Issue using num_beams parameter for T5 / DeepSpeed
Using a fine-turned seq2seq model, I'd like to generate some number of possible different generations for a given input.  One way of typically doing this is using beam search. 

Using @stas00 's amazing DeepSpeed additions so that T5-11B will fit in my GPUs, I'm calling the trainer ( finetune_trainer.py 
) with only the --do_predict (no train/eval) and (critically) the --num_beams parameter, but this is throwing an error. 

I think the issue is likely one of the following:

1) That this is an unexpected bug/error

2) That this is normal/expected, and that beam search isn't supported on trainer prediction, but rather normally accomplished using run_distributed_eval.py (as described in https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md ).  But if I remember correctly I don't think run_distributed_eval.py currently works with DeepSpeed (though I could be wrong?). 

I am using a pull from around Feb 4th, so if things have changed in the past week, it's possible that's my issue, too. 

### Run Script
```
export BS=1; rm -rf $OUTPUTDIR; PYTHONPATH=../../src USE_TF=0 /usr/bin/time -v deepspeed --num_gpus=4 ./finetune_trainer.py --model_name_or_path allenai/unifiedqa-t5-11b --output_dir $OUTPUTDIR --adam_eps 1e-06 --data_dir $DATADIR \
--do_predict \
--num_beams 8 \
--evaluation_strategy=steps --freeze_embeds --label_smoothing 0.1 --learning_rate 3e-5 \
--logging_first_step --logging_steps 1000 --max_source_length $SEQLEN --max_target_length $SEQLEN --num_train_epochs $EPOCHS \
--overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS \
--predict_with_generate --sortish_sampler \
--test_max_target_length $SEQLEN --val_max_target_length $SEQLEN \
--warmup_steps 5 \
--deepspeed ds_config.json --fp16 \
```

### Error
```
[2021-02-12 01:02:55,207] [WARNING] [runner.py:117:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
[2021-02-12 01:02:55,861] [INFO] [runner.py:355:main] cmd = /home/pajansen/anaconda3/envs/transformers-feb4-2020/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgM119 --master_addr=127.0.0.1 --master_port=29500 ./finetune_trainer.py --model_name_or_path allenai/unifiedqa-t5-11b --output_dir output_dir_compexpl-feb10-epoch1-uqa-11b-pretrain-teacher-min6-max8-step2-beam --adam_eps 1e-06 --data_dir /home/pajansen/github/compositional-expl/pretrain/min-6-max-8-noduptest/ --do_predict --num_beams 8 --evaluation_strategy=steps --freeze_embeds --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 256 --max_target_length 256 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size 1 --per_device_train_batch_size 1 --predict_with_generate --sortish_sampler --test_max_target_length 256 --val_max_target_length 256 --warmup_steps 5 --deepspeed ds_config.json --fp16
[2021-02-12 01:02:56,753] [INFO] [launch.py:78:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3]}
[2021-02-12 01:02:56,753] [INFO] [launch.py:84:main] nnodes=1, num_local_procs=4, node_rank=0
[2021-02-12 01:02:56,753] [INFO] [launch.py:99:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1, 2, 3]})
[2021-02-12 01:02:56,753] [INFO] [launch.py:100:main] dist_world_size=4
[2021-02-12 01:02:56,753] [INFO] [launch.py:102:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
[2021-02-12 01:02:59,580] [INFO] [distributed.py:39:init_distributed] Initializing torch distributed with backend: nccl
[2021-02-12 01:02:59,723] [INFO] [distributed.py:39:init_distributed] Initializing torch distributed with backend: nccl
[2021-02-12 01:02:59,828] [INFO] [distributed.py:39:init_distributed] Initializing torch distributed with backend: nccl
[2021-02-12 01:02:59,976] [INFO] [distributed.py:39:init_distributed] Initializing torch distributed with backend: nccl
Traceback (most recent call last):
  File "./finetune_trainer.py", line 367, in <module>
    main()
  File "./finetune_trainer.py", line 160, in main
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
  File "/home/pajansen/github/transformers-feb4-2021/transformers/src/transformers/hf_argparser.py", line 189, in parse_args_into_dataclasses
    raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
ValueError: Some specified arguments are not used by the HfArgumentParser: ['--num_beams', '8']
Traceback (most recent call last):
  File "./finetune_trainer.py", line 367, in <module>
    main()
  File "./finetune_trainer.py", line 160, in main
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
  File "/home/pajansen/github/transformers-feb4-2021/transformers/src/transformers/hf_argparser.py", line 189, in parse_args_into_dataclasses
    raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
ValueError: Some specified arguments are not used by the HfArgumentParser: ['--num_beams', '8']
Traceback (most recent call last):
  File "./finetune_trainer.py", line 367, in <module>
Traceback (most recent call last):
  File "./finetune_trainer.py", line 367, in <module>
    main()
  File "./finetune_trainer.py", line 160, in main
    main()
  File "./finetune_trainer.py", line 160, in main
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()  File "/home/pajansen/github/transformers-feb4-2021/transformers/src/transformers/hf_argparser.py", line 189, in parse_args_into_dataclasses

  File "/home/pajansen/github/transformers-feb4-2021/transformers/src/transformers/hf_argparser.py", line 189, in parse_args_into_dataclasses
        raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")

ValueErrorValueError: : Some specified arguments are not used by the HfArgumentParser: ['--num_beams', '8']Some specified arguments are not used by the HfArgumentParser: ['--num_beams', '8']
```

