CRWF tensorflow runtime_error ai_generated true

InternalError: CUDNN_STATUS_BAD_PARAM: Invalid weight format for cuDNN RNN. Expected format: [num_layers, input_size, num_units] but got [3, 128, 64].

ID: tensorflow/cudnn-rnn-weight-format-error

Also available as: JSON · Markdown · 中文
80%Fix Rate
82%Confidence
1Evidence
2023-03-10First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow==2.9.0 active — — —
tensorflow==2.11.0 active — — —
tensorflow==2.14.0 active — — —

Root Cause

The weight tensor passed to cuDNN RNN has an incorrect shape or layout, often due to a mismatch between the model's RNN configuration and the actual weight initialization.

generic

中文

传递给cuDNN RNN的权重张量形状或布局不正确，通常由模型的RNN配置与实际权重初始化之间的不匹配引起。

Official Documentation

https://www.tensorflow.org/api_docs/python/tf/keras/layers/RNN

Workarounds

  1. 85% success Ensure the RNN layer is initialized with the correct input shape and units, and use `return_sequences=True` if the next layer expects 3D output. 
    Ensure the RNN layer is initialized with the correct input shape and units, and use `return_sequences=True` if the next layer expects 3D output.
  2. 75% success Use `tf.keras.layers.RNN` with a custom cell that explicitly defines weight shapes to avoid cuDNN assumptions. 
    Use `tf.keras.layers.RNN` with a custom cell that explicitly defines weight shapes to avoid cuDNN assumptions.

中文步骤

  1. Ensure the RNN layer is initialized with the correct input shape and units, and use `return_sequences=True` if the next layer expects 3D output.
  2. Use `tf.keras.layers.RNN` with a custom cell that explicitly defines weight shapes to avoid cuDNN assumptions.

Dead Ends

Common approaches that don't work:

  1. 85% fail

    Changing the RNN type (e.g., LSTM to GRU) does not fix the weight shape issue; it may introduce new shape requirements.

  2. 90% fail

    Increasing the number of units exacerbates the mismatch because weight dimensions become larger but still wrong.