It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)

What I have in mind (but lmk if you have other ideas or comments):

For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker.

For iterable datasets, this requires to save the state of the dataset iterator, which includes:
- the current shard idx and row position in the current shard
- the epoch number
- the rng state
- the shuffle buffer

Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point.

cc @stas00 @sgugger 
Save and resume the state of a DataLoader
Something that'd be nice to have is "manual update of state". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy. Your outline spec is very sound and clear, @lhoestq - thank you!

@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one). Hi there! I think this is a critical issue and have an urgent need for it, in my attempt to train on a super large-scale dataset using `datasets`. It is impossible to resume a time-consuming (like one month) experiment by iterating all seen data again, which could possibly cost several days.

@stas00 @thomasw21 @lhoestq Any updates on this problem after 1 year passed? any update？ No update so far, I wonder if someone implemented a resumable pytorch Sampler somwhere.

Then regarding resuming a streaming dataset, we'd first like to have an efficient way to skip shards automatically but this is not implemented yet I opened a draft here for IterableDataset: https://github.com/huggingface/datasets/pull/6658



```python
"""Requires https://github.com/huggingface/datasets/pull/6658 (WIP)"""
from datasets import load_dataset
from torch.utils.data import DataLoader

ds = load_dataset(..., streaming=True)
# ds = ds.map(tokenize)
# ds = ds.shuffle(seed=42, buffer_size=1000)

# Init the dataset state_dict, or load it from a checkpoint
dataset_state_dict = ds.state_dict()

# Resumable training loop
ds.load_state_dict(dataset_state_dict)
dataloader = DataLoader(ds, batch_size=batch_size)
for step, batch in enumerate(dataloader):
    ...
    if step % save_steps == 0:
        dataset_state_dict = ds.state_dict()
``` Hi @lhoestq - can you provide more information and how to implement on saving and restoring vanilla DataLoader states with map-style datasets?

 For now the easiest is probably to use the vanilla DataLoader only for batching and multiprocessing, and implement the resuming logic using a `Dataset` (it has `.select()` to skip examples) and a `dataset_state_dict`:


```python
from datasets import load_dataset
from torch.utils.data import DataLoader

ds = load_dataset(...)
# ds = ds.map(tokenize)
# ds = ds.shuffle(seed=42)

# Init the dataset state_dict, or load it from a checkpoint
dataset_state_dict = {"step": 0}  

# Resumable training loop
start_step = dataset_state_dict["step"]
dataloader = DataLoader(ds.select(range(start_step * batch_size, len(ds))), batch_size=batch_size)
for step, batch in enumerate(dataloader, start=start_step):
    ...
    if step % save_steps == 0:
        dataset_state_dict = {"step": step}
``` Hello, I found a similar implementation online that seems to solve your problem.  https://github.com/facebookresearch/vissl/blob/main/vissl/data/data_helper.py#L93
it looks like we can set_start_iter in StatefulDistributedSampler to implement the stateful  resume requirement we want.

