transformers seems to have recently been "bricked"
## 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: 4.12.0.dev0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyTorch version (GPU?): 1.9.0+cu102 (False)
- Tensorflow version (GPU?): 2.6.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: Yes

### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @Rocketknight1

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

Model hub:

- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

 -->
@sgugger

## Information

The example script below was working fine until today. I believe that it was working in version `4.11.0.dev0`. If you can please tell me how to checkout the source for `4.11.0.dev0` from github, I will confirm that it works. 

## To reproduce

Steps to reproduce the behavior:

On a TPU colab instance with High-RAM, run:

```
CHECKPOINT=bert-large-uncased
DATASET=rte
EPOCHS=2
BATCH_SIZE=16
LEARNING_RATE=3e-5

python transformers/examples/pytorch/xla_spawn.py --num_cores 8 \
  transformers/examples/pytorch/text-classification/run_glue.py \
  --model_name_or_path $CHECKPOINT \
  --task_name $DATASET \
  --seed 10000 \
  --output_dir results \
  --overwrite_output_dir \
  --num_train_epochs $EPOCHS \
  --evaluation_strategy no \
  --logging_strategy epoch \
  --save_strategy epoch \
  --per_device_train_batch_size $BATCH_SIZE \
  --per_device_eval_batch_size $BATCH_SIZE \
  --learning_rate $LEARNING_RATE \
  --do_train
```

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
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Gives the error:

```
Exception in device=TPU:7: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:4: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:2: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:1: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:6: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:5: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:3: zero-dimensional tensor (at position 0) cannot be concatenated
Exception in device=TPU:0: zero-dimensional tensor (at position 0) cannot be concatenated

  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data

uted/xla_multiprocessing.py", line 329, in _mp_start_fn
    _start_fn(index, pf_cfg, fn, args)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 323, in _start_fn
    fn(gindex, *args)
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 564, in _mp_fn
    main()
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 329, in _mp_start_fn
    _start_fn(index, pf_cfg, fn, args)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 323, in _start_fn
    fn(gindex, *args)
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 564, in _mp_fn
    main()
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 329, in _mp_start_fn
    _start_fn(index, pf_cfg, fn, args)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 323, in _start_fn
    fn(gindex, *args)
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 564, in _mp_fn
    main()
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
Traceback (most recent call last):
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 329, in _mp_start_fn
    _start_fn(index, pf_cfg, fn, args)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 323, in _start_fn
    fn(gindex, *args)
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 564, in _mp_fn
    main()
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated

  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/examples/pytorch/text-classification/run_glue.py", line 486, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1383, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 1467, in _maybe_log_save_evaluate
    tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer.py", line 2373, in _nested_gather
    tensors = nested_xla_mesh_reduce(tensors, name)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/content/transformers/src/transformers/trainer_pt_utils.py", line 155, in nested_xla_mesh_reduce
    return xm.mesh_reduce(name, tensors, torch.cat)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/core/xla_model.py", line 916, in mesh_reduce
    return reduce_fn(xldata) if xldata else cpu_data
RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated
RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated
 50%|█████████████▌             | 20/40 [08:22<08:22, 25.15s/it]
Traceback (most recent call last):
  File "transformers/examples/pytorch/xla_spawn.py", line 85, in <module>
    main()
  File "transformers/examples/pytorch/xla_spawn.py", line 81, in main
    xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)
  File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 394, in spawn
    start_method=start_method)
  File "/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py", line 188, in start_processes
    while not context.join():
  File "/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py", line 144, in join
    exit_code=exitcode
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with exit code 17
```

## Expected behavior

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

No error. 
I see where the problem comes from. Will push a fix tonight or tomorrow morning, then we will do a patch release.
In the meantime you should have no error by staying on v4.10<|||||>I run out of memory using transformers v4.X where X > 10 training `led-large-16384-arxiv` with four gradient accumulation steps and a batch size of two like in [this notebook](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing) on an A6000 with 48 GB of RAM. I had to bump gradient accumulation steps and batch size down to 1 each to fit the model + batch on the GPU. Wild. Don't really feel like opening an issue, but yeah just thought I'd chirp in here and say that with v4.10.1 I can fit up to 8 samples per batch with four gradient accumulation steps on the A6000.

If you upgrade to `4.11.1` in the colab notebook I shared [it fails](https://gist.github.com/odellus/e1c637860acbd66280429fe1f99d4071), but for `4.10.1` it works just fine.