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PiperOrigin-RevId: 279798675

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PiperOrigin-RevId: 276518206

Using this code on GPU, was not logging "learning_rate" and "gradient norm" etc. We need to use simple "tf.summary.scalar" instead of contrib one to log these params. Tested on Tensorflow 1.14.

This reverts commit b4e560dc.

* Revert "Update tf.contrib.data to tf.data.experimental. (#7650)"

This reverts commit faf4bbb3.

* revert research

change benchmark's log verbosity to logging.INFO. it seems to me that DEBUG map to ---v=1 internally, which is way to verbose for the purpose of benchmarking.

PiperOrigin-RevId: 274040907

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Instead of needing to ensure variables are float32, casting inputs to float32, etc, instead dtype="float32" is passed to the layer constructor, which will do all that logic automatically.

The only difference is the output of LayerNorm is now float32 instead of float16, so an extra cast is needed elsewhere.

PiperOrigin-RevId: 273833286

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Refactor basic utils to modeling/

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--stop_threshold, --num_gpu, --hooks, --export_dir, and --distribution_strategy have been unexposed from models which do not use them

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--clean, --train_epochs, and --epochs_between_evals have been unexposed from models which do not use them

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I soon plan on making mixed precision policies exposed in TF2 only.

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The transformer model requires TF2.

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The old infer_float32_policies policy will be removed from TensorFlow soon.

PiperOrigin-RevId: 264300408

An earlier change made it so running this file would cause an error. The unit tests still passed, as the unit tests do not directly call the main() function

PiperOrigin-RevId: 264268400

Small tweak to the range based on seeing values
slightly over 29 and slightly under 28. This avoids
false positives that even if valid are not actionable.

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