Google Colab Bert instantiation Error using Tensorflow

Question

I'm trying to construct a Bert Model using Tensorflow on Colab. This code was perfectly working weeks ago. Now if I try to instantiate the model I obtain the following error:

Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).
All the weights of TFBertModel were initialized from the PyTorch model.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-14-b0e769ef7890> in <cell line: 7>()
      5 SC_mask_layer = Input(shape=(max_seq_length,), dtype=tf.int32, name="attention_mask")
      6 SC_bert_model = TFBertModel.from_pretrained("bert-base-uncased")
----> 7 SC_pooler_output = SC_bert_model(SC_input_layer, attention_mask=SC_mask_layer)[1]  # Estrai il secondo output, che è il pooler_output
      8 
      9 # Aggiungi un layer di Dropout

36 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/type_spec.py in type_spec_from_value(value)
   1002         3, "Failed to convert %r to tensor: %s" % (type(value).__name__, e))
   1003 
-> 1004   raise TypeError(f"Could not build a TypeSpec for {value} of "
   1005                   f"unsupported type {type(value)}.")
   1006 

TypeError: Exception encountered when calling layer 'embeddings' (type TFBertEmbeddings).

Could not build a TypeSpec for name: "tf.debugging.assert_less_5/assert_less/Assert/Assert"
op: "Assert"
input: "tf.debugging.assert_less_5/assert_less/All"
input: "tf.debugging.assert_less_5/assert_less/Assert/Assert/data_0"
input: "tf.debugging.assert_less_5/assert_less/Assert/Assert/data_1"
input: "tf.debugging.assert_less_5/assert_less/Assert/Assert/data_2"
input: "Placeholder"
input: "tf.debugging.assert_less_5/assert_less/Assert/Assert/data_4"
input: "tf.debugging.assert_less_5/assert_less/y"
attr {
  key: "summarize"
  value {
    i: 3
  }
}
attr {
  key: "T"
  value {
    list {
      type: DT_STRING
      type: DT_STRING
      type: DT_STRING
      type: DT_INT32
      type: DT_STRING
      type: DT_INT32
    }
  }
}
 of unsupported type <class 'tensorflow.python.framework.ops.Operation'>.

Call arguments received by layer 'embeddings' (type TFBertEmbeddings):
  • input_ids=<KerasTensor: shape=(None, 128) dtype=int32 (created by layer 'input_ids')>
  • position_ids=None
  • token_type_ids=<KerasTensor: shape=(None, 128) dtype=int32 (created by layer 'tf.fill_5')>
  • inputs_embeds=None
  • past_key_values_length=0
  • training=False

The code for the model is:

SC_input_layer = Input(shape=(max_seq_length,), dtype=tf.int32, name="input_ids")
SC_mask_layer = Input(shape=(max_seq_length,), dtype=tf.int32, name="attention_mask")
SC_bert_model = TFBertModel.from_pretrained("bert-base-uncased")
SC_pooler_output = SC_bert_model(SC_input_layer, attention_mask=SC_mask_layer)[1]  

# Aggiungi un layer di Dropout
SC_dropout_layer = Dropout(dropout_rate)(SC_pooler_output)
SC_output_layer = Dense(6, activation='sigmoid')(SC_dropout_layer)
SC_model = Model(inputs=[SC_input_layer, SC_mask_layer], outputs=SC_output_layer)

I found that installing tensorflow 2.10.0 it works but, using Google Colab i have problems with the CUDA version and using tensorflow 2.10 it doesn't recognize the GPU. This code was working weeks ago, someone has a solution?

EDIT: the same error appears on Kaggle.

Answer 1

UPDATE: The problem is related to the version of Transformers. Using version 4.31.0 should solve.