RuntimeError: DataLoader worker (pid 11011) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit
Labels: module: dataloader, triaged, module: data

### 🐛 Describe the bug

torch version: 1.13
job: kubernetes pytorch job, one master and three workers
launch: torchrun
resource: 4 * [GPU: 1* A100-80GiB | CPU: 12Cores | Memory: 84GB]
log: [r2307290098-33183-worker-1.log](https://github.com/pytorch/pytorch/files/12558740/r2307290098-33183-worker-1.log)
errlog:
`Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1161, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/opt/conda/lib/python3.8/queue.py", line 179, in get
    self.not_empty.wait(remaining)
  File "/opt/conda/lib/python3.8/threading.py", line 306, in wait
    gotit = waiter.acquire(True, timeout)
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 11011) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.`

memory used monitor:
<img width="839" alt="image" src="https://github.com/pytorch/pytorch/assets/96805673/9c3afba7-31f1-4548-9379-04eb6b925127">

node shm used monitor, the pod's shm is set by node host's total shared memory by mount the /dev/shm:
<img width="1699" alt="image" src="https://github.com/pytorch/pytorch/assets/96805673/248e9418-d479-45c3-95ad-97ce2c33d144">

this worker failed and i didn't found out of shm or memory, i do not known what's the problem, who can help me?

### Versions

Collecting environment information...
PyTorch version: 1.13.0a0+d0d6b1f
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.22.2
Libc version: glibc-2.31

Python version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:10)  [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-18-shopee-generic-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB
Nvidia driver version: 510.73.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.6.0
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 57 bits virtual
CPU(s):                          128
On-line CPU(s) list:             0-127
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           106
Model name:                      Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
Stepping:                        6
CPU MHz:                         2600.000
CPU max MHz:                     3400.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5200.00
Virtualization:                  VT-x
L1d cache:                       3 MiB
L1i cache:                       2 MiB
L2 cache:                        80 MiB
L3 cache:                        96 MiB
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
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; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bwavx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] flake8==3.7.9
[pip3] functorch==0.3.0a0
[pip3] numpy==1.22.2
[pip3] pytorch-quantization==2.1.2
[pip3] torch==1.13.0a0+d0d6b1f
[pip3] torch-tensorrt==1.2.0a0
[pip3] torchtext==0.11.0a0
[pip3] torchvision==0.14.0a0
[conda] functorch                 0.3.0a0                  pypi_0    pypi
[conda] mkl                       2020.4             h726a3e6_304    conda-forge
[conda] mkl-include               2020.4             h726a3e6_304    conda-forge
[conda] numpy                     1.22.2           py38h6ae9a64_0    conda-forge
[conda] pytorch-quantization      2.1.2                    pypi_0    pypi
[conda] torch                     1.13.0a0+d0d6b1f          pypi_0    pypi
[conda] torch-tensorrt            1.2.0a0                  pypi_0    pypi
[conda] torchtext                 0.11.0a0                 pypi_0    pypi
[conda] torchvision               0.14.0a0                 pypi_0    pypi

cc @SsnL @VitalyFedyunin @ejguan @dzhulgakov