Fix: local variable 'dataset' referenced before assignment

[hiyouga]

What does this PR do?

Fixes local variable 'dataset' referenced before assignment

Reproduce

from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig

model_id = "facebook/opt-125m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = [{"input_ids": [0, 1, 2, 3, 4], "attention_mask": [1, 1, 1, 1, 1]}]
gptq_config = GPTQConfig(bits=4, dataset=dataset, tokenizer=tokenizer)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config)

The logic is broken if we pass a tokenized dataset to the GPTQQuantizer.

optimum/optimum/gptq/quantizer.py

Lines 358 to 382 in 7376c6a

	# Step 1: Prepare the data
	if isinstance(self.dataset, list) and not isinstance(self.dataset[0], str):
	logger.info("GPTQQuantizer dataset appears to be already tokenized. Skipping tokenization.")
	else:
	if isinstance(tokenizer, str):
	try:
	tokenizer = AutoTokenizer.from_pretrained(tokenizer)
	except Exception:
	raise ValueError(
	f"""We were not able to get the tokenizer using `AutoTokenizer.from_pretrained`
	with the string that you have passed {tokenizer}. If you have a custom tokenizer, you can pass it as input.
	For now, we only support quantization for text model. Support for vision, speech and multimodel will come later."""
	)
	if self.dataset is None:
	raise ValueError("You need to pass `dataset` in order to quantize your model")
	elif isinstance(self.dataset, str):
	dataset = get_dataset(self.dataset, tokenizer, seqlen=self.model_seqlen, split="train")
	elif isinstance(self.dataset, list):
	dataset = [tokenizer(data, return_tensors="pt") for data in self.dataset]
	else:
	raise ValueError(
	f"You need to pass a list of string, a list of tokenized data or a string for `dataset`. Found: {type(self.dataset)}."
	)
	dataset = prepare_dataset(dataset, pad_token_id=self.pad_token_id, batch_size=self.batch_size)

Traceback (most recent call last):
  File "lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 566, in from_pretrained
    return model_class.from_pretrained(
  File "lib/python3.10/site-packages/transformers/modeling_utils.py", line 3768, in from_pretrained
    quantizer.quantize_model(model, quantization_config.tokenizer)
  File "lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "lib/python3.10/site-packages/optimum/gptq/quantizer.py", line 382, in quantize_model
    dataset = prepare_dataset(dataset, pad_token_id=self.pad_token_id, batch_size=self.batch_size)
UnboundLocalError: local variable 'dataset' referenced before assignment

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?

[fxmarty]

LGTM!