UnimplementedError: The Conv2D op currently does not support grouped convolutions on the CPU.

Info

Coalb TPU v2
Kaggle TPU v3
TensorFlow: 2.4.1
Transformer: 4.22.0.dev0

Who can help?

@Rocketknight1 @NielsRogge @sgugger @amyeroberts

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, …)
• My own task or dataset (give details below)

Reproduction

Please, get the file form HERE. A notebook scripts, just plug-n-play.

What to do

1. Run the script in Colab with TPU.
2. Run the script in Kaggle with TPU.

You may not need to change anything, just through the file to these platform and run all.

Expected behavior

What was I doing

With the given script above, I was trying to run a vision transformer model on Kaggle TPU (with TF 2.4.1 by default). And I got

      2     prime_input = tf.keras.Input(shape=(*IMAGE_SIZE, 3))
      3     mode_inputs = tf.keras.layers.Permute(dims=(3, 1, 2))(prime_input)
----> 4     backbone = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
      5     backbone.trainable = False
....
    171     def call(self, hidden_states, training=False):
    172         input = hidden_states
--> 173         x = self.dwconv(hidden_states)
    174         x = self.layernorm(x)
    175         x = self.pwconv1(x)

UnimplementedError: The Conv2D op currently does not support grouped convolutions on the CPU. A grouped convolution was attempted to be run because the input depth of 96 does not match the filter input depth of 1

A known tf issue, discussed also here. But this issue didn’t appear when I ran the same script on Colab TPU (with tf 2.4.1) system. The model build successfully.

As I am currently using transformer on kaggle platform, I need to make it work. The given script above is just about model construction code. Any pointer what’s going on here?

Please note again, Kaggle TPU v3 and Colab TPU v2. Not sure if it’s something to do with this.

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[gante]

Hi @innat – as I’ve mentioned above this is an issue for the Kaggle and/or the TensorFlow team, there is nothing the transformers team can do.

We don’t have the power to go back in time and add code to a repository that isn’t ours, nor to update Kaggle’s TPU runtimes.