Warning on loading quantized model

[andxalex]

Hello,

I'm running base mistral and using bnb to quantize as below:

from optimum_benchmark.backends.pytorch.config import PyTorchConfig
from optimum_benchmark.benchmarks.inference.config import InferenceConfig
from optimum_benchmark.experiment import ExperimentConfig, launch
from optimum_benchmark.launchers.process.config import ProcessConfig
from optimum_benchmark.logging_utils import setup_logging
import torch

if __name__ == "__main__":
    setup_logging(level="INFO")
    launcher_config = ProcessConfig(device_isolation=False)
    benchmark_config = InferenceConfig(
        memory=True,
        latency=True,
        input_shapes={"batch_size": 4, "sequence_length": 128},
        generate_kwargs={"max_new_tokens": 128, "min_new_tokens": 128},
    )
    backend_config = PyTorchConfig(
        model="mistralai/Mistral-7B-v0.1",
        device="cuda",
        device_ids="0",
        torch_dtype="float16",
        no_weights=True,
        quantization_scheme="bnb",
        quantization_config={"load_in_4bit": True,
                                            "bnb_4bit_compute_dtype": "float16",
                                            "bnb_4bit_quant_type": "nf4", 
                                            },
    )
    experiment_config = ExperimentConfig(
        experiment_name="mistral-fp16-base",
        benchmark=benchmark_config,
        launcher=launcher_config,
        backend=backend_config,
    )
    benchmark_report = launch(experiment_config)

I get this error about the model already having a quantization config. Is this expected behaviour?

Is there a mistake in how I'm passing the quantization config in the pytorch backend?

[IlyasMoutawwakil]

This seems to be an edge case with BitsAndBytes and no weights integration (no_weights=true + quantization_scheme=bnb).
TLDR; It will work as intended

Basically when trying to create a no weights model, for AWQ and GPTQ we add the quant config to the model's pretrained config so that the dummy (no weights) model gets loaded in its quantized format. However this is not necessary for BnB models as they're quantized on the fly during model loading, the warning is shown because bnb pre-quantized models (saved in quantized format) don't expect any quantization config rewriting.

If you want to avoid this warning, please consider submitting a PR to change the condition here to only include awq and gptq 🤗

optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py

Lines 217 to 220 in d2d1e62

	if self.is_quantized:
	LOGGER.info("\t+ Adding quantization config to no weights model's pretrained config")
	self.pretrained_config.quantization_config = self.quantization_config.to_dict()
	# tricking from_pretrained to load the model as if it was quantized