`set_model_state_dict` not adding "module" prefix to buffers.

[STomoya]

🐛 Describe the bug

The set_model_state_dict in torch.distributed.checkpoint.state_dict does not add module prefix to buffers when loading state_dicts to models wrapped with DDP.

Code

import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed.checkpoint.state_dict import (
    StateDictOptions,
    get_model_state_dict,
    set_model_state_dict,
)
from torch.nn.parallel import DistributedDataParallel as DDP

dist.init_process_group('nccl')
device = torch.device('cuda', dist.get_rank())

model = nn.Sequential(nn.Linear(10, 10), nn.ReLU(), nn.BatchNorm1d(10), nn.Linear(10, 10))
model.to(device)
model = DDP(model)

options = StateDictOptions(cpu_offload=True, strict=True)
model_state_dict = get_model_state_dict(model, options=options)
set_model_state_dict(model, model_state_dict, options=options)

Error

$ torchrun --nproc-per-node=2 example.py

...

Traceback (most recent call last):
  File "/usr/src/example.py", line 20, in <module>
    set_model_state_dict(model, model_state_dict, options=options)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py", line 797, in set_model_state_dict
    return _load_model_state_dict(model, model_state_dict, info)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py", line 408, in _load_model_state_dict
    _state_dict_fn(model, "load_state_dict")(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2153, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for DistributedDataParallel:
        Missing key(s) in state_dict: "module.2.running_mean", "module.2.running_var". 
        Unexpected key(s) in state_dict: "2.running_mean", "2.running_var", "2.num_batches_tracked". 

Versions

PyTorch version: 2.2.0 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.26.4 Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.4.0-171-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA TITAN RTX GPU 1: NVIDIA TITAN RTX

Nvidia driver version: 525.147.05 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6 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): 24 On-line CPU(s) list: 0-23 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 6 Socket(s): 2 Stepping: 4 CPU max MHz: 3700.0000 CPU min MHz: 1200.0000 BogoMIPS: 6800.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 384 KiB (12 instances) L1i cache: 384 KiB (12 instances) L2 cache: 12 MiB (12 instances) L3 cache: 38.5 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-5,12-17 NUMA node1 CPU(s): 6-11,18-23 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries: [pip3] numpy==1.26.3 [pip3] optree==0.10.0 [pip3] torch==2.2.0 [pip3] torchaudio==2.2.0 [pip3] torchelastic==0.2.2 [pip3] torchvision==0.17.0 [pip3] triton==2.2.0 [conda] blas 1.0 mkl
[conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch [conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-service 2.4.0 py310h5eee18b_1
[conda] mkl_fft 1.3.8 py310h5eee18b_0
[conda] mkl_random 1.2.4 py310hdb19cb5_0
[conda] numpy 1.26.3 py310h5f9d8c6_0
[conda] numpy-base 1.26.3 py310hb5e798b_0
[conda] optree 0.10.0 pypi_0 pypi [conda] pytorch 2.2.0 py3.10_cuda11.8_cudnn8.7.0_0 pytorch [conda] pytorch-cuda 11.8 h7e8668a_5 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] torchaudio 2.2.0 py310_cu118 pytorch [conda] torchelastic 0.2.2 pypi_0 pypi [conda] torchtriton 2.2.0 py310 pytorch [conda] torchvision 0.17.0 py310_cu118 pytorch

cc @LucasLLC

[fegin]

https://github.com/pytorch/pytorch/pull/119573 should fixes the issue. Thanks for reporting the bug.

[fegin]

The fix is landed. Please let me know if you still encounter the same error. Close the issue for now.