Hi,  i switched from TF/Keras to PyTorch/Lightning but got this error can anyone help me with this ?
Is there any recommended way to debug some errors in pytorch ?
data example:
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1 B-DATE
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import pandas as pd
import numpy as np
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
from pytorch_lightning.loggers import TensorBoardLogger

from transformers import BertTokenizer, BertForTokenClassification, AdamW
from sklearn.model_selection import train_test_split


FILE_PATH = 'data/own.txt'

def read_conll_from_txt_to_df(file_path):  
    df = pd.DataFrame(columns=['SENTENCE', 'TOKEN', 'LABEL'])
    sent = 1

    with open(file_path, 'r') as f:
        for i, line in enumerate(f):
            line = line.replace('\n', '')
            splitted = line.split()
            if not splitted:
                sent += 1
            else:
                df.loc[i] = [sent, splitted[0], splitted[1]]   
    return df       


data = read_conll_from_txt_to_df(FILE_PATH)

class SentenceGetter(object):

    def __init__(self, data):
        self.n_sent = 1
        self.data = data
        self.empty = False
        agg_func = lambda s: [(token, tag) for token, tag in zip(s["TOKEN"].values.tolist(),
                                                                 s["LABEL"].values.tolist())]
        self.grouped = self.data.groupby("SENTENCE").apply(agg_func)
        self.sentences = [s for s in self.grouped]

    def get_next(self):
        try:
            s = self.grouped["SENTENCE: {}".format(self.n_sent)]
            self.n_sent += 1
            return s
        except:
            return None

getter = SentenceGetter(data)

tags_vals = list(set(data["LABEL"].values))
tag2idx = {t: i for i, t in enumerate(tags_vals)}
sentences = [' '.join([s[0] for s in sent]) for sent in getter.sentences]

labels = [[s[1] for s in sent] for sent in getter.sentences]
labels = [[tag2idx.get(l) for l in lab] for lab in labels]


##### only overview #####
tags = ["[PAD]"]
tags.extend(list(set(data["LABEL"].values)))
tag2idx = {t: i for i, t in enumerate(tags)}
print('Length of Labels : ' + str(len(tags)))

words = ["[PAD]", "[UNK]"]
words.extend(list(set(data["TOKEN"].values)))
word2idx = {t: i for i, t in enumerate(words)}
print('Length of unique words : ' + str(len(words)))
##### only overview #####


train_sent, test_sent, train_label, test_label = train_test_split(sentences, labels, test_size=0.05)
train_sent, val_sent, train_label, val_label = train_test_split(train_sent, train_label, test_size=0.15)

print('FULL DATASET SENT: ' + str(len(sentences)))
print('FULL DATASET LABELS: ' + str(len(labels)))
print('Train sent size : ' + str(len(train_sent)))
print('Train label size : ' + str(len(train_label)))
print('Test sent size : ' + str(len(test_sent)))
print('Test label size : ' + str(len(test_label)))
print('Val sent size : ' + str(len(val_sent)))
print('Val label size : ' + str(len(val_label)))

class ConllDataset(Dataset):
    def __init__(self, tokenizer, sentences, labels, max_len):
        self.len = len(sentences)
        self.sentences = sentences
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_len = max_len
    
    def __len__(self):
        return self.len
        
    def __getitem__(self, index):
        sentence = str(self.sentences[index])
        inputs = self.tokenizer.encode_plus(
            sentence,
            None,
            add_special_tokens=False,
            max_length=self.max_len,
            pad_to_max_length=False,
            truncation=True,
            padding=True,
            return_token_type_ids=False,
            return_attention_mask=True,
            return_tensors='pt'
        )
        ids = inputs['input_ids']
        mask = inputs['attention_mask']
        label = self.labels[index]
        label.extend([4]*200)
        label=label[:200]

        return {
            'ids': ids,
            'mask': mask,
            'tags': label
        } 
    

class NERConllDataset(pl.LightningDataModule):
    
    def __init__(self, tokenizer, train_sent, train_label, val_sent, val_label, test_sent, test_label, max_len, batch_size):
        super().__init__()
        self.tokenizer = tokenizer
        self.train_sent = train_sent
        self.train_label = train_label
        self.val_sent = val_sent
        self.val_label = val_label
        self.test_sent = test_sent
        self.test_label = test_label
        self.max_len = max_len
        self.batch_size = batch_size
    
    def setup(self, stage=None):
        self.train_dataset = ConllDataset(
            self.tokenizer, 
            self.train_sent,
            self.train_label,
            self.max_len,
        )
        
        self.val_dataset = ConllDataset(
            self.tokenizer, 
            self.train_sent,
            self.train_label,
            self.max_len,
        )
        
        self.test_dataset = ConllDataset(
            self.tokenizer, 
            self.train_sent,
            self.train_label,
            self.max_len,
        )

    def train_dataloader(self):
        return DataLoader(
            self.train_dataset,
            batch_size=self.batch_size,
            shuffle=False,
            num_workers=8
        )
        
