huggingface type_error ai_generated true

ValueError: batch_size must be None for IterableDataset, but got 32

ID: huggingface/dataset-iterable-batch-size

Also available as: JSON · Markdown · 中文
90%Fix Rate
82%Confidence
1Evidence
2023-06-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
datasets>=2.10.0 active — — —
transformers>=4.28.0 active — — —
torch>=2.0.0 active — — —

Root Cause

When using an IterableDataset with the Trainer or DataLoader, a fixed batch_size is provided, but IterableDataset requires dynamic batching via `batch_size=None`.

generic

中文

当使用 IterableDataset 与 Trainer 或 DataLoader 时，提供了固定的 batch_size，但 IterableDataset 需要通过 `batch_size=None` 进行动态批处理。

Official Documentation

https://huggingface.co/docs/datasets/en/iterable_dataset#batch-size

Workarounds

  1. 95% success Set `batch_size=None` in the DataLoader or Trainer: `from transformers import Trainer; trainer = Trainer(model=model, args=training_args, train_dataset=iterable_dataset, data_collator=collator, batch_size=None)` 
    Set `batch_size=None` in the DataLoader or Trainer: `from transformers import Trainer; trainer = Trainer(model=model, args=training_args, train_dataset=iterable_dataset, data_collator=collator, batch_size=None)`
  2. 90% success Use `DataLoader` with `batch_size=None` and `batch_sampler` if needed: `from torch.utils.data import DataLoader; dl = DataLoader(iterable_dataset, batch_size=None, collate_fn=collator)` 
    Use `DataLoader` with `batch_size=None` and `batch_sampler` if needed: `from torch.utils.data import DataLoader; dl = DataLoader(iterable_dataset, batch_size=None, collate_fn=collator)`
  3. 85% success If using Trainer, override `get_train_dataloader` to handle batching: `class CustomTrainer(Trainer): def get_train_dataloader(self): return DataLoader(self.train_dataset, batch_size=None, collate_fn=self.data_collator)` 
    If using Trainer, override `get_train_dataloader` to handle batching: `class CustomTrainer(Trainer): def get_train_dataloader(self): return DataLoader(self.train_dataset, batch_size=None, collate_fn=self.data_collator)`

中文步骤

  1. Set `batch_size=None` in the DataLoader or Trainer: `from transformers import Trainer; trainer = Trainer(model=model, args=training_args, train_dataset=iterable_dataset, data_collator=collator, batch_size=None)`
  2. Use `DataLoader` with `batch_size=None` and `batch_sampler` if needed: `from torch.utils.data import DataLoader; dl = DataLoader(iterable_dataset, batch_size=None, collate_fn=collator)`
  3. If using Trainer, override `get_train_dataloader` to handle batching: `class CustomTrainer(Trainer): def get_train_dataloader(self): return DataLoader(self.train_dataset, batch_size=None, collate_fn=self.data_collator)`

Dead Ends

Common approaches that don't work:

  1. Setting batch_size=1 for IterableDataset 90% fail

    IterableDataset requires batch_size=None; any integer value raises the same error.

  2. Converting IterableDataset to a regular Dataset with `.to_iterable_dataset()` 80% fail

    This creates another IterableDataset; the correct fix is to use `with_format('torch')` and handle batching manually.

  3. Downgrading datasets to version 2.8.0 70% fail

    Older versions had the same restriction; the error is by design.