Computation of compression parameters via OpenVINO models

[nikita-savelyevv]

Changes

• Implemented OpenVINO model graphs which are used for calculation of compressed and decompressed weights. Since these models are compiled, calculation become significantly faster especially for larger models and int4 compression.
• This functionality is exposed by two methods at weight_lowering.py:
  • do_int_quantization() is used for computing a compressed weight. Possible signatures:
    • weight -> compressed_weight, scale, (zero_point for asymmetric compression)
    • weight, scale, (zero_point) -> compressed_weight, scale, (zero_point)
  • calculate_quantized_dequantized_weight() is used for computing a decompressed weight. Possible signatures:
    • weight -> decompressed_weight
    • weight, scale, (zero_point) -> decompressed_weight
    • weight -> decompressed_weight, compressed_weight, scale, (zero_point)
    • weight, scale, (zero_point) -> decompressed_weight, compressed_weight, scale, (zero_point)
  • Output scale and zero_point are the same as the ones given as input (if they were given at all).
  • Computation is done via OV models only if openvino package is installed and input tensors are not torch tensors.
• Introduce a new NNCF Tensor backend for storing instances of openvino.Tensor. Implementation for this backend is limited by only the required functionality, e.g. addition of OV Tensors is not supported because it is not needed.
  • Introduction of OV Tensors is required for seamless handling of tensors in bf16, u4 and i4 data types. For example, bf16 constants are read from an OpenVINO LLM and given as inputs to a compressing OpenVINO model. u4 and i4 compressed weights are seamlessly inserted into the resulting compressed OpenVINO model.
  • Added tensor.to_backend() method to convert an NNCF Tensor from one backend to another. Currently on OV<->NP conversion is required.
• All calculations are aligned with reference numpy implementation. Some performance and memory sacrifices had to be made for such alignment.

Data-free asymmetric compression:

Data-free symmetric compression:

Data-aware compression:

Reason for changes

Reducing model compression time. Only OpenVINO model compression backend is affected.

Related tickets

139047

Tests

• tests/openvino/native/quantization/test_ov_modeling_compression.py::test_quantization_alignment -- check aligment with reference numpy implementation
• tests/openvino/native/test_openvino_modeling.py -- checks OV modeling framework hyperparameters
• tests/openvino/native/test_tensor.py -- NNCF OV Tensor backend tests

Validation jobs:

• NNCF/job/manual/job/post_training_weight_compression/286/
• OVVP validation ✅
• optimum-intel test job https://github.com/huggingface/optimum-intel/actions/runs/12378718192/job/34551283045?pr=734