[FastBiasCorrection] Support models without the batch dimension

[daniil-lyakhov]

🚀 Feature request

Currently FastBiasCorrection algorithm does not support models without the batch dimension:
Reproducer:

From the branch:
https://github.com/daniil-lyakhov/nncf/tree/dl/fbc_no_batch_repro

python -m pytest /home/dlyakhov/Projects/nncf/tests/openvino/native/test_fast_bias_correction.py -k OneDimMM

Output:

    @pytest.mark.parametrize(
        "model_cls, ref_bias",
        (
            (ConvTestModel, [0.0288348, 1.0838453]),
            (ConvBNTestModel, [0.08396978, 1.1676897]),
            (OneDimMM, [0.95773065, 1.3218939, 0.81694865]),
        ),
    )
    def test_update_bias(self, model_cls, ref_bias, tmpdir):
        model = self.backend_specific_model(model_cls(), tmpdir)
        dataset = get_static_dataset(model_cls.INPUT_SIZE, self.get_transform_fn(), self.fn_to_type)
    
        quantization_algorithm = self.get_quantization_algorithm()
        graph = NNCFGraphFactory.create(model)
>       quantized_model = quantization_algorithm.apply(model, graph, dataset=dataset)

tests/cross_fw/test_templates/test_fast_bias_correction.py:121: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
nncf/quantization/algorithms/post_training/algorithm.py:113: in apply
    return self._pipeline.run_from_step(model, dataset, graph, 0, step_index_to_statistics)
nncf/quantization/algorithms/pipeline.py:164: in run_from_step
    step_model = self.run_step(step_index, step_statistics, step_model, step_graph)
nncf/quantization/algorithms/pipeline.py:119: in run_step
    current_model = pipeline_step[-1].apply(current_model, current_graph, step_statistics)
nncf/quantization/algorithms/fast_bias_correction/algorithm.py:182: in apply
    input_blob = self._backend_entity.create_input_data(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

shape = [3], data = [nncf.Tensor(0.6222554)], input_name = 'Parameter_/linear/MatMul.0', channel_axis = 0

    @staticmethod
    def create_input_data(
        shape: tuple[int], data: list[Tensor], input_name: str, channel_axis: int
    ) -> dict[str, np.ndarray]:
        blob = np.zeros(shape, dtype=data[0].data.dtype)
        for j, idx in enumerate(np.ndindex(blob.shape[channel_axis])):
            index = tuple(slice(None) if i != channel_axis else idx for i in range(blob.ndim))
>           blob[index] = data[j].data
E           IndexError: list index out of range

The task is to support models without the batch dimension in the FastBiasCorrection

Feature Use Case

To be able to use FastBiasCorrection model with models without the batch dimension

Are you going to submit a PR?

• Yes I'd like to help by submitting a PR!