您可能使用的是旧版本的PyTorch，如Pytorch1.10，它不具有此功能。
要在旧版本中复制此功能，只需复制新版本的源代码即可：

import math
from torch import default_generator, randperm
from torch._utils import _accumulate
from torch.utils.data.dataset import Subset
def random_split(dataset, lengths,
                 generator=default_generator):
    r"""
    Randomly split a dataset into non-overlapping new datasets of given lengths.
    If a list of fractions that sum up to 1 is given,
    the lengths will be computed automatically as
    floor(frac * len(dataset)) for each fraction provided.
    After computing the lengths, if there are any remainders, 1 count will be
    distributed in round-robin fashion to the lengths
    until there are no remainders left.
    Optionally fix the generator for reproducible results, e.g.:
    >>> random_split(range(10), [3, 7], generator=torch.Generator().manual_seed(42))
    >>> random_split(range(30), [0.3, 0.3, 0.4], generator=torch.Generator(
    ...   ).manual_seed(42))
    Args:
        dataset (Dataset): Dataset to be split
        lengths (sequence): lengths or fractions of splits to be produced
        generator (Generator): Generator used for the random permutation.
    """
    if math.isclose(sum(lengths), 1) and sum(lengths) <= 1:
        subset_lengths: List[int] = []
        for i, frac in enumerate(lengths):
            if frac < 0 or frac > 1:
                raise ValueError(f"Fraction at index {i} is not between 0 and 1")
            n_items_in_split = int(
                math.floor(len(dataset) * frac)  # type: ignore[arg-type]
            )
            subset_lengths.append(n_items_in_split)
        remainder = len(dataset) - sum(subset_lengths)  # type: ignore[arg-type]
        # add 1 to all the lengths in round-robin fashion until the remainder is 0
        for i in range(remainder):
            idx_to_add_at = i % len(subset_lengths)
            subset_lengths[idx_to_add_at] += 1
        lengths = subset_lengths
        for i, length in enumerate(lengths):
            if length == 0:
                warnings.warn(f"Length of split at index {i} is 0. "
                              f"This might result in an empty dataset.")
    # Cannot verify that dataset is Sized
    if sum(lengths) != len(dataset):    # type: ignore[arg-type]
        raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
    indices = randperm(sum(lengths), generator=generator).tolist()  # type: ignore[call-overload]
    return [Subset(dataset, indices[offset - length : offset]) for offset, length in zip(_accumulate(lengths), lengths)]