Error on training XLNet. RuntimeError: CUDA error: device-side assert triggered
Currently trying to make pretraining using XLNet architecture.

script to reproduce:

```

def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)



from transformers import XLNetTokenizerFast
from tokenizers import Tokenizer
from transformers import XLNetLMHeadModel
from transformers import XLNetConfig
from transformers import DataCollatorForLanguageModeling


tokenizer = XLNetTokenizerFast.from_pretrained("xlnet-base-cased")



config=XLNetConfig(vocab_size=16000, 
)

model = XLNetLMHeadModel(config=config)


from datasets import load_dataset

raw_datasets = load_dataset("imdb")

tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))


training_args = TrainingArguments(
    output_dir="./models/custom_pasona",
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_device_train_batch_size=1,
    save_steps=1000,
    save_total_limit=2,
    prediction_loss_only=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=small_train_dataset,
)

trainer.train()


```

This would results on an error

```
ING=1 python pretrain.py 
Reusing dataset imdb (~/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a)
DatasetDict({
    train: Dataset({
        features: ['label', 'text'],
        num_rows: 25000
    })
    test: Dataset({
        features: ['label', 'text'],
        num_rows: 25000
    })
    unsupervised: Dataset({
        features: ['label', 'text'],
        num_rows: 50000
    })
})
  0%|                                                                                                                     | 0/25 [00:00<?, ?ba/s]Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:17<00:00,  1.43ba/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:17<00:00,  1.46ba/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:35<00:00,  1.42ba/s]
The following columns in the training set  don't have a corresponding argument in `XLNetLMHeadModel.forward` and have been ignored: text.
***** Running training *****
  Num examples = 1000
  Num Epochs = 1
  Instantaneous batch size per device = 1
  Total train batch size (w. parallel, distributed & accumulation) = 1
  Gradient Accumulation steps = 1
  Total optimization steps = 1000
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Traceback (most recent call last):
  File "~/Desktop/workspace/recommendation/pretrain.py", line 74, in <module>
    trainer.train()
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/transformers/trainer.py", line 1269, in train
    tr_loss += self.training_step(model, inputs)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/transformers/trainer.py", line 1762, in training_step
    loss = self.compute_loss(model, inputs)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/transformers/trainer.py", line 1794, in compute_loss
    outputs = model(**inputs)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1432, in forward
    transformer_outputs = self.transformer(
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1189, in forward
    output_h = self.dropout(word_emb_k)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/torch/nn/modules/dropout.py", line 58, in forward
    return F.dropout(input, self.p, self.training, self.inplace)
  File "~/anaconda3/envs/deltalake/lib/python3.9/site-packages/torch/nn/functional.py", line 983, in dropout
    else _VF.dropout(input, p, training))
RuntimeError: CUDA error: device-side assert triggered
```
I could not find out what is the source of the problem.
Why is this happening?
Is there any walkthrough on training with XLNet?
I found out that changing vocab_size  to 32000 fixes this error.

How do I change this number to other than 32000?

I made a custom tokenizer 
```
tokenizer.train(files=paths, vocab_size=16000, special_tokens=special_tokens)
tokenizer.save('unigram.json', pretty=True)
```

loaded it
```
tokenizer = Tokenizer.from_file('unigram.json')
tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)
```

using this with vocab_size 16000 causing an error.
How do I load this custom tokenizer with XLNet?<|||||>It seems to me that the issue here is that you're loading a specific tokenizer, `xlnet-base-cased`, with a dictionary of 32k tokens:
```py
>>> from transformers import XLNetTokenizerFast
>>> tokenizer = XLNetTokenizerFast.from_pretrained("xlnet-base-cased")
>>> len(tokenizer)
32000
```

But you're then using a randomly initialized model that you initialized at 16k tokens. So if your model receives a token that has an ID superior to 15999, it will crash with the error above. 