ICR tensorflow gpu_error ai_generated partial

InternalError: cuDNN RNN initialization failed: CUDNN_STATUS_BAD_PARAM

ID: tensorflow/internal-error-cudnn-rnn-init

Also available as: JSON · Markdown · 中文
75%Fix Rate
83%Confidence
1Evidence
2024-03-10First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.14.0 active — — —
cudnn 8.9.0 active — — —

Root Cause

cuDNN RNN layer initialization fails due to unsupported hidden size, batch size, or sequence length for the given cuDNN version.

generic

中文

cuDNN RNN层初始化失败,原因是给定cuDNN版本不支持的隐藏大小、批大小或序列长度。

Official Documentation

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

Workarounds

  1. 80% success Reduce the hidden size or batch size to a value supported by cuDNN (e.g., hidden size divisible by 32 or 64): model.add(tf.keras.layers.LSTM(units=256, return_sequences=True)) # Try units=128 or 64 if 256 fails
    Reduce the hidden size or batch size to a value supported by cuDNN (e.g., hidden size divisible by 32 or 64):
    model.add(tf.keras.layers.LSTM(units=256, return_sequences=True))
    # Try units=128 or 64 if 256 fails
  2. 70% success Set the environment variable TF_CUDNN_USE_AUTOTUNE=0 to disable cuDNN autotuning, which may bypass the BAD_PARAM error: export TF_CUDNN_USE_AUTOTUNE=0 python train.py
    Set the environment variable TF_CUDNN_USE_AUTOTUNE=0 to disable cuDNN autotuning, which may bypass the BAD_PARAM error:
    export TF_CUDNN_USE_AUTOTUNE=0
    python train.py

中文步骤

  1. Reduce the hidden size or batch size to a value supported by cuDNN (e.g., hidden size divisible by 32 or 64):
    model.add(tf.keras.layers.LSTM(units=256, return_sequences=True))
    # Try units=128 or 64 if 256 fails
  2. Set the environment variable TF_CUDNN_USE_AUTOTUNE=0 to disable cuDNN autotuning, which may bypass the BAD_PARAM error:
    export TF_CUDNN_USE_AUTOTUNE=0
    python train.py

Dead Ends

Common approaches that don't work:

  1. 90% fail

    The error is about invalid parameters, not memory.

  2. 75% fail

    Older cuDNN versions may not support required RNN operations.