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Hi all, I was experimenting with the tf-metal (v0.4) framework and noticed some odd interactions with the tensorflow_probability package: import tensorflow as tf import numpy as np from tensorflow_probability.python.stats import percentile if __name__ == "__main__": data = np.array([0.12941672, 0.22039098, 0.33956015, 0.3787993, 0.5329178, 0.62175393, 0.5906472, 0.97234255, 0.7709932, 0.76639813, 1.0468946, 1.1515584, 1.0470238, 1.1140094, 1.2083299, 1.051311, 1.0782655, 1.0192754, 0.8690998, 0.9439713, 0.6992503, 0.7017522, 0.6524739, 0.536425, 0.47863948, 0.46657538, 0.45757294, 0.2988146, 0.19273241, 0.1494804, 0., 0.], dtype=np.float64) data16 = tf.convert_to_tensor(data, dtype=tf.float16) data32 = tf.convert_to_tensor(data, dtype=tf.float32) data64 = tf.convert_to_tensor(data, dtype=tf.float64) p = percentile(data, 99, keepdims=True, interpolation="lower") print(f"Percentile based on Numpy array (float64): {p}") p = percentile(data16, 99, keepdims=True, interpolation="lower") print(f"Percentile based on TF (float16): {p}") p = percentile(data32, 99, keepdims=True, interpolation="lower") print(f"Percentile based on TF (float32): {p}") p = percentile(data64, 99, keepdims=True, interpolation="lower") print(f"Percentile based on TF (float64): {p}") This results in: Percentile based on Numpy array (float64): [1.1515584] Percentile based on TF (float16): [1.151] Percentile based on TF (float32): [-0.] Percentile based on TF (float64): [1.1515584] The float32 value here is obviously corrupted, whereas the others are fine (presumably because only float32 is sent to the gpu?). When I uninstall tf-metal the float32 values are computed correctly. Any thoughts on when a fix might be available? Also, is there any timeline for supporting float16 on gpu?

Normally when a model is instantiated multiple times, it gets different weights each time (important for statistical significance testing). The current version of TF-Metal (0.4) seems to cache the model weights or something, resulting in the weights being equal for multiple model instantiations: from tensorflow.keras import Sequential, layers def get_model(): model = Sequential() model.add(layers.Dense(5, activation='relu', input_shape=(4, 4))) return model if __name__ == "__main__": model1 = get_model() model2 = get_model() print(model1.weights[0] - model2.weights[0]) Response without TF-Metal (as desired, weights for the two models are different): tf.Tensor( [[ 1.0636648 -0.10816181 0.8423695 1.3752697 0.38691664] [ 0.2402662 0.38139135 -0.19254395 -0.24551326 0.13166189] [-0.24854952 1.3374841 0.9716329 -0.21249878 -0.34604508] [ 0.5040202 0.120031 0.13515717 -0.40721053 0.29544616]], shape=(4, 5), dtype=float32) Response with TF-Metal (weights are all equal): tf.Tensor( [[0. 0. 0. 0. 0.] [0. 0. 0. 0. 0.] [0. 0. 0. 0. 0.] [0. 0. 0. 0. 0.]], shape=(4, 5), dtype=float32) Any ideas on the root cause or timeline for a fix?