(f" Number of devices = {jax.device_count()}")
	logger.info(
	f" Gradient accumulation steps = {training_args.gradient_accumulation_steps}"
	)
	logger.info(f" Batch size per update = {batch_size_per_step}")
	logger.info(f" Model parameters = {num_params:,}")

	# set up wandb run
	if jax.process_index() == 0:
	# set default x-axis as 'train/step'
	wandb.define_metric("*", step_metric="train/step")

	# add interesting config parameters
	wandb.config.update(
	{
	"len_train_dataset": len_train_dataset,
	"len_eval_dataset": len_eval_dataset,
	"batch_size_per_step": batch_size_per_step,
	"num_params": num_params,
	"model_config": model.config.to_dict(),
	"num_devices": jax.device_count(),
	"versions": {
	"jax": jax.__version__,
	"jaxlib": jaxlib.__version__,
	"flax": flax.__version__,
	"transformers": transformers.__version__,
	"datasets": datasets.__version__,
	"wandb": wandb.__version__,
	"dalle_mini": dalle_mini.__version__,
	},
	}
	)

	# Create learning rate schedule
	def create_learning_rate_fn() -> Callable[[int], jnp.array]:
	"""Create the learning rate function."""
	warmup_fn = optax.linear_schedule(
	init_value=0.0,
	end_value=training_args.learning_rate,
	transition_steps=training_args.warmup_steps + 1, # ensure not 0
	)
	last_boundary = training_args.warmup_steps
	# offset step when resuming
	if training_args.lr_offset:
	warmup_fn = optax.join_schedules(
	schedules=[optax.constant_schedule(0.0), warmup_fn],
	boundaries=[training_args.lr_offset],
	)
	last_boundary += training_args.lr_offset
	if training_args.lr_decay is None:
	return warmup_fn
	elif training_args.lr_decay == "linear":
	assert (
	num_train_steps is not None
	), "linear decay requires knowing the dataset length"
	decay_fn = optax.linear_schedule(
	init_value=training_args.learning_rate,
	end_value=0,
	transition_steps=num_train_steps - training_args.warmup_steps,
	)
	elif training_args.lr_decay == "exponential":
	decay_fn = optax.exponential_decay(
	init_value=training_args.learning_rate,
	transition_steps=training_args.lr_transition_steps,
	decay_rate=training_args.lr_decay_rate,
	staircase=training_args.lr_staircase,
	)
	schedule_fn = optax.join_schedules(
	schedules=[warmup_fn, decay_fn],
	boundaries=[last_boundary],
	)
	return schedule_fn

	learning_rate_fn = create_learning_rate_fn()

	# create optimizer
	trainable_params_shape = trainable_params(
	params_shape, training_args.embeddings_only
	)
	if training_args.optim == "distributed_shampoo":
	# parameters from https://github.com/tensorflow/lingvo/blob/03ee9d7cd50764b0424c7c863733c91fc0b053ec/lingvo/jax/optimizers.py#L729
	graft_type = {
	"sgd": GraftingType.SGD,
	"adagrad": GraftingType.ADAGRAD,
	"rmsprop": GraftingType.RMSPROP,
	"rmsprop_normalized": GraftingType.RMSPROP_NORMALIZED,
	"sqrt_n": GraftingType.SQRT_N,
	"adagrad_normalized": GraftingType.ADAGRAD_NORMALIZED,
	}[training_args.graft_type]
	statistics_partition_spec = (
	PartitionSpec(None, training_args.shard_shampoo_across, None)
	if training_args.shard_shampoo_across != "2d"
	else PartitionSpec(None, "dp", "mp")
	)
	opt = distributed_shampoo(
	learning_rate_fn,
	block_size=training_args.block_size,
	beta1=training_args.beta1,
	beta2=training_args.beta2,
	diagonal_epsilon=1e-10,
	matrix_epsilon=1e-6,
	weight_decay=training_args.weight_decay,
	start_preconditioning_step=max(
	training_args.preconditioning_compute_steps + 1, 101
	),
	preconditioning_compute_steps=training_args.preconditioning_compute_steps,
	statistics_compute_steps=1,
	best_effort_shape_interpretation=True,
	graft_type=graft_type,
	nesterov=training_args.nesterov,
	exponent_override=0,
	statistics_partition_spec=statistics_partition_spec,
	preconditioner_partition_spec=PartitionSpec(
	training_args.shard_shampoo_across, None, None
	)
	if training_args.shard_shampoo_across != "2d"
	else PartitionSpec(
	"mp" if training_args.mp_devices > training_args.dp_devices else "dp",
	None,
	None,
	),
	num_devices_for_pjit=training_args.dp_devices,
	shard_optimizer_states=True,
	inverse_failure_threshold=0.1,
	moving_average_for_momentum=True,
	skip_preconditioning_dim_size_gt=training_args.skip_preconditioning_dim_size_gt,
	clip_by_scaled_gradient_norm=None,
	precision=jax.lax.Precision.HIGHEST,
	best_effort_memory_usage_reduction=training_args.optim_quantized,
	)
	# get the real optimizer and helper functions
	update_fn = opt.update

