Clinical longformer - IndexError: index out of range in self
**Description:** 
While using clinical Longformer (AutoModelForSequenceClassification.from_pretrained("yikuan8/Clinical-Longformer", num_labels=3)) and its tokenizer (AutoTokenizer.from_pretrained("yikuan8/Clinical-Longformer")), I am running into the "IndexError: index out of range in self" error.

**Source code:**
import torch
from torch.utils.data import Dataset, TensorDataset, DataLoader
from torch.nn.utils.rnn import pad_sequence
import pickle
import os
from transformers import AutoTokenizer

#Longformer
from transformers import AutoModelForSequenceClassification, AdamW
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("yikuan8/Clinical-Longformer")
model = AutoModelForSequenceClassification.from_pretrained("yikuan8/Clinical-Longformer", num_labels=3)
model.to(device)
class NLIDataBert(Dataset):

  def __init__(self, train_df, val_df, input_tokenizer):
    self.label_dict = {'entailment': 0, 'contradiction': 1, 'neutral': 2}

    self.train_df = train_df
    self.val_df = val_df

    self.tokenizer = input_tokenizer
    self.train_data = None
    self.val_data = None
    self.init_data()

  def init_data(self):
    self.train_data = self.load_data(self.train_df)
    self.val_data = self.load_data(self.val_df)

  def load_data(self, df):
    max_seq_length = 3072
    token_ids = []
    mask_ids = []
    seg_ids = []
    y = []

    premise_list = df['premise'].to_list()
    hypothesis_list = df['hypothesis'].to_list()
    label_list = df['gold_label'].to_list()

    for (premise, hypothesis, label) in zip(premise_list, hypothesis_list, label_list):
      premise_id = self.tokenizer.encode(premise, add_special_tokens = False)
      hypothesis_id = self.tokenizer.encode(hypothesis, add_special_tokens = False)
      pair_token_ids = [self.tokenizer.cls_token_id] + premise_id + [self.tokenizer.sep_token_id] + hypothesis_id + [self.tokenizer.sep_token_id]
      premise_len = len(premise_id)
      hypothesis_len = len(hypothesis_id)

      segment_ids = torch.tensor([0] * (premise_len + 2) + [1] * (hypothesis_len + 1))  # sentence 0 and sentence 1
      attention_mask_ids = torch.tensor([1] * (premise_len + hypothesis_len + 3))  # mask padded values

      token_ids.append(torch.tensor(pair_token_ids))
      seg_ids.append(segment_ids)
      mask_ids.append(attention_mask_ids)
      y.append(self.label_dict[label])
    
    token_ids = pad_sequence(token_ids, batch_first=True)
    mask_ids = pad_sequence(mask_ids, batch_first=True)
    seg_ids = pad_sequence(seg_ids, batch_first=True)
    
    '''
    padding = [0] * (max_seq_length - len(token_ids))
    token_ids += padding
    mask_ids += padding
    seg_ids += padding
    '''
    y = torch.tensor(y)
    dataset = TensorDataset(token_ids, mask_ids, seg_ids, y)
    print(len(dataset))
    return dataset

  def get_data_loaders(self, batch_size=32, shuffle=True):
    train_loader = DataLoader(
      self.train_data,
      shuffle=shuffle,
      batch_size=batch_size
    )

    val_loader = DataLoader(
      self.val_data,
      shuffle=shuffle,
      batch_size=batch_size
    )

    return train_loader, val_loader
nli_dataset = NLIDataBert(df_train, df_val, tokenizer)
train_loader, val_loader = nli_dataset.get_data_loaders(batch_size=16)

param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [
    {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
     'weight_decay_rate': 0.01},
    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
     'weight_decay_rate': 0.0}
]

optimizer = AdamW(optimizer_grouped_parameters, lr=2e-5, correct_bias=False)

def multi_acc(y_pred, y_test):
  acc = (torch.log_softmax(y_pred, dim=1).argmax(dim=1) == y_test).sum().float() / float(y_test.size(0))
  return acc

import time

EPOCHS = 5

def train(model, train_loader, val_loader, optimizer):  
  total_step = len(train_loader)

