{
  "id": "huggingface/dataset-iterable-batch-size",
  "signature": "ValueError: batch_size must be None for IterableDataset, but got 32",
  "signature_zh": "ValueError: IterableDataset 的 batch_size 必须为 None，但得到了 32",
  "regex": "ValueError: batch_size must be None for IterableDataset, but got \\d+",
  "domain": "huggingface",
  "category": "type_error",
  "subcategory": null,
  "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`.",
  "root_cause_type": "generic",
  "root_cause_zh": "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。",
  "versions": [
    {
      "version": "datasets>=2.10.0",
      "introduced": null,
      "deprecated": null,
      "removed": null,
      "behavior_change": null,
      "status": "active"
    },
    {
      "version": "transformers>=4.28.0",
      "introduced": null,
      "deprecated": null,
      "removed": null,
      "behavior_change": null,
      "status": "active"
    },
    {
      "version": "torch>=2.0.0",
      "introduced": null,
      "deprecated": null,
      "removed": null,
      "behavior_change": null,
      "status": "active"
    }
  ],
  "os_specific": {},
  "dead_ends": [
    {
      "action": "Setting batch_size=1 for IterableDataset",
      "why_fails": "IterableDataset requires batch_size=None; any integer value raises the same error.",
      "fail_rate": 0.9,
      "condition": "",
      "sources": []
    },
    {
      "action": "Converting IterableDataset to a regular Dataset with `.to_iterable_dataset()`",
      "why_fails": "This creates another IterableDataset; the correct fix is to use `with_format('torch')` and handle batching manually.",
      "fail_rate": 0.8,
      "condition": "",
      "sources": []
    },
    {
      "action": "Downgrading datasets to version 2.8.0",
      "why_fails": "Older versions had the same restriction; the error is by design.",
      "fail_rate": 0.7,
      "condition": "",
      "sources": []
    }
  ],
  "workarounds": [
    {
      "action": "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)`",
      "success_rate": 0.95,
      "how": "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)`",
      "condition": "",
      "sources": []
    },
    {
      "action": "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)`",
      "success_rate": 0.9,
      "how": "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)`",
      "condition": "",
      "sources": []
    },
    {
      "action": "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)`",
      "success_rate": 0.85,
      "how": "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)`",
      "condition": "",
      "sources": []
    }
  ],
  "workarounds_zh": [
    "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)`",
    "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)`",
    "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)`"
  ],
  "transition_graph": {
    "leads_to": [],
    "preceded_by": [],
    "frequently_confused_with": []
  },
  "official_doc_url": "https://huggingface.co/docs/datasets/en/iterable_dataset#batch-size",
  "official_doc_section": null,
  "error_code": null,
  "verification_tier": "ai_generated",
  "confidence": 0.82,
  "fix_success_rate": 0.9,
  "resolvable": "true",
  "first_seen": "2023-06-20",
  "last_confirmed": "2024-06-01",
  "last_updated": "2024-06-01",
  "evidence_count": 1,
  "tags": [],
  "locale": "en",
  "aliases": []
}