Longformer crashes for position embeddings indexing?

## 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: 3.4.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.5.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: apex ddp

### Who can help
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 albert, bert, GPT2, XLM: @LysandreJik
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 TextGeneration: @TevenLeScao
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @TevenLeScao
 blenderbot: @mariamabarham
 Bart: @sshleifer
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 T5: @patrickvonplaten
 Longformer/Reformer: @patrickvonplaten
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 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 --> @patrickvonplaten maybe?

## Information

Model I am using (Bert, XLNet ...): Longformer

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. use apex ddp with longformerforsequenceclassification

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my code snippet:

```python
def train(self):

    self.model.train()
    losses = []

    if isinstance(self.train_loader.sampler, DistributedSampler):
        self.train_loader.sampler.set_epoch(self.epoch)

    for qids, dids, queries, documents, y in self.train_loader:

        encoded = self._tokenizer.batch_encode_plus(batch_text_or_text_pairs=list(zip(queries, documents)),
                                                truncation="longest_first", add_special_tokens=True,
                                                max_length = self.max_len, padding="max_length",
                                                is_pretokenized=False, return_tensors="pt",
                                                return_attention_mask=True, return_token_type_ids=True)

        input_ids = encoded["input_ids"].cuda()
        attention_mask = encoded["attention_mask"].cuda()
        token_type_ids = encoded["token_type_ids"].cuda()
        y = torch.tensor(y).unsqueeze(1).cuda()
        global_attention_mask = self.get_global_attention(encoded["input_ids"], self.max_len, self._tokenizer.sep_token_id)[0].cuda()

        self.optimizer.zero_grad()

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            global_attention_mask=global_attention_mask,
            labels=y
        )

        loss = outputs[0]

        with amp.scale_loss(loss, self.optimizer) as scaled_loss:
            scaled_loss.backward()

        self.optimizer.step()

```

Where the data are queries and documents that are either relevant (y=1) or irrelevant (y=0). Each input is the concatenation of a query and a document. ```get_global_attention()``` is a function to give global attention to query tokens.

I find that for some batches (no all batches!), the code would give the following errors, which are very confusing to me:

```
INFO:__main__:Namespace(apex_level='O2', batch_size=1, cased=1, debug=0, encoder_lr=1e-05, eval_step=1, finetune_embedding=0, local_rank=0, model_path='allenai/longformer-base-4096', model_type='longformer', num_epochs=20, num_ft_encoders=2, num_neg=1, projector_lr=1e-05, seed=611)
Some weights of the model checkpoint at allenai/longformer-base-4096 were not used when initializing LongformerForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']
- This IS expected if you are initializing LongformerForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing LongformerForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of LongformerForSequenceClassification were not initialized from the model checkpoint at allenai/longformer-base-4096 and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
INFO:__main__:Reading data from /....../sampled
INFO:root:Number of positive query-document pairs in [train] set: 67
INFO:root:Number of labelled query-document pairs in [dev] set: 2000
INFO:root:Number of labelled query-document pairs in [test] set: 2000
INFO:__main__:Data reading done ...
INFO:__main__:adding 10-th encoder to optimizer...
INFO:__main__:adding 11-th encoder to optimizer...
Selected optimization level O2:  FP16 training with FP32 batchnorm and FP32 master weights.

Defaults for this optimization level are:
enabled                : True
opt_level              : O2
cast_model_type        : torch.float16
patch_torch_functions  : False
keep_batchnorm_fp32    : True
master_weights         : True
loss_scale             : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled                : True
opt_level              : O2
cast_model_type        : torch.float16
patch_torch_functions  : False
keep_batchnorm_fp32    : True
master_weights         : True
loss_scale             : dynamic
INFO:__main__:process[0]: training epoch 0 ...
/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/tokenization_utils.py:547: FutureWarning: `is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.
  warnings.warn(
Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 32768.0
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/opt/conda/conda-bld/pytorch_1591914886554/work/aten/src/THC/THCTensorIndex.cu:272: indexSelectLargeIndex: block: [5,0,0], thread: [61,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1591914886554/work/aten/src/THC/THCTensorIndex.cu:272: indexSelectLargeIndex: block: [5,0,0], thread: [62,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1591914886554/work/aten/src/THC/THCTensorIndex.cu:272: indexSelectLargeIndex: block: [5,0,0], thread: [63,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
Traceback (most recent call last):
  File "finetune-marco.py", line 93, in <module>
    marco.run()
  File "/mnt/nfs/work1/allan/user/LF-for-IR/Marco.py", line 167, in run
    self.train()
  File "/mnt/nfs/work1/allan/user/LF-for-IR/Marco.py", line 223, in train
    outputs = self.model(
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/apex/amp/_initialize.py", line 196, in new_fwd
    output = old_fwd(*applier(args, input_caster),
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/apex/parallel/distributed.py", line 560, in forward
    result = self.module(*inputs, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/modeling_longformer.py", line 1442, in forward
    outputs = self.longformer(
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/modeling_longformer.py", line 1262, in forward
    encoder_outputs = self.encoder(
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/modeling_longformer.py", line 903, in forward
    layer_outputs = layer_module(
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/modeling_longformer.py", line 849, in forward
    self_attn_outputs = self.attention(
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/modeling_longformer.py", line 793, in forward
    self_outputs = self.self(
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/transformers/modeling_longformer.py", line 246, in forward
    is_global_attn = is_index_global_attn.flatten().any().item()
RuntimeError: CUDA error: device-side assert triggered
NCCL error in: /opt/conda/conda-bld/pytorch_1591914886554/work/torch/lib/c10d/../c10d/NCCLUtils.hpp:69, unhandled cuda error, NCCL version 2.4.8
Traceback (most recent call last):
  File "/home/user/miniconda3/envs/marco/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/user/miniconda3/envs/marco/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/distributed/launch.py", line 263, in <module>
    main()
  File "/home/user/miniconda3/envs/marco/lib/python3.8/site-packages/torch/distributed/launch.py", line 258, in main
    raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/home/user/miniconda3/envs/marco/bin/python', '-u', 'finetune-marco.py', '--local_rank=0', '--model_type', 'longformer', '--model_path', 'allenai/longformer-base-4096', '--batch_size', '1', '--finetune_embedding', '0', '--cased', '1', '--num_neg', '1', '--eval_step', '1', '--num_epochs', '20', '--apex_level', 'O2', '--encoder_lr', '1e-5', '--projector_lr', '1e-5', '--num_ft_encoders', '2', '--seed', '611']' died with <Signals.SIGABRT: 6>.

```

## Expected behavior

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

I think this error is uncalled for. I have tried our pretrained models like base BERT models and they ran just fine. Can someone help interpret the error message here? Thanks!