[NNCF] Enable data-aware weight compression for MatMul with transpose_b=False

[Varshith-Yadav]

Changes

fixes #3494
Added full support for data-aware weight compression when MatMul nodes use transpose_b=False.
Updated and validated test_compression_with_transpose to ensure it passes for transpose_b=False.

Reason for changes

Previously, NNCF’s weight compression flow assumed that the weight input of MatMul operations was always transposed (transpose_b=True).

Related tickets

Tests

pytest tests/openvino/native/quantization/test_weights_compression.py -v
(All tests pass; test_scale_estimation[True] remains the expected XFAIL for ticket 176465.)

[ljaljushkin]

@Varshith-Yadav, thank you for the contribution!
Is it possible to avoid many if conditions and do transpose once to keep original logic for transposed weights?

[ljaljushkin]

please also add unit tests.
at least you can copy-paste from #3725: https://github.com/openvinotoolkit/nncf/pull/3725/files#diff-223ea638f7751f7c0c3e8f867ec9c8c132a3ccd62a9dcea2a5d158836c71c222R1955-R1979 and make sure exception is not raised for transpose_b=False

[ljaljushkin]

@Varshith-Yadav, thank you for the contribution! Is it possible to avoid many if conditions and do transpose once to keep original logic for transposed weights?

I have reconsidered and now believe that transposing each weight can extend the total compression duration. What about implementing and utilizing a "slice_weight" method with a transpose parameter?

[Varshith-Yadav]

@ljaljushkin
That makes sense. I agree that explicitly transposing the full weight tensor could introduce unnecessary overhead

I will update the implementation to use a slice_weight helper method . This way, we can fetch the necessary channels dynamically based on the transpose_b parameter without physically reshaping the underlying tensor.

I'll proceed with this approach and update the PR shortly.

[Varshith-Yadav]

@ljaljushkin
I've updated the implementation as requested. I added a slice_weight helper in utils.py to handle the data access without performing a full transpose, and refactored the GPTQ logic to use it.

I also added a new test file test_utils_slice_weight.py to verify the helper works correctly for both Numpy and PyTorch tensors with different transpose_b settings.

[ljaljushkin]

I believe you need just 2 methods

def get_weight_slice(weight: Tensor, slice_obj: Union[int, slice, Tensor], is_transposed: bool) -> Tensor:
     return weight[:, slice_obj] if is_transposed else weight[slice_obj, :]

def set_weight_slice(weight: Tensor, slice_obj: Union[int, slice, Tensor], value: Tensor, is_transposed: bool) -> None:
    if is_transposed:
        weight[:, slice_obj] = value
    else:
        weight[slice_obj, :] = value

[ljaljushkin]

the same issue should be in other data-aware algorithms: awq, lora_correction, scale_estimation
Support copy-pasting a test for transpose_b=False + all these methods and check whether it fails: https://github.com/openvinotoolkit/nncf/pull/3725/files#diff-223ea638f7751f7c0c3e8f867ec9c8c132a3ccd62a9dcea2a5d158836c71c222R1960-R1961

[ljaljushkin]

utils name violates the code style: https://github.com/openvinotoolkit/nncf/blob/develop/docs/styleguide/PyGuide.md#474-file-naming
Possible name: tensor_slicing.py

[ljaljushkin]

also recommend configuring automatic code formating: https://github.com/openvinotoolkit/nncf/blob/develop/docs/styleguide/PyGuide.md#2-automating-code-formatting