Support backup precision option for WC (#2978)

### Changes

Add functionality to determine the backup precision to be used for
layers that are not quantized to the primary precision, which is set to
INT8_ASYM by default.

Example: Compress weights to INT4_ASYM channel-wise (group size=-1),
except embeddings, convolutions and last linear layers - they are remain
in original floating-point precision.

```python
from nncf import compress_weights, BackupMode, CompressWeightsMode
compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, group size=-1, backup_mode=BackupMode.NONE) # model is openvino.Model object
```

![image](https://github.com/user-attachments/assets/b99ce421-af1c-4a9d-83ad-a97841b44e41)
statistics:

![image](https://github.com/user-attachments/assets/744f177c-f212-456c-b426-ba60ec747183)


For mode=CompressWeightsMode.INT4_ASYM,
backup_mode=BackupMode.INT8_ASYM, and a non-empty ignored_scope, the
statistics string contains three different precisions:


![image](https://github.com/user-attachments/assets/abab1a1b-c932-4001-9c84-858d10e23cda)

### Reason for changes

 To define backup mode for compress_weight

### Related tickets

* 152056

### Tests

* test_data_free_compression_with_backup_mode
* test_data_based_compression_with_backup_mode
* tinyllama_awq_backup_mode_none

docs/usage/post_training_compression/weights_compression/Usage.md

@@ -11,8 +11,8 @@ The Weights Compression algorithm is aimed at compressing the weights of the mod
 By default, weights are compressed asymmetrically to 8-bit integer data type - "INT8_ASYM" mode.
 OpenVINO backend also supports 4 modes of mixed precision weight quantization with a 4-bit data type as a primary precision - INT4_SYM, INT4_ASYM, NF4, E2M1. The primary precision in case of INT4_SYM mode is signed 4-bit integer and weights are quantized to it [symmetrically](/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#symmetric-quantization) without zero point. In case of INT4_ASYM mode - unsigned 4-bit integer and weight are quantized to it [asymmetrically](/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#asymmetric-quantization) with a typical non-fixed zero point. In case of NF4 mode - [nf4](https://arxiv.org/pdf/2305.14314v1.pdf) data type without zero point. In case of E2M1 mode - [e2m1](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) data type without zero point and has 8bit [E8M0](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) scale.
 All 4-bit modes have a grouped quantization support, when small group of weights (e.g. 128) in the channel dimension share quantization parameters (scale).
-All embeddings, convolutions and last linear layers are always compressed to 8-bit integer data type. To quantize embeddings and last linear layers to 4-bit, use `all_layers=True`.
-Percent of the rest layers compressed to 4-bit can be configured by "ratio" parameter. E.g. ratio=0.9 means 90% of layers compressed to the corresponding 4-bit data type and the rest to 8-bit asymmetric integer data type.
+All embeddings, convolutions and last linear layers are always compressed to a backup mode, which is "INT8_ASYM", by default. To quantize embeddings and last linear layers to 4-bit, use `all_layers=True`.
+Percent of the rest layers compressed to 4-bit can be configured by "ratio" parameter. E.g. ratio=0.9 means 90% of layers compressed to the corresponding 4-bit data type and the rest to a backup mode. OpenVINO backend supports 3 backup modes: INT8_SYM, INT8_ASYM, and NONE, which retains the original floating-point precision of the model weights. Backup mode is supported only for mixed-precision weight quantization.
 
