CUDNN_STATUS_BAD_PARAM cuda runtime_error ai_generated true

RuntimeError: cuDNN error: CUDNN_STATUS_BAD_PARAM when calling cudnnSetRNNDescriptor_v8 with hiddenSize=4096, numLayers=4, and dropout=0.0

ID: cuda/cudnn-rnn-hidden-size-mismatch

Also available as: JSON · Markdown · 中文
90%Fix Rate
85%Confidence
1Evidence
2023-08-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
cuDNN 8.9 active — — —
cuDNN 9.0 active — — —
PyTorch 2.1 active — — —
PyTorch 2.2 active — — —

Root Cause

cuDNN RNN descriptor initialization fails because the hidden size is not a multiple of the alignment requirement (typically 32 or 64) for the chosen RNN mode and data type.

generic

中文

cuDNN RNN 描述符初始化失败，因为隐藏层大小不是所选 RNN 模式和数据类型对齐要求（通常为 32 或 64）的倍数。

Official Documentation

https://docs.nvidia.com/deeplearning/cudnn/api/index.html#cudnnSetRNNDescriptor_v8

Workarounds

  1. 95% success Pad the hidden size to the nearest multiple of 64. For example: hidden_size = ((hidden_size + 63) // 64) * 64. Then adjust your model's hidden dimension accordingly. 
    Pad the hidden size to the nearest multiple of 64. For example: hidden_size = ((hidden_size + 63) // 64) * 64. Then adjust your model's hidden dimension accordingly.
  2. 85% success Switch to a non-cuDNN RNN implementation by setting torch.backends.cudnn.enabled = False before creating the RNN module. This uses PyTorch's native RNN which doesn't have alignment constraints. 
    Switch to a non-cuDNN RNN implementation by setting torch.backends.cudnn.enabled = False before creating the RNN module. This uses PyTorch's native RNN which doesn't have alignment constraints.
  3. 60% success Use a different RNN mode like GRU or LSTM with the same hidden size; sometimes the alignment requirement differs per mode. 
    Use a different RNN mode like GRU or LSTM with the same hidden size; sometimes the alignment requirement differs per mode.

中文步骤

  1. 将隐藏层大小填充到最接近的 64 的倍数。例如：hidden_size = ((hidden_size + 63) // 64) * 64。然后相应调整模型的隐藏维度。
  2. 在创建 RNN 模块之前设置 torch.backends.cudnn.enabled = False，切换到非 cuDNN 的 RNN 实现。这会使用 PyTorch 的原生 RNN，没有对齐约束。
  3. 使用不同的 RNN 模式，如 GRU 或 LSTM，并保持相同的隐藏层大小；有时不同模式的对齐要求不同。

Dead Ends

Common approaches that don't work:

  1. 95% fail

    The error is caused by hidden size alignment, not layer count. Reducing layers may change the model architecture but doesn't fix the alignment issue.

  2. 98% fail

    Dropout doesn't affect descriptor alignment requirements; it only controls regularization. Changing it has no effect on the BAD_PARAM error.

  3. 70% fail

    Older cuDNN versions may have different alignment constraints but often are more restrictive. This can introduce other compatibility issues with PyTorch or CUDA.