train_clm_flax_v2.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.
"""
Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) 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=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.

import transformers
import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Callable, Optional

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

import jax
import jax.numpy as jnp
import optax
from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import Repository
from transformers import (
    CONFIG_MAPPING,
    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
    AutoConfig,
    AutoTokenizer,
    FlaxAutoModelForCausalLM,
    HfArgumentParser,
    is_tensorboard_available,
    set_seed,
)
# from transformers.testing_utils import CaptureLogger
from transformers.utils import get_full_repo_name, send_example_telemetry
from traceback import print_exc
import wandb

logger = logging.getLogger(__name__)

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


@dataclass
class TrainingArguments:
    run_name: str = field(
        metadata={
            "help": "The run name for wandb."
        },
    )
    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."}
    )
    gradient_accumulation_steps: int = field(
        default=1, metadata={"help": "Gradient Accumulation Steps"}
    )
    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."}
    )
    save_strategy: str = field(
        default="no", metadata={"help": "Save strategy. no, epoch, steps"}
    )
    eval_steps: int = field(
        default=None, metadata={"help": "Run an evaluation every X steps."}
    )
    eval_strategy: str = field(
        default="no", metadata={"help": "Run an evaluation every epoch, steps or at the end"}
    )
    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."}
    )

    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"
        },
    )
    revision: Optional[str] = field(
        default=None,
        metadata={
            "help": "Pretrained model reivison 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]`."
            )
        },
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        },
    )


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

    dataset_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)."},
    )
    dataset_config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The configuration name of the dataset to use (via the datasets library)."
        },
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a text file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets"},
    )
    validation_split_percentage: Optional[int] = field(
        default=5,
        metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        },
    )
    block_size: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "Optional input sequence length after tokenization. "
                "The training dataset will be truncated in block of this size for training. "
                "Default to the model max input length for single sentence inputs (take into account special tokens)."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets"},
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    keep_linebreaks: bool = field(
        default=True,
        metadata={"help": "Whether to keep line breaks when using TXT files or not."},
    )

    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",
        #             "jsonl"
        #         ], "`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",
        #             "jsonl"
        #         ], "`validation_file` should be a csv, a json or a txt file."


class TrainState(train_state.TrainState):
    dropout_rng: jnp.ndarray
    grad_accum: jnp.ndarray
    step: int
    optimizer_step: int

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


def data_loader(
    rng: Optional[jax.random.PRNGKey],
    dataset: Dataset,
    batch_size: int,
    shuffle: bool = False,
    drop_last=True,
):
    """
    Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
    and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
    """
    if shuffle:
        batch_idx = jax.random.permutation(rng, len(dataset))
        batch_idx = np.asarray(batch_idx)
    else:
        batch_idx = np.arange(len(dataset))

    if drop_last:
        steps_per_epoch = len(dataset) // batch_size
        batch_idx = batch_idx[: steps_per_epoch * batch_size]  # Skip incomplete batch.
        batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
    else:
        steps_per_epoch = math.ceil(len(dataset) / batch_size)
        batch_idx = np.array_split(batch_idx, steps_per_epoch)

    for idx in batch_idx:
        batch = dataset[idx]
        try:
            batch = {k: np.array(v, dtype=np.int64) for k, v in batch.items()}
            batch["labels"] = batch["input_ids"]
            yield batch
        except Exception as e:
            print_exc()
            print(batch)



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

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


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


def create_learning_rate_fn(
    train_ds_size: int,
    train_batch_size: int,
    num_train_epochs: int,
    num_warmup_steps: int,
    learning_rate: float,
) -> Callable[[int], jnp.array]:
    """Returns a linear warmup, linear_decay learning rate function."""
    steps_per_epoch = train_ds_size // train_batch_size
    num_train_steps = steps_per_epoch * num_train_epochs
    warmup_fn = optax.linear_schedule(
        init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps
    )
    decay_fn = optax.linear_schedule(
        init_value=learning_rate,
        end_value=0.1 * learning_rate,
        transition_steps=num_train_steps - num_warmup_steps,
    )
    schedule_fn = optax.join_schedules(
        schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps]
    )
    return schedule_fn


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

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

    # 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_clm", 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."
        )

    # 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}")

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

    train_dataset = load_dataset(
        "json",
        data_files={
            "train": data_args.train_file
        },
        cache_dir=model_args.cache_dir,
        split="train",
        use_auth_token=True if model_args.use_auth_token else None,
    )

    if data_args.validation_file is not None:
        eval_dataset = load_dataset(
            "json",
            data_files={
                "train": data_args.validation_file
            },
            cache_dir=model_args.cache_dir,
            split="train",
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        eval_dataset = None


    # 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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    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."
        )

    special_tokens_dict = {'additional_special_tokens': [f'<unused{i}>' for i in range(1, 10)]}
    num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
    
    if model_args.model_name_or_path:
        model = FlaxAutoModelForCausalLM.from_pretrained(
            model_args.model_name_or_path,
            config=config,
            seed=training_args.seed,
            revision=model_args.revision,
            dtype=getattr(jnp, model_args.dtype),
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        model = FlaxAutoModelForCausalLM.from_config(
            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
        )


