# AttributeError Traceback (most recent call last) It would be nice to be able to either use modern optimizers, or have a legacy-compatible version of AdamW, since weight-decay is an important tool in modern ML research, and currently cannot be used on mac.įrom import Modelįrom import Input, Denseįrom import AdamW Note: a warning message suggests using the optimizer located at tf., but this does not exist Then the model will train correctly with a valid AdamW optimizer when calling model.fit(). Later, when we call model.fit(), this leads to an AttributeError, because () does not exist when model.optimizer is a string.Įxpected behaviour: correctly compile the model with either a v2.11+ optimiser without slowdown, or a legacy-compatible implementation of the AdamW optimizer. In the current nightly, this error is not thrown - however, after calling pile(), the attribute model.optimizer is set to string 'adamw' instead of an optimizer object. In a previous tf-macos version 2.12, this lead to an error during pile(). However, no legacy version of the AdamW optimizer exists. When calling pile() with the AdamW optimizer, a warning is thrown saying that v2.11+ optimizers have a known slowdown on M1/M2 devices, and so the backend attempts to fallback to a legacy version. I initially raised this issue in the tensorflow forum, and they directed me back here since this is a tf-macos specific problem. I tested the same code in a colab environment and the result was as expectedĪnd this is correct and consistent with the documentation Which is obviously wrong at every passage Print('Final Sorted Indices:', sorted_indices.numpy())

Sorted_indices = multi_column_argsort(points, columns_order) To test this I used the following example: Print('Sorted Indices After:', sorted_indices.numpy())Īfter debugging this function for a while I found out that it was not sorting the 3 columns as expected because the argsort were not stable i.e. Sorted_indices = tf.gather(sorted_indices, col_argsort)

Print('Sorted Indices Before:', sorted_indices.numpy()) Print('Col Argsort:', col_argsort.numpy()) Print('Column Values:', col_vals.numpy()) Sorted_indices = tf.range(start=0, limit=tf.shape(tensor), dtype=tf.int32)Ĭol_vals = tf.gather(tensor, sorted_indices)Ĭol_argsort = tf.argsort(col_vals, stable=True)

So I implemented the following function:ĭef multi_column_argsort(tensor, columns_order): For a tensorflow layer I need a multi column argsort.

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