ValueError when trying to fine-tune GPT-2 model in TensorFlow - tensorflow

Question

I am encountering a ValueError in my Python code when trying to fine-tune Hugging Face's distribution of the GPT-2 model. Specifically:
ValueError: Dimensions must be equal, but are 64 and 0 for
'{{node Equal_1}} = Equal[T=DT_FLOAT, incompatible_shape_error=true](Cast_18, Cast_19)'
with input shapes: [64,0,1024], [2,0,12,1024].
I have around 100 text files that I concatenate into a string variable called raw_text and then pass into the following function to create training and testing TensorFlow datasets:
def to_datasets(raw_text):
# split the raw text in smaller sequences
seqs = [
raw_text[SEQ_LEN * i:SEQ_LEN * (i + 1)]
for i in range(len(raw_text) // SEQ_LEN)
]
# set up Hugging Face GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
tokenizer.pad_token = tokenizer.eos_token
# tokenize the character sequences
tokenized_seqs = [
tokenizer(seq, padding="max_length", return_tensors="tf")["input_ids"]
for seq in seqs
]
# convert tokenized sequences into TensorFlow datasets
trn_seqs = tf.data.Dataset \
.from_tensor_slices(tokenized_seqs[:int(len(tokenized_seqs) * TRAIN_PERCENT)])
tst_seqs = tf.data.Dataset \
.from_tensor_slices(tokenized_seqs[int(len(tokenized_seqs) * TRAIN_PERCENT):])
def input_and_target(x):
return x[:-1], x[1:]
# map into (input, target) tuples, shuffle order of elements, and batch
trn_dataset = trn_seqs.map(input_and_target) \
.shuffle(SHUFFLE_BUFFER_SIZE) \
.batch(BATCH_SIZE, drop_remainder=True)
tst_dataset = tst_seqs.map(input_and_target) \
.shuffle(SHUFFLE_BUFFER_SIZE) \
.batch(BATCH_SIZE, drop_remainder=True)
return trn_dataset, tst_dataset
I then try to train my model, calling train_model(*to_datasets(raw_text)):
def train_model(trn_dataset, tst_dataset):
# import Hugging Face GPT-2 model
model = TFGPT2Model.from_pretrained("gpt2")
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=tf.metrics.SparseCategoricalAccuracy()
)
model.fit(
trn_dataset,
epochs=EPOCHS,
initial_epoch=0,
validation_data=tst_dataset
)
The ValueError is triggered on the model.fit() call. The variables in all-caps are settings pulled in from a JSON file. Currently, they are set to:
{
"BATCH_SIZE":64,
"SHUFFLE_BUFFER_SIZE":10000,
"EPOCHS":500,
"SEQ_LEN":2048,
"TRAIN_PERCENT":0.9
}
Any information regarding what this error means or ideas on how to resolve it would be greatly appreciated. Thank you!

Answer 1

I'm having the same problem but when I change the batch size to 12 (same as n_layer parameter in the gpt-2 config file) it works.
I don't Know why it works but you can try it...
If you manage to solve it on different way I will be glad to hear.