I am running distributed training codes on a single nodes with 8 GPUs. It raises the following errors:
Traceback (most recent call last):
File “/home/hqjo/anaconda3/envs/torch/lib/python3.9/site-packages/torch/multiprocessing/spawn.py”, line 74, in _wrap
fn(i, *args)
File “/home/hqjo/lgame-community/test_dist.py”, line 16, in example
ddp_model = DDP(model, device_ids=[rank])
File “/home/hqjo/anaconda3/envs/torch/lib/python3.9/site-packages/torch/nn/parallel/distributed.py”, line 795, in init
_verify_param_shape_across_processes(self.process_group, parameters)
File “/home/hqjo/anaconda3/envs/torch/lib/python3.9/site-packages/torch/distributed/utils.py”, line 265, in _verify_param_shape_across_processes
return dist._verify_params_across_processes(process_group, tensors, logger)
torch.distributed.DistBackendError: NCCL error in: /opt/conda/conda-bld/pytorch_1695392035629/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1331, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.5
ncclUnhandledCudaError: Call to CUDA function failed.
Besides, when I use 2 gpus(setting wolrd_size to 2 in the code), it works well. This error only occurs when I use more than 2 gpus.
Any idea to solve this problem?
My example codes:
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
import os
from torch.nn.parallel import DistributedDataParallel as DDP
def example(rank, world_size):
# create default process group
dist.init_process_group("nccl", rank=rank, world_size=world_size)
# create local model
model = nn.Linear(10, 10).to(rank)
# construct DDP model
ddp_model = DDP(model, device_ids=[rank])
# define loss function and optimizer
loss_fn = nn.MSELoss()
optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
# forward pass
outputs = ddp_model(torch.randn(20, 10).to(rank))
labels = torch.randn(20, 10).to(rank)
# backward pass
loss_fn(outputs, labels).backward()
# update parameters
optimizer.step()
def main():
world_size = 4
mp.spawn(example,
args=(world_size,),
nprocs=world_size,
join=True)
if __name__=="__main__":
# Environment variables which need to be
# set when using c10d's default "env"
# initialization mode.
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "29500"
main()
print("Done.")
Environment:
Collecting environment information…
Collecting environment information…
PyTorch version: 2.1.0
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/AOS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35Python version: 3.9.20 (main, Oct 3 2024, 07:27:41) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-28-generic-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 H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3Nvidia driver version: 535.183.06
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: TrueCPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 192
On-line CPU(s) list: 0-191
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8468
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
Stepping: 8
CPU max MHz: 3800.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow 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 avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 4.5 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 192 MiB (96 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142,144,146,148,150,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143,145,147,149,151,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,189,191Versions of relevant libraries:
[pip3] numpy==1.24.0
[pip3] torch==2.1.0
[pip3] torch_cluster==1.6.3+pt22cu121
[pip3] torch-geometric==2.6.1
[pip3] torch_scatter==2.1.2+pt22cu121
[pip3] torch_sparse==0.6.18+pt22cu121
[pip3] torch_spline_conv==1.2.2+pt22cu121
[pip3] torchaudio==2.1.0
[pip3] torchvision==0.16.0
[pip3] triton==2.1.0
[conda] blas 1.0 mkl
[conda] cudatoolkit 10.2.89 hfd86e86_1
[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 py39h5eee18b_1
[conda] mkl_fft 1.3.10 py39h5eee18b_0
[conda] mkl_random 1.2.7 py39h1128e8f_0
[conda] numpy 1.24.0 pypi_0 pypi
[conda] numpy-base 1.26.4 py39hb5e798b_0
[conda] pytorch 2.1.0 py3.9_cuda12.1_cudnn8.9.2_0 pytorch
[conda] pytorch-cuda 12.1 ha16c6d3_6 pytorch
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-cluster 1.6.3+pt22cu121 pypi_0 pypi
[conda] torch-geometric 2.6.1 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt22cu121 pypi_0 pypi
[conda] torch-sparse 0.6.18+pt22cu121 pypi_0 pypi
[conda] torch-spline-conv 1.2.2+pt22cu121 pypi_0 pypi
[conda] torchaudio 2.4.0+cu121 pypi_0 pypi
[conda] torchtriton 2.1.0 py39 pytorch
[conda] torchvision 0.19.0+cu121 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi