Tokenization slows towards end of dataset

### Describe the bug

Mapped tokenization slows down substantially towards end of dataset.

train set started off very slow, caught up to 20k then tapered off til the end.

what's particularly strange is that the tokenization crashed a few times before due to errors with invalid tokens somewhere or corrupted downloads, and the speed ups/downs consistently happened the same times

```bash
Running tokenizer on dataset (num_proc=48):   0%|          | 847000/881416735 [12:18<252:45:45, 967.72 examples/s]
Running tokenizer on dataset (num_proc=48):   0%|          | 848000/881416735 [12:19<224:16:10, 1090.66 examples/s]

Running tokenizer on dataset (num_proc=48):  10%|▉         | 84964000/881416735 [3:48:00<11:21:34, 19476.01 examples/s]
Running tokenizer on dataset (num_proc=48):  10%|▉         | 84967000/881416735 [3:48:00<12:04:01, 18333.79 examples/s]

Running tokenizer on dataset (num_proc=48):  61%|██████    | 538631977/881416735 [13:46:40<27:50:04, 3420.84 examples/s]
Running tokenizer on dataset (num_proc=48):  61%|██████    | 538632977/881416735 [13:46:40<23:48:20, 3999.77 examples/s]

Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881365886/881416735 [38:30:19<04:34, 185.10 examples/s]
Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881366886/881416735 [38:30:25<04:36, 180.57 examples/s]
```

and validation set as well

```bash
Running tokenizer on dataset (num_proc=48):  90%|████████▉ | 41544000/46390354 [28:44<02:37, 30798.76 examples/s]
Running tokenizer on dataset (num_proc=48):  90%|████████▉ | 41550000/46390354 [28:44<02:08, 37698.08 examples/s]

Running tokenizer on dataset (num_proc=48):  96%|█████████▋| 44747422/46390354 [2:15:48<12:22:44, 36.87 examples/s]
Running tokenizer on dataset (num_proc=48):  96%|█████████▋| 44747422/46390354 [2:16:00<12:22:44, 36.87 examples/s]

```



### Steps to reproduce the bug

    using the following kwargs
    
```python
with accelerator.main_process_first():
        lm_datasets = tokenized_datasets.map(
            group_texts,
            batched=True,
            num_proc=48
            load_from_cache_file=True,
            desc=f"Grouping texts in chunks of {block_size}",
        )
```

running through slurm script 
```bash
#SBATCH --partition=gpu-nvidia-a100
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --gpus-per-task=8
#SBATCH --cpus-per-task=96
```

using this dataset https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T





### Expected behavior

Constant speed throughout

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-5.15.0-1049-aws-x86_64-with-glibc2.10
- Python version: 3.8.18
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0

Comments:
Hi ! First note that if the dataset is not heterogeneous / shuffled, there might be places in the data with shorter texts that are faster to tokenize.

Moreover, the way `num_proc` works is by slicing the dataset and passing each slice to a process to run the `map()` function. So at the very end of `map()`, some processes might have finished transforming their slice of data while others are still running, causing the throughput to become lower.

I did see some comments about how num_proc=None could help and outputting numpy arrays can also help in the docs, but this seems quite odd now dropping down to 1it/s

```bash
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46048888/46390354 [12:33:30<4:20:32, 21.84 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46049888/46390354 [12:36:11<8:37:59, 10.95 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46050888/46390354 [12:46:35<24:56:56,  3.78 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46051888/46390354 [12:56:43<35:08:10,  2.68 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46052888/46390354 [13:06:58<42:05:41,  2.23 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46053888/46390354 [13:16:01<44:40:18,  2.09 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46054888/46390354 [13:25:11<46:35:28,  2.00 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46055888/46390354 [13:34:23<47:55:34,  1.94 examples/s]
```



@ethansmith2000 Hi, did you solve this problem? I'm strugging with the same problem now.

So, is there a way to solve this problem now?

You may need to set the preprocessing_num_workers as None like:
```
accelerate launch \
    --num_processes 8 \
    --num_machines 1 \
    --main_process_port 44144 \
    --mixed_precision fp16 \
    --dynamo_backend no \
    pretrain.py \
    --dataset_name \
        /data/***/datasets/STORIES \
    --model_name_or_path bert-base-uncased \
    --per_device_train_batch_size 64 \
    --per_device_eval_batch_size 64 \
    --learning_rate 2e-4 \
    --max_train_steps 500000 \
    --num_warmup_steps 5000 \
    --output_dir ./base \
    --max_seq_length 128 \
    --checkpointing_steps 50000 \
    # --preprocessing_num_workers None \
    --with_tracking \
    --report_to wandb
```
Result in:
```
Running tokenizer on every text in dataset:  95%|██████████████████████████████████████████████████████████████████████████████████████     | 77660856/82086247 [1:09:52<6:53:00, 178.58 examples/s]
```
For me, I just set the `preprocessing_num_workers 16` at the beginning and remove the  `preprocessing_num_workers` apporaching the end. This will result in almost 20000 examples/s at the beginning and 180 examples/s at the end, but still better then 2 examples/s. However, I still don't understand why single process will run faster.