It's `--eval_beams` in that particular script:
```
./finetune_trainer.py -h | grep beams
                           [--tgt_lang TGT_LANG] [--eval_beams EVAL_BEAMS]
  --eval_beams EVAL_BEAMS
                        # num_beams to use for evaluation.
```

This script is going to be retired soon and `run_seq2seq.py` is the replacement, and there by my suggestions we switched to `num_beams` to match the `model.config.num_beams`<|||||>Thanks -- I was doing it the complex way and looking through the seqtrainer to verify the num_beams was being passed, when really I should have started with funetune_trainer.py to verify the name was the same. :)

That did get rid of the argument error.  But I am now seeing different errors:

1) I received the "RuntimeError: Input, output and indices must be on the current device" error, but then realized that was fixed in #10039 , so I did a pull of master.

2) Then I was getting OOM errors when calling trainer with just --do_predict.  I tried reducing eval_beams to 1, then excluding the argument all together, and the OOM is still thrown. 

3) To figure out if this was a broader issue from the pull, I've went back to rerunning my fine tuning script, but it's also now throwing OOM on T5-11B (but worked okay on my pull from ~Feb 4th).  I'm running a few more tests to try to rule out if it's something I accidentally changed (so far nothing).  I should probably start a fresh issue. 

<|||||>You probably need to start transitioning to `run_seq2seq.py` as `funetune_trainer.py` is about to be demoted into the legacy underworld. 

I haven't full figured out how to do it as not everything was ported, but I'm updating notes here: https://github.com/huggingface/transformers/issues/10036 as I learn new nuances - one of the main changes is that datasets are now done in a complete different way. 



----

> To figure out if this was a broader issue from the pull, I've went back to rerunning my fine tuning script, but it's also now throwing OOM on T5-11B

Yes, I remember I had encountered that too - I went back to the original scripts that I know worked (https://github.com/huggingface/transformers/issues/9996) and then started comparing what changes I have done and then discovered which differences I made that led to more GPU usage.

Also note that since the merge of https://github.com/huggingface/transformers/pull/10114 the DeepSpeed process is completely contained in the `train()` stage (since it doesn't have anything to offer during eval at the moment). I think this then would impact the ability to load t5-11b 45GB model onto 40GB gpu, because DeepSpeed was loading it in fp16 (22GB), but HF trainer can't do that. But this is a very recent change. I started looking at doing fp16 during eval in HF Trainer, but it looks like this is a wildcard and many models fail to deliver when `.half`ed.

Before this PR was merged, if you were to train and then eval then the smaller model would avail itself to eval. Not yet sure how to best to proceed - surely if one can train a model, they should be able to eval it too.

**edit**: looking closer, `self.model` will remain as it were in `train` anyway, so actually this PR shouldn't have affected the eval stage - i.e. should remain in fp16 if the trainer set the model. But if `train` wasn't run it surely won't be able to load in fp32 (45GB>40GB).<|||||>Thanks -- I migrated to ```run_seq2seq.py``` and I'm now able to replicate the OOM error on the README examples (assuming I have DeepSpeed configured correctly).  So it does seem like it's a broader issue, and we may back to not being able to train T5-11B on the 40gb cards on the current master (though I can always go back and try to see if there's a commit from the past week that's post-eval-issue fix and pre-new issue). 

Since this is unrelated to the ```--num_beams`` argument, I put it in a new issue: #10161 and we can probably close this one. 