	optimizer = {}
	opt_fn = {}
	for k, p in split_params(trainable_params_shape).items():
	if "scanned" in k:
	p = jax.eval_shape(
	lambda x: jax.tree_util.tree_map(lambda y: y[0], x), p
	)
	optimizer[k] = opt.init(p)
	opt_fn[k] = NamedTuple("opt_fn", pspec_fn=Any, shape_and_dtype_fn=Any)(
	optimizer[k].pspec_fn, optimizer[k].shape_and_dtype_fn
	)
	optimizer[k] = optax.GradientTransformation(optimizer[k].init_fn, update_fn)

	elif training_args.optim == "adam":
	optimizer = optax.adamw(
	learning_rate=learning_rate_fn,
	b1=training_args.beta1,
	b2=training_args.beta2,
	eps=training_args.adam_epsilon,
	weight_decay=training_args.weight_decay,
	)
	optimizer = {k: optimizer for k in split_params(trainable_params_shape)}

	elif training_args.optim == "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=learning_rate_fn,
	clipping_threshold=training_args.max_grad_norm,
	weight_decay_rate=training_args.weight_decay,
	)
	optimizer = {k: optimizer for k in split_params(trainable_params_shape)}

	# get PartitionSpec for optimizer state
	def get_opt_state_spec_and_shape():
	# get opt_state shape without actual init
	opt_state_shape = {}
	for k, p in split_params(trainable_params_shape).items():
	if "scanned" not in k:
	opt_state_shape[k] = jax.eval_shape(optimizer[k].init, p)
	else:
	opt_state_shape[k] = jax.eval_shape(jax.vmap(optimizer[k].init), p)

	if training_args.optim == "adafactor":
	# factorized state must be replicated (rank different than params)
	opt_state_spec = {k: None for k in split_params(trainable_params_shape)}

	elif training_args.optim in ["adam", "distributed_shampoo"]:

	def _opt_state_spec_per_leaf(x, spec):
	if isinstance(x, FrozenDict):
	# variables with same structure as params
	return spec
	else:
	# other variables such as count
	return None

	split_spec = split_params(set_partitions(trainable_params_shape, False))
	opt_state_spec = {}
	for k, p in split_params(trainable_params_shape).items():
	if "scanned" in k:
	p = jax.eval_shape(
	lambda x: jax.tree_util.tree_map(lambda y: y[0], x), p
	)
	if training_args.optim == "adam":
	opt_state_spec[k] = jax.tree_util.tree_map(
	partial(_opt_state_spec_per_leaf, spec=split_spec[k]),
	opt_state_shape[k],
	# return None spec for empty elements
	is_leaf=lambda x: isinstance(x, (FrozenDict, optax.EmptyState)),
	)
	elif training_args.optim == "distributed_shampoo":
	opt_state_spec[k] = opt_fn[k].pspec_fn(
	p,
	split_spec[k],
	statistics_partition_spec,
	)
	# add dimension for scanned params
	if "scanned" in k:
	opt_state_spec[k] = jax.tree_util.tree_map(
	lambda x: PartitionSpec(*(None,) + x)
	if x is not None
	else None,
	opt_state_spec[k],
	is_leaf=lambda x: isinstance(x, PartitionSpec),
	)

