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pytorch - 💡(How to fix) Fix [CUDA] illegal memory Write on _convert_indices_from_coo_to_csr [1 comments, 2 participants]

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pytorch/pytorch#182064•Fetched 2026-05-01 05:32:36
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Fix Action

Fix / Workaround

CPU: 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): 104 On-line CPU(s) list: 0-103 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 26 Socket(s): 2 Stepping: 6 CPU max MHz: 3400.0000 CPU min MHz: 800.0000 BogoMIPS: 4400.00 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 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 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 2.4 MiB (52 instances) L1i cache: 1.6 MiB (52 instances) L2 cache: 65 MiB (52 instances) L3 cache: 78 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-25,52-77 NUMA node1 CPU(s): 26-51,78-103 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected 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; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected

Code Example

import torch
print(torch.__version__)
torch.cuda.init()

gpu_dev = torch.device('cuda')

tensor_0 = torch.full((1,), 2147480000, dtype=torch.int64, requires_grad=False, device=gpu_dev)
int_1 = -1
bool_2 = False
tensor_3 = torch.full((1,), 2147480000, dtype=torch.int64, requires_grad=False, device=gpu_dev)
torch.ops.aten._convert_indices_from_coo_to_csr(tensor_0, int_1, out_int32=bool_2)
torch.cuda.synchronize()

---

========= COMPUTE-SANITIZER
========= Invalid __global__ write of size 8 bytes
=========     at void at::native::<unnamed>::convert_indices_from_coo_to_csr_cuda_kernel<long, long>(T2 *, const T1 *, long, long)+0xa80
=========     by thread (0,0,0) in block (0,0,0)
=========     Address 0x0 is out of bounds
=========     and is 140604711370752 bytes before the nearest allocation at 0x7fe115e00000 of size 2097152 bytes
=========     Saved host backtrace up to driver entry point at kernel launch time
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

import torch
print(torch.__version__)
torch.cuda.init()

gpu_dev = torch.device('cuda')

tensor_0 = torch.full((1,), 2147480000, dtype=torch.int64, requires_grad=False, device=gpu_dev)
int_1 = -1
bool_2 = False
tensor_3 = torch.full((1,), 2147480000, dtype=torch.int64, requires_grad=False, device=gpu_dev)
torch.ops.aten._convert_indices_from_coo_to_csr(tensor_0, int_1, out_int32=bool_2)
torch.cuda.synchronize()
========= COMPUTE-SANITIZER
========= Invalid __global__ write of size 8 bytes
=========     at void at::native::<unnamed>::convert_indices_from_coo_to_csr_cuda_kernel<long, long>(T2 *, const T1 *, long, long)+0xa80
=========     by thread (0,0,0) in block (0,0,0)
=========     Address 0x0 is out of bounds
=========     and is 140604711370752 bytes before the nearest allocation at 0x7fe115e00000 of size 2097152 bytes
=========     Saved host backtrace up to driver entry point at kernel launch time

Versions

Collecting environment information... PyTorch version: 2.11.0+cu126 Is debug build: False CUDA used to build PyTorch: 12.6 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0 Clang version: 14.0.0-1ubuntu1.1 CMake version: version 3.22.1 Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 3 2026, 11:56:32) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.15.0-174-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.6.20 CUDA_MODULE_LOADING set to: GPU models and configuration: GPU 0: NVIDIA A30 GPU 1: NVIDIA A30

Nvidia driver version: 560.28.03 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.3.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7 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: 46 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 104 On-line CPU(s) list: 0-103 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 26 Socket(s): 2 Stepping: 6 CPU max MHz: 3400.0000 CPU min MHz: 800.0000 BogoMIPS: 4400.00 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 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 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 2.4 MiB (52 instances) L1i cache: 1.6 MiB (52 instances) L2 cache: 65 MiB (52 instances) L3 cache: 78 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-25,52-77 NUMA node1 CPU(s): 26-51,78-103 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected 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; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected

Versions of relevant libraries: [pip3] numpy==2.2.6 [pip3] nvidia-cublas-cu12==12.6.4.1 [pip3] nvidia-cuda-cupti-cu12==12.6.80 [pip3] nvidia-cuda-nvrtc-cu12==12.6.85 [pip3] nvidia-cuda-runtime-cu12==12.6.77 [pip3] nvidia-cudnn-cu12==9.10.2.21 [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==0.7.1 [pip3] nvidia-nccl-cu12==2.28.9 [pip3] nvidia-nvjitlink-cu12==12.6.85 [pip3] nvidia-nvtx-cu12==12.6.77 [pip3] torch==2.11.0+cu126 [pip3] torchaudio==2.11.0+cu126 [pip3] torchvision==0.26.0+cu126 [pip3] triton==3.6.0 [pip3] tritonclient==2.67.0 [conda] _anaconda_depends 2023.09 py311_mkl_1
[conda] blas 1.0 mkl
[conda] intel-openmp 2023.1.0 hdb19cb5_46306
[conda] magma-cuda121 2.6.1 1 pytorch [conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-include 2023.2.0 intel_49495 intel [conda] mkl-service 2.4.0 py311h5eee18b_1
[conda] mkl-static 2023.2.0 intel_49495 intel [conda] mkl_fft 1.3.8 py311h5eee18b_0
[conda] mkl_random 1.2.4 py311hdb19cb5_0
[conda] numpy 1.26.4 py311h08b1b3b_0
[conda] numpy-base 1.26.4 py311hf175353_0
[conda] numpydoc 1.5.0 py311h06a4308_0
[conda] optree 0.10.0 pypi_0 pypi [conda] pytorch-triton 3.0.0+989adb9a29 pypi_0 pypi [conda] tbb 2021.8.0 hdb19cb5_0
[conda] tbb4py 2021.8.0 py311hdb19cb5_0
[conda] torch 2.3.0a0+git443444d dev_0 <develop>

cc @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jcaip @ptrblck @msaroufim @eqy @jerryzh168 @tinglvv @nWEIdia

extent analysis

TL;DR

The issue is likely caused by an invalid memory access in the convert_indices_from_coo_to_csr_cuda_kernel function, and a potential fix is to update the PyTorch version or modify the input parameters to avoid the out-of-bounds access.

Guidance

  • The error message indicates an invalid __global__ write of size 8 bytes, which suggests an out-of-bounds memory access in the CUDA kernel.
  • The bool_2 variable is set to False, which might be causing the issue, as the out_int32 parameter is expected to be a boolean value indicating whether the output should be in int32 format.
  • To mitigate the issue, try setting bool_2 to True or modify the input parameters to ensure that the memory access is within bounds.
  • Verify that the CUDA kernel is correctly handling the input parameters and memory allocation.

Example

torch.ops.aten._convert_indices_from_coo_to_csr(tensor_0, int_1, out_int32=True)

Note: This example is based on the assumption that setting out_int32 to True might resolve the issue.

Notes

  • The issue might be specific to the PyTorch version (2.11.0+cu126) or the CUDA version (12.6.20) used.
  • Further investigation is required to determine the root cause of the issue and to develop a more robust solution.

Recommendation

Apply workaround: Modify the input parameters to avoid the out-of-bounds access, as shown in the example above. This might resolve the issue temporarily, but a more permanent fix might require updating the PyTorch version or modifying the CUDA kernel.

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pytorch - 💡(How to fix) Fix [CUDA] illegal memory Write on _convert_indices_from_coo_to_csr [1 comments, 2 participants]