Repository: pytorch
Issue: nn.Parallel
Labels: oncall: distributed,triaged

### 🐛 Describe the bug

<img width="298" alt="1" src="https://github.com/user-attachments/assets/da1a07e5-bb5b-4edd-a15c-294372a375a7">
<img width="288" alt="2" src="https://github.com/user-attachments/assets/943e2071-e593-4efd-8eae-4295177e62ff">
When I use `nn.DataParallel(create_model().to(device), device_ids=[0, 1])` the output of the model comes up with the above two scenarios, resulting in a mismatch of dimensions in the final calculation of the loss.

### Versions

Collecting environment information...
PyTorch version: 2.1.2+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.25.0
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-6.5.0-15-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 GeForce RTX 2080 Ti
GPU 1: NVIDIA GeForce RTX 2080 Ti

Nvidia driver version: 535.154.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构：                              x86_64
CPU 运行模式：                      32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
字节序：                            Little Endian
CPU:                                20
在线 CPU 列表：                     0-19
厂商 ID：                           GenuineIntel
型号名称：                          Intel(R) Core(TM) i9-9820X CPU @ 3.30GHz
CPU 系列：                          6
型号：                              85
每个核的线程数：                    2
每个座的核数：                      10
座：                                1
步进：                              4
CPU 最大 MHz：                      4200.0000
CPU 最小 MHz：                      1200.0000
BogoMIPS：                          6599.98
标记：                              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 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 ssbd mba ibrs ibpb stibp tpr_shadow 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 vnmi md_clear flush_l1d arch_capabilities
虚拟化：                            VT-x
L1d 缓存：                          320 KiB (10 instances)
L1i 缓存：                          320 KiB (10 instances)
L2 缓存：                           10 MiB (10 instances)
L3 缓存：                           16.5 MiB (1 instance)
NUMA 节点：                         1
NUMA 节点0 CPU：                    0-19
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
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 rstack overflow: Not affected
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; 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] efficientnet-pytorch==0.7.1
[pip3] ema-pytorch==0.4.8
[pip3] numpy==1.23.5
[pip3] torch==2.1.2+cu118
[pip3] torch-dct==0.1.6
[pip3] torchaudio==2.1.2+cu118
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.16.2+cu118
[pip3] triton==2.1.0
[conda] efficientnet-pytorch      0.7.1                    pypi_0    pypi
[conda] ema-pytorch               0.4.8                    pypi_0    pypi
[conda] numpy                     1.23.5                   pypi_0    pypi
[conda] torch                     2.1.2+cu118              pypi_0    pypi
[conda] torch-dct                 0.1.6                    pypi_0    pypi
[conda] torchaudio                2.1.2+cu118              pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchvision               0.16.2+cu118             pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi


cc @XilunWu @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o