Avoid use of deprecated openvino.runtime (#1274)

* Avoid use of deprecate openvino.runtime namespace in favor of openvino

Signed-off-by: Kazantsev, Roman <[email protected]>

* Fix code-style

Signed-off-by: Kazantsev, Roman <[email protected]>

* Fix code-style

Signed-off-by: Kazantsev, Roman <[email protected]>

---------

Signed-off-by: Kazantsev, Roman <[email protected]>

Author: rkazants

optimum/exporters/openvino/__main__.py

@@ -25,7 +25,7 @@
 from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizerBase, ProcessorMixin
 from transformers.utils import is_torch_available
 
-from openvino.runtime import Core, Type, save_model
+from openvino import Core, Type, save_model
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx.base import OnnxConfig
 from optimum.exporters.onnx.constants import SDPA_ARCHS_ONNX_EXPORT_NOT_SUPPORTED

optimum/exporters/openvino/convert.py

@@ -25,8 +25,8 @@
 from transformers.models.speecht5.modeling_speecht5 import SpeechT5HifiGan
 from transformers.utils import is_tf_available, is_torch_available
 
-from openvino.runtime import Model, save_model
-from openvino.runtime.exceptions import OVTypeError
+from openvino import Model, save_model
+from openvino.exceptions import OVTypeError
 from openvino.tools.ovc import convert_model
 from optimum.exporters import TasksManager
 from optimum.exporters.utils import (

optimum/exporters/openvino/stateful.py

@@ -19,7 +19,7 @@
 from transformers import PretrainedConfig
 
 import openvino as ov
-from openvino.runtime import opset13
+from openvino import opset13
 from optimum.intel.utils.import_utils import _openvino_version, is_openvino_version, is_transformers_version
 
 from .utils import MULTI_MODAL_TEXT_GENERATION_MODELS

optimum/exporters/openvino/utils.py

@@ -21,8 +21,8 @@
 from transformers import PretrainedConfig
 from transformers.utils import is_torch_available
 
-from openvino.runtime import Dimension, PartialShape, Symbol
-from openvino.runtime.utils.types import get_element_type
+from openvino import Dimension, PartialShape, Symbol
+from openvino.utils.types import get_element_type
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx.base import OnnxConfig
 from optimum.intel.utils import is_transformers_version

optimum/intel/openvino/loaders.py

@@ -19,9 +19,9 @@
 import torch
 from diffusers.loaders.textual_inversion import TextualInversionLoaderMixin, load_textual_inversion_state_dicts
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
-from openvino.runtime import Type
-from openvino.runtime import opset11 as ops
-from openvino.runtime.passes import Manager, Matcher, MatcherPass, WrapType
+from openvino import Type
+from openvino import opset11 as ops
+from openvino.passes import Manager, Matcher, MatcherPass, WrapType
 from transformers import PreTrainedTokenizer
 
 from .utils import TEXTUAL_INVERSION_EMBEDDING_KEYS
@@ -81,7 +81,7 @@ def load_textual_inversion(
         pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],
         token: Optional[Union[str, List[str]]] = None,
         tokenizer: Optional["PreTrainedTokenizer"] = None,  # noqa: F821
-        text_encoder: Optional["openvino.runtime.Model"] = None,  # noqa: F821
+        text_encoder: Optional["openvino.Model"] = None,  # noqa: F821
         **kwargs,
     ):
         if not hasattr(self, "tokenizer"):
@@ -97,9 +97,9 @@ def load_textual_inversion(
             raise ValueError(
                 f"{self.__class__.__name__} requires `self.text_encoder` for calling `{self.load_textual_inversion.__name__}`"
             )
-        elif not isinstance(self.text_encoder.model, openvino.runtime.Model):
+        elif not isinstance(self.text_encoder.model, openvino.Model):
             raise ValueError(
-                f"{self.__class__.__name__} requires `self.text_encoder` of type `openvino.runtime.Model` for calling `{self.load_textual_inversion.__name__}`"
+                f"{self.__class__.__name__} requires `self.text_encoder` of type `openvino.Model` for calling `{self.load_textual_inversion.__name__}`"
             )
 
