fix setup.py

Author: eaidova

optimum/exporters/openvino/__init__.py

@@ -13,4 +13,4 @@
 #  limitations under the License.
 
 from .__main__ import main_export
-from .convert import export, export_models
+from .convert import export, export_models

optimum/exporters/openvino/__main__.py

@@ -30,11 +30,13 @@
 from ...intel.openvino.utils import OV_XML_FILE_NAME
 from .convert import export_models
 
+
 logger = logging.getLogger(__name__)
 
 if is_torch_available():
     import torch
 
+
 def main_export(
     model_name_or_path: str,
     output: Union[str, Path],

optimum/exporters/openvino/convert.py

@@ -19,18 +19,23 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-from openvino.runtime import PartialShape, serialize
-from openvino.tools.ovc import convert_model
-from openvino.runtime.utils.types import get_element_type
 from transformers.utils import is_tf_available, is_torch_available
 
+from openvino.runtime import PartialShape, serialize
+from openvino.runtime.utils.types import get_element_type
+from openvino.tools.ovc import convert_model
 from optimum.exporters.onnx.base import OnnxConfig
 from optimum.exporters.onnx.convert import check_dummy_inputs_are_allowed, export_tensorflow
 from optimum.exporters.onnx.convert import export_pytorch as export_pytorch_to_onnx
 from optimum.utils import is_diffusers_available
 
-from ...intel.openvino.utils import OV_XML_FILE_NAME, ONNX_WEIGHTS_NAME
-from .utils import clear_class_registry, remove_none_from_dummy_inputs, flattenize_inputs, get_input_shapes, is_torch_model
+from ...intel.openvino.utils import ONNX_WEIGHTS_NAME, OV_XML_FILE_NAME
+from .utils import (
+    clear_class_registry,
+    flattenize_inputs,
+    get_input_shapes,
+    remove_none_from_dummy_inputs,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -197,7 +202,11 @@ def ts_patched_forward(*args, **kwargs):
         except Exception:
             model.config.torchscript = False
             model.config.return_dict = True
-            onnx_output = output.with_suffix(".onnx") if not output.name != OV_XML_FILE_NAME else output.parent / ONNX_WEIGHTS_NAME
+            onnx_output = (
+                output.with_suffix(".onnx")
+                if not output.name != OV_XML_FILE_NAME
+                else output.parent / ONNX_WEIGHTS_NAME
+            )
             input_names, output_names = export_pytorch_to_onnx(
                 model, config, opset, onnx_output, device, input_shapes, model_kwargs
             )

optimum/exporters/openvino/utils.py

@@ -12,9 +12,11 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from openvino.runtime import PartialShape
 from transformers.utils import is_torch_available
 
+from openvino.runtime import PartialShape
+
+
 if is_torch_available():
     import torch
     import torch.nn as nn
@@ -25,6 +27,7 @@ def is_torch_model(model):
         return False
     return isinstance(model, nn.Module)
 
+
 def flattenize_inputs(inputs):
     flatten_inputs = []
     for input_data in inputs:
@@ -74,4 +77,4 @@ def get_input_shapes(dummy_inputs, inputs):
 def clear_class_registry():
     torch._C._jit_clear_class_registry()
     torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
-    torch.jit._state._clear_class_state()
+    torch.jit._state._clear_class_state()

optimum/intel/openvino/modeling_base.py

@@ -28,9 +28,9 @@
 from optimum.exporters import TasksManager
 from optimum.modeling_base import OptimizedModel
 
-from ..utils.import_utils import is_transformers_version
 from ...exporters.openvino import export
 from ...exporters.openvino.utils import is_torch_model
+from ..utils.import_utils import is_transformers_version
 from .utils import ONNX_WEIGHTS_NAME, OV_XML_FILE_NAME
 
 
@@ -131,7 +131,9 @@ def fix_op_names_duplicates(model: openvino.runtime.Model):
             file_name = Path(file_name)
         bin_file_name = file_name.with_suffix(".bin") if file_name.suffix == ".xml" else None
         model = (
-            core.read_model(file_name, bin_file_name) if not file_name.suffix == ".onnx" else convert_model(str(file_name))
+            core.read_model(file_name, bin_file_name)
+            if not file_name.suffix == ".onnx"
+            else convert_model(str(file_name))
         )
         if file_name.suffix == ".onnx":
             model = fix_op_names_duplicates(model)  # should be called during model conversion to IR

optimum/intel/openvino/modeling_base_seq2seq.py

@@ -27,8 +27,8 @@
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx import get_encoder_decoder_models_for_export
 
-from ..utils.import_utils import is_transformers_version
 from ...exporters.openvino import export_models
+from ..utils.import_utils import is_transformers_version
 from .modeling_base import OVBaseModel
 from .utils import (
     ONNX_DECODER_NAME,

optimum/intel/openvino/modeling_decoder.py

@@ -30,10 +30,10 @@
 from optimum.exporters import TasksManager
 from optimum.utils import NormalizedConfigManager
 
-from ..utils.import_utils import is_transformers_version
-from ..utils.modeling_utils import _prepare_attn_mask, _prepare_decoder_attention_mask
 from ...exporters.openvino import export
 from ...exporters.openvino.utils import is_torch_model
+from ..utils.import_utils import is_transformers_version
+from ..utils.modeling_utils import _prepare_attn_mask, _prepare_decoder_attention_mask
 from .modeling import _TOKENIZER_FOR_DOC, INPUTS_DOCSTRING, MODEL_START_DOCSTRING, OVModel
 from .utils import ONNX_WEIGHTS_NAME, OV_XML_FILE_NAME, STR_TO_OV_TYPE
 

optimum/intel/openvino/quantization.py

@@ -30,17 +30,17 @@
 from nncf.torch.initialization import PTInitializingDataLoader
 from openvino._offline_transformations import compress_quantize_weights_transformation
 from openvino.runtime import Core, Tensor
-from torch.utils.data import DataLoader, RandomSampler, TensorDataset
+from torch.utils.data import DataLoader, RandomSampler
 from transformers import DataCollator, PreTrainedModel, default_data_collator
 from transformers.pytorch_utils import Conv1D
 
 from optimum.exporters.onnx import export as onnx_export
 from optimum.exporters.tasks import TasksManager
 from optimum.quantization_base import OptimumQuantizer
 
-from ..utils.constant import _TASK_ALIASES
-from .configuration import INT8_WEIGHT_COMPRESSION_CONFIG, OVConfig
 from ...exporters.openvino import export
+from ..utils.constant import _TASK_ALIASES
+from .configuration import OVConfig
 from .modeling_base import OVBaseModel
 from .modeling_decoder import OVBaseDecoderModel
 from .utils import (
@@ -353,7 +353,7 @@ def _quantize_torchmodel(
                 "No configuration describing the quantization process was provided, a default OVConfig will be generated."
             )
             quantization_config = OVConfig()
-            
+
         if weights_only:
             compressed_model = compress_weights(self.model)
             self.model = compressed_model

setup.py

@@ -41,7 +41,7 @@
         "onnxruntime<1.15.0",
     ],
     "openvino": ["openvino==2023.1.0.dev20230728", "onnx", "onnxruntime"],
-    "nncf": ["git+https://github.com/openvinotoolkit/nncf.git"],
+    "nncf": ["nncf @ git+https://github.com/openvinotoolkit/nncf.git"],
     "ipex": ["intel-extension-for-pytorch", "onnx"],
     "diffusers": ["diffusers", "invisible-watermark>=0.2.0"],
     "quality": QUALITY_REQUIRE,