CUBLAS_STATUS_INVALID_VALUE cuda runtime_error ai_generated true

RuntimeError: CUBLAS_STATUS_INVALID_VALUE when calling cublasGemmStridedBatchedEx with batch_count > 0 but A/B/C matrices have incompatible dimensions

ID: cuda/cublas-gemm-batched-wrong-rank

Also available as: JSON · Markdown · 中文
80%Fix Rate
82%Confidence
1Evidence
2023-11-12First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
CUDA 11.7 active — — —
CUDA 12.0 active — — —
cuBLAS 11.10 active — — —
cuBLAS 12.0 active — — —

Root Cause

cuBLAS batched GEMM requires that the leading dimensions (lda, ldb, ldc) and strides of matrices A, B, and C are consistent with the matrix dimensions and batch count; mismatched sizes cause an invalid value error.

generic

中文

cuBLAS 批量 GEMM 要求矩阵 A、B 和 C 的前导维度（lda、ldb、ldc）和步幅与矩阵维度和批次数一致；大小不匹配会导致无效值错误。

Official Documentation

https://docs.nvidia.com/cuda/cublas/index.html#cublas-gemm-strided-batched-ex

Workarounds

  1. 85% success Verify that lda >= m, ldb >= k, ldc >= m, and that strideA >= m*k, strideB >= k*n, strideC >= m*n for each batch. Adjust matrix allocation accordingly. 
    Verify that lda >= m, ldb >= k, ldc >= m, and that strideA >= m*k, strideB >= k*n, strideC >= m*n for each batch. Adjust matrix allocation accordingly.
  2. 90% success Use PyTorch's `torch.bmm` or `torch.matmul` with batched tensors instead of raw cuBLAS calls, as these handle dimension validation internally. 
    Use PyTorch's `torch.bmm` or `torch.matmul` with batched tensors instead of raw cuBLAS calls, as these handle dimension validation internally.

中文步骤

  1. Verify that lda >= m, ldb >= k, ldc >= m, and that strideA >= m*k, strideB >= k*n, strideC >= m*n for each batch. Adjust matrix allocation accordingly.
  2. Use PyTorch's `torch.bmm` or `torch.matmul` with batched tensors instead of raw cuBLAS calls, as these handle dimension validation internally.

Dead Ends

Common approaches that don't work:

  1. 90% fail

    Transposition changes the memory layout and may cause silent data corruption; the correct fix is to compute proper strides and leading dimensions.

  2. 70% fail

    This bypasses the error but loses the performance benefit of batching; the underlying dimension issue remains for actual batched use.