T5-base goes out of memory on 4 GPUs with as small batch size as 4 
## 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: 3.5.1 
- Platform: LINUX
- Python version: 3.7 
- PyTorch version (GPU?): 1.7
- Tensorflow version (GPU?): - 
- Using GPU in script?: yes 
- Using distributed or parallel set-up in script?: yes 

### Who can help
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 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
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 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
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 Blenderbot: @patrickvonplaten
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 Trainer: @sgugger
 T5: @patrickvonplaten
 examples/seq2seq: @patil-suraj

## Information

Model I am using T5-base with batch size of 8 and on 4 GPUs, I am always getting out of memory even with small batch sizes, This looks like a bug as this model is not really big. I am under time pressure. Is there anyone who could help me with this bug? thanks

The tasks I am working on is:
* GLUE benchmark 


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


## Error Stack
```
  0%|                                                                                                                                                                                                                 | 0/148395 [00:00<?, ?it/s]Traceback (most recent call last):
  File "finetune_trainer.py", line 303, in <module>
    main()
  File "finetune_trainer.py", line 239, in main
    training_args.optimize_from_scratch) else None
  File "/julia/codes/trainers/trainer.py", line 804, in train
    self.optimizer.step()
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/torch-1.7.1-py3.7-linux-x86_64.egg/torch/optim/lr_scheduler.py", line 67, in wrapper
    return wrapped(*args, **kwargs)
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/transformers-3.5.1-py3.7.egg/transformers/optimization.py", line 285, in step
    state["exp_avg_sq"] = torch.zeros_like(p.data)
RuntimeError: CUDA out of memory. Tried to allocate 36.00 MiB (GPU 2; 15.78 GiB total capacity; 14.10 GiB already allocated; 20.25 MiB free; 14.42 GiB reserved in total by PyTorch)
Traceback (most recent call last):
  File "finetune_trainer.py", line 303, in <module>
    main()
  File "finetune_trainer.py", line 239, in main
    training_args.optimize_from_scratch) else None
  File "/julia/codes/trainers/trainer.py", line 804, in train
    self.optimizer.step()
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/torch-1.7.1-py3.7-linux-x86_64.egg/torch/optim/lr_scheduler.py", line 67, in wrapper
    return wrapped(*args, **kwargs)
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/transformers-3.5.1-py3.7.egg/transformers/optimization.py", line 296, in step
    denom = exp_avg_sq.sqrt().add_(group["eps"])
RuntimeError: CUDA out of memory. Tried to allocate 36.00 MiB (GPU 0; 15.78 GiB total capacity; 14.06 GiB already allocated; 4.25 MiB free; 14.44 GiB reserved in total by PyTorch)
Traceback (most recent call last):
  File "finetune_trainer.py", line 303, in <module>
    main()
  File "finetune_trainer.py", line 239, in main
Traceback (most recent call last):
  File "finetune_trainer.py", line 303, in <module>
    training_args.optimize_from_scratch) else None
  File "/julia/codes/trainers/trainer.py", line 804, in train
    main()
  File "finetune_trainer.py", line 239, in main
    self.optimizer.step()
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/torch-1.7.1-py3.7-linux-x86_64.egg/torch/optim/lr_scheduler.py", line 67, in wrapper
        training_args.optimize_from_scratch) else Nonereturn wrapped(*args, **kwargs)

  File "/julia/codes/trainers/trainer.py", line 804, in train
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/transformers-3.5.1-py3.7.egg/transformers/optimization.py", line 296, in step
    denom = exp_avg_sq.sqrt().add_(group["eps"])
RuntimeError: CUDA out of memory. Tried to allocate 36.00 MiB (GPU 1; 15.78 GiB total capacity; 14.13 GiB already allocated; 10.25 MiB free; 14.46 GiB reserved in total by PyTorch)
    self.optimizer.step()
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/torch-1.7.1-py3.7-linux-x86_64.egg/torch/optim/lr_scheduler.py", line 67, in wrapper
    return wrapped(*args, **kwargs)
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/transformers-3.5.1-py3.7.egg/transformers/optimization.py", line 285, in step
    state["exp_avg_sq"] = torch.zeros_like(p.data)
RuntimeError: CUDA out of memory. Tried to allocate 36.00 MiB (GPU 3; 15.78 GiB total capacity; 14.10 GiB already allocated; 26.25 MiB free; 14.44 GiB reserved in total by PyTorch)
  0%|                                                                                                                                                                                                                 | 0/148395 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/opt/conda/envs/t5/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/opt/conda/envs/t5/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/torch-1.7.1-py3.7-linux-x86_64.egg/torch/distributed/launch.py", line 260, in <module>
    main()
  File "/opt/conda/envs/t5/lib/python3.7/site-packages/torch-1.7.1-py3.7-linux-x86_64.egg/torch/distributed/launch.py", line 256, in main
    cmd=cmd)
subprocess.CalledProcessError: Command '['/opt/conda/envs/t5/bin/python', '-u', 'finetune_trainer.py', '--local_rank=3', 'configs/glue.json']' returned non-zero exit status 1.
```
Here are things you may try (they are unrelated to each other, so you can try in any order that resonates):

1. turn off `--fp16` or keep it but switch to [pytorch-nightly](https://pytorch.org/get-started/locally/) - there was a large memory leak fixed a few weeks ago related to autocast (fp16) - if this is not related to `autocast`/ftp16 this won't help then. `--fp16` was triggering the leak. Switching to apex amp is another option to try if you're hitting this memory leak in pytorch.

2. If you are using huggingface trainer (I assume `finetune_trainer.py` is from examples/seq2seq then you're good) and if you can use `transformers` master, I'd suggest using the just added `--sharded_ddp` option. In my few experiments I was able to get 2-3 times bigger batches. It's documented in this PR https://github.com/huggingface/transformers/pull/9208 (we are just waiting for a new fairscale release to merge it). But you can just use it w/o needing to understand if you are short on time. So if you want to try it, install both transformers and [fairscale](https://github.com/facebookresearch/fairscale/) from master and then that new option will be available. 

And please edit your Issue to show the command line you use, so we can see what cl args and/or hyper parameters you're using.<|||||>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.