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

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

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

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

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

    # We use Optax's "masking" functionality to not apply weight decay
    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
    # mask boolean with the same structure as the parameters.
    # The mask is True for parameters that should be decayed.
    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 = set(
            [
                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 = TrainState.create(
        apply_fn=model.__call__,
        params=model.params,
        tx=optimizer,
        dropout_rng=dropout_rng,
        grad_accum=jax.tree_map(jnp.zeros_like, model.params),
        optimizer_step=0
    )

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

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

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

        grad_fn = jax.value_and_grad(compute_loss)
        loss, grads = grad_fn(state.params)
        grad_accum = jax.tree_map(lambda x, y: x + y, grads, state.grad_accum)
        
        def update_fn():
            grads = jax.tree_map(lambda x: x / training_args.gradient_accumulation_steps, grad_accum)
            grads = jax.lax.pmean(grads, "batch")
            new_state = state.apply_gradients(
                grads=grads, 
                grad_accum=jax.tree_map(jnp.zeros_like, grads), 
                optimizer_step=state.step
            )
            new_state = new_state.replace(step=state.step + 1)
            return new_state

        new_state = jax.lax.cond(
            state.step % training_args.gradient_accumulation_steps == 0,
            lambda _: update_fn(),
            lambda _: state.replace(grad_accum=grad_accum, step=state.step + 1),
            None,
        )

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

        return new_state.replace(dropout_rng=new_dropout_rng), metrics

    # Define eval fn
    def eval_step(params, batch):
        labels = batch.pop("labels")
        logits = model(**batch, params=params, train=False)[0]
        loss = loss_fn(logits, labels)

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

    def evaluate(cur_step, epochs_tqdm=None):
        # ======================== Evaluating ==============================
        eval_metrics = []
        eval_loader = data_loader(
            None, eval_dataset, eval_batch_size, drop_last=False
        )
        eval_steps = math.ceil(len(eval_dataset) / eval_batch_size)
        for _ in tqdm(
            range(eval_steps), desc="Evaluating...", position=2, leave=False
        ):
            # Model forward
            batch = next(eval_loader)
            metrics = pad_shard_unpad(p_eval_step, static_return=True)(
                state.params, batch, 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.mean, eval_metrics)

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

        # Print metrics and update progress bar
        if epochs_tqdm is not None:
            desc = (
                f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity:"
                f" {eval_metrics['perplexity']})"
            )
            epochs.write(desc)
            epochs.desc = desc

        if jax.process_index() == 0:
            eval_metrics = {
                f"eval/{metric_name}": value
                for metric_name, value in eval_metrics.items()
            }
            eval_metrics['eval/loss'] = eval_metrics['eval/loss'].item()
            print(eval_metrics)
            if wandb.run is not None and jax.process_index() == 0:
                wandb.log(eval_metrics)

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

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

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

    wandb.init(name=training_args.run_name)

    train_time = 0
    train_metrics = []
    save_strategy = training_args.save_strategy.split(",")

    if training_args.do_train:
        epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
        for epoch in epochs:
            # ======================== Training ================================
            train_start = time.time()

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

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

                cur_step = epoch * (len(train_dataset) // train_batch_size) + step 
                # cur_step = cur_step // training_args.gradient_accumulation_steps

                if cur_step % training_args.logging_steps == 0 and cur_step > 0:
                    # Save metrics
                    train_metric = unreplicate(train_metric)
                    train_time += time.time() - train_start

                    # epochs.write(
                    #     f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
                    #     f" {train_metric['learning_rate'].mean()})"
                    # )
                    wandb.log({
                        "train/loss": train_metric['loss'].mean().item(),
                        "train/learning_rate": train_metric['learning_rate'].mean().item(),
                        "train/global_step": cur_step
                    })

                    train_metrics = []

                if training_args.do_eval and training_args.eval_strategy == "steps" and cur_step % training_args.eval_steps == 0 and cur_step > 0:
                    evaluate(cur_step, epochs)


                if "steps" in save_strategy and cur_step % training_args.save_steps == 0 and cur_step > 0:
                    # save checkpoint after each epoch and push checkpoint to the hub
                    if jax.process_index() == 0:
                        params = jax.device_get(unreplicate(state.params))
                        model.save_pretrained(f"{training_args.output_dir}/checkpoint-{cur_step}", params=params)
                        tokenizer.save_pretrained(f"{training_args.output_dir}/checkpoint-{cur_step}")
            
            if f"epoch-{epoch}" in save_strategy or "epoch" in save_strategy:
                # save checkpoint after each epoch and push checkpoint to the hub
                if jax.process_index() == 0:
                    params = jax.device_get(unreplicate(state.params))
                    model.save_pretrained(f"{training_args.output_dir}/checkpoint-epoch-{epoch}", params=params)
                    tokenizer.save_pretrained(f"{training_args.output_dir}/checkpoint-epoch-{epoch}")

            if training_args.do_eval and training_args.eval_strategy == "epoch":
                evaluate(cur_step, epochs)

        if "last" in save_strategy and jax.process_index() == 0:
            # save checkpoint after each epoch and push checkpoint to the hub
            params = jax.device_get(unreplicate(state.params))
            model.save_pretrained(f"{training_args.output_dir}/checkpoint-epoch-{cur_step}-last", params=params)
            tokenizer.save_pretrained(f"{training_args.output_dir}/checkpoint-epoch-{cur_step}-last")

    # Eval after training
    if training_args.do_eval and training_args.eval_strategy == "last":
        evaluate(0)

    if wandb.run is not None and jax.process_index() == 0:
        wandb.finish()

if __name__ == "__main__":
    main()