         # 1. Set correct tokenizer and text encoder

optimum/intel/openvino/modeling.py

@@ -66,7 +66,7 @@
     This model inherits from [`optimum.intel.openvino.modeling.OVBaseModel`]. Check the superclass documentation for the generic methods the
     library implements for all its model (such as downloading or saving)
     Parameters:
-        model (`openvino.runtime.Model`): is the main class used to run OpenVINO Runtime inference.
+        model (`openvino.Model`): is the main class used to run OpenVINO Runtime inference.
         config (`transformers.PretrainedConfig`): [PretrainedConfig](https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig)
             is the Model configuration class with all the parameters of the model.
             Initializing with a config file does not load the weights associated with the model, only the configuration.
@@ -119,7 +119,7 @@ class OVModel(OVBaseModel):
     base_model_prefix = "openvino_model"
     auto_model_class = AutoModel
 
-    def __init__(self, model: openvino.runtime.Model, config: transformers.PretrainedConfig = None, **kwargs):
+    def __init__(self, model: openvino.Model, config: transformers.PretrainedConfig = None, **kwargs):
         super().__init__(model, config, **kwargs)
         # Avoid warnings when creating a transformers pipeline
         AutoConfig.register(self.base_model_prefix, AutoConfig)

optimum/intel/openvino/modeling_base.py

@@ -67,7 +67,7 @@ class OVBaseModel(OptimizedModel):
 
     def __init__(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         config: PretrainedConfig = None,
         device: str = "CPU",
         dynamic_shapes: bool = True,
@@ -206,7 +206,7 @@ def dtype(self) -> Optional[torch.dtype]:
         return None
 
     @property
-    def ov_submodels(self) -> Dict[str, openvino.runtime.Model]:
+    def ov_submodels(self) -> Dict[str, openvino.Model]:
         return {submodel_name: getattr(self, submodel_name) for submodel_name in self._ov_submodel_names}
 
     @property
@@ -220,7 +220,7 @@ def _ov_submodel_names(self) -> List[str]:
     def load_model(
         file_name: Union[str, Path],
         quantization_config: Union[OVWeightQuantizationConfig, Dict] = None,
-    ) -> openvino.runtime.Model:
+    ) -> openvino.Model:
         """
         Loads the model.
 
@@ -231,7 +231,7 @@ def load_model(
                 Quantization config to apply after model is loaded.
         """
 
-        def fix_op_names_duplicates(model: openvino.runtime.Model):
+        def fix_op_names_duplicates(model: openvino.Model):
             names = set()
             for op in model.get_ops():
                 friendly_name = op.get_friendly_name()
@@ -705,7 +705,7 @@ def compile(self):
 
     def _reshape(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         batch_size: int,
         sequence_length: int,
         height: int = None,

optimum/intel/openvino/modeling_base_seq2seq.py

@@ -53,9 +53,9 @@ class OVBaseModelForSeq2SeqLM(OVBaseModel):
 
     def __init__(
         self,
-        encoder: openvino.runtime.Model,
-        decoder: openvino.runtime.Model,
-        decoder_with_past: openvino.runtime.Model = None,
+        encoder: openvino.Model,
+        decoder: openvino.Model,
+        decoder_with_past: openvino.Model = None,
         config: PretrainedConfig = None,
         device: str = "CPU",
         dynamic_shapes: bool = True,
@@ -446,7 +446,7 @@ def _from_transformers(
             **kwargs,
         )
 
-    def _reshape(self, model: openvino.runtime.Model, batch_size: int, sequence_length: int, is_decoder=True):
+    def _reshape(self, model: openvino.Model, batch_size: int, sequence_length: int, is_decoder=True):
         shapes = {}
         for inputs in model.inputs:
             shapes[inputs] = inputs.get_partial_shape()

optimum/intel/openvino/modeling_decoder.py

@@ -103,7 +103,7 @@
 class OVBaseDecoderModel(OVModel):
     def __init__(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         config: PretrainedConfig = None,
         device: str = "CPU",
         dynamic_shapes: bool = True,
@@ -360,7 +360,7 @@ def _from_transformers(
 
     def _reshape(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         batch_size: int,
         sequence_length: int,
         height: int = None,

