Error seen with nightly build, e.g. torch==2.8.0.dev20250327+cu126
[2025-04-08 08:39:46] devgpu263:589012:591652 [0] transport/nvls.cc:254 NCCL WARN Cuda failure 1 'invalid argument'
devgpu263:589012:591652 [0] NCCL INFO transport/nvls.cc:409 -> 1
devgpu263:589012:591652 [0] NCCL INFO init.cc:1141 -> 1
devgpu263:589012:591652 [0] NCCL INFO init.cc:1409 -> 1
devgpu263:589012:591652 [0] NCCL INFO group.cc:75 -> 1 [Async thread]
devgpu263:589012:589012 [0] NCCL INFO group.cc:422 -> 1
devgpu263:589012:589012 [0] NCCL INFO group.cc:581 -> 1
devgpu263:589012:589012 [0] NCCL INFO init.cc:1836 -> 1
[rank0]: Traceback (most recent call last):
[rank0]: File "/home/kw2501/local/driver_12.2/repro.py", line 12, in <module>
[rank0]: dist.all_reduce(x)
[rank0]: File "/home/kw2501/.conda/envs/titan/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper
[rank0]: return func(*args, **kwargs)
[rank0]: File "/home/kw2501/.conda/envs/titan/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 2868, in all_reduce
[rank0]: work = group.allreduce([tensor], opts)
[rank0]: torch.distributed.DistBackendError: NCCL error in: /pytorch/torch/csrc/distributed/c10d/NCCLUtils.cpp:77, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.26.2
[rank0]: ncclUnhandledCudaError: Call to CUDA function failed.
[rank0]: Last error:
[rank0]: Cuda failure 1 'invalid argument'
Mini repro:
import os
import torch
import torch.distributed as dist
if __name__ == "__main__":
rank = int(os.getenv("RANK"))
world_size = int(os.getenv("WORLD_SIZE"))
device = torch.device("cuda", rank)
dist.init_process_group("nccl", rank=rank, world_size=world_size)
x = torch.empty(4, device=device)
dist.all_reduce(x)
print(x)
Command line:
NCCL_DEBUG=INFO torchrun --standalone --nproc-per-node 4 repro.py
Fails with 12.2 driver:Driver Version: 535.154.05 CUDA Version: 12.2
Works with 12.4 driver:Driver Version: 550.90.07 CUDA Version: 12.4
| Run w/ 12.2 driver | Run w/ 12.4 driver or higher
torch Built w/ |
12.2 toolkit | Works | Not tested
--------------------------------------------------------------------
torch Built w/ |
12.6 toolkit | Fails | Works
Line 254 in nvls.cc:
CUCHECKGOTO(cuMulticastBindMem(*mcHandle, 0/*mcOffset*/, *ucHandle, 0/*memOffset*/, ucsize, 0/*flags*/), ret, fail);
[kw2501@devgpu263.prn2 ~]$ python collect_env.py
Collecting environment information...
PyTorch version: 2.8.0.dev20250327+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A
OS: CentOS Stream 9 (x86_64)
GCC version: (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)
Clang version: Could not collect
CMake version: version 3.26.5
Libc version: glibc-2.34
Python version: 3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.4.3-0_fbk20_zion_2830_g3e5ab162667d-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 12.6.85
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100
GPU 1: NVIDIA H100
GPU 2: NVIDIA H100
GPU 3: NVIDIA H100
GPU 4: NVIDIA H100
GPU 5: NVIDIA H100
GPU 6: NVIDIA H100
GPU 7: NVIDIA H100
Nvidia driver version: 535.154.05
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.8.0
/usr/lib64/libcudnn.so.9.6.0
/usr/lib64/libcudnn_adv.so.9.6.0
/usr/lib64/libcudnn_adv_infer.so.8.8.0
/usr/lib64/libcudnn_adv_train.so.8.8.0
/usr/lib64/libcudnn_cnn.so.9.6.0
/usr/lib64/libcudnn_cnn_infer.so.8.8.0
/usr/lib64/libcudnn_cnn_train.so.8.8.0
/usr/lib64/libcudnn_engines_precompiled.so.9.6.0
/usr/lib64/libcudnn_engines_runtime_compiled.so.9.6.0
/usr/lib64/libcudnn_graph.so.9.6.0
/usr/lib64/libcudnn_heuristic.so.9.6.0
/usr/lib64/libcudnn_ops.so.9.6.0
/usr/lib64/libcudnn_ops_infer.so.8.8.0
/usr/lib64/libcudnn_ops_train.so.8.8.0
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
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 384
On-line CPU(s) list: 0-383
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9654 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 2
Core(s) per socket: 96
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU(s) scaling MHz: 84%
CPU max MHz: 3707.8120
CPU min MHz: 1500.0000
BogoMIPS: 4792.81
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization: AMD-V
L1d cache: 6 MiB (192 instances)
L1i cache: 6 MiB (192 instances)
L2 cache: 192 MiB (192 instances)
L3 cache: 768 MiB (24 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-95,192-287
NUMA node1 CPU(s): 96-191,288-383
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: 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: Vulnerable: eIBRS with unprivileged eBPF
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.1.2
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pytorch-triton==3.3.0+git96316ce5
[pip3] torch==2.8.0.dev20250327+cu126
[pip3] torchaudio==2.6.0.dev20250329+cu126
[pip3] torchdata==0.11.0
[pip3] torchtitan==0.0.2
[pip3] torchvision==0.22.0.dev20250329+cu126
[pip3] triton==3.2.0
[conda] numpy 2.1.2 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
[conda] pytorch-triton 3.3.0+git96316ce5 pypi_0 pypi
[conda] torch 2.8.0.dev20250327+cu126 pypi_0 pypi
[conda] torchaudio 2.6.0.dev20250329+cu126 pypi_0 pypi
[conda] torchdata 0.11.0 pypi_0 pypi
[conda] torchtitan 0.0.2 pypi_0 pypi
[conda] torchvision 0.22.0.dev20250329+cu126 pypi_0 pypi
[conda] triton 3.2.0 pypi_0 pypi
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k
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