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[misc] Support split eval_dataset when explict set "predict_with_generate" (#9604)

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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ZIYI ZENG
2025-12-20 01:46:00 +08:00
committed by GitHub
parent ddd7dcc722
commit b0d49e137f
4 changed files with 37 additions and 30 deletions
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31
src/llamafactory/data/data_utils.py
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@@ -81,41 +81,48 @@ def split_dataset(
eval_dataset: Optional[Union["Dataset", "IterableDataset", dict[str, "Dataset"]]],
data_args: "DataArguments",
seed: int,
) -> "DatasetDict":
r"""Split the dataset and returns a dataset dict containing train set and validation set.
) -> tuple[dict, dict]:
r"""Split the dataset and returns two dicts containing train set and validation set.
Support both map dataset and iterable dataset.
Returns:
train_dict: Dictionary containing training data with key "train"
eval_dict: Dictionary containing evaluation data with keys "validation" or "validation_{name}"
"""
if eval_dataset is not None and data_args.val_size > 1e-6:
raise ValueError("Cannot specify `val_size` if `eval_dataset` is not None.")
dataset_dict = {}
# the train and eval better to in dict dtype and separately return for cpode clearly and good handle outside
train_dict, eval_dict = {}, {}
if dataset is not None:
if data_args.streaming:
dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=seed)
if data_args.val_size > 1e-6:
if data_args.streaming:
dataset_dict["validation"] = dataset.take(int(data_args.val_size))
dataset_dict["train"] = dataset.skip(int(data_args.val_size))
eval_dict["validation"] = dataset.take(int(data_args.val_size))
train_dict["train"] = dataset.skip(int(data_args.val_size))
else:
val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
dataset_dict = dataset.train_test_split(test_size=val_size, seed=seed)
dataset = dataset.train_test_split(test_size=val_size, seed=seed)
dataset_dict = {"train": dataset["train"], "validation": dataset["test"]}
split_result = dataset.train_test_split(test_size=val_size, seed=seed)
train_dict["train"] = split_result["train"]
eval_dict["validation"] = split_result["test"]
else:
dataset_dict["train"] = dataset
train_dict["train"] = dataset
if eval_dataset is not None:
if isinstance(eval_dataset, dict):
dataset_dict.update({f"validation_{name}": data for name, data in eval_dataset.items()})
for name, data in eval_dataset.items():
eval_dict[f"validation_{name}"] = data
else:
if data_args.streaming:
eval_dataset = eval_dataset.shuffle(buffer_size=data_args.buffer_size, seed=seed)
dataset_dict["validation"] = eval_dataset
eval_dict["validation"] = eval_dataset
return DatasetDict(dataset_dict)
return train_dict, eval_dict
def get_dataset_module(dataset: Union["Dataset", "DatasetDict"]) -> "DatasetModule":