Fix imports

Author: nikita-savelyevv

notebooks/openvino/stable_diffusion_hybrid_quantization.ipynb

@@ -53,7 +53,7 @@
     "from pathlib import Path\n",
     "from openvino.runtime import Core\n",
     "from optimum.intel import OVConfig, OVQuantizer, OVStableDiffusionPipeline, OVWeightQuantizationConfig\n",
-    "from optimum.intel.openvino.quantization.configuration import OVQuantizationMethod\n",
+    "from optimum.intel.openvino.quantization import OVQuantizationMethod\n",
     "\n",
     "transformers.logging.set_verbosity_error()\n",
     "datasets.logging.set_verbosity_error()"

optimum/exporters/openvino/__main__.py

@@ -52,7 +52,7 @@
 FORCE_ATTN_MODEL_CLASSES = {"phi3-v": "eager", "gemma2": "sdpa"}
 
 if TYPE_CHECKING:
-    from optimum.intel.openvino.quantization.configuration import OVConfig
+    from optimum.intel.openvino.quantization import OVConfig
 
 
 if is_torch_available():

optimum/exporters/openvino/convert.py

@@ -90,7 +90,7 @@
 
 if TYPE_CHECKING:
     from optimum.exporters.onnx.base import OnnxConfig
-    from optimum.intel.openvino.quantization.configuration import OVConfig
+    from optimum.intel.openvino.quantization import OVConfig
 
 
 def _set_runtime_options(

optimum/intel/openvino/configuration.py

@@ -33,6 +33,6 @@
 logger = logging.getLogger(__name__)
 
 logger.warning(
-    "`optimum.intel.configuration` import path is deprecated and will be removed in optimum-intel v.1.26."
+    "`optimum.intel.configuration` import path is deprecated and will be removed in optimum-intel v1.25."
     "Please use `optimum.intel.quantization.configuration` instead."
 )

optimum/intel/openvino/modeling_seq2seq.py

@@ -38,7 +38,7 @@
 from ...exporters.openvino.stateful import model_has_state
 from ..utils import is_transformers_version
 from .modeling_base_seq2seq import OVBaseModelForSeq2SeqLM
-from .quantization import OVConfig, OVQuantizationConfig, OVQuantizationConfigBase, OVQuantizer
+from .quantization import OVConfig, OVQuantizationConfig, OVQuantizationConfigBase
 from .utils import OV_TO_PT_TYPE, _print_compiled_model_properties
 
 
@@ -1030,6 +1030,8 @@ def _from_pretrained(
 
         quantization_config = cls._prepare_quantization_config(quantization_config, load_in_8bit)
         if not compile_only and isinstance(quantization_config, OVQuantizationConfig):
+            from .quantization import OVQuantizer
+
             model = super(OVModelForSpeechSeq2Seq, cls)._from_pretrained(
                 model_id, config, load_in_8bit=False, **kwargs
             )

optimum/intel/openvino/quantization/__init__.py

@@ -13,6 +13,8 @@
 #  limitations under the License.
 
 
+from optimum.intel.utils.import_utils import is_nncf_available
+
 from .configuration import (
     OVConfig,
     OVDynamicQuantizationConfig,
@@ -22,4 +24,7 @@
     OVQuantizationMethod,
     OVWeightQuantizationConfig,
 )
-from .quantizer import OVQuantizer
+
+
+if is_nncf_available():
+    from .quantizer import OVQuantizer

optimum/intel/openvino/quantization/calibration_dataset_builder.py

@@ -28,7 +28,6 @@
 from tqdm import tqdm
 from transformers import AutoProcessor, AutoTokenizer, DataCollator, default_data_collator
 
-from optimum.intel.openvino.modeling_decoder import OVBaseDecoderModel
 from optimum.intel.openvino.utils import (
     PREDEFINED_DIFFUSION_DATASETS,
     PREDEFINED_SPEECH_TO_TEXT_DATASETS,
@@ -168,6 +167,7 @@ def build_from_dataset(
         # TODO: deprecate remove_unused_columns ?
 
         from optimum.intel import OVModelForVisualCausalLM
+        from optimum.intel.openvino.modeling_decoder import OVBaseDecoderModel
         from optimum.intel.openvino.modeling_seq2seq import _OVModelForWhisper
 
         if is_diffusers_available():
@@ -181,7 +181,7 @@ def build_from_dataset(
             return self._prepare_visual_causal_lm_calibration_data(quantization_config, dataloader)
         elif isinstance(self.model, _OVModelForWhisper):
             return self._prepare_speech_to_text_calibration_data(quantization_config, dataloader)
-        elif isinstance(self.model, OVDiffusionPipeline):
+        elif is_diffusers_available() and isinstance(self.model, OVDiffusionPipeline):
             return self._prepare_diffusion_calibration_data(quantization_config, dataloader)
         else:
             # Torch model quantization scenario
@@ -334,7 +334,7 @@ def preprocess_function(item):
                 )
             else:
                 raise Exception
-        elif isinstance(self.model, OVDiffusionPipeline):
+        elif is_diffusers_available() and isinstance(self.model, OVDiffusionPipeline):
             dataset = config.dataset
 
             dataset_metadata = None

optimum/intel/openvino/quantization/quantizer.py

@@ -44,7 +44,6 @@
     is_diffusers_available,
 )
 from ...utils.modeling_utils import get_model_device
-from ..modeling_base import OVBaseModel
 from ..utils import (
     MAX_ONNX_OPSET,
     MIN_ONNX_QDQ_OPSET,
@@ -193,7 +192,8 @@ def quantize(
                 calibration_dataset = calibration_dataset.select_columns(["caption"])
 
             if (
-                isinstance(self.model, OVDiffusionPipeline)
+                is_diffusers_available()
+                and isinstance(self.model, OVDiffusionPipeline)
                 and isinstance(calibration_dataset, list)
                 and all(isinstance(it, str) for it in calibration_dataset)
             ):
@@ -204,6 +204,8 @@ def quantize(
                 quantization_config, calibration_dataset, batch_size, data_collator, remove_unused_columns
             )
 
+        from ..modeling_base import OVBaseModel
+
         if isinstance(self.model, OVBaseModel):
             if self.model._compile_only:
                 raise ValueError(
@@ -284,7 +286,11 @@ def _quantize_ovbasemodel(
             if calibration_datasets is None:
                 raise ValueError("Calibration dataset is required to run data-aware quantization.")
             if (
-                not isinstance(self.model, (OVDiffusionPipeline, _OVModelForWhisper))
+                not (
+                    is_diffusers_available()
+                    and isinstance(self.model, OVDiffusionPipeline)
+                    or isinstance(self.model, _OVModelForWhisper)
+                )
                 and "model" not in calibration_datasets
             ):
                 raise RuntimeError("Calibration datasets should contain a key 'model' with a dataset.")