When using TorchRec version 0.8.0 or later, we cannot train for more than one epoch when set `persistent_workers=true` in the dataloader.

[tiankongdeguiji]

In the _next_batch method of TrainPipelineSparseDist, we check whether the new dataloader_iter is the same as the original dataloader_iter. We proceed to fetch the next batch only if they are different. However, when we set persistent_workers=true in the dataloader, the dataloader_iter remains the same instance for each epoch. As a result, we can not get data when the epoch exceeds 1.

https://github.com/pytorch/torchrec/blob/main/torchrec/distributed/train_pipeline/train_pipelines.py#L578

    def _next_batch(self, dataloader_iter: Iterator[In]) -> Optional[In]:
        if dataloader_iter is not self._dataloader_iter:
            self._dataloader_iter = dataloader_iter
            self._dataloader_exhausted = False

        if self._dataloader_exhausted:
            batch = None
        else:
            with record_function("## next_batch ##"):
                batch = next(dataloader_iter, None)
            if batch is None:
                self._dataloader_exhausted = True
        return batch

[tiankongdeguiji]

Hi, @henrylhtsang @IvanKobzarev @joshuadeng @PaulZhang12 can you see this problem?

[gouchangjiang]

Try setting the num_workers = 0, and see if it solves your problem. In my case, it works.