What is the meaning of "AttributeError: 'Tensor' object has no attribute 'nested_row_splits'"

General Discussion
,
#1

I am having this error “AttributeError: ‘Tensor’ object has no attribute ‘nested_row_splits’” when trying to train a decoder based transformer for word prediction
this is the code

**BATCH_SIZE = 64
 EPOCHS = 30
 MAX_SEQUENCE_LENGTH = 394
 VOCAB_SIZE = 15000
 EMBED_DIM = 256
 INTERMEDIATE_DIM = 512
 NUM_HEADS = 8**

def process_batch(ds):
    ds = tokenizer(ds)

    ## padd short senteces to max len using the [PAD] id
    ## add special tokens [START] and [END]

    ds_start_end_packer = StartEndPacker(
        sequence_length=MAX_SEQUENCE_LENGTH,
        start_value = tokenizer.token_to_id("[START]")
     )

    output = ds_start_end_packer(ds)


    return (output, ds)


def make_ds(seq):
    dataset = tf.data.Dataset.from_tensor_slices(seq)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.map(process_batch, num_parallel_calls=tf.data.AUTOTUNE)
    dataset = dataset.shuffle(2048).prefetch(16).cache()

    return dataset

Data tokenization

train_ds = make_ds(train_seq)
val_ds = make_ds(val_seq)


for x,y in train_ds.take(1):
    print(f"inputs.shape: {x.shape}")
    print(f"features.shape: {y.shape}")


 inputs.shape: (64, 394)
 features.shape: (64, None)

And the code for the transformer

decoder_inputs = Input(shape=(None,), dtype="int64", name="decoder_inputs")

x = TokenAndPositionEmbedding(
    vocabulary_size= VOCAB_SIZE,
    sequence_length = MAX_SEQUENCE_LENGTH,
    embedding_dim = EMBED_DIM,
    mask_zero =True
    )(decoder_inputs)


for _ in range(NUM_LAYERS):

    decoder_layer = TransformerDecoder(
        intermediate_dim = INTERMEDIATE_DIM, num_heads= NUM_HEADS
    )

    x = decoder_layer(x)


x = Dropout(0.5)(x)

decoder_ouput = Dense(VOCAB_SIZE)(x)
transformer = Model(inputs=decoder_inputs, outputs=decoder_ouput, name="transformer")

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
perplexity = Perplexity(from_logits=True, mask_token_id = 0)

transformer.compile(optimizer = "adam", loss="mse", metrics=[perplexity])

transformer.fit(train_ds, epochs=EPOCHS,batch_size=128 ,verbose=2 , 
              validation_data=val_ds,callbacks=[model_checkpoint_callback])

AND THE ERROR

 File "c:\users\hp\appdata\local\programs\python\python38\lib\site- 
 packages\keras\engine\compile_utils.py", line 265, in __call__
    loss_value = loss_obj(y_t, y_p, sample_weight=sw)
 File "c:\users\hp\appdata\local\programs\python\python38\lib\site-packages\keras\losses.py", 
 line 152, in __call__
    losses = call_fn(y_true, y_pred)
 File "c:\users\hp\appdata\local\programs\python\python38\lib\site-packages\keras\losses.py", 
 line 272, in call  **
    return ag_fn(y_true, y_pred, **self._fn_kwargs)
 File "c:\users\hp\appdata\local\programs\python\python38\lib\site-packages\keras\losses.py", 
 line 2115, in _ragged_tensor_sparse_categorical_crossentropy
    return _ragged_tensor_apply_loss(fn, y_true, y_pred, y_pred_extra_dim=True)
 File "c:\users\hp\appdata\local\programs\python\python38\lib\site-packages\keras\losses.py", 
 line 1564, in _ragged_tensor_apply_loss
    nested_splits_list = [rt.nested_row_splits for rt in (y_true, y_pred)]
 File "c:\users\hp\appdata\local\programs\python\python38\lib\site-packages\keras\losses.py", 
 line 1564, in <listcomp>
    nested_splits_list = [rt.nested_row_splits for rt in (y_true, y_pred)]

AttributeError: 'Tensor' object has no attribute 'nested_row_splits'