AttributeError: 'DistributedDataParallel' object has no attribute 'generate'
### System Info

transformer version: 4.21.1
python: 3.8
pytorch: 1.12

### Who can help?

@NielsRogge 

### Reproduction

Steps:

1. Loaded the trOCR model by huggingface:
2. Coping the model to all GPUs
3. Predicting the validation set using generate function

### Expected behavior

I'm training the trOCR model on my customized Arabic Dataset on Sagemaker instance, I'm running a distributed data training job and have added the model into all GPUs using pytorch as follows:
```
from torch.nn.parallel import DistributedDataParallel as DDP
model = DDP(model)

```

When I'm running the validation function it raises this error 

> "AttributeError: 'DistributedDataParallel' object has no attribute 'generate'"

when I'm trying the generate function:
```
inputs: torch.Tensor = batch["input"].to('cuda')            
generated_ids = model.generate(inputs)
```

What could be the problem pleaes?
Hi, just unwrap the model:

model.module.generate(inputs)

(I didn't verify, but this should work)<|||||>> Hi, just unwrap the model:
> 
> model.module.generate(inputs)
> 
> (I didn't verify, but this should work)

Yes, this actually works, thank you @nitaytech .. I have just faced one more issue now.

when I decode the batch after generation I get no prediction strings (means generated_text is always empty string), should I load the processor as well at the devices?
hint: this behavior happened only with the DistributedDataParallel, It was working all together on a single GPU
```
def test(processor: TrOCRProcessor, model: VisionEncoderDecoderModel, dataloader: DataLoader):
    output: dict[int, str] = []
    model.eval()
    with torch.no_grad():
        for i, batch in enumerate(dataloader):
            inputs: torch.Tensor = batch["input"].cuda(non_blocking=True)

            generated_ids = model.module.generate(inputs)
            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)

            ids = [t.item() for t in batch["idx"]]
            output.extend(zip(ids, generated_text))
    return output
```
am I missing anything else?
<|||||>The distributed model expects distribution of the function `forward` and not `generate`. In case you want to distribute inference, what you can do is create a class inheriting from `torch.nn.Module` with a `forward` function that interfaces TrOCR's `generate` function and then distribute an instance of that class.

```python
class DitributionCompatibleTrOCR(torch.nn.Module):
    def __init__(self, trocr_model):
        self.trocr_model = trocr_model

    def forward(self, x):
        return self.trocr_model.generate(x)
```

You will probably get a concatenation error since the distribution function will try to concatenate outputs from each GPU but I can't remember how I solved that 🥲.<|||||>I'd recommend using [HuggingFace Accelerate](https://github.com/huggingface/accelerate) for training TrOCR in a distributed set-up.

You can then use [unwrap_model](https://huggingface.co/docs/accelerate/v0.12.0/en/package_reference/accelerator#accelerate.Accelerator.unwrap_model) to turn the distributed module back into a regular nn.Module (on which you can call generate)<|||||>We do provide an example for that, see here: https://github.com/huggingface/transformers/blob/8edf1963103127247ae3ef96fc5ba6a96eb4a290/examples/pytorch/summarization/run_summarization_no_trainer.py#L675

This is taken from the example script for summarization, but it would be equivalent for TrOCR<|||||>> We do provide an example for that, see here:
> 
> https://github.com/huggingface/transformers/blob/8edf1963103127247ae3ef96fc5ba6a96eb4a290/examples/pytorch/summarization/run_summarization_no_trainer.py#L675
> 
> This is taken from the example script for summarization, but it would be equivalent for TrOCR

Yet, I already switched to huggingface Accelerate (as I am working on Sagemaker so I installed "accelerate[sagemaker]" version), however the same issue present. 

Here's the training script, that's already working properly on a single GPU.. I couldn't figure out the what is the root issue.

> 
```
import os
import torch
print(f"TORCH_VERSION: {torch.__version__}")
print(f"CUDA AVAILABILITY: {torch.cuda.is_available()} GPUs: {torch.cuda.get_device_name()}")
import pandas as pd
import random
import math
import re
import numpy as np
import itertools
from PIL import Image
import PIL.ImageOps
import cv2
from smart_open import open as smart_open
import io
from torch.utils.data import DataLoader
from transformers import AdamW, TrOCRProcessor, VisionEncoderDecoderModel, get_scheduler
from Data_pipeline import Context, HCRDataset, OCRDataLoad
from Validation_Metrics import getWordLevelError, getCharacterLevelError
from accelerate import Accelerator
import accelerate
accelerator = Accelerator(kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=True)])
accelerator.print(f"ACCELERATOR DEVICE:{accelerator.distributed_type}---- NUM OF PROCESSES: {accelerator.num_processes }")

from datasets import load_metric
cer_metric = load_metric("cer")
wer_metric = load_metric("wer")

# LOAD MODEL
def load_model() -> VisionEncoderDecoderModel:
    model: VisionEncoderDecoderModel = VisionEncoderDecoderModel.from_pretrained('gagan3012/ArOCRv4')
    return model.to(accelerator.device)

# SETUP MODEL CONFIGUATIONS
def init_model_for_training(model: VisionEncoderDecoderModel, processor: TrOCRProcessor):
    model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
    model.config.pad_token_id = processor.tokenizer.pad_token_id
    model.config.vocab_size = model.config.decoder.vocab_size
    model.config.bos_token_id = processor.tokenizer.bos_token_id
    model.config.max_length = 162
    model.config.decoder.is_decoder = True
    model.config.decoder.add_cross_attention = True
    torch.cuda.manual_seed_all(42)
    model.config.num_beams = 4

def predict(processor: TrOCRProcessor, model: VisionEncoderDecoderModel, dataloader: DataLoader):
    output: dict[int, str] = []
    with torch.no_grad():
        for i, batch in enumerate(dataloader):
            inputs: torch.Tensor = batch["input"].to(accelerator.device)

            generated_ids = model.generate(inputs)
            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)

            ids = [t.item() for t in batch["idx"]]
            output.extend(zip(ids, generated_text))

    return output


def validate(context: Context, print_wrong: bool = False) -> float:
    predictions = predict(context.processor, context.model, context.val_dataloader)
    assert len(predictions) > 0
    
    CER_avg = []
    WER_avg = []
    correct_count = 0
    wrong_count = 0
    for id, prediction in predictions:
        label = context.val_dataset.get_label(id)
        path = context.val_dataset.get_path(id)
        
        CER = getCharacterLevelError(label, prediction)
        WER = getWordLevelError(label, prediction)
        
        CER_avg.append(CER)
        WER_avg.append(WER)
        accelerator.print(f"validation-batch--------------{id}-----------Label--------{label}---------Prediction-----------{prediction} -----CER----- {CER}----")

    return round(sum(CER_avg)/len(CER_avg),2), round(sum(WER_avg)/len(WER_avg),2)

# LOAD PRE_PROCESSOR
def load_processor() -> TrOCRProcessor:
    return TrOCRProcessor.from_pretrained('gagan3012/ArOCRv4')

def train(context, train_epochs, learning_rate):
    model = context.model
    optimizer = AdamW(model.parameters(), lr=learning_rate)
    
    num_training_steps = train_epochs * len(context.train_dataloader)
    lr_scheduler = get_scheduler("linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps)
    
    model, optimizer, context.training_dataloader,context.val_dataloader = accelerator.prepare(model, optimizer, context.train_dataloader, context.val_dataloader)
    
    overall_loss = 0.0
    overall_cer = 0.0
    overall_wer = 0.0
    for epoch in range(train_epochs):
        context.model.train() 
        train_loss = 0.0
        min_cer = 1.0
        min_train_loss = 1.0
        for j, batch in enumerate(context.train_dataloader):
                inputs: torch.Tensor = batch["input"].to(accelerator.device)
                labels: torch.Tensor = batch["label"].to(accelerator.device)
                #print(inputs)
                #print(labels)
                
                outputs = model(pixel_values=inputs, labels=labels)
                loss = outputs.loss
                accelerator.backward(loss)
                #loss.backward()
                optimizer.step()
                
                lr_scheduler.step()
                optimizer.zero_grad()
                train_loss+=loss
                #accelerator.print(f"Batch: {j}----Loss: {loss}")
        overall_loss+=train_loss
        if (loss < min_train_loss) or (min_train_loss==1.0):
            min_train_loss = loss
        accelerator.print(f"Epoch {epoch}-----Loss---{train_loss/len(context.train_dataloader)}--------- min-loss: {min_train_loss}")
        # evaluate
        unwrapped_model = accelerator.unwrap_model(model)
        context.model = unwrapped_model
        cer, wer = validate(context)

        del loss, outputs, train_loss

        
        overall_cer+=cer
        overall_wer+=wer
    accelerator.print(f"\n---- overall loss: {overall_loss/train_epochs}\n\n")
    accelerator.print(f"\n---- overall cer: {overall_cer/train_epochs}\n\n")
    accelerator.print(f"\n---- overall wer: {overall_wer/train_epochs}\n\n")



def main():
    batch_size = 8
    train_epochs = 10
    learning_rate = 0.001
    checkpoints_path = "checkpoints"
    
    processor = load_processor()
    (x_train,y_train),(x_valid,y_valid),(x_test,y_test) = OCRDataLoad()
    train_dataset = HCRDataset(x_train, y_train, processor)

    train_dataloader = DataLoader(train_dataset, batch_size, shuffle=True, num_workers=4)#, sampler=train_sampler)
    
    val_dataset = HCRDataset(x_valid, y_valid, processor)

    val_dataloader = DataLoader(val_dataset, batch_size, shuffle=False, num_workers=4)#, sampler=val_sampler)
 
    # SageMaker data parallel: Wrap the PyTorch model with the library's DDP
    model = load_model()
    init_model_for_training(model, processor)
    
    #model = DDP(model, broadcast_buffers=False)
    context = Context(model, processor, train_dataset, train_dataloader, val_dataset, val_dataloader)
    train(context, train_epochs, learning_rate)
    unwraped_model = accelerator.unwrap_model(context.model)
    # SageMaker data parallel: Save model on master node.
    unwraped_model.save_pretrained(checkpoints_path)

if __name__ == '__main__':
    main()
```<|||||>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.