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pytorch - 💡(How to fix) Fix torch.dot crashes with SIGFPE (exit code 136) on RTX 5060 Ti (Blackwell, sm_120) [4 comments, 4 participants]

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pytorch/pytorch#178038•Fetched 2026-04-08 01:07:37
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Error Message

Calling torch.dot on CUDA tensors causes a floating point exception (SIGFPE, process exit code 136) on an NVIDIA RTX 5060 Ti (Blackwell, sm_120). The process crashes with a core dump and no Python-level traceback.

Fix Action

Fix / Workaround

The workaround (x * y).sum() produces correct results on the same hardware, suggesting the issue is specific to the torch.dot kernel path.

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 7700X 8-Core Processor CPU family: 25 Model: 97 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 2 Frequency boost: enabled CPU(s) scaling MHz: 78% CPU max MHz: 5575.8662 CPU min MHz: 403.0750 BogoMIPS: 8982.87 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 xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 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 cpuid_fault cpb cat_l3 cdp_l3 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 user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic 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 amd_lbr_pmc_freeze Virtualization: AMD-V L1d cache: 256 KiB (8 instances) L1i cache: 256 KiB (8 instances) L2 cache: 8 MiB (8 instances) L3 cache: 32 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Indirect target selection: 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 Old microcode: 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 Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsa: Mitigation; Clear CPU buffers Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Code Example

import torch
x = torch.rand(4, device='cuda')
y = torch.rand(4, device='cuda')
torch.dot(x, y)  # SIGFPE on RTX 5060 Ti, PyTorch 2.10.0+cu128
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

Calling torch.dot on CUDA tensors causes a floating point exception (SIGFPE, process exit code 136) on an NVIDIA RTX 5060 Ti (Blackwell, sm_120). The process crashes with a core dump and no Python-level traceback.

The workaround (x * y).sum() produces correct results on the same hardware, suggesting the issue is specific to the torch.dot kernel path.

To reproduce:

import torch
x = torch.rand(4, device='cuda')
y = torch.rand(4, device='cuda')
torch.dot(x, y)  # SIGFPE on RTX 5060 Ti, PyTorch 2.10.0+cu128

PyTorch version: 2.10.0+cu128 CUDA version: 12.8 Triton version: 3.6.0 GPU: NVIDIA GeForce RTX 5060 Ti (sm_120, Blackwell) Driver version: 580.126.18 OS: Linux 6.18.16-200.fc43.x86_64 Python: 3.14

Versions

Collecting environment information... PyTorch version: 2.10.0+cu128 Is debug build: False CUDA used to build PyTorch: 12.8 ROCM used to build PyTorch: N/A

OS: Fedora Linux 43 (Workstation Edition) (x86_64) GCC version: (GCC) 15.2.1 20260123 (Red Hat 15.2.1-7) Clang version: Could not collect CMake version: version 3.31.10 Libc version: glibc-2.42

Python version: 3.14.3 (main, Feb 4 2026, 00:00:00) [GCC 15.2.1 20260123 (Red Hat 15.2.1-7)] (64-bit runtime) Python platform: Linux-6.18.16-200.fc43.x86_64-x86_64-with-glibc2.42 Is CUDA available: True CUDA runtime version: 12.6.85 CUDA_MODULE_LOADING set to: GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5060 Ti Nvidia driver version: 580.126.18 cuDNN version: Could not collect Is XPU available: False HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True Caching allocator config: N/A

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 7700X 8-Core Processor CPU family: 25 Model: 97 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 2 Frequency boost: enabled CPU(s) scaling MHz: 78% CPU max MHz: 5575.8662 CPU min MHz: 403.0750 BogoMIPS: 8982.87 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 xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 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 cpuid_fault cpb cat_l3 cdp_l3 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 user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic 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 amd_lbr_pmc_freeze Virtualization: AMD-V L1d cache: 256 KiB (8 instances) L1i cache: 256 KiB (8 instances) L2 cache: 8 MiB (8 instances) L3 cache: 32 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Indirect target selection: 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 Old microcode: 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 Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsa: Mitigation; Clear CPU buffers Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Versions of relevant libraries: [pip3] numpy==2.4.2 [pip3] nvidia-cublas-cu12==12.8.4.1 [pip3] nvidia-cuda-cupti-cu12==12.8.90 [pip3] nvidia-cuda-nvrtc-cu12==12.8.93 [pip3] nvidia-cuda-runtime-cu12==12.8.90 [pip3] nvidia-cudnn-cu12==9.10.2.21 [pip3] nvidia-cufft-cu12==11.3.3.83 [pip3] nvidia-curand-cu12==10.3.9.90 [pip3] nvidia-cusolver-cu12==11.7.3.90 [pip3] nvidia-cusparse-cu12==12.5.8.93 [pip3] nvidia-cusparselt-cu12==0.7.1 [pip3] nvidia-nccl-cu12==2.27.5 [pip3] nvidia-nvjitlink-cu12==12.8.93 [pip3] nvidia-nvtx-cu12==12.8.90 [pip3] onnx==1.20.1 [pip3] torch==2.10.0 [pip3] torchaudio==2.10.0+cu128 [pip3] torchcodec==0.10.0 [pip3] torchvision==0.25.0+cu128 [pip3] triton==3.6.0 [conda] Could not collect

cc @ptrblck @msaroufim @eqy @jerryzh168 @tinglvv @nWEIdia @csarofeen @jianyuh @nikitaved @mruberry @walterddr @xwang233 @Lezcano

extent analysis

Fix Plan

To fix the issue with torch.dot causing a floating point exception on NVIDIA RTX 5060 Ti, we can use the workaround provided in the issue description.

  • Replace torch.dot(x, y) with (x * y).sum().
  • This change should be applied to all instances where torch.dot is used with CUDA tensors.

Example code:

import torch
x = torch.rand(4, device='cuda')
y = torch.rand(4, device='cuda')
result = (x * y).sum()  # Replace torch.dot(x, y) with this line

Verification

To verify that the fix worked, run the modified code and check that it no longer produces a floating point exception. You can also compare the results of (x * y).sum() with the expected results to ensure that the fix does not affect the accuracy of the calculations.

Extra Tips

  • Make sure to update the code in all relevant places to use the workaround.
  • If you are using torch.dot in a library or framework, you may need to update the library or framework to use the workaround.
  • Keep in mind that this is a workaround and not a permanent fix. If you encounter any issues with the workaround, you may need to investigate further to find a more permanent solution.

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