use datasets streaming mode in trainer ddp mode cause memory leak
Author: gromzhu | Pull request: no | State: open

### Describe the bug

use datasets streaming mode in trainer ddp mode cause memory leak


### Steps to reproduce the bug

import os
import time
import datetime
import sys
import numpy as np
import random

import torch
from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler
torch.manual_seed(42)

from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM
from transformers import AdamW, get_linear_schedule_with_warmup

hf_model_path ='./Wenzhong-GPT2-110M'
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
tokenizer.add_special_tokens({'pad_token': '
'})

from datasets import load_dataset

gpus=8
max_len = 576
batch_size_node = 17
save_step = 5000
gradient_accumulation = 2
dataloader_num = 4
max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus
#max_step = -1
print("total_step:%d"%(max_step))
import datasets

datasets.version

dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True)

print('load over')
shuffled_dataset = dataset.shuffle(seed=42)
print('shuffle over')

def dataset_tokener(example,max_lenth=max_len):
example['text'] = list(map(lambda x : x.strip()+'
',example['text'] ))
return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest")


new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"])
print('map over')

configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False)

model = AutoModelForCausalLM.from_pretrained(hf_model_path)

model.resize_token_embeddings(len(tokenizer))

seed_val = 42

random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)

from transformers import Trainer,TrainingArguments
import os
print("strat train")
training_args = TrainingArguments(output_dir="./test_trainer",
num_train_epochs=1.0,
report_to="none",
do_train=True,
dataloader_num_workers=dataloader_num,
local_rank=int(os.environ.get('LOCAL_RANK', -1)),
overwrite_output_dir=True,
logging_strategy='steps',
logging_first_step=True,
logging_dir="./logs",
log_on_each_node=False,
per_device_train_batch_size=batch_size_node,
warmup_ratio=0.03,
save_steps=save_step,
save_total_limit=5,
gradient_accumulation_steps=gradient_accumulation,
max_steps=max_step,
disable_tqdm=False,
data_seed=42
)

trainer = Trainer(
model=model,
args=training_args,
train_dataset=new_new_dataset,
eval_dataset=None,
tokenizer=tokenizer,
data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False),
#compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
#preprocess_logits_for_metrics=preprocess_logits_for_metrics
#if training_args.do_eval and not is_torch_tpu_available()
#else None,
)
trainer.train(resume_from_checkpoint=True)

### Expected behavior

use the train code uppper
my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb
start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py
here is result:

![image](https://user-images.githubusercontent.com/15223544/223026042-1a81489f-897a-43e4-8339-65a202fd5dc7.png)

here is memory usage monitor in 12 hours

![image](https://user-images.githubusercontent.com/15223544/223027076-14e32e8b-9608-4282-9a80-f15d0277026d.png)

every dataloader work allocate over 24gb cpu memory
according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase.
i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak.

### Environment info

pytorch 1.11.0
py 3.8
cuda 11.3
transformers 4.26.1
datasets 2.9.0