Training runs 50% slower when using 2 GPUs comparing to 1
Labels: oncall: distributed

### 🐛 Describe the bug

Hi,

I'm trying to train an InternImage model and I'm running into a weird issue using either Torchrun or torch.distributed.launch

I'm using the PyTorch Nvidia docker 22.04 with CUDA 11.6.2 and PyTorch 1.12.
When I'm training the model using "python train.py ..." the model runs well, however, when I try to take advantage of my 2 GPUs, I"m running into problems. 

I can see that both my GPUs are running since their temperature is going up and their memory usage is going up, but while training the model with 1 GPU takes me 2.5 days, using the 2 GPUs, the model ETA is > 3.5 days.

I don't care that much about the time, but the main problem is that the training crashes after 1000 iterations, when it's saving the first checkpoint. None of these issues is observed when I use 1 GPU.

I'll appreciate your help troubleshooting this problem.

I'm using WSL2, with Nvidia docker 22.04, and I have two RTX3090 GPUs


This is the error I'm getting:

2023-04-06 21:18:27,943 - mmseg - INFO - Saving checkpoint at 1000 iterations
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 19682 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -9) local_rank: 0 (pid: 19681) of binary: /opt/conda/bin/python
Traceback (most recent call last):
  File "/opt/conda/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==1.12.0a0+bd13bc6', 'console_scripts', 'torchrun')())
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 345, in wrapper
    return f(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 761, in main
    run(args)
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run
    elastic_launch(
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
======================================================
train.py FAILED
------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2023-04-06_21:18:35
  host      : f56b97aeeef1
  rank      : 0 (local_rank: 0)
  exitcode  : -9 (pid: 19681)
  error_file: <N/A>
  traceback : Signal 9 (SIGKILL) received by PID 19681
======================================================
root@f56b97aeeef1:/workspace/InternImage/segmentation# /opt/conda/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 38 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
/opt/conda/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 38 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '


### Versions

Collecting environment information...
PyTorch version: 1.12.0a0+bd13bc6
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.22.3
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-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to:
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 531.18
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.0
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, 48 bits virtual
CPU(s):                          20
On-line CPU(s) list:             0-19
Thread(s) per core:              2
Core(s) per socket:              10
Socket(s):                       1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Core(TM) i9-10900X CPU @ 3.70GHz
Stepping:                        7
CPU MHz:                         3695.996
BogoMIPS:                        7391.99
Hypervisor vendor:               Microsoft
Virtualization type:             full
L1d cache:                       320 KiB
L1i cache:                       320 KiB
L2 cache:                        10 MiB
L3 cache:                        19.3 MiB
Vulnerability Itlb multihit:     KVM: Mitigation: VMX unsupported
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
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 and seccomp
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; TSX disabled
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 arch_perfmon rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_vnni flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.22.3
[pip3] pytorch-quantization==2.1.2
[pip3] torch==1.12.0a0+bd13bc6
[pip3] torch-tensorrt==1.1.0a0
[pip3] torchtext==0.13.0a0
[pip3] torchvision==0.13.0a0
[conda] magma-cuda110             2.5.2                         5    local
[conda] mkl                       2019.5                      281    conda-forge
[conda] mkl-include               2019.5                      281    conda-forge
[conda] numpy                     1.22.3           py38h1d589f8_2    conda-forge
[conda] pytorch-quantization      2.1.2                    pypi_0    pypi
[conda] torch                     1.12.0a0+bd13bc6          pypi_0    pypi
[conda] torch-tensorrt            1.1.0a0                  pypi_0    pypi
[conda] torchtext                 0.13.0a0                 pypi_0    pypi
[conda] torchvision               0.13.0a0                 pypi_0    pypi

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu