RET tensorflow runtime_error ai_generated true

WARNING:tensorflow:5 out of the last 5 calls to <function train_step> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.

ID: tensorflow/graph-execution-function-retracing

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85%Fix Rate
87%Confidence
1Evidence
2024-03-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.15 active — — —
tensorflow 2.16 active — — —
tensorflow 2.17 active — — —

Root Cause

Repeated retracing of a tf.function due to dynamic inputs like varying tensor shapes or Python arguments, degrading performance.

generic

Official Documentation

https://www.tensorflow.org/guide/function#controlling_retracing

Workarounds

  1. 85% success Define `@tf.function` outside the training loop and ensure inputs are tensors with consistent shapes. For example: `@tf.function(reduce_retracing=True) def train_step(x, y): ...`. Then call `train_step(batch_x, batch_y)` inside the loop. 
    Define `@tf.function` outside the training loop and ensure inputs are tensors with consistent shapes. For example: `@tf.function(reduce_retracing=True) def train_step(x, y): ...`. Then call `train_step(batch_x, batch_y)` inside the loop.
  2. 75% success If shapes vary, pad inputs to a fixed shape or use `tf.ensure_shape` to constrain tensor shapes before passing to the function. 
    If shapes vary, pad inputs to a fixed shape or use `tf.ensure_shape` to constrain tensor shapes before passing to the function.

中文步骤

  1. Define `@tf.function` outside the training loop and ensure inputs are tensors with consistent shapes. For example: `@tf.function(reduce_retracing=True) def train_step(x, y): ...`. Then call `train_step(batch_x, batch_y)` inside the loop.
  2. If shapes vary, pad inputs to a fixed shape or use `tf.ensure_shape` to constrain tensor shapes before passing to the function.

Dead Ends

Common approaches that don't work:

  1. 60% fail

    Adding `@tf.function` inside a loop repeatedly creates new functions, but users often move it outside incorrectly without fixing the input types.

  2. 70% fail

    Setting `experimental_compile=True` may suppress the warning but cause compilation errors if shapes are dynamic.