Getting error while saving model
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.5.1
- Platform: linux
- Python version : 3.6
- PyTorch version (GPU?): gpu
- Tensorflow version (GPU?):
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: yes
- 
- @sgugger Need help with trainer module

Models:
- BERT





- I am using BERT model. The problem arises in trainer module

#15300 File "~/lib/python3.6/site-packages/transformers/trainer.py", line 1608, in save_model
    ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp
TypeError: 'in <string>' requires string as left operand, not ShardedDDPOption

I am training a Bert Model for a multi-class classification task
## To reproduce

Steps to reproduce the behavior:

1.  Code
    ```python
     import logging
import os
from statistics import mean, stdev
import sys
from typing import Callable, Dict
import pandas as pd

import numpy as np
from pprint import pformat
from scipy.special import softmax
import tensorboard
import torch

from transformers import (
    AutoTokenizer,
    AutoConfig,
    HfArgumentParser,
    Trainer,
    EvalPrediction,
    set_seed
)

from multimodal_args import ModelArguments, MultiModalDataArguments, MultiModalTrainingArguments
from evaluation import calc_classification_metrics, calc_regression_metrics
from load_dataset  import load_datadir
from config import TabularConfig
from auto_fusion_model import AutoModelFusion
from utils import create_dir_if_not_exists

os.environ['COMET_MODE'] = 'DISABLED'
logger = logging.getLogger(__name__)


def main():

    #Define text and tabular features
    text_cols = ['keywords',"browse_node_name","pod","ORDERING_GL_PRODUCT_GROUP","gl_product_group_desc"]
    label_col = 'label'
    cat_features = []
    non_num_col = text_cols + ["shipping_address_id","postal_code","browse_node_id","label","asin","customer_id","order_day"]
    #features = pd.read_csv("/efs/avimodi/static_model/feature_importance_static_rhm.csv")
    #features_list = features.head(50)["Feature"].to_list()
    logger.info("Reading sample File")
    sample = pd.read_csv("/efs/avimodi/.MultiModal_Model/input_sample/val.csv")
    features_list = sample.columns.to_list()
    num_features = [col for col in features_list if col not in non_num_col]
    logger.info(len(num_features))
    label_list = ["0","1","2"] # what each label class represents
    column_info_dict = {
    'text_cols': text_cols,
    'num_cols': num_features,
    'cat_cols': cat_features,
    'label_col': 'label',
    'label_list': ["0","1","2"]
    }


    model_args = ModelArguments(
        model_name_or_path='bert-base-uncased'
    )

    data_args = MultiModalDataArguments(
        data_path='/efs/avimodi/.MultiModal_Model/input_sample',
        fusion_method='attention',
        features_info=column_info_dict,
        task='classification',
        numerical_encoding='min-max',
        categorical_encoding = 'none'
    )

    training_args = MultiModalTrainingArguments(
        output_dir="/efs/avimodi/unified_model/run_sample/output",
        logging_dir="/efs/avimodi/unified_model/run_sample/logs",
        overwrite_output_dir=True,
        do_train=True,
        do_eval=True,
        per_device_train_batch_size=256,
        per_device_eval_batch_size=256,
        num_train_epochs=10,
        evaluate_during_training=True,
        logging_steps=25,
        eval_steps=500,
        save_steps=500,
        debug_dataset=True,
        report_to = ["tensorboard"],
    )

    set_seed(training_args.seed)
    
    # Setup logging
    create_dir_if_not_exists(training_args.output_dir)
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
        datefmt="%m/%d/%Y %H:%M:%S",
        filename = os.path.join(training_args.output_dir,'train_log.txt'),
        filemode = 'w+'
    )

    
    

    logger.info(f"======== Model Args ========\n{(model_args)}\n")
    logger.info(f"======== Data Args ========\n{(data_args)}\n")
    logger.info(f"======== Training Args ========\n{(training_args)}\n")


    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
    )

    
    train_dataset, val_dataset, test_dataset = load_datadir(
        data_args.data_path,
        data_args.features_info['text_cols'],
        tokenizer,
        label_col=data_args.features_info['label_col'],
        label_list=data_args.features_info['label_list'],
        categorical_cols=data_args.features_info['cat_cols'],
        numerical_cols=data_args.features_info['num_cols'],
        categorical_encoding=data_args.categorical_encoding,
        numerical_encoding=data_args.numerical_encoding,
        sep_text_token_str=tokenizer.sep_token,
        max_token_length=training_args.max_token_length,
        debug=training_args.debug_dataset
    )
    train_datasets = [train_dataset]
    val_datasets = [val_dataset]
    test_datasets = [test_dataset]
    
    train_dataset = train_datasets[0]

    num_labels = len(np.unique(train_dataset.labels)) if data_args.num_classes == -1 else data_args.num_classes

    def compute_metrics_fn(p: EvalPrediction):
        if data_args.task == "classification":
            preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
            preds_labels = np.argmax(preds, axis=1)
            if p.predictions.shape[-1] == 2:
                pred_scores = softmax(preds, axis=1)[:, 1]
            else:
                pred_scores = softmax(preds, axis=1)
            return calc_classification_metrics(pred_scores, preds_labels,
                                                p.label_ids)
        elif data_args.task == "regression":
            preds = np.squeeze(p.predictions)
            return calc_regression_metrics(preds, p.label_ids)
        else:
            return {}

    total_results = []
    for i, (train_dataset, val_dataset, test_dataset) in enumerate(zip(train_datasets, val_datasets, test_datasets)):
        logger.info(f'======== Fold {i+1} ========')
        config = AutoConfig.from_pretrained(
            model_args.config_name if model_args.config_name else model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
        )
        tabular_config = TabularConfig(
                               num_labels=num_labels,
                               cat_feat_dim=train_dataset.cat_feats.shape[1] if train_dataset.cat_feats is not None else 0,
                               numerical_feat_dim=train_dataset.numerical_feats.shape[1] if train_dataset.numerical_feats is not None else 0,
                               **vars(data_args)
                               )
        config.tabular_config = tabular_config

        model = AutoModelFusion.from_pretrained(
            model_args.config_name if model_args.config_name else model_args.model_name_or_path,
            config=config,
            cache_dir=model_args.cache_dir
        )
        if i == 0:
            logger.info(tabular_config)
            logger.info(model)

