[Good First Issue][NNCF]: Support not transposed weight for data-aware weight compression methods

[ljaljushkin]

Context

Matmul operation in OpenVINO assumes an implicit shape alignment for input arguments. It applies transpositions specified by optional transpose_a and transpose_b attributes: OV spec.
Currently, weight compression in NNCF does not support transpose_b=False.
Here's the test.
Potentially, it affects Mixed-Precision, AWQ, Scale Estimation, GPTQ and Lora Correction algorithms.

What needs to be done?

The task is to enable data-aware weight compression methods (Mixed-Precision, AWQ, Scale Estimation, Lora Correction, GPTQ) for models with matrix multiplications having not transposed weight.

test_compression_with_transpose shouldn't raise an error for transpose_b=False

Example Pull Requests

#3230
#3296

Resources

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Contact points

@ljaljushkin

Ticket

No response

[yuanjua]

.take

[github-actions]

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