Divide by zero error when following ch7 tutorial

I am using the ch 7 tutorial () to fine-tune an LM on my dataset. And, here is the error log:

The following columns in the training set don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: text. If text are not expected by `DistilBertForMaskedLM.forward`,  you can safely ignore this message.
***** Running training *****
  Num examples = 4595
  Num Epochs = 3
  Instantaneous batch size per device = 64
  Total train batch size (w. parallel, distributed & accumulation) = 64
  Gradient Accumulation steps = 1
  Total optimization steps = 216
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
Input In [69], in <module>
----> 1 trainer.train()

File /opt/conda/lib/python3.8/site-packages/transformers/trainer.py:1317, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1312     self.model_wrapped = self.model
   1314 inner_training_loop = find_executable_batch_size(
   1315     self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
   1316 )
-> 1317 return inner_training_loop(
   1318     args=args,
   1319     resume_from_checkpoint=resume_from_checkpoint,
   1320     trial=trial,
   1321     ignore_keys_for_eval=ignore_keys_for_eval,
   1322 )

File /opt/conda/lib/python3.8/site-packages/transformers/trainer.py:1528, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1525     self._load_rng_state(resume_from_checkpoint)
   1527 step = -1
-> 1528 for step, inputs in enumerate(epoch_iterator):
   1529 
   1530     # Skip past any already trained steps if resuming training
   1531     if steps_trained_in_current_epoch > 0:
   1532         steps_trained_in_current_epoch -= 1

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py:530, in _BaseDataLoaderIter.__next__(self)
    528 if self._sampler_iter is None:
    529     self._reset()
--> 530 data = self._next_data()
    531 self._num_yielded += 1
    532 if self._dataset_kind == _DatasetKind.Iterable and \
    533         self._IterableDataset_len_called is not None and \
    534         self._num_yielded > self._IterableDataset_len_called:

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py:570, in _SingleProcessDataLoaderIter._next_data(self)
    568 def _next_data(self):
    569     index = self._next_index()  # may raise StopIteration
--> 570     data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    571     if self._pin_memory:
    572         data = _utils.pin_memory.pin_memory(data)

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py:49, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
     47 def fetch(self, possibly_batched_index):
     48     if self.auto_collation:
---> 49         data = [self.dataset[idx] for idx in possibly_batched_index]
     50     else:
     51         data = self.dataset[possibly_batched_index]

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py:49, in <listcomp>(.0)
     47 def fetch(self, possibly_batched_index):
     48     if self.auto_collation:
---> 49         data = [self.dataset[idx] for idx in possibly_batched_index]
     50     else:
     51         data = self.dataset[possibly_batched_index]

File /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:2124, in Dataset.__getitem__(self, key)
   2122 def __getitem__(self, key):  # noqa: F811
   2123     """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2124     return self._getitem(
   2125         key,
   2126     )

File /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:2108, in Dataset._getitem(self, key, decoded, **kwargs)
   2106 format_kwargs = format_kwargs if format_kwargs is not None else {}
   2107 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
-> 2108 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
   2109 formatted_output = format_table(
   2110     pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
   2111 )
   2112 return formatted_output

File /opt/conda/lib/python3.8/site-packages/datasets/formatting/formatting.py:491, in query_table(table, key, indices)
    489     pa_subtable = _query_table(table, key)
    490 else:
--> 491     pa_subtable = _query_table_with_indices_mapping(table, key, indices=indices)
    492 return pa_subtable

File /opt/conda/lib/python3.8/site-packages/datasets/formatting/formatting.py:57, in _query_table_with_indices_mapping(table, key, indices)
     55 if isinstance(key, int):
     56     key = indices.fast_slice(key % indices.num_rows, 1).column(0)[0].as_py()
---> 57     return _query_table(table, key)
     58 if isinstance(key, slice):
     59     key = range(*key.indices(indices.num_rows))

File /opt/conda/lib/python3.8/site-packages/datasets/formatting/formatting.py:81, in _query_table(table, key)
     77 """
     78 Query a pyarrow Table to extract the subtable that correspond to the given key.
     79 """
     80 if isinstance(key, int):
---> 81     return table.fast_slice(key % table.num_rows, 1)
     82 if isinstance(key, slice):
     83     key = range(*key.indices(table.num_rows))

ZeroDivisionError: integer division or modulo by zero

The only difference between the tutorial and what I have done is to load the dataset from a csv, as opposed to hub. Can someone please tell me what could I be doing wrong?

1 Like

Exactly same problem here too.
Training Wav2Vec2 by following Blog link. Same change made, loading dataset from csv file.

tried

df=pd.read_csv("kspsp.csv")
dataset=Dataset.from_pandas(df)\

and

dataset = load_dataset("csv",data_files="kspsp.csv")

The super class of ZeroDivisionError is ArithmeticError. This exception raised when the second argument of a division or modulo operation is zero. In Mathematics, when a number is divided by a zero, the result is an infinite number. It is impossible to write an Infinite number physically. Python interpreter throws “ZeroDivisionError: division by zero” error if the result is infinite number. While implementing any program logic and there is division operation make sure always handle ArithmeticError or ZeroDivisionError so that program will not terminate.

try:
    z = x / y
except ZeroDivisionError:
    z = 0

Or check before you do the division:

if y == 0:
    z = 0
else:
    z = x / y

The latter can be reduced to:

z = 0 if y == 0 else (x / y)

ronaldgevern’s suggestion, while a reasonable explanation of what a ZeroDivisionError is, isn’t applicable in this instance because the exception is coming from PyArrow, not from any code likely written by the people with this issue.

It looks like the dataset that you pass to your trainer is empty. For me, this error was coming when I accidentally passed the untokenized dataset to the trainer instead of the tokenized one. There could be other reasons, so if that wasn’t it, investigate other possible causes of the data being missing.