pytorch - 💡(How to fix) Fix CPU SEGV crash in `torch.Tensor.to_dense()` for malformed torch.sparse_coo_tensor [2 comments, 3 participants]

#!/usr/bin/env python3
import torch

print("torch version:", torch.__version__)

# Malformed parameters: negative out-of-bounds index with valid tensor size
# Index value is far outside the legal range [0, size[0]-1]
indices = torch.tensor([[-4702111234474983746]], dtype=torch.int64)
values = torch.tensor([-16.0], dtype=torch.float32)
size = (1,)

print(f"[*] Indices shape: {indices.shape}")
print(f"[*] Values shape: {values.shape}")
print(f"[*] Tensor size: {size}")

# Create sparse tensor with invariant checks disabled
t = torch.sparse_coo_tensor(
    indices=indices,
    values=values,
    size=size,
    dtype=torch.float32,
    device='cpu',
    check_invariants=False
)

# Trigger crash
print("[*] Triggering to_dense()...")
result = t.to_dense()
print("Success:", result.shape)

---

==2583004==ERROR: AddressSanitizer: SEGV on unknown address (pc 0x7fffdab6ffdf bp 0x7fffffffcfc0 sp 0x7fffffffce20 T0)
==2583004==The signal is caused by a READ memory access.
==2583004==Hint: this fault was caused by a dereference of a high value address.
    #0 0x7fffdab6ffdf in void at::native::add_dense_sparse_worker_non_hybrid_cpu<float>(at::Tensor&, c10::Scalar const&, at::Tensor const&, at::Tensor const&, at::Tensor const&)::'lambda'(long, long)::operator()(long, long) const /root/pytorch/aten/src/ATen/native/sparse/SparseTensorMath.cpp:613:20
    #1 0x7fffdab6ffdf in void at::parallel_for<void at::native::add_dense_sparse_worker_non_hybrid_cpu<float>(at::Tensor&, c10::Scalar const&, at::Tensor const&, at::Tensor const&, at::Tensor const&)::'lambda'(long, long)>(long, long, long, void at::native::add_dense_sparse_worker_non_hybrid_cpu<float>(at::Tensor&, c10::Scalar const&, at::Tensor const&, at::Tensor const&, at::Tensor const&)::'lambda'(long, long) const&) /root/pytorch/aten/src/ATen/Parallel-inl.h:29:5
    #2 0x7fffdab6ffdf in void at::native::add_dense_sparse_worker_non_hybrid_cpu<float>(at::Tensor&, c10::Scalar const&, at::Tensor const&, at::Tensor const&, at::Tensor const&) /root/pytorch/aten/src/ATen/native/sparse/SparseTensorMath.cpp:607:3
    #3 0x7fffdab5c673 in at::native::add_out_dense_sparse_cpu(at::Tensor&, at::Tensor const&, at::Tensor const&, c10::Scalar const&) /root/pytorch/aten/src/ATen/native/sparse/SparseTensorMath.cpp:754:7
    #4 0x7fffdab5c673 in at::native::add_out_sparse_cpu(at::Tensor const&, at::Tensor const&, c10::Scalar const&, at::Tensor&) /root/pytorch/aten/src/ATen/native/sparse/SparseTensorMath.cpp:558:12
    #5 0x7fffda6a52c6 in at::native::sparse_to_dense(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<bool>) /root/pytorch/aten/src/ATen/native/TensorConversions.cpp:697:14
    #6 0x7ffff1e10fde in at::Tensor::to_dense(std::optional<c10::ScalarType>, std::optional<bool>) const /root/pytorch/build/aten/src/ATen/core/TensorBody.h:4060:12
    #7 0x5555556dfc16  (/usr/bin/python3.10+0x18bc16) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #8 0x5555556c9966 in _PyEval_EvalFrameDefault (/usr/bin/python3.10+0x175966) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #9 0x5555557ae565  (/usr/bin/python3.10+0x25a565) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #10 0x5555557ae435 in PyEval_EvalCode (/usr/bin/python3.10+0x25a435) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #11 0x5555557d4ed7  (/usr/bin/python3.10+0x280ed7) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #12 0x5555557cf6de  (/usr/bin/python3.10+0x27b6de) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #13 0x5555557d4c74  (/usr/bin/python3.10+0x280c74) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #14 0x5555557d4257 in _PyRun_SimpleFileObject (/usr/bin/python3.10+0x280257) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #15 0x5555557d3f36 in _PyRun_AnyFileObject (/usr/bin/python3.10+0x27ff36) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #16 0x5555557c83ad in Py_RunMain (/usr/bin/python3.10+0x2743ad) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #17 0x5555557a247c in Py_BytesMain (/usr/bin/python3.10+0x24e47c) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)
    #18 0x7ffff7106d8f in __libc_start_call_main csu/../sysdeps/nptl/libc_start_call_main.h:58:16
    #19 0x7ffff7106e3f in __libc_start_main csu/../csu/libc-start.c:392:3
    #20 0x5555557a2374 in _start (/usr/bin/python3.10+0x24e374) (BuildId: b2fd9010dc75aa747aee5296c31a07d210d124ad)

AddressSanitizer can not provide additional info.
SUMMARY: AddressSanitizer: SEGV /root/pytorch/aten/src/ATen/native/sparse/SparseTensorMath.cpp:613:20 in void at::native::add_dense_sparse_worker_non_hybrid_cpu<float>(at::Tensor&, c10::Scalar const&, at::Tensor const&, at::Tensor const&, at::Tensor const&)::'lambda'(long, long)::operator()(long, long) const
==2583004==ABORTING

---

PyTorch version: 2.10.0a0+gitf2bb22f
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 4.1.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Aug 15 2025, 14:32:43) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-200-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
Nvidia driver version: Could not collect
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: False
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):                             128
On-line CPU(s) list:                0-127
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen Threadripper PRO 5995WX 64-Cores
CPU family:                         25
Model:                              8
Thread(s) per core:                 2
Core(s) per socket:                 64
Socket(s):                          1
Stepping:                           2
Frequency boost:                    enabled
CPU max MHz:                        2700.0000
CPU min MHz:                        1800.0000
BogoMIPS:                           5389.77
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 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 vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca
Virtualization:                     AMD-V
L1d cache:                          2 MiB (64 instances)
L1i cache:                          2 MiB (64 instances)
L2 cache:                           32 MiB (64 instances)
L3 cache:                           256 MiB (8 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-127
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 and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==2.2.6
[pip3] optree==0.17.0
[pip3] torch==2.10.0a0+gitf2bb22f
[conda] Could not collect