CUDA_LAUNCH_BLOCKING=1 python3 /workspace/examples/rev_gnn.py
Traceback (most recent call last):
  File "/workspace/examples/rev_gnn.py", line 187, in <module>
    loss = train(epoch)
  File "/workspace/examples/rev_gnn.py", line 125, in train
    out = model(data.x, data.adj_t)[data.train_mask]
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/examples/rev_gnn.py", line 76, in forward
    x = conv(x, edge_index, mask)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/models/rev_gnn.py", line 166, in forward
    return self._fn_apply(args, self._forward, self._inverse)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/models/rev_gnn.py", line 181, in _fn_apply
    out = InvertibleFunction.apply(
  File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 539, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/models/rev_gnn.py", line 52, in forward
    outputs = ctx.fn(*x)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/models/rev_gnn.py", line 283, in _forward
    y_in = xs[i] + self.convs[i](y_in, edge_index, *args[i])
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/examples/rev_gnn.py", line 35, in forward
    return self.conv(x, edge_index)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/conv/sage_conv.py", line 130, in forward
    out = self.propagate(edge_index, x=x, size=size)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/conv/message_passing.py", line 431, in propagate
    out = self.message_and_aggregate(edge_index, **msg_aggr_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/nn/conv/sage_conv.py", line 149, in message_and_aggregate
    return spmm(adj_t, x[0], reduce=self.aggr)
  File "/usr/local/lib/python3.10/dist-packages/torch_geometric/utils/spmm.py", line 99, in spmm
    return torch.sparse.mm(src, other) / deg.view(-1, 1).clamp_(min=1)
RuntimeError: CUDA error: an illegal memory access was encountered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

python collect_env.py
Collecting environment information...
PyTorch version: 2.1.0a0+32f93b1
Is debug build: False
CUDA used to build PyTorch: 12.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.27.6
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA RTX A5000
GPU 1: NVIDIA RTX A5000

Nvidia driver version: 530.41.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
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:                   46 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          16
On-line CPU(s) list:             0-15
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Core(TM) i7-9800X CPU @ 3.80GHz
CPU family:                      6
Model:                           85
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
Stepping:                        4
CPU max MHz:                     4500.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        7599.80
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 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 cdp_l3 invpcid_single pti ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       256 KiB (8 instances)
L1i cache:                       256 KiB (8 instances)
L2 cache:                        8 MiB (8 instances)
L3 cache:                        16.5 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-15
Vulnerability Itlb multihit:     KVM: Mitigation: Split huge pages
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; IBRS
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; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] flake8==6.1.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.22.2
[pip3] onnx==1.14.0
[pip3] pytorch-quantization==2.1.2
[pip3] torch==2.1.0a0+32f93b1
[pip3] torch_geometric==2.4.0
[pip3] torch-tensorrt==0.0.0
[pip3] torchdata==0.6.0+5bbcd77
[pip3] torchmetrics==1.2.0
[pip3] torchtext==0.16.0a0
[pip3] torchvision==0.16.0a0
[pip3] triton==2.1.0+e621604
[pip3] tritonclient==2.38.0.69485441
[conda] Could not collect