TPUEV tensorflow config_error ai_generated true

NotFoundError: Variable embedding_table not found in TPUEmbedding. Make sure the embedding config matches the model variables.

ID: tensorflow/tpu-embedding-variable-not-found

Also available as: JSON · Markdown · 中文
85%Fix Rate
88%Confidence
1Evidence
2023-09-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow==2.11.0 active — — —
tensorflow==2.13.0 active — — —
tensorflow==2.16.0 active — — —

Root Cause

The TPUEmbedding layer's variable names or table config do not align with the model's embedding variables, often due to name scoping differences.

generic

中文

TPUEmbedding层的变量名或表配置与模型的嵌入变量不匹配，通常由名称作用域差异引起。

Official Documentation

https://www.tensorflow.org/api_docs/python/tf/tpu/experimental/embedding/TPUEmbedding

Workarounds

  1. 90% success Ensure the TPUEmbedding config uses the same variable names as the model by setting 'name' parameters explicitly in both the embedding layer and the config. 
    Ensure the TPUEmbedding config uses the same variable names as the model by setting 'name' parameters explicitly in both the embedding layer and the config.
  2. 75% success Use tf.tpu.experimental.embedding.TPUEmbedding with the exact variable names from the model's get_weights() output. 
    Use tf.tpu.experimental.embedding.TPUEmbedding with the exact variable names from the model's get_weights() output.

中文步骤

  1. Ensure the TPUEmbedding config uses the same variable names as the model by setting 'name' parameters explicitly in both the embedding layer and the config.
  2. Use tf.tpu.experimental.embedding.TPUEmbedding with the exact variable names from the model's get_weights() output.

Dead Ends

Common approaches that don't work:

  1. 95% fail

    Adding more embedding layers without updating the config only increases the mismatch.

  2. 80% fail

    Changing the learning rate does not affect variable registration.