[TorchFX] PTQ MinMax algorithm test

tests/cross_fw/test_templates/test_min_max.py

@@ -30,9 +30,9 @@ class TemplateTestMinMaxAlgorithm:
     @abstractmethod
     def backend(self) -> MinMaxAlgoBackend:
         """
-        Get backend specific BiasCorrectionAlgoBackend
+        Get backend specific MinMaxAlgoBackend
 
-        :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend
+        :return MinMaxAlgoBackend: Backend specific MinMaxAlgoBackend
         """
 
     @property
@@ -164,7 +164,6 @@ def test_get_channel_axes_matmul_torch(self, weight_shape, ref_axes):
         Checks MatMul quantization axes in MinMax for Torch.
         """
         matmul_node = NNCFNode({"metatype": self.matmul_metatype})
-        matmul_node.layer_attributes = self.get_matmul_node_attrs(None, None, weight_shape)
 
         class DummyTargetPoint:
             input_port_id = 0

tests/torch/fx/test_min_max.py

@@ -0,0 +1,85 @@
+# Copyright (c) 2024 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Tuple
+
+import pytest
+
+from nncf.common.graph.graph import NNCFGraph
+from nncf.common.graph.layer_attributes import BaseLayerAttributes
+from nncf.common.graph.transformations.commands import TargetType
+from nncf.quantization.algorithms.min_max.backend import MinMaxAlgoBackend
+from nncf.quantization.algorithms.min_max.torch_fx_backend import FXMinMaxAlgoBackend
+from nncf.torch.graph.graph import PTNNCFGraph
+from nncf.torch.graph.operator_metatypes import PTConstNoopMetatype
+from nncf.torch.graph.operator_metatypes import PTConv2dMetatype
+from nncf.torch.graph.operator_metatypes import PTDepthwiseConv2dSubtype
+from nncf.torch.graph.operator_metatypes import PTLinearMetatype
+from nncf.torch.graph.transformations.commands import PTTargetPoint
+from tests.cross_fw.test_templates.models import NNCFGraphToTest
+from tests.cross_fw.test_templates.test_min_max import TemplateTestGetChannelAxes
+from tests.cross_fw.test_templates.test_min_max import TemplateTestGetTargetPointShape
+from tests.cross_fw.test_templates.test_min_max import TemplateTestMinMaxAlgorithm
+
+
+class TestTorchFXMinMaxAlgorithm(TemplateTestMinMaxAlgorithm):
+    @property
+    def backend(self) -> MinMaxAlgoBackend:
+        return FXMinMaxAlgoBackend
+
+    @property
+    def conv_metatype(self):
+        return PTConv2dMetatype
+
+    def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> PTTargetPoint:
+        if target_point_type == TargetType.POST_LAYER_OPERATION:
+            port_id = None
+        return PTTargetPoint(target_point_type, name, input_port_id=port_id)
+
+
+class TestTorchFXGetTargetPointShape(TemplateTestGetTargetPointShape, TestTorchFXMinMaxAlgorithm):
+    def get_nncf_graph(self, weight_port_id: int, weight_shape: Tuple[int]) -> NNCFGraph:
+        return NNCFGraphToTest(
+            conv_metatype=PTConