ERTL tensorflow resource_error ai_generated true

ResourceExhaustedError: The function 'train_step' has been retraced 1000 times. The tracing limit has been reached. This may be caused by passing Python literals or tensors with changing shapes.

ID: tensorflow/tf-function-recompilation-cache-limit

Also available as: JSON · Markdown · 中文
88%Fix Rate
89%Confidence
1Evidence
2023-08-12First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
TensorFlow 2.6.0 active — — —
TensorFlow 2.10.0 active — — —

Root Cause

A tf.function-decorated function (e.g., train_step) is being re-traced excessively because it receives arguments with varying shapes or Python values that are not cached as part of the function's input signature, exhausting the tracing cache.

generic

中文

被 tf.function 装饰的函数（例如 train_step）因接收形状变化或 Python 值的参数而被过度重跟踪，这些参数未作为函数输入签名的一部分被缓存，耗尽了跟踪缓存。

Official Documentation

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

Workarounds

  1. 90% success Ensure that all tensor arguments to the tf.function have consistent shapes. Pad or resize inputs to a fixed shape before passing them. For Python arguments, convert them to tensors or use tf.constant() to make them part of the graph signature. 
    Ensure that all tensor arguments to the tf.function have consistent shapes. Pad or resize inputs to a fixed shape before passing them. For Python arguments, convert them to tensors or use tf.constant() to make them part of the graph signature.
  2. 85% success Define the input signature explicitly using tf.TensorSpec to prevent retracing due to shape or dtype variations. This tells TensorFlow to use a single graph for all calls matching the signature. 
    Define the input signature explicitly using tf.TensorSpec to prevent retracing due to shape or dtype variations. This tells TensorFlow to use a single graph for all calls matching the signature.
  3. 80% success If the function uses Python integer or boolean arguments that change, convert them to tensors or move them outside the tf.function by using tf.cond() or tf.switch_case() for control flow. 
    If the function uses Python integer or boolean arguments that change, convert them to tensors or move them outside the tf.function by using tf.cond() or tf.switch_case() for control flow.

中文步骤

  1. 确保传递给 tf.function 的所有张量参数具有一致的形状。在传递之前将输入填充或调整为固定形状。对于 Python 参数，将其转换为张量或使用 tf.constant() 使其成为图签名的一部分。
  2. 使用 tf.TensorSpec 显式定义输入签名，以防止因形状或数据类型变化而重跟踪。这告诉 TensorFlow 对匹配签名的所有调用使用单个图。
  3. 如果函数使用变化的 Python 整数或布尔参数，将其转换为张量，或使用 tf.cond() 或 tf.switch_case() 进行控制流，将其移出 tf.function。

Dead Ends

Common approaches that don't work:

  1. 90% fail

    Running eagerly defeats the purpose of tf.function (performance), and increasing the limit only delays the error without fixing the root cause of shape variability.

  2. 70% fail

    While this can reduce retracing for shape changes, it does not address retracing caused by changing Python literal values (e.g., integer arguments), and may still hit the limit.

  3. 60% fail

    This eliminates retracing but also removes the performance benefits of graph compilation, potentially slowing training significantly.