Segmentation fault when loading audio #4025
Description
🐛 Describe the bug
When trying to load an audio file on an AMD machine, I get a segmentation fault. The machine has 8 MI300 GPUs.
import torchaudio
torchaudio.load('hindi.ogg')
# coming from https://huggingface.co/datasets/Narsil/asr_dummy/blob/main/hindi.ogg
and in the stdout I only get Segmentation fault. I have looked at the stack trace in GDB:
#0 0x00007f64534d4d55 in __GI___libc_free (mem=0x1) at ./malloc/malloc.c:3375
#1 0x00007f64427121e7 in llvm::cl::Option::~Option() () from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so
#2 0x00007f645346ea76 in __run_exit_handlers (status=0, listp=<optimized out>, run_list_atexit=run_list_atexit@entry=true, run_dtors=run_dtors@entry=true) at ./stdlib/exit.c:108
#3 0x00007f645346ebbe in __GI_exit (status=<optimized out>) at ./stdlib/exit.c:138
#4 0x00007f64534511d1 in __libc_start_call_main (main=main@entry=0x559510f0f520 <main>, argc=argc@entry=2, argv=argv@entry=0x7ffc19390318) at ../sysdeps/nptl/libc_start_call_main.h:74
#5 0x00007f645345128b in __libc_start_main_impl (main=0x559510f0f520 <main>, argc=2, argv=0x7ffc19390318, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>,
stack_end=0x7ffc19390308) at ../csu/libc-start.c:360
#6 0x0000559510f0f47d in _start ()
using gdb python and then run wip.py which corresponds to the reproducible code block I pasted at the start.
Thanks in advance for the help!
Versions
Collecting environment information...
PyTorch version: 2.7.0+git77a7b6c
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.4.43482-0f2d60242
OS: Ubuntu 24.04.3 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: 19.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.4.0 25133 c7fe45cf4b819c5991fe208aaa96edf142730f1d)
CMake version: version 3.31.6
Libc version: glibc-2.39
Python version: 3.12.10 | packaged by conda-forge | (main, Apr 10 2025, 22:21:13) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-116-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Instinct MI300X (gfx942:sramecc+:xnack-)
Nvidia driver version: Could not collect
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: 6.4.43482
MIOpen runtime version: 3.4.0
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): 192
On-line CPU(s) list: 0-191
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9654 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 1
Core(s) per socket: 96
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU(s) scaling MHz: 42%
CPU max MHz: 3707.8120
CPU min MHz: 1500.0000
BogoMIPS: 4793.00
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 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 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 v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 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
NUMA node1 CPU(s): 96-191
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 Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
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 / Automatic IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] mypy==1.14.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] onnx==1.17.0
[pip3] onnxruntime==1.22.1
[pip3] onnxruntime-tools==1.7.0
[pip3] onnxscript==0.2.2
[pip3] optree==0.13.0
[pip3] torch==2.7.0+git77a7b6c
[pip3] torchaudio==2.7.0+rocm6.3
[pip3] torchvision==0.22.0+9eb57cd
[pip3] triton==3.3.0
[conda] No relevant packages