	else:
	raise NotImplementedError
	return freeze(opt_state_spec), freeze(opt_state_shape)

	opt_state_spec, opt_state_shape = get_opt_state_spec_and_shape()

	# create a mesh
	mesh_shape = (training_args.dp_devices, training_args.mp_devices)
	devices = np.asarray(jax.devices()).reshape(*mesh_shape)
	mesh = maps.Mesh(devices, ("dp", "mp"))
	logger.info(f" Mesh shape: {mesh_shape}")

	# define TrainState
	class TrainState(struct.PyTreeNode):
	step: int
	params: core.FrozenDict[str, Any]
	opt_state: optax.OptState
	apply_fn: Callable = struct.field(pytree_node=False)
	tx: optax.GradientTransformation = struct.field(pytree_node=False)
	dropout_rng: jnp.ndarray = None
	epoch: int = 0
	train_time: float = 0.0 # total time the model trained
	train_samples: int = 0 # number of samples seen

	def apply_gradients(self, , grads, *kwargs):
	grads = split_params(trainable_params(grads, training_args.embeddings_only))
	params = split_params(
	trainable_params(self.params, training_args.embeddings_only)
	)
	opt_state = {}
	# we loop over keys: "standard", "scanned_encoder", "scanned_decoder"
	for k, param in params.items():
	update_fn = self.tx[k].update
	if "scanned" in k:
	update_fn = jax.vmap(update_fn, in_axes=(0, 0, 0), out_axes=(0, 0))
	updates, new_opt_state = update_fn(grads[k], self.opt_state[k], param)
	params[k] = optax.apply_updates(param, updates)
	opt_state[k] = new_opt_state
	params = unsplit_params(params)
	# merge with non-trainable params
	params, new_params = traverse_util.flatten_dict(
	unfreeze(self.params)
	), traverse_util.flatten_dict(unfreeze(params))
	params.update(new_params)
	params = freeze(traverse_util.unflatten_dict(params))

	return self.replace(
	step=self.step + 1,
	params=params,
	opt_state=freeze(opt_state),
	**kwargs,
	)

	@classmethod
	def create(cls, , apply_fn, params, tx, *kwargs):
	opt_state = {}
	for k, p in split_params(
	trainable_params(params, training_args.embeddings_only)
	).items():
	init_fn = tx[k].init
	if "scanned" in k:
	init_fn = jax.vmap(init_fn)
	opt_state[k] = init_fn(p)
	return cls(
	step=0,
	apply_fn=apply_fn,
	params=params,
	tx=tx,
	opt_state=freeze(opt_state),
	**kwargs,
	)

	# define state spec
	state_spec = TrainState(
	params=param_spec,
	opt_state=opt_state_spec,
	dropout_rng=None,
	step=None,
	epoch=None,
	train_time=None,
	train_samples=None,
	apply_fn=model.__call__,
	tx=optimizer,
	)

	# init params if not available yet
	def maybe_init_params(params):
	if params is not None:
	# model params are correctly loaded
	return params
	else:
	# params have not been initialized yet
	return model.init_weights(model.key, model.input_shape)

	with mesh:
	logger.info(" Creating state")

