apply black

Author: l-bat

optimum/intel/openvino/configuration.py

@@ -240,7 +240,7 @@ def post_init(self):
             stable_diffusion_datasets = [
                 "conceptual_captions",
                 "laion/220k-GPT4Vision-captions-from-LIVIS",
-                "laion/filtered-wit"
+                "laion/filtered-wit",
             ]
             if self.dataset not in llm_datasets + stable_diffusion_datasets:
                 raise ValueError(

optimum/intel/openvino/modeling_diffusion.py

@@ -306,7 +306,7 @@ def _from_pretrained(
             supported_pipelines = (
                 OVStableDiffusionPipeline,
                 OVStableDiffusionXLPipeline,
-                OVLatentConsistencyModelPipeline
+                OVLatentConsistencyModelPipeline,
             )
             if not isinstance(sd_model, supported_pipelines):
                 raise NotImplementedError(f"Quantization in hybrid mode is not supported for {cls.__name__}")
@@ -316,14 +316,18 @@ def _from_pretrained(
 
             if isinstance(quantization_config.dataset, str):
                 from .quantization import get_stable_diffusion_dataset
+
                 dataset_name = quantization_config.dataset
                 num_samples = math.ceil(quantization_config.subset_size / num_inference_steps)
                 quantization_config.dataset = get_stable_diffusion_dataset(dataset_name, num_samples)
 
-            unet_inputs = sd_model.prepare_inputs(quantization_config.dataset, quantization_config.subset_size, num_inference_steps)
+            unet_inputs = sd_model.prepare_inputs(
+                quantization_config.dataset, quantization_config.subset_size, num_inference_steps
+            )
             quantization_config.dataset = unet_inputs
 
             from .quantization import _hybrid_quantization
+
             unet = _hybrid_quantization(sd_model.unet.model, quantization_config)
 
         return cls(
@@ -348,6 +352,7 @@ def prepare_inputs(
         calibration_data = []
 
         from .quantization import InferRequestWrapper
+
         self.unet.request = InferRequestWrapper(self.unet.request, calibration_data)
         for prompt in dataset.get_inference_data():
             _ = self.__call__(prompt, num_inference_steps=num_inference_steps, height=height, width=width)
@@ -356,8 +361,8 @@ def prepare_inputs(
         self.unet.request = self.unet.request.request
 
         from nncf import Dataset
-        return Dataset(calibration_data)
 
+        return Dataset(calibration_data)
 
     @classmethod
     def _from_transformers(

optimum/intel/openvino/quantization.py

@@ -640,11 +640,7 @@ def _collect_ops_with_weights(model):
 def get_stable_diffusion_dataset(
     dataset_name: str, nsamples: int = 50, seed: int = 0, text_column: str = "caption"
 ) -> nncf.Dataset:
-    if dataset_name not in [
-            "conceptual_captions",
-            "laion/220k-GPT4Vision-captions-from-LIVIS",
-            "laion/filtered-wit"
-        ]:
+    if dataset_name not in ["conceptual_captions", "laion/220k-GPT4Vision-captions-from-LIVIS", "laion/filtered-wit"]:
         raise ValueError(
             f"""You have entered a string value for dataset. You can only choose between
              ['conceptual_captions','laion/220k-GPT4Vision-captions-from-LIVIS','laion/filtered-wit'],
@@ -656,9 +652,7 @@ def get_stable_diffusion_dataset(
     return nncf.Dataset(dataset)
 
 
-def _hybrid_quantization(
-    model: openvino.runtime.Model, quantization_config: Union[OVWeightQuantizationConfig, Dict]
-):
+def _hybrid_quantization(model: openvino.runtime.Model, quantization_config: Union[OVWeightQuantizationConfig, Dict]):
     dataset = quantization_config.dataset
     wc_ignored_scope = deepcopy(quantization_config.ignored_scope)
 

tests/openvino/test_quantization.py

@@ -384,9 +384,7 @@ def test_ovmodel_hybrid_quantization_with_custom_dataset(
         model = model_cls.from_pretrained(
             model_id,
             export=True,
-            quantization_config=OVWeightQuantizationConfig(
-                bits=8, dataset=quantization_dataset, subset_size=3
-            ),
+            quantization_config=OVWeightQuantizationConfig(bits=8, dataset=quantization_dataset, subset_size=3),
         )
         num_fake_quantize, num_int8, num_int4 = get_num_quantized_nodes(model.unet)
         self.assertEqual(expected_num_fake_quantize, num_fake_quantize)