Comments (2)
What's the performance like with JAX? Please try it.
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Thanks for the suggestion. Here is our situation. Previously, we were running a BERT pretrain with TensorFlow 2.11 and TensorFlow Models (TFM) with XLA on mulitple GPUs. This took about an hour.
We are migrating to Keras 3 and testing:
- With Keras 3.3.3, JAX, Keras NLP, XLA and Data Parallel, we are seeing about a 50% increase in train time.
- With Keras 3.3.3, TF, Keras NLP, and TF Distribute, and no XLA we are seeing a 200% increase in train time. (We also need to reduce batch size when we do not enable XLA)
The train is modified:
- Before, we used
add_loss
. Now, we specify the loss in thecompile()
. - Keras 3 only has one loss as a metric, so we duplicate these losses as metrics to see the separate MLM and NSP losses.
- Keras 3 does not support
add_metric()
so we moved these also tocompile()
.
from keras.
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from keras.