	# restore metadata
	attr_state = {}
	keys = ["train_time", "train_samples"]
	if model_args.restore_state:
	keys += ["step", "epoch"]
	attr_state = {k: v for k, v in model_metadata.items() if k in keys}

	if not model_args.restore_state:

	def init_state(params):
	return TrainState.create(
	apply_fn=model.__call__,
	tx=optimizer,
	params=maybe_init_params(params),
	dropout_rng=dropout_rng,
	**attr_state,
	)

	state = pjit(
	init_state,
	in_axis_resources=(param_spec,)
	if model_args.model_name_or_path
	else None,
	out_axis_resources=state_spec,
	donate_argnums=(0,),
	)(params)

	else:
	# load opt_state
	opt_state = from_bytes(opt_state_shape, model_args.get_opt_state())

	def restore_state(params, opt_state):
	return TrainState(
	apply_fn=model.__call__,
	tx=optimizer,
	params=params,
	opt_state=opt_state,
	dropout_rng=dropout_rng,
	**attr_state,
	)

	state = pjit(
	restore_state,
	in_axis_resources=(
	param_spec,
	opt_state_spec,
	),
	out_axis_resources=state_spec,
	donate_argnums=(0, 1),
	)(params, opt_state)

	# remove opt_state from CPU
	del opt_state

	# free CPU memory
	del params, opt_state_spec, opt_state_shape

	# define batch specs
	batch_spec = PartitionSpec("dp")
	grad_batch_spec = PartitionSpec(None, "dp")

	# define loss
	def loss_fn(logits, labels):
	loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))
	loss = loss.mean()
	return loss

	# "vmap trick" avoids a crash when mp_devices > 1 (not sure why it happens)
	# lead to better perf: see https://wandb.ai/dalle-mini/dalle-mini/reports/JAX-pmap-vs-pjit--VmlldzoxNDg1ODA2
	use_vmap_trick = training_args.use_vmap_trick

	# make grad_param_spec for vmap
	if use_vmap_trick:
	grad_param_spec = jax.tree_util.tree_map(
	lambda x: PartitionSpec(*("dp",) + (x if x is not None else (None,))),
	param_spec,
	)

	# Define gradient update step fn
	def train_step(state, batch, train_time):
	# get a minibatch (one gradient accumulation slice)
	def get_minibatch(batch, grad_idx):
	return jax.tree_util.tree_map(
	lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False),
	batch,
	)

	def compute_loss(params, minibatch, dropout_rng):
	# minibatch has dim (batch_size, ...)
	minibatch, labels = minibatch.pop("labels")
	logits = state.apply_fn(
	**minibatch, params=params, dropout_rng=dropout_rng, train=True
	)[0]
	return loss_fn(logits, labels)

	grad_fn = jax.value_and_grad(compute_loss)

	def loss_and_grad(grad_idx, dropout_rng):
	# minibatch at grad_idx for gradient accumulation (None otherwise)
	minibatch = (
	get_minibatch(batch, grad_idx) if grad_idx is not None else batch
	)
	# ensure it is sharded properly
	minibatch = with_sharding_constraint(minibatch, batch_spec)
	# only 1 single rng per grad step, let us handle larger batch size (not sure why)
	dropout_rng, _ = jax.random.split(dropout_rng)

	if use_vmap_trick:
	# "vmap trick", calculate loss and grads independently per dp_device
	loss, grads = jax.vmap(
	grad_fn, in_axes=(None, 0, None), out_axes=(0, 0)
	)(state.params, minibatch, dropout_rng)
	# ensure they are sharded correctly
	loss = with_sharding_constraint(loss, batch_spec)
	grads = with_sharding_constraint(grads, grad_param_spec)
	# average across all devices
	# Note: we could average per device only after gradient accumulation, right before params update
	loss, grads = jax.tree_util.tree_map(
	lambda x: jnp.mean(x, axis=0), (loss, grads)
	)
	else:
	# "vmap trick" does not work in multi-hosts and requires too much hbm
	loss, grads = grad_fn(state.params, minibatch, dropout_rng)
	# ensure grads are sharded
	grads = with_sharding_constraint(grads, param_spec)
	# return loss and grads
	return loss, grads, dropout_rng

	if training_args.gradient_accumulation_steps == 1:
	loss, grads, dropout_rng = loss_and_grad(None, state.dropout_rng)
	else:
	# create initial state for cumul_minibatch_step loop
	init_minibatch_step = (
	0.0,
	with_sharding_constraint(
	jax.tree_util.tree_map(jnp.zeros_like, state.params), param_spec
	),
	state.dropout_rng,
	)

	# accumulate gradients
	def cumul_minibatch_step(grad_idx, cumul_loss_grad_dropout):
	cumul_loss, cumul_grads, dropout_rng = cumul_loss_grad_dropout
	loss, grads, dropout_rng = loss_and_grad(grad_idx, dropout_rng)
	cumul_loss, cumul_grads = jax.tree_util.tree_map(
	jnp.add, (cumul_loss, cumul_grads), (loss, grads)
	)
	cumul_grads = with_sharding_constraint(cumul_grads, param_spec)
	return cumul_loss, cumul_grads, dropout_rng

	# loop over gradients
	loss, grads, dropout_rng = jax.lax.fori_loop(
	0,
	training_args.gradient_accumulation_steps,
	cumul_minibatch_step,
	init_minibatch_step,
	)
	grads = with_sharding_constraint(grads, param_spec)
	# sum -> mean
	loss, grads = jax.tree_util.tree_map(
	lambda x: x / training_args.gradient_accumulation_steps, (loss, grads)
	)

	grads = with_sharding_constraint(grads, param_spec)

	# update state
	state = state.apply_gradients(
	grads=grads,
	dropout_rng=dropout_rng,
	train_time=train_time,
	train_samples=state.train_samples + batch_size_per_step,
	)

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

	def maybe_fn(fn, val, zeros, freq):
	"""Call fn only if it is a logging step"""
	return jax.lax.cond(
	state.step % freq == 0,
	fn,
	lambda _: zeros,
	val,
	)

	# log additional metrics
	params = trainable_params(state.params, training_args.embeddings_only)
	grads = trainable_params(grads, training_args.embeddings_only)
	if training_args.log_norm_steps:
	zeros_norm = jax.tree_util.tree_map(lambda _: jnp.float32(0), params)

	def norm(val):
	return jax.tree_util.tree_map(lambda x: jnp.linalg.norm(x), val)

	gradients_norm = maybe_fn(
	norm, grads, zeros_norm, training_args.log_norm_steps
	)
	params_norm = maybe_fn(
	norm, params, zeros_norm, training_args.log_norm_steps
	)

	metrics.update(
	{
	"gradients_norm": gradients_norm,
	"params_norm": params_norm,
	}
	)

	if training_args.log_histogram_steps:
	zeros_hist = jax.tree_util.tree_map(
	lambda _: jnp.histogram(jnp.zeros(1), density=True), params
	)

	def histogram(val):
	return jax.tree_util.tree_map(
	lambda x: jnp.histogram(x, density=True), val
	)

	gradients_hist = maybe_fn(
	histogram, grads, zeros_hist, training_args.log_histogram_steps
	)
	params_hist = maybe_fn(
	histogram, params, zeros_hist, training_args.log_histogram_steps
	)

	metrics.update(
	{
	"params_hist": params_hist,
	"gradients_hist": gradients_hist,
	}
	)

	return state, metrics

	# Define eval fn
	eval_model = (
	model
	if model_args.dtype == "float32"
	else DalleBart(
	model.config,
	seed=training_args.seed_model,
	dtype=jnp.float32,
	_do_init=False,
	)
	)

	def eval_step(state, batch):
	def compute_eval_loss(batch):
	batch, labels = batch.pop("labels")
	logits = eval_model(**batch, params=state.params, train=False)[0]
	return loss_fn(logits, labels)

	if use_vmap_trick:
	loss = jax.vmap(compute_eval_loss)(batch)
	# ensure they are sharded correctly
	loss = with_sharding_constraint(loss, batch_spec)
	# average across all devices
	loss = jnp.mean(loss)
	else:
	loss = compute_eval_loss(batch)

	return loss

	# Create parallel version of the train and eval step
	p_train_step = pjit(
	train_step,
	in_axis_resources=(
	state_spec,
	grad_batch_spec
	if training_args.gradient_accumulation_steps > 1
	else batch_spec,
	None,
	),
	out_axis_resources=(state_spec, None),
	donate_argnums=(0,),
	)
	p_eval_step = pjit(
	eval_step,
	in_axis_resources=(state_spec, batch_spec),
	out_axis_resources=None,
	)

