ValueError: No gradients provided for any variable: ['tf_bert_for_masked_lm_6/bert/embeddings/word_embeddings/weight:0', 'tf_bert_for_masked_lm_6/bert/embeddings/position_embeddings/embeddings:0'......
Hi! Everyone.
I encounter some problems with TFBertForMaskedLM. 
I modify TFBertForMaskedLM layer according to "Condition-Bert Contextual Augmentation" paper.

In short, my dataset sentences have 5 labels, then change type_token_ids to label_ids.
so, i change bert.embeddings.token_type_embeddings .

my model code as follows:
```python
from_pretrain = 'bert-base-chinese'
def create_model():
    mlm_model = TFBertForMaskedLM.from_pretrained(from_pretrain, return_dict=True)
    mlm_model.bert.embeddings.token_type_embeddings = tf.keras.layers.Embedding(6, 768)
    
    return tf_bert_mlm_model

model = create_model()
``` 

then, my tf dataset tensor as follows(batch_size=2):
```
{'input_ids': <tf.Tensor: shape=(2, 128), dtype=int32, numpy=
 array([[  101,   103,   103,   928,  6249,  6244,   103,  7361,  4534,
          5022,  3300,  3126,   511,  6313,  4825,  6291,  5206,  7514,
          6352,  1162,   103,   100,  6745,  1057,  5080,  6244,   103,
          6349,  4826,   103,  9039,  8599,  1564,  4500,  6257,   991,
          6291,  6349,  3302,  1243,   103,  6313,  1257,  2200,  7710,
          6349,  4826,  6752,  4761,   800,   103,  8024,  7344,  3632,
          3300,  2552,   782,  1894,  4671,  4500,   511,   102,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0],
        [  101,   523,   791,  8532,   677,  5221,   524,  1920,   686,
           103,  6240,  4634,  2466,   103,  2695,   103,   519,  5064,
          1918,   736,  2336,   520,  4158,  2695,  1564,  4923,  8013,
           678,  6734,  8038,  8532,   131,   120,   120,  8373,   119,
           103,  9989,   119,  8450,   103,   100,   120, 12990,  8921,
          8165,   102,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0,     0,     0,     0,     0,     0,     0,     0,
             0,     0]], dtype=int32)>,
 'attention_mask': <tf.Tensor: shape=(2, 128), dtype=int32, numpy=
 array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
       dtype=int32)>,
 'token_type_ids': <tf.Tensor: shape=(2, 128), dtype=int32, numpy=
 array([[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
         3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
         3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
       dtype=int32)>,
 'labels': <tf.Tensor: shape=(2, 128), dtype=int32, numpy=
 array([[ -100,   704,  1751,  -100,  -100,  -100,  2622,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  4826,  -100,  6745,  -100,  -100,  -100,  7710,
          -100,  -100, 10873,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  8024,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,   782,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100],
        [ -100,  -100,  -100,  3189,  -100,  -100,  -100,  -100,  -100,
          4518,  -100,  -100,  -100,  2204,  -100,   100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  2695,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          8429,  -100,  -100,  -100,   120,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,
          -100,  -100]], dtype=int32)>}
```

and model.compile() and model.fit() as follows:
```python
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)
model.fit(tf_sms_dataset, epochs=2)
```

But I always get the error message of 
```
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:757 train_step
        self.trainable_variables)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
        trainable_variables))
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
        filtered_grads_and_vars = _filter_grads(grads_and_vars)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
        ([v.name for _, v in grads_and_vars],))

    ValueError: No gradients provided for any variable: ['tf_bert_for_masked_lm_6/bert/embeddings/word_embeddings/weight:0', 'tf_bert_for_masked_lm_6/bert/embeddings/position_embeddings/embeddings:0', 'tf_bert_for_masked_lm_6/bert/embeddings/LayerNorm/gamma:0', ..........]
```

How to solve the problem? Thanks

change other questions.<|||||>How did u solve this issue?<|||||>How did u solve this problem? I got same problem like yours