MPRS with DL2 memory use increases over time vs. using older DL

[rejuvyesh]

🐛 Describe the bug

MPRS version:

    train_dp = IterableWrapper(train_ds).batch(batch_size=batch_size).collate(collate_fn=encode_processor)

    rs = MultiProcessingReadingService(num_workers=num_workers)
    train_dl = DataLoader2(train_dp, reading_service=rs)
    for batch_idx, batch in enumerate(train_dl):
        print(batch_idx)
        if batch_idx > 500:
            break

Running with mprof run --multiprocess --include-children <script.py> results in increasing memory usage over time:

vs. older DataLoader:

    train_dl = DataLoader(train_ds, batch_size=batch_size, num_workers=num_workers, collate_fn=encode_processor)
    for batch_idx, batch in enumerate(train_dl):
        print(batch_idx)
        if batch_idx > 500:
            break

Running with mprof run --multiprocess --include-children <script.py> results in reasonable memory usage over time:

Any possible recommendations for debugging this further. For context train_ds is a MapDataPipe generating some strings for each index.

Versions

PyTorch version: 2.0.1+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: version 3.26.3 Libc version: glibc-2.35

Python version: 3.10.11 (main, Apr 20 2023, 19:02:41) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB Nvidia driver version: 530.30.02 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 12 On-line CPU(s) list: 0-11 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU @ 2.20GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 6 Socket(s): 1 Stepping: 7 BogoMIPS: 4400.42 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 192 KiB (6 instances) L1i cache: 192 KiB (6 instances) L2 cache: 6 MiB (6 instances) L3 cache: 38.5 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-11 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown

Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.3 [pip3] pytorch-lightning==2.0.2 [pip3] torch==2.0.1 [pip3] torchdata==0.6.1 [pip3] torchmetrics==0.11.4 [pip3] torchvision==0.15.2 [pip3] triton==2.0.0 [conda] numpy 1.24.3 pypi_0 pypi [conda] pytorch-lightning 2.0.2 pypi_0 pypi [conda] torch 2.0.1 pypi_0 pypi [conda] torchdata 0.6.1 pypi_0 pypi [conda] torchmetrics 0.11.4 pypi_0 pypi [conda] torchvision 0.15.2 pypi_0 pypi [conda] triton 2.0.0 pypi_0 pypi