	# define metrics logger
	class MetricsLogger:
	def __init__(self, step):
	# keep state
	self.state_dict = {}
	# estimate speed
	self.step = step
	self.time = time.perf_counter()
	self.offset_time = 0.0

	def update_state_metrics(self, state):
	"""Update internal state metrics (logged at each call to be used as x-axis)"""
	self.state_dict = {
	f'train/{k.split("_")[-1]}': state[k]
	for k in ["step", "epoch", "train_time", "train_samples"]
	}
	# timing metrics
	new_step = int(state["step"])
	new_time = time.perf_counter()
	if new_step > self.step:
	# remove time for eval & save
	delta_time = new_time - self.time - self.offset_time
	self.offset_time = 0
	time_per_step = delta_time / (new_step - self.step)
	self.step = new_step
	self.time = new_time
	self.log_time("train_per_step", time_per_step, offset=False)
	self.log_time("train_per_log", delta_time, offset=False)

	def log_time(self, key, duration, offset=True):
	if jax.process_index() == 0:
	wandb.log({f"time/{key}": duration, **self.state_dict})
	if offset:
	self.offset_time += duration

	def log(self, metrics, prefix=None):
	if jax.process_index() == 0:
	log_metrics = {}
	for k, v in metrics.items():
	if "_norm" in k:
	if self.step % training_args.log_norm_steps == 0:
	log_metrics[f"{k}/"] = unfreeze(v)
	elif "_hist" in k:
	if self.step % training_args.log_histogram_steps == 0:
	v = jax.tree_util.tree_map(
	lambda x: jax.device_get(x), unfreeze(v)
	)
	v = jax.tree_util.tree_map(
	lambda x: wandb.Histogram(np_histogram=x),
	v,
	is_leaf=lambda x: isinstance(x, tuple),
	)
	log_metrics[f"{k}/"] = v
	else:
	if prefix is not None:
	k = f"{prefix}/{k}"
	log_metrics[k] = v
	wandb.log({log_metrics, self.state_dict})

	# keep local copy of state
	local_state = {
	k: jax.device_get(getattr(state, k)).item()
	for k in ["step", "epoch", "train_time", "train_samples"]
	}
	# init variables
	start_time = time.perf_counter() - local_state["train_time"]
	train_metrics = None
	evaluation_ran = False
	save_model_ran = False
	metrics_logger = MetricsLogger(local_state["step"])
	epochs = tqdm(
	range(local_state["epoch"], num_epochs),
	desc=f"Epoch ... (1/{num_epochs})",
	position=0,
	disable=jax.process_index() > 0,
	)

	def run_evaluation():
	# ======================== Evaluating ==============================
	if training_args.do_eval:
	start_eval_time = time.perf_counter()
	# get validation datasets
	val_datasets = list(
	dataset.other_eval_datasets.keys()
	if hasattr(dataset, "other_eval_datasets")
	else []
	)
	val_datasets += ["eval"]
	for val_dataset in val_datasets:
	eval_loader = dataset.dataloader(
	val_dataset,
	eval_batch_size_per_step
	* max(1, training_args.mp_devices // jax.local_device_count()),
	)
	eval_steps = (
	len_eval_dataset // eval_batch_size_per_step
	if len_eval_dataset is not None
	else None
	)
	eval_loss = []
	for batch in tqdm(
	eval_loader,
	desc="Evaluating...",
	position=2,
	leave=False,
	total=eval_steps,
	disable=jax.process_index() > 0,
	):
	# need to keep only eval_batch_size_per_node items relevant to the node
	batch = jax.tree_util.tree_map(
	lambda x: x.reshape(
	(jax.process_count(), eval_batch_size_per_node)
	+ x.shape[1:]
	),
	batch,
	)
	batch = jax.tree_util.tree_map(
	lambda x: x[jax.process_index()], batch
	)

