[TorchFX] Refines for ExecuTorch OpenVINO backend integration (#3270)

### Changes Refines for ExecuTorch OpenVINO backend integration ### Reason for changes To enable the OpenVINOQuantizer and quantize_pt2e exectutorch integration ### Related tickets
openvinotoolkit · Feb 12, 2025 · 77ef40c · 77ef40c
1 parent 99f0c44
commit 77ef40c
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Showing 3 changed files with 6 additions and 4 deletions.
diff --git a/nncf/experimental/torch/fx/quantization/quantize_pt2e.py b/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
@@ -94,7 +94,7 @@ def quantize_pt2e(
         model = deepcopy(model)
 
     _fuse_conv_bn_(model)
-    if isinstance(quantizer, OpenVINOQuantizer):
+    if isinstance(quantizer, OpenVINOQuantizer) or hasattr(quantizer, "get_nncf_quantization_setup"):
         quantizer = OpenVINOQuantizerAdapter(quantizer)
     else:
         quantizer = TorchAOQuantizerAdapter(quantizer)

diff --git a/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py b/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py
@@ -29,4 +29,4 @@ def transform_prior_quantization(self, model: torch.fx.GraphModule) -> torch.fx.
         return self._quantizer.transform_for_annotation(model)
 
     def get_quantization_setup(self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> SingleConfigQuantizerSetup:
-        return self._quantizer.get_quantization_setup(model, nncf_graph)
+        return self._quantizer.get_nncf_quantization_setup(model, nncf_graph)
diff --git a/nncf/experimental/torch/fx/quantization/quantizer/openvino_quantizer.py b/nncf/experimental/torch/fx/quantization/quantizer/openvino_quantizer.py
@@ -104,13 +104,15 @@ def __init__(
             quantizer_propagation_rule=quantizer_propagation_rule,
         )
 
-    def get_quantization_setup(self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> SingleConfigQuantizerSetup:
+    def get_nncf_quantization_setup(
+        self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph
+    ) -> SingleConfigQuantizerSetup:
         self._min_max_algo._set_backend_entity(model)
         return self._min_max_algo.find_quantization_setup(model, nncf_graph)
 
     def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
         nncf_graph = GraphConverter.create_nncf_graph(model)
-        quantization_setup = self.get_quantization_setup(model, nncf_graph)
+        quantization_setup = self.get_nncf_quantization_setup(model, nncf_graph)
 
         graph = model.graph
         node_vs_torch_annotation = defaultdict(TorchAOQuantizationAnnotation)