    def val_dataloader(self):
        return DataLoader(
            self.val_dataset,
            batch_size=self.batch_size,
            shuffle=False,
            num_workers=8
        )
    
    def test_dataloader(self):
        return DataLoader(
            self.test_dataset,
            batch_size=self.batch_size,
            shuffle=False,
            num_workers=8
        )

class NerBertModel(pl.LightningModule):
    def __init__(self):
        super(NerBertModel, self).__init__()
        self.l1 = BertForTokenClassification.from_pretrained('bert-base-german-cased', num_labels=20, return_dict=True)
        #self.l2 = torch.nn.Dropout(0.3)
        #self.l3 = torch.nn.Linear(768, 200)
    
    def forward(self, ids, mask, labels):
        loss, logits = self.l1(ids, mask, labels)
        return loss, logits
        #output_1= self.l1(ids, mask, labels = labels)
        #output_2 = self.l2(output_1[0])
        #output_3 = self.l3(output_2)
        #return output_1
    
    def training_step(self, batch, batch_idx):
        ids = batch["ids"]
        mask = batch["mask"]
        labels = batch["tags"]
        
        loss, outputs = self(
            ids=ids,
            mask=mask,
            labels=labels
            )
        self.log("train_loss", loss, prog_bar=True, logger=True)
        return loss, outputs
    
    def validation_step(self, batch, batch_idx):
        ids = batch["ids"]
        mask = batch["mask"]
        labels = batch["tags"]
        
        loss, outputs = self(
            ids=ids,
            mask=mask,
            labels=labels
            )
        self.log("val_loss", loss, prog_bar=True, logger=True)
        return loss, outputs
    
    def test_step(self, batch, batch_idx):
        ids = batch["ids"]
        mask = batch["mask"]
        labels = batch["tags"]
        
        loss, outputs = self(
            ids=ids,
            mask=mask,
            labels=labels
            )
        self.log("test_loss", loss, prog_bar=True, logger=True)
        return loss, outputs
    
    def configure_optimizers(self):
        return AdamW(self.parameters(), lr=0.0001)
    

MAX_LEN = 128
BATCH_SIZE = 8
tokenizer = BertTokenizer.from_pretrained('bert-base-german-cased', do_lower_case=False)

data_module = NERConllDataset(tokenizer, train_sent, train_label, val_sent, val_label, test_sent, test_label, max_len=MAX_LEN, batch_size=BATCH_SIZE)
data_module.setup()
model = NerBertModel()
      
checkpoint_callback = ModelCheckpoint(
    dirpath="checkpoints",
    filename="best-checkpoint",
    save_top_k=1,
    verbose=True,
    monitor="val_loss",
    mode="min"
)

logger = TensorBoardLogger(save_dir="Lightning_logs", name="news_summary")

early_stopping_callback = EarlyStopping(
    monitor="val_loss", 
    min_delta=0.01,
    patience=2,
    verbose=True
)

trainer = pl.Trainer(logger=logger,
                     callbacks=[checkpoint_callback, early_stopping_callback],
                     max_epochs=2,
                     gpus=0,
                     progress_bar_refresh_rate=1
                     )

trainer.fit(model, data_module)

Error:
Length of Labels : 20
Length of unique words : 2440
FULL DATASET SENT: 710
FULL DATASET LABELS: 710
Train sent size : 572
Train label size : 572
Test sent size : 36
Test label size : 36
Val sent size : 102
Val label size : 102
Some weights of the model checkpoint at bert-base-german-cased were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-german-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
EarlyStopping mode set to min for monitoring val_loss.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores

  | Name | Type                       | Params
----------------------------------------------------
0 | l1   | BertForTokenClassification | 108 M 
----------------------------------------------------
108 M     Trainable params
0         Non-trainable params
108 M     Total params
434.025   Total estimated model params size (MB)
Validation sanity check:   0%|                                                                                                                                | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/home/felix/Desktop/machine-learning/NER_torch/torch_model.py", line 276, in <module>
    trainer.fit(model, data_module)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 499, in fit
    self.dispatch()
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 546, in dispatch
    self.accelerator.start_training(self)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 73, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 114, in start_training
    self._results = trainer.run_train()
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 607, in run_train
    self.run_sanity_check(self.lightning_module)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 864, in run_sanity_check
    _, eval_results = self.run_evaluation(max_batches=self.num_sanity_val_batches)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 713, in run_evaluation
    for batch_idx, batch in enumerate(dataloader):
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1085, in _next_data
    return self._process_data(data)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1111, in _process_data
    data.reraise()
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/_utils.py", line 428, in reraise
    raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in default_collate
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in <dictcomp>
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate
    return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [1, 11] at entry 0 and [1, 41] at entry 1

Thanks 
Edit:
after change in Dataset class:
        ids = inputs['input_ids'].flatten()
        mask = inputs['attention_mask'].flatten()

i get this:
RuntimeError: stack expects each tensor to be equal size, but got [16] at entry 0 and [116] at entry 1
so i think the problem is, that the labels list has a different size ?