	# add dp dimension when using "vmap trick"
	if use_vmap_trick:
	bs_shape = (
	jax.local_device_count() // training_args.mp_devices,
	training_args.per_device_eval_batch_size,
	)
	batch = jax.tree_util.tree_map(
	lambda x: x.reshape(bs_shape + x.shape[1:]), batch
	)

	# freeze batch to pass safely to jax transforms
	batch = freeze(batch)
	# accumulate losses async
	eval_loss.append(p_eval_step(state, batch))

	# get the mean of the loss
	eval_loss = jnp.stack(eval_loss)
	eval_loss = jnp.mean(eval_loss)
	eval_metrics = {"loss": eval_loss}

	# log metrics
	metrics_logger.log(eval_metrics, prefix=val_dataset)

	# Print metrics and update progress bar
	desc = f"Epoch... ({epoch + 1}/{num_epochs} \| {val_dataset} Loss: {eval_metrics['loss']})"
	epochs.write(desc)
	epochs.desc = desc

	# log time
	metrics_logger.log_time("eval", time.perf_counter() - start_eval_time)

	return eval_metrics

	def run_save_model(state, eval_metrics=None):
	if jax.process_index() == 0:
	start_save_time = time.perf_counter()
	output_dir = training_args.output_dir
	use_bucket = output_dir.startswith("gs://")
	if use_bucket:
	bucket_path = Path(output_dir[5:]) / wandb.run.id / f"step_{state.step}"
	bucket, dir_path = str(bucket_path).split("/", 1)
	tmp_dir = tempfile.TemporaryDirectory()
	output_dir = tmp_dir.name

	# save model
	params = jax.device_get(state.params)
	model.save_pretrained(
	output_dir,
	params=params,
	)

	# save tokenizer
	tokenizer.save_pretrained(output_dir)

	# copy to bucket
	if use_bucket:
	client = storage.Client()
	bucket = client.bucket(bucket)
	for filename in Path(output_dir).glob("*"):
	blob_name = str(Path(dir_path) / "model" / filename.name)
	blob = bucket.blob(blob_name)
	blob.upload_from_filename(str(filename))
	tmp_dir.cleanup()

	# save state
	opt_state = jax.device_get(state.opt_state)
	if use_bucket:
	blob_name = str(Path(dir_path) / "state" / "opt_state.msgpack")
	blob = bucket.blob(blob_name)
	blob.upload_from_file(io.BytesIO(to_bytes(opt_state)))
	else:
	with (Path(output_dir) / "opt_state.msgpack").open("wb") as f:
	f.write(to_bytes(opt_state))

	# save to W&B
	if training_args.log_model:
	# save some space
	c = wandb.wandb_sdk.wandb_artifacts.get_artifacts_cache()
	c.cleanup(wandb.util.from_human_size("20GB"))

	metadata = {
	k: jax.device_get(getattr(state, k)).item()
	for k in ["step", "epoch", "train_time", "train_samples"]
	}
	metadata["num_params"] = num_params
	if eval_metrics is not None:
	metadata["eval"] = eval_metrics

	# create model artifact
	if use_bucket:
	metadata["bucket_path"] = f"gs://{bucket_path}/model"
	artifact = wandb.Artifact(
	name=f"model-{wandb.run.id}",
	type="DalleBart_model",
	metadata=metadata,
	)
	if use_bucket:
	artifact.add_reference(metadata["bucket_path"])
	else:
	for filename in [
	"config.json",
	"flax_model.msgpack",
	"merges.txt",
	"special_tokens_map.json",
	"tokenizer.json",
	"tokenizer_config.json",
	"vocab.json",
	]:
	artifact.add_file(
	f"{Path(training_args.output_dir) / filename}"
	)
	wandb.run.log_artifact(artifact)

