[OpenVINO] Fix Quantizer for PTQ (#15891)

Author: anzr299

backends/openvino/quantizer/quantizer.py

@@ -20,12 +20,19 @@
     INT8WeightObserver,
 )
 from nncf.common.graph.graph import NNCFGraph  # type: ignore[import-untyped]
+from nncf.common.logging import nncf_logger  # type: ignore[import-untyped]
+from nncf.quantization.algorithms.min_max.algorithm import (  # type: ignore[import-untyped]
+    MinMaxQuantization,
+)
 from nncf.quantization.algorithms.weight_compression.config import (  # type: ignore[import-untyped]
     WeightCompressionParameters,
 )
 from nncf.quantization.quantize_model import (  # type: ignore[import-untyped]
     get_weight_compression_configuration,
 )
+from nncf.torch.model_graph_manager import (  # type: ignore[import-untyped]
+    get_weight_tensor_port_ids,
+)
 from torchao.quantization.pt2e import (
     HistogramObserver,
     PerChannelMinMaxObserver,
@@ -105,16 +112,15 @@ def __init__(
             else:
                 preset = None
                 model_type = nncf.parameters.ModelType.TRANSFORMER
-            self._algo = (
-                nncf.quantization.algorithms.min_max.algorithm.MinMaxQuantization(
-                    preset=preset, model_type=model_type, **kwargs
-                )
+            self._algo = MinMaxQuantization(
+                preset=preset, model_type=model_type, **kwargs
             )
         else:
+            compression_mode = mode.value.replace(
+                "wo", ""
+            )  # Mode value has to match NNCF CompressWeightsMode
             weight_compression_configuration = get_weight_compression_configuration(
-                mode.value.replace(
-                    "wo", ""
-                ),  # Mode value has to match NNCF CompressWeightsMode
+                nncf.CompressWeightsMode(compression_mode),
                 **kwargs,
             )
             subset_size = 1  # Doesn't really matter in this case since it is data-free. Should just be +ve
@@ -354,12 +360,10 @@ def _get_weight_edge(
         :return: Edge represented by a Tuple of (weight_node, target_node), where weight_node is the FX node supplying the weight.
         """
         nncf_node = nncf_graph.get_node_by_name(target_node.name)
-        weights_ports_ids = nncf.torch.model_graph_manager.get_weight_tensor_port_ids(
-            nncf_node, nncf_graph
-        )
+        weights_ports_ids = get_weight_tensor_port_ids(nncf_node, nncf_graph)
         if len(weights_ports_ids) > 1:
             # TODO(dlyakhov): support quantization for nodes with several weights
-            nncf.common.logging.nncf_logger.warning(
+            nncf_logger.warning(
                 f"Quantization of the weighted node {target_node.name}"
                 " is not yet supported by the OpenVINOQuantizer."
                 f" Only the weight on port ID {weights_ports_ids[0]} will be quantized."
@@ -384,7 +388,7 @@ def _get_edge_or_node(
         """
         ip = qp.insertion_point
         if qp.is_weight_quantization_point():
-            OpenVINOQuantizer._get_weight_edge(target_node, nncf_graph)
+            return OpenVINOQuantizer._get_weight_edge(target_node, nncf_graph)
 
         if ip.input_port_id is None:
             return target_node