 ### User guide
 
@@ -37,6 +37,13 @@ from nncf import compress_weights, CompressWeightsMode
 compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM) # model is openvino.Model object
 ```
 
+- Compress weights to NF4 with group size = 128, except embeddings, convolutions and last linear layers - they are remain in original floating-point precision.
+
+```python
+from nncf import compress_weights, BackupMode, CompressWeightsMode
+compressed_model = compress_weights(model, mode=CompressWeightsMode.NF4, backup_mode=BackupMode.NONE) # model is openvino.Model object
+```
+
 - Generally, `INT4_SYM` mode is the fastest mixed-precision mode, but it may lead to a significant accuracy degradation or perplexity increase.
   Compressing weights asymmetrically (`INT4_ASYM` mode) is the way to increase accuracy, however in turns it slows down inference a bit.
   If the accuracy or perplexity is still not satisfying, there are 2 more hyper-parameters to tune: `group_size` and `ratio`. Please refer to the [example](https://github.com/openvinotoolkit/nncf/blob/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams) how to automatically tune these parameters.

nncf/__init__.py

@@ -32,6 +32,7 @@
 from nncf.errors import UnsupportedModelError as UnsupportedModelError
 from nncf.errors import UnsupportedVersionError as UnsupportedVersionError
 from nncf.errors import ValidationError as ValidationError
+from nncf.parameters import BackupMode as BackupMode
 from nncf.parameters import CompressWeightsMode as CompressWeightsMode
 from nncf.parameters import DropType as DropType
 from nncf.parameters import ModelType as ModelType

nncf/experimental/torch/fx/quantization/quantize_model.py

@@ -29,6 +29,7 @@
 from nncf.experimental.torch.fx.transformations import apply_quantization_transformations
 from nncf.experimental.torch.fx.transformations import revert_quantization_transformations
 from nncf.experimental.torch.fx.transformations import shared_constants_unification_transformation
+from nncf.parameters import BackupMode
 from nncf.parameters import CompressWeightsMode
 from nncf.parameters import ModelType
 from nncf.parameters import QuantizationMode
@@ -124,6 +125,7 @@ def compress_weights_impl(
     scale_estimation: bool,
     gptq: bool,
     lora_correction: bool,
+    backup_mode: BackupMode,
     advanced_parameters: Optional[AdvancedCompressionParameters] = None,
 ) -> torch.fx.GraphModule:
     """
@@ -142,6 +144,7 @@ def compress_weights_impl(
         scale_estimation,
         gptq,
         lora_correction,
+        backup_mode,
         advanced_parameters,
     )
     shared_constants_unification_transformation(model)

nncf/openvino/quantization/quantize_model.py

@@ -29,6 +29,7 @@
 from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed
 from nncf.openvino.quantization.quantize_ifmodel import apply_algorithm_if_bodies
 from nncf.openvino.rt_info import dump_parameters
+from nncf.parameters import BackupMode
 from nncf.parameters import CompressWeightsMode
 from nncf.parameters import DropType
 from nncf.parameters import ModelType
@@ -379,6 +380,7 @@ def compress_weights_impl(
     scale_estimation: bool,
     gptq: bool,
     lora_correction: bool,
+    backup_mode: BackupMode,
     advanced_parameters: Optional[AdvancedCompressionParameters] = None,
 ) -> ov.Model:
     """
@@ -398,6 +400,7 @@ def compress_weights_impl(
         scale_estimation,
         gptq,
         lora_correction,
+        backup_mode,
         advanced_parameters,
     )
     graph = NNCFGraphFactory.create(model)

nncf/parameters.py

@@ -96,6 +96,23 @@ class CompressWeightsMode(StrEnum):
     E2M1 = "e2m1"
 
 
+@api(canonical_alias="nncf.BackupMode")
+class BackupMode(StrEnum):
+    """
+    Defines a backup mode for weight compression.
+    :param NONE: Stands for original floating-point precision of the model weights.
+        In this mode, weights are retained in their original precision without any quantization.
+    :param INT8_SYM: Stands for 8-bit integer symmetric quantization without zero point.
+        https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#symmetric-quantization
+    :param INT8_ASYM: Stands for 8-bit integer asymmetric quantization with a typical non-fixed zero point.
+        https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#asymmetric-quantization
+    """
+
+    NONE = "none"
+    INT8_SYM = "int8_sym"
+    INT8_ASYM = "int8_asym"
+
+
 @api(canonical_alias="nncf.SensitivityMetric")
 class SensitivityMetric(StrEnum):
     """