	# create state artifact
	if use_bucket:
	metadata["bucket_path"] = f"gs://{bucket_path}/state"
	artifact_state = wandb.Artifact(
	name=f"state-{wandb.run.id}",
	type="DalleBart_state",
	metadata=metadata,
	)
	if use_bucket:
	artifact_state.add_reference(metadata["bucket_path"])
	else:
	artifact_state.add_file(
	f"{Path(training_args.output_dir) / 'opt_state.msgpack'}"
	)
	wandb.run.log_artifact(artifact_state)
	metrics_logger.log_time("save_model", time.perf_counter() - start_save_time)

	logger.info(" Ready to start training")
	with mesh:
	for epoch in epochs:
	state = state.replace(epoch=epoch)
	local_state["epoch"] = epoch
	# ======================== Training ================================
	metrics_logger.update_state_metrics(local_state)
	metrics_logger.log({})

	if training_args.do_train:
	# load data - may be replicated on multiple nodes
	node_groups = max(
	1, training_args.mp_devices // jax.local_device_count()
	)
	loader_bs = batch_size_per_node * node_groups
	train_loader = dataset.dataloader(
	"train",
	loader_bs,
	epoch,
	)
	# train
	for batch in tqdm(
	train_loader,
	desc="Training...",
	position=1,
	leave=False,
	total=steps_per_epoch,
	disable=jax.process_index() > 0,
	):
	# calculate delta time (we have a lag of one step but it's ok)
	train_time = time.perf_counter() - start_time

	# reset control variables
	evaluation_ran = False
	save_model_ran = False

	# set correct shape to batch
	# - add grad_step dim if gradient_accumulation_steps > 1
	bs_shape = (
	(batch_size_per_node_per_grad_step * node_groups,)
	if not use_vmap_trick
	else (
	jax.local_device_count()
	* node_groups
	// training_args.mp_devices, # local dp devices
	training_args.per_device_train_batch_size,
	)
	)
	if training_args.gradient_accumulation_steps > 1:
	# reshape data into (gradient_accumulation_steps, batch_per_node, ...)
	# to avoid any data redistribution when sharding
	bs_shape = (
	training_args.gradient_accumulation_steps,
	) + bs_shape

	# reshape batch
	batch = jax.tree_util.tree_map(
	lambda x: x.reshape(bs_shape + x.shape[1:]),
	batch,
	)
	# freeze batch to pass safely to jax transforms
	batch = freeze(batch)

	# train step
	state, train_metrics = p_train_step(state, batch, train_time)
	local_state["step"] += 1
	local_state["train_time"] = train_time
	local_state["train_samples"] += batch_size_per_step

	if (
	local_state["step"] % training_args.logging_steps == 0
	and jax.process_index() == 0
	):
	metrics_logger.update_state_metrics(local_state)
	metrics_logger.log(train_metrics, prefix="train")

	eval_metrics = None
	if local_state["step"] % training_args.eval_steps == 0:
	eval_metrics = run_evaluation()
	evaluation_ran = True

	if local_state["step"] % training_args.save_steps == 0:
	run_save_model(state, eval_metrics)
	save_model_ran = True

	# log final train metrics
	if train_metrics is not None:
	metrics_logger.update_state_metrics(local_state)
	metrics_logger.log(train_metrics, prefix="train")

	epochs.write(
	f"Epoch... ({epoch + 1}/{num_epochs} \| Loss: {train_metrics['loss']}, Learning Rate: {train_metrics['learning_rate']})"
	)

	# Final evaluation at the end of each epoch
	if not evaluation_ran:
	eval_metrics = run_evaluation()

	# save checkpoint after each epoch
	if not save_model_ran:
	run_save_model(state, eval_metrics)


	if __name__ == "__main__":
	main()