AttributeError: 'DataParallel' object has no attribute 'model'
### System Info

System Info

- torch==1.8.1+cu101
- transformers==4.10.1
- Python 3.8
- "Ubuntu 18.04.6 LTS"

I am training parallel GPUs and not using pretrained weight. However, during training, I got this issue and break training:
```python
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 15%|█▌        | 247/1617 [09:03<48:21,  2.12s/it]
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 15%|█▌        | 249/1617 [09:07<48:17,  2.12s/it]
 15%|█▌        | 250/1617 [09:10<48:35,  2.13s/it]***** Running Evaluation *****
  Num examples = 1500
  Batch size = 512
{'loss': 1.2497, 'learning_rate': 4.941249226963513e-05, 'epoch': 0.04}
{'loss': 0.6803, 'learning_rate': 4.879406307977737e-05, 'epoch': 0.07}
{'loss': 0.6134, 'learning_rate': 4.817563388991961e-05, 'epoch': 0.11}
{'loss': 0.5777, 'learning_rate': 4.7557204700061845e-05, 'epoch': 0.15}
{'loss': 0.5626, 'learning_rate': 4.6938775510204086e-05, 'epoch': 0.19}
{'loss': 0.5413, 'learning_rate': 4.6320346320346326e-05, 'epoch': 0.22}
{'loss': 0.5249, 'learning_rate': 4.570191713048856e-05, 'epoch': 0.26}
{'loss': 0.5015, 'learning_rate': 4.50834879406308e-05, 'epoch': 0.3}
{'loss': 0.5017, 'learning_rate': 4.4465058750773034e-05, 'epoch': 0.33}
{'loss': 0.4924, 'learning_rate': 4.3846629560915274e-05, 'epoch': 0.37}
{'loss': 0.4831, 'learning_rate': 4.3228200371057515e-05, 'epoch': 0.41}
{'loss': 0.4695, 'learning_rate': 4.2609771181199755e-05, 'epoch': 0.45}
Traceback (most recent call last):
  File "examples/run_train.py", line 105, in <module>
    main()
  File "examples/run_train.py", line 99, in main
    train_result = trainer.train()
  File "/root/data/huyhuynh/clrcmd-master/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1340, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/root/data/huyhuynh/clrcmd-master/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1445, in _maybe_log_save_evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
  File "/root/data/huyhuynh/clrcmd-master/venv/lib/python3.8/site-packages/transformers/trainer.py", line 2051, in evaluate
    output = eval_loop(
  File "/root/data/huyhuynh/clrcmd-master/venv/lib/python3.8/site-packages/transformers/trainer.py", line 2223, in evaluation_loop
    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
  File "/root/data/huyhuynh/clrcmd-master/src/clrcmd/trainer.py", line 29, in prediction_step
    score = model.model(inputs1, inputs2)
  File "/root/data/huyhuynh/clrcmd-master/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 947, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'DataParallel' object has no attribute 'model'

 15%|█▌        | 250/1617 [09:10<50:11,  2.20s/it]
```


This is training code:

```python
import argparse
import logging
import os
import uuid

from transformers import TrainingArguments, set_seed

from clrcmd.data.dataset import (
    ContrastiveLearningCollator,
    NLIContrastiveLearningDataset,
    STSBenchmarkDataset,
)
from clrcmd.data.sts import load_stsb_dev
from clrcmd.models import create_contrastive_learning, create_tokenizer
from clrcmd.trainer import STSTrainer, compute_metrics
import torch

logger = logging.getLogger(__name__)

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# fmt: off
parser.add_argument("--data-dir", type=str, help="Data directory", default="data")
parser.add_argument("--model", type=str, help="Model", default="bert-cls",
                    choices=["bert-cls", "bert-avg", "bert-rcmd", "roberta-cls", "roberta-avg", "roberta-rcmd"])
parser.add_argument("--output-dir", type=str, help="Output directory", default="ckpt")
parser.add_argument("--temp", type=float, help="Softmax temperature", default=0.05)
parser.add_argument("--seed", type=int, help="Seed", default=0)
# fmt: on


def main():
    args = parser.parse_args()

    experiment_name = f"{args.model}-{uuid.uuid4()}"
    training_args = TrainingArguments(
        os.path.join(args.output_dir, experiment_name),
        per_device_train_batch_size=128,
        per_device_eval_batch_size=128,
        learning_rate=5e-5,
        num_train_epochs=3,
        fp16=True,
        logging_strategy="steps",
        logging_steps=20,
        evaluation_strategy="steps",
        eval_steps=250,
        save_strategy="steps",
        save_steps=250,
        metric_for_best_model="eval_spearman",
        load_best_model_at_end=True,
        greater_is_better=True,
        save_total_limit=1,
        seed=args.seed,
    )
    if training_args.local_rank == -1 or training_args.local_rank == 0:
        logging.basicConfig(
            level=logging.INFO,
            format="%(asctime)s - %(message)s",
            filename=f"log/train-{experiment_name}.log",
        )
    logger.info("Hyperparameters")
    for k, v in vars(args).items():
        logger.info(f"{k} = {v}")

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, "
        f"device: {training_args.device}, "
        f"n_gpu: {training_args.n_gpu}, "
        f"distributed training: {bool(training_args.local_rank != -1)}, "
        f"16-bits training: {training_args.fp16} "
    )

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

    # Load pretrained model and tokenizer
    tokenizer = create_tokenizer(args.model)
    model = create_contrastive_learning(args.model, args.temp)
    ### model = torch.nn.DataParallel(model) --> tried but not fix ...
    model.train()

    train_dataset = NLIContrastiveLearningDataset(
        os.path.join(args.data_dir, "nli_for_simcse.csv"), tokenizer
    )
    eval_dataset = STSBenchmarkDataset(
        load_stsb_dev(os.path.join(args.data_dir, "STS", "STSBenchmark"))["dev"], tokenizer
    )

    trainer = STSTrainer(
        model=model,
        data_collator=ContrastiveLearningCollator(),
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
        
    )
    train_result = trainer.train()
    logger.info(train_result)
    trainer.module.save_model(os.path.join(training_args.output_dir, "checkpoint-best"))


if __name__ == "__main__":
    main()
```

I searched problem, but I didn't find any solution for this.
Could you help me?



### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Wrap the model with train_result = trainer.train()


### Expected behavior

Can solve issue
contact me, I fixed it <|||||>Hi, @huynhhoanghuy. I think that clrcmd trainer is trying to access `model.model` while your model is wrapped into DataParallel, hence there is no `.model` attribute.
See addressed [issue](https://github.com/sh0416/clrcmd/issues/2) <|||||>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/main/CONTRIBUTING.md) are likely to be ignored.