Hello! do you have any update regarding this issue? It looks like LSTMs and sequential networks in general are very slow and do not give correct results when using the GPU, as other users have reported here in the forum for various versions of tf-metal. It would be great if this bug in tf-metal is addressed. Thank you!

I tried running the notebook using the new tensorflow-metal version 0.5.1 The problem when training using the GPU still persists. Things are fine when using the CPU only. I tried the following combinations: tensorflow-macos 2.8.0, tensorflow-metal 0.5.1, python 3.8.13 tensorflow-macos 2.9.0, tensorflow-metal 0.5.1, python 3.9.13 tensorflow-macos 2.9.2, tensorflow-metal 0.5.1, python 3.8.13 tensorflow-macos 2.9.2, tensorflow-metal 0.5.1, python 3.9.13 Can you confirm you face the same issue? Can you please fix the bug in tensorflow-metal, since sequential networks are really important and it is really beneficial to accelerate the training using the GPU?

I tried running the notebook using tensorflow-macos 2.10.0,  tensorflow-metal 0.6.0 and python 3.9.13 and the problem with the GPU still persists even with the new tensorflow-metal version. Things are fine when using the CPU only. The code I am running https://www.tensorflow.org/text/tutorials/text_classification_rnn is from the official TensorFlow website. Can you confirm you face the same issue and please fix the bug in tensorflow-metal? Sequential networks using LSTMs are really basic and important.

I face exactly the same problem as I mention on my post https://developer.apple.com/forums/thread/709142 Still waiting for this issue to be addressed.