optimum/intel/openvino/modeling_diffusion.py

@@ -49,8 +49,8 @@
 from huggingface_hub import snapshot_download
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
 from huggingface_hub.utils import validate_hf_hub_args
+from openvino import Core
 from openvino._offline_transformations import compress_model_transformation
-from openvino.runtime import Core
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 from transformers.modeling_outputs import ModelOutput
 from transformers.utils import http_user_agent
@@ -141,14 +141,14 @@ class OVDiffusionPipeline(OVBaseModel, DiffusionPipeline):
     def __init__(
         self,
         scheduler: SchedulerMixin,
-        unet: Optional[openvino.runtime.Model] = None,
-        vae_decoder: Optional[openvino.runtime.Model] = None,
+        unet: Optional[openvino.Model] = None,
+        vae_decoder: Optional[openvino.Model] = None,
         # optional pipeline models
-        vae_encoder: Optional[openvino.runtime.Model] = None,
-        text_encoder: Optional[openvino.runtime.Model] = None,
-        text_encoder_2: Optional[openvino.runtime.Model] = None,
-        text_encoder_3: Optional[openvino.runtime.Model] = None,
-        transformer: Optional[openvino.runtime.Model] = None,
+        vae_encoder: Optional[openvino.Model] = None,
+        text_encoder: Optional[openvino.Model] = None,
+        text_encoder_2: Optional[openvino.Model] = None,
+        text_encoder_3: Optional[openvino.Model] = None,
+        transformer: Optional[openvino.Model] = None,
         # optional pipeline submodels
         tokenizer: Optional[CLIPTokenizer] = None,
         tokenizer_2: Optional[CLIPTokenizer] = None,
@@ -183,7 +183,7 @@ def __init__(
                 )
 
             main_model = unet if unet is not None else transformer
-            if not isinstance(main_model, openvino.runtime.CompiledModel):
+            if not isinstance(main_model, openvino.CompiledModel):
                 raise ValueError("`compile_only` expect that already compiled model will be provided")
 
             model_is_dynamic = model_has_dynamic_inputs(main_model)
@@ -278,7 +278,7 @@ def __init__(
             self.compile()
 
     @property
-    def ov_submodels(self) -> Dict[str, openvino.runtime.Model]:
+    def ov_submodels(self) -> Dict[str, openvino.Model]:
         return {name: getattr(getattr(self, name), "model") for name in self._ov_submodel_names}
 
     @property
@@ -709,7 +709,7 @@ def batch_size(self) -> int:
 
     def _reshape_unet(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         batch_size: int = -1,
         height: int = -1,
         width: int = -1,
@@ -757,7 +757,7 @@ def _reshape_unet(
 
     def _reshape_transformer(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         batch_size: int = -1,
         height: int = -1,
         width: int = -1,
@@ -824,17 +824,15 @@ def _reshape_transformer(
         model.reshape(shapes)
         return model
 
-    def _reshape_text_encoder(
-        self, model: openvino.runtime.Model, batch_size: int = -1, tokenizer_max_length: int = -1
-    ):
+    def _reshape_text_encoder(self, model: openvino.Model, batch_size: int = -1, tokenizer_max_length: int = -1):
         if batch_size != -1:
             shapes = {input_tensor: [batch_size, tokenizer_max_length] for input_tensor in model.inputs}
             model.reshape(shapes)
         return model
 
     def _reshape_vae_encoder(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         batch_size: int = -1,
         height: int = -1,
         width: int = -1,
@@ -858,7 +856,7 @@ def _reshape_vae_encoder(
 
     def _reshape_vae_decoder(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         height: int = -1,
         width: int = -1,
         num_images_per_prompt: int = -1,
@@ -1098,7 +1096,7 @@ class OVPipelinePart(ConfigMixin):
 
     def __init__(
         self,
-        model: openvino.runtime.Model,
+        model: openvino.Model,
         parent_pipeline: OVDiffusionPipeline,
         model_name: str = "",
     ):
@@ -1184,7 +1182,7 @@ def modules(self):
 
 
 class OVModelTextEncoder(OVPipelinePart):
-    def __init__(self, model: openvino.runtime.Model, parent_pipeline: OVDiffusionPipeline, model_name: str = ""):
+    def __init__(self, model: openvino.Model, parent_pipeline: OVDiffusionPipeline, model_name: str = ""):
         super().__init__(model, parent_pipeline, model_name)
         self.hidden_states_output_names = [
             name for out in self.model.outputs for name in out.names if name.startswith("hidden_states")