  for epoch in range(EPOCHS):
    start = time.time()
    model.train()
    total_train_loss = 0
    total_train_acc  = 0
    for batch_idx, (pair_token_ids, mask_ids, seg_ids, y) in enumerate(train_loader):
      optimizer.zero_grad()
      pair_token_ids = pair_token_ids.to(device)
      mask_ids = mask_ids.to(device)
      seg_ids = seg_ids.to(device)
      labels = y.to(device)

      loss, prediction = model(pair_token_ids, 
                             token_type_ids=seg_ids, 
                             attention_mask=mask_ids, 
                             labels=labels).values()

      acc = multi_acc(prediction, labels)

      loss.backward()
      optimizer.step()
      
      total_train_loss += loss.item()
      total_train_acc  += acc.item()

    train_acc  = total_train_acc/len(train_loader)
    train_loss = total_train_loss/len(train_loader)
    model.eval()
    total_val_acc  = 0
    total_val_loss = 0
    with torch.no_grad():
      for batch_idx, (pair_token_ids, mask_ids, seg_ids, y) in enumerate(val_loader):
        optimizer.zero_grad()
        pair_token_ids = pair_token_ids.to(device)
        mask_ids = mask_ids.to(device)
        seg_ids = seg_ids.to(device)
        labels = y.to(device)
        
        loss, prediction = model(pair_token_ids, 
                             token_type_ids=seg_ids, 
                             attention_mask=mask_ids, 
                             labels=labels).values()
        
        acc = multi_acc(prediction, labels)

        total_val_loss += loss.item()
        total_val_acc  += acc.item()

    val_acc  = total_val_acc/len(val_loader)
    val_loss = total_val_loss/len(val_loader)
    end = time.time()
    hours, rem = divmod(end-start, 3600)
    minutes, seconds = divmod(rem, 60)

    print(f'Epoch {epoch+1}: train_loss: {train_loss:.4f} train_acc: {train_acc:.4f} | val_loss: {val_loss:.4f} val_acc: {val_acc:.4f}')
    print("{:0>2}:{:0>2}:{:05.2f}".format(int(hours),int(minutes),seconds))

train(model, train_loader, val_loader, optimizer)

**Exception:**
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<[command-2383037512822880]()> in <module>
----> 1 train(model, train_loader, val_loader, optimizer)

<[command-2383037512822878]()> in train(model, train_loader, val_loader, optimizer)
     21                              token_type_ids=seg_ids,
     22                              attention_mask=mask_ids,
---> 23                              labels=labels).values()
     24 
     25       acc = multi_acc(prediction, labels)

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/transformers/models/longformer/modeling_longformer.py in forward(self, input_ids, attention_mask, global_attention_mask, head_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
   1895             output_attentions=output_attentions,
   1896             output_hidden_states=output_hidden_states,
-> 1897             return_dict=return_dict,
   1898         )
   1899         sequence_output = outputs[0]

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/transformers/models/longformer/modeling_longformer.py in forward(self, input_ids, attention_mask, global_attention_mask, head_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)
   1702 
   1703         embedding_output = self.embeddings(
-> 1704             input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
   1705         )
   1706 

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/transformers/models/longformer/modeling_longformer.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
    484             inputs_embeds = self.word_embeddings(input_ids)
    485         position_embeddings = self.position_embeddings(position_ids)
--> 486         token_type_embeddings = self.token_type_embeddings(token_type_ids)
    487 
    488         embeddings = inputs_embeds + position_embeddings + token_type_embeddings

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/torch/nn/modules/sparse.py in forward(self, input)
    158         return F.embedding(
    159             input, self.weight, self.padding_idx, self.max_norm,
--> 160             self.norm_type, self.scale_grad_by_freq, self.sparse)
    161 
    162     def extra_repr(self) -> str:

/local_disk0/.ephemeral_nfs/envs/pythonEnv-ed86c010-21bd-4af0-ad0f-9c449ce44eac/lib/python3.7/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   2181         # remove once script supports set_grad_enabled
   2182         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2183     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   2184 
   2185 

IndexError: index out of range in self

**Environment:**
Databricks
Python: '3.7.5  \n[GCC 8.4.0]'
torch: '1.11.0+cu102'
transformers: '4.17.0'

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