[OpenVINO] Adopt new mxfp4 quantization logic (#1465)

* Remove cb4 pre-release logic; adopt new mxfp4 logic

* Fix tests

* Fix tests 2

* Update setup.py

Author: nikita-savelyevv

optimum/intel/openvino/configuration.py

@@ -715,7 +715,7 @@ def __init__(
         )
         self.bits = bits
         self.sym = sym
-        self.group_size = group_size or (-1 if bits == 8 else 128)
+        self.group_size = group_size
         self.ratio = ratio
         self.all_layers = all_layers
         self.sensitivity_metric = sensitivity_metric
@@ -794,7 +794,7 @@ def post_init(self):
                 raise ValueError(
                     f"For 8-bit quantization, `ratio` is expected to be set to 1.0, but was set to {self.ratio}"
                 )
-            if self.group_size != -1:
+            if self.group_size is not None and self.group_size != -1:
                 raise ValueError(
                     f"For 8-bit quantization, `group_size` is expected to be set to -1, but was set to {self.group_size}"
                 )
@@ -843,11 +843,6 @@ def post_init(self):
                 f"['int4', 'int8', 'mxfp4', 'nf4', 'cb4'], but found: {self.dtype}."
             )
         if self.dtype in ["mxfp4", "nf4", "cb4"]:
-            if self.dtype == "cb4" and is_nncf_version("<=", "2.17"):
-                raise ImportError(
-                    "Codebook quantization is currently supported only with NNCF develop. "
-                    "Please run `pip install git+https://github.com/openvinotoolkit/nncf.git`."
-                )
             if self.bits != 4:
                 raise ValueError(
                     f"When applying weight compression with '{self.dtype}' data type, the `bits` parameter must be set to 4, but found {self.bits}"
@@ -874,7 +869,7 @@ def to_nncf_dict(self) -> Dict[str, Any]:
         if mode in signed_bitness.values():
             mode += "_sym" if self.sym else "_asym"
         if mode == "mxfp4":
-            mode = "e2m1"
+            mode = "e2m1" if is_nncf_version("<=", "2.18") else "mxfp4"
         if mode == "cb4":
             mode = "cb4_f8e4m3"
         mode = nncf.CompressWeightsMode(mode)

setup.py

@@ -66,8 +66,8 @@
 QUALITY_REQUIRE = ["black~=23.1", "ruff==0.4.4"]
 
 EXTRAS_REQUIRE = {
-    "nncf": ["nncf>=2.16.0"],
-    "openvino": ["nncf>=2.16.0", "openvino>=2025.1.0", "openvino-tokenizers>=2025.1.0"],
+    "nncf": ["nncf>=2.18.0"],
+    "openvino": ["nncf>=2.18.0", "openvino>=2025.1.0", "openvino-tokenizers>=2025.1.0"],
     "neural-compressor": ["neural-compressor[pt]>=3.4.1", "accelerate", "transformers<4.46"],
     "ipex": ["intel-extension-for-pytorch>=2.8", "transformers>4.54,<4.56", "accelerate"],
     "diffusers": ["diffusers"],

