单机多卡训练时存在内存泄漏现象

进行单机双卡训练时，不定时间异常中止，进行排查发现训练过程中内存泄漏。

使用代码：

import paddle
import paddle.nn as nn
import paddle.optimizer as opt
import paddle.distributed as dist
import paddlex as pdx
from paddlex import transforms as T

def train_1(model, train_dataset, eval_dataset):
    # print(paddle.distributed.is_initialized(),'------------')
    # # 1. 初始化并行训练环境
    # 注释paddlex/__init__.py中的init_parallel_env()
    dist.init_parallel_env()

    # model = pdx.det.PPYOLOv2(num_classes=num_classes, backbone='ResNet50_vd_dcn')

    model.train(
        num_epochs=100,
        train_dataset=train_dataset,
        train_batch_size=4,
        eval_dataset=eval_dataset,
        pretrain_weights='IMAGENET',
        learning_rate=0.005 / 12,
        warmup_steps=1000,
        warmup_start_lr=0.0,
        lr_decay_epochs=[105, 135, 150],
        save_interval_epochs=5,
        save_dir='output/ppyolov2_r50vd_dcn')

if __name__ == '__main__':
    dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
    pdx.utils.download_and_decompress(dataset, path='./')
    print(dist.get_rank(),'----------------get_rank')
    train_transforms = T.Compose([
        T.MixupImage(mixup_epoch=-1), T.RandomDistort(),
        T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
        T.RandomHorizontalFlip(), T.BatchRandomResize(
            target_sizes=[
                320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704,
                736, 768
            ],
            interp='RANDOM'), T.Normalize(
                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    eval_transforms = T.Compose([
        T.Resize(
            target_size=640, interp='CUBIC'), T.Normalize(
                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    # 定义训练和验证所用的数据集
    # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/apis/datasets.md
    train_dataset = pdx.datasets.VOCDetection(
        data_dir='insect_det',
        file_list='insect_det/train_list.txt',
        label_list='insect_det/labels.txt',
        transforms=train_transforms,
        shuffle=True)

    eval_dataset = pdx.datasets.VOCDetection(
        data_dir='insect_det',
        file_list='insect_det/val_list.txt',
        label_list='insect_det/labels.txt',
        transforms=eval_transforms,
        shuffle=False)

    num_classes = len(train_dataset.labels)
    import os
    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
    model = pdx.det.PPYOLOv2(num_classes=num_classes, backbone='ResNet50_vd_dcn')
    dist.spawn(train_1, gpus='0,1', args=(model, train_dataset, eval_dataset))

同时注释了paddlex/init.py中的init_parallel_env()

环境

PaddlePaddle ：v2.5.1
PaddleX：2.1.0
操作系统：ubuntu20.04
请问您使用的Python版本是？ python3.10
请问您使用的CUDA/cuDNN的版本号是？ cuda：11.2.2

PaddlePaddle / PaddleX

单机多卡训练时存在内存泄漏现象 #1739

环境