        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=val_dataset,
            compute_metrics=compute_metrics_fn
        )
        if training_args.do_train:
            train_result = trainer.train(
                resume_from_checkpoint=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
            )
            metrics = train_result.metrics
            # max_train_samples = (
            #     data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
            # )
            metrics["train_samples"] = 500 if training_args.debug_dataset else len(train_dataset)
            trainer.save_model()  # Saves the tokenizer too for easy upload
            trainer.log_metrics("train", metrics)
            trainer.save_metrics("train", metrics)
            trainer.save_state()


        # Evaluation
        eval_results = {}
        if training_args.do_eval:
            logger.info("*** Evaluate ***")
            eval_result = trainer.evaluate(eval_dataset=val_dataset)
            logger.info(pformat(eval_result, indent=4))

            output_eval_file = os.path.join(
                training_args.output_dir, f"eval_metric_results_{task}_fold_{i+1}.txt"
            )
            if trainer.is_world_master():
                with open(output_eval_file, "w") as writer:
                    logger.info("***** Eval results {} *****".format(task))
                    for key, value in eval_result.items():
                        logger.info("  %s = %s", key, value)
                        writer.write("%s = %s\n" % (key, value))

            eval_results.update(eval_result)

        if training_args.do_predict:
            logging.info("*** Test ***")

            predictions = trainer.predict(test_dataset=test_dataset).predictions
            output_test_file = os.path.join(
                training_args.output_dir, f"test_results_{task}_fold_{i+1}.txt"
            )
            eval_result = trainer.evaluate(eval_dataset=test_dataset)
            logger.info(pformat(eval_result, indent=4))
            if trainer.is_world_master():
                with open(output_test_file, "w") as writer:
                    logger.info("***** Test results {} *****".format(task))
                    writer.write("index\tprediction\n")
                    if task == "classification":
                        predictions = np.argmax(predictions, axis=1)
                    for index, item in enumerate(predictions):
                        if task == "regression":
                            writer.write("%d\t%3.3f\t%d\n" % (index, item, test_dataset.labels[index]))
                        else:
                            item = test_dataset.get_labels()[item]
                            writer.write("%d\t%s\n" % (index, item))
                output_test_file = os.path.join(
                    training_args.output_dir, f"test_metric_results_{task}_fold_{i+1}.txt"
                )
                with open(output_test_file, "w") as writer:
                    logger.info("***** Test results {} *****".format(task))
                    for key, value in eval_result.items():
                        logger.info("  %s = %s", key, value)
                        writer.write("%s = %s\n" % (key, value))
                eval_results.update(eval_result)
        del model
        del config
        del tabular_config
        del trainer
        torch.cuda.empty_cache()
        total_results.append(eval_results)
    aggr_res = aggregate_results(total_results)
    logger.info('========= Aggr Results ========')
    logger.info(pformat(aggr_res, indent=4))

    output_aggre_test_file = os.path.join(
        training_args.output_dir, f"all_test_metric_results_{task}.txt"
    )
    with open(output_aggre_test_file, "w") as writer:
        logger.info("***** Aggr results {} *****".format(task))
        for key, value in aggr_res.items():
            logger.info("  %s = %s", key, value)
            writer.write("%s = %s\n" % (key, value))


def aggregate_results(total_test_results):
    metric_keys = list(total_test_results[0].keys())
    aggr_results = dict()

    for metric_name in metric_keys:
        if type(total_test_results[0][metric_name]) is str:
            continue
        res_list = []
        for results in total_test_results:
            res_list.append(results[metric_name])
        if len(res_list) == 1:
            metric_avg = res_list[0]
            metric_stdev = 0
        else:
            metric_avg = mean(res_list)
            metric_stdev = stdev(res_list)

        aggr_results[metric_name + '_mean'] = metric_avg
        aggr_results[metric_name + '_stdev'] = metric_stdev
    return aggr_results


if __name__ == '__main__':
    main()
 ```
2. Error
Traceback (most recent call last):
  File "run.py", line 289, in <module>
    main()
  File "run.py", line 191, in main
    trainer.save_model()  # Saves the tokenizer too for easy upload
  File "/home/avimodi/anaconda3/envs/chakanik_transformer/lib/python3.6/site-packages/transformers/trainer.py", line 1608, in save_model
    **ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp
TypeError: 'in <string>' requires string as left operand, not ShardedDDPOption**
Killing subprocess 122966
Traceback (most recent call last):
  File "/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File /lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/avimodi/anaconda3/envs/chakanik_transformer/lib/python3.6/site-packages/torch/distributed/launch.py", line 340, in <module>
    main()
  File "/home/avimodi/anaconda3/envs/chakanik_transformer/lib/python3.6/site-packages/torch/distributed/launch.py", line 326, in main
    sigkill_handler(signal.SIGTERM, None)  # not coming back
  File "/lib/python3.6/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
    raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)

3.

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

The error comes from the value you have in `args.sharded_ddp` where `args` is your `MultiModalTrainingArguments` object. Since you did not share the code of that class, there is little we can do to help fix the issue.

Also, please use the [forums](https://discuss.huggingface.co/) to debug your code as we keep the issues for bugs and feature requests only :-)<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.