tests/openvino/test_exporters_cli.py

@@ -18,7 +18,6 @@
 from typing import Dict
 from unittest.mock import Mock
 
-import pytest
 from parameterized import parameterized
 from transformers import AutoModelForCausalLM, AutoModelForZeroShotImageClassification, AutoProcessor, AutoTokenizer
 from utils_tests import (
@@ -63,7 +62,6 @@
 from optimum.intel.openvino.utils import _HEAD_TO_AUTOMODELS, TemporaryDirectory
 from optimum.intel.utils.import_utils import (
     compare_versions,
-    is_nncf_version,
     is_openvino_tokenizers_available,
     is_openvino_version,
     is_tokenizers_version,
@@ -443,7 +441,7 @@ class OVCLIExportTestCase(unittest.TestCase):
             if is_transformers_version("<=", "4.45")
             else {
                 "encoder": 30,
-                "decoder": 62 if is_nncf_version("<=", "2.17") and is_openvino_version("<", "2025.3") else 52,
+                "decoder": 52,
             },
             (
                 {"encoder": {"int8": 32}, "decoder": {"int8": 52}, "decoder_with_past": {"int8": 42}}
@@ -1026,8 +1024,6 @@ def test_exporters_cli_hybrid_quantization(
     def test_exporters_cli_4bit(
         self, task: str, model_type: str, option: str, expected_num_weight_nodes_per_model: Dict[str, Dict[str, int]]
     ):
-        if option.startswith("cb4") and is_nncf_version("<=", "2.17"):
-            pytest.skip("Codebook quantization is supported starting from NNCF 2.18")
         with TemporaryDirectory() as tmpdir:
             result = subprocess.run(
                 f"optimum-cli export openvino --model {MODEL_NAMES[model_type]} --task {task} --weight-format {option} {tmpdir}",
@@ -1084,8 +1080,6 @@ def test_exporters_cli_full_quantization(
         expected_fake_nodes_per_model: Dict[str, int],
         expected_num_weight_nodes_per_model: Dict[str, Dict[str, int]],
     ):
-        if quant_mode == "cb4_f8e4m3" and is_nncf_version("<=", "2.17"):
-            pytest.skip("Codebook quantization is supported starting from NNCF 2.18")
         with TemporaryDirectory() as tmpdir:
             subprocess.run(
                 f"optimum-cli export openvino --task {task} --model {MODEL_NAMES[model_type]} "

tests/openvino/test_quantization.py

@@ -80,7 +80,7 @@
 from copy import deepcopy
 
 from optimum.intel.openvino.quantization import InferRequestWrapper, OVCalibrationDatasetBuilder
-from optimum.intel.utils.import_utils import is_openvino_version, is_transformers_version, is_nncf_version
+from optimum.intel.utils.import_utils import is_openvino_version, is_transformers_version
 from utils_tests import (
     MODEL_NAMES,
     get_num_quantized_nodes,
@@ -394,7 +394,7 @@ class OVQuantizerTest(unittest.TestCase):
             if is_transformers_version("<=", "4.45")
             else {
                 "encoder": 30,
-                "decoder": 62 if is_nncf_version("<=", "2.17") and is_openvino_version("<", "2025.3") else 52,
+                "decoder": 52,
             },
             (
                 {"encoder": {"int8": 32}, "decoder": {"int8": 52}, "decoder_with_past": {"int8": 42}}
@@ -568,12 +568,6 @@ def test_ov_model_static_quantization_with_auto_dataset(
         expected_fake_nodes_per_model,
         expected_num_weight_nodes_per_model,
     ):
-        if (
-            isinstance(quantization_config, dict)
-            and quantization_config.get("weight_quantization_config", {}).get("dtype") == "cb4"
-            and is_nncf_version("<=", "2.17")
-        ):
-            pytest.skip("Codebook quantization is supported starting from NNCF 2.18")
         model_id = MODEL_NAMES[model_name]
 
         with TemporaryDirectory() as tmp_dir:
@@ -1303,13 +1297,6 @@ def test_ovmodel_4bit_auto_compression(self, model_cls, model_type, expected_ov_
     def test_ovmodel_4bit_auto_compression_with_config(
         self, model_cls, model_name, trust_remote_code, quantization_config, expected_num_weight_nodes_per_model
     ):
-        if (
-            isinstance(quantization_config, dict)
-            and quantization_config.get("dtype") == "cb4"
-            and is_nncf_version("<=", "2.17")
-        ):
-            pytest.skip("Codebook quantization is supported starting from NNCF 2.18")
-
         model_id = MODEL_NAMES[model_name]
         with TemporaryDirectory() as tmp_dir:
             quantization_config = OVWeightQuantizationConfig.from_dict(quantization_config)
@@ -1390,7 +1377,7 @@ def main_export_in_stacktrace(*args, **kwargs):
                 compression_params = {
                     "mode": nncf.CompressWeightsMode.INT8_ASYM,
                     "ratio": 1.0,
-                    "group_size": -1,
+                    "group_size": None,
                     "all_layers": None,
                     "sensitivity_metric": None,
                     "dataset": None,
@@ -1805,6 +1792,8 @@ def eval_expression_if_possible(expression):
                             config_value = (
                                 "max_activation_variance" if sub_config.bits == 4 else "weight_quantization_error"
                             )
+                        if param_name == "group_size" and config_value is None:
+                            config_value = -1 if sub_config.bits == 8 else 128
 
                         if config_value is None and rt_info_value is False:
                             continue