comments

Author: AlexanderDokuchaev

nncf/experimental/torch2/function_hook/extractor.py

@@ -61,7 +61,6 @@ def apply_args_to_kwargs(
     :param args: The positional arguments.
     :param kwargs: The keyword arguments.
     :param indexed_args: The list of pairs of indexes and names.
-
     :return: A dictionary of keyword arguments with the applied arguments and keyword arguments.
     """
     args_dict: Dict[str, Any] = dict()
@@ -77,7 +76,6 @@ def apply_args_to_kwargs(
 def extract_bn(model: nn.Module, graph: PTNNCFGraph, node: NNCFNode) -> ExtractedFunc:
     """
     Extract batch_norm operation.
-    If source modules inhered from nn.BatchNorm1d, nn.BatchNorm2d, or nn.BatchNorm3d return torch BatchNorm module.
 
     :param model: Source model.
     :param graph: Graph of source model.

nncf/experimental/torch2/model_transformer.py

@@ -38,10 +38,10 @@ class PT2ModelTransformer(ModelTransformer[GraphModelWrapper]):
     def __init__(self, model: GraphModelWrapper):
         super().__init__(model)
 
-        self._command_transformation_ordered_pairs: TRANSFORMATION_PAIRS = [
+        self._command_transformation_ordered_pairs: TRANSFORMATION_PAIRS = (
             (PT2InsertionCommand, self._apply_insertion_transformations),
             (PTBiasCorrectionCommand, self._apply_bias_correction_transformations),
-        ]
+        )
 
     def transform(self, transformation_layout: TransformationLayout) -> GraphModelWrapper:
         """

tests/torch/ptq/test_fast_bias_correction.py

@@ -55,7 +55,7 @@ def check_bias(model: NNCFNetwork, ref_bias: list):
         for node in nncf_graph.get_all_nodes():
             if not is_node_with_fused_bias(node, nncf_graph):
                 continue
-            bias_value = get_fused_bias_value(node, nncf_graph, model)
+            bias_value = get_fused_bias_value(node, nncf_graph, model).cpu()
             # TODO(AlexanderDokuchaev): return atol=0.0001 after fix 109189
             assert torch.all(torch.isclose(bias_value, ref_bias, atol=0.02)), f"{bias_value} != {ref_bias}"
             return
@@ -77,17 +77,3 @@ def backend_specific_model(model: bool, tmp_dir: str):
     @staticmethod
     def fn_to_type(tensor):
         return torch.Tensor(tensor).cuda()
-
-    @staticmethod
-    def check_bias(model: NNCFNetwork, ref_bias: list):
-        ref_bias = torch.Tensor(ref_bias)
-        nncf_graph = NNCFGraphFactory.create(model)
-        for node in nncf_graph.get_all_nodes():
-            if not is_node_with_fused_bias(node, nncf_graph):
-                continue
-            bias_value = get_fused_bias_value(node, nncf_graph, model).cpu()
-            # TODO(AlexanderDokuchaev): return atol=0.0001 after fix 109189
-            assert torch.all(torch.isclose(bias_value, ref_bias, atol=0.02)), f"{bias_value} != {ref_bias}"
-            return
-        msg = "Not found node with bias"
-        raise ValueError(msg)

tests/torch2/function_hook/quantization/test_fast_bias_correction.py

@@ -54,7 +54,7 @@ def check_bias(model: GraphModelWrapper, ref_bias: list):
         for node in nncf_graph.get_all_nodes():
             if not is_node_with_fused_bias(node, nncf_graph):
                 continue
-            bias_value = get_fused_bias_value(node, nncf_graph, model.model)
+            bias_value = get_fused_bias_value(node, nncf_graph, model.model).cpu()
             # TODO(AlexanderDokuchaev): return atol=0.0001 after fix 109189
             assert torch.all(torch.isclose(bias_value, ref_bias, atol=0.02)), f"{bias_value} != {ref_bias}"
             return
@@ -76,17 +76,3 @@ def backend_specific_model(model: bool, tmp_dir: str):
     @staticmethod
     def fn_to_type(tensor):
         return torch.Tensor(tensor).cuda()
-
-    @staticmethod
-    def check_bias(model: GraphModelWrapper, ref_bias: list):
-        ref_bias = torch.Tensor(ref_bias)
-        nncf_graph = model.get_graph()
-        for node in nncf_graph.get_all_nodes():
-            if not is_node_with_fused_bias(node, nncf_graph):
-                continue
-            bias_value = get_fused_bias_value(node, nncf_graph, model.model).cpu()
-            # TODO(AlexanderDokuchaev): return atol=0.0001 after fix 109189
-            assert torch.all(torch.isclose(bias_value, ref_bias, atol=0.02)), f"{bias_value} != {ref_bias}"
-            return
-        msg = "Not found node with bias"
-        raise ValueError(msg)