Does the M2 chip have 64 bit registers in the GPU?

https://github.com/pytorch/pytorch/issues/77867

Confirmed. It's an Intel based iMac 27" (2020) with an AMD Radeo Pro 5700 XT GPU running OS X 12.3.1 % ipython Python 3.8.5 ... % pip show tensorflow-macos WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages) Name: tensorflow-macos Version: 2.8.0 Summary: TensorFlow is an open source machine learning framework for everyone. Home-page: https://www.tensorflow.org/ Author: Google Inc. Author-email: packages@tensorflow.org License: Apache 2.0 Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, keras-preprocessing, libclang, numpy, opt-einsum, protobuf, setuptools, six, tensorboard, termcolor, tf-estimator-nightly, typing-extensions, wrapt Required-by: (tensorflow-metal) (base) davidlaxer@x86_64-apple-darwin13 top2vec % pip show tensorflow-metal WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages) Name: tensorflow-metal Version: 0.4.0 Summary: TensorFlow acceleration for Mac GPUs. Home-page: https://developer.apple.com/metal/tensorflow-plugin/ Author: Author-email: License: MIT License. Copyright © 2020-2021 Apple Inc. All rights reserved. Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages Requires: six, wheel Required-by: (tensorflow-metal) (base) davidlaxer@x86_64-apple-darwin13 top2vec %

Show us the code ...

Tried: optimizer=tfa.optimizers.RectifiedAdam(). It's not utilizing GPU on iMac 27" with AMD Radeon Pro 5700 XT in this code example: `inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1])) x = layers.LSTM(32, recurrent_dropout=0.25)(inputs) x = layers.Dropout(0.5)(x) outputs = layers.Dense(1)(x) model = keras.Model(inputs, outputs) callbacks = [ keras.callbacks.ModelCheckpoint("jena_lstm_dropout.keras", save_best_only=True) ] model.compile(optimizer=tfa.optimizers.RectifiedAdam(), loss="mse", metrics=["mae"]) history = model.fit(train_dataset, epochs=50, validation_data=val_dataset, callbacks=callbacks)`

I'm getting some results with the 'Adagrad' optimizer. At epoch 57: Epoch 58/100 6332/6332 [==============================] - 1621s 256ms/step - d_loss: 0.6239 - g_loss: 0.8338 Epoch 59/100 1342/6332 [=====>........................] - ETA: 21:07 - d_loss: 0.6044 - g_loss: 0.8635