ERNAL cuda library_error ai_generated partial

RuntimeError: cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR

ID: cuda/cusolver-internal-error

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78%Fix Rate
85%Confidence
50Evidence
2023-01-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
12 active — — —

Root Cause

cuSOLVER failure in linear algebra operations. Matrix may be singular or GPU memory corrupted.

generic

Workarounds

  1. 90% success Check if input matrix is singular or has NaN/Inf values 
    torch.isnan(matrix).any()  # check for NaN
torch.isinf(matrix).any()  # check for Inf

    Sources: https://pytorch.org/docs/stable/generated/torch.isnan.html

  2. 85% success Add regularization to prevent singular matrices: A + eps * I 
    A = A + 1e-6 * torch.eye(A.size(0), device=A.device)

    Sources: https://pytorch.org/docs/stable/generated/torch.linalg.solve.html

  3. 78% success Update CUDA and cuSOLVER to latest version for bug fixes 
    # Check current CUDA version:
nvcc --version
nvidia-smi

# Update CUDA toolkit (Ubuntu):
sudo apt-get update
sudo apt-get install cuda-toolkit-12-4

# Or update via conda for PyTorch:
conda install pytorch pytorch-cuda=12.4 -c pytorch -c nvidia

# Verify cuSOLVER works:
python -c "import torch; torch.linalg.solve(torch.eye(3, device='cuda'), torch.ones(3, device='cuda'))"

    Sources: https://docs.nvidia.com/cuda/cusolver/index.html

Dead Ends

Common approaches that don't work:

  1. Retry the same operation 70% fail

    If matrix is singular, retrying gives the same error

  2. Switch to CPU computation as fallback 50% fail

    Works but defeats the purpose of GPU acceleration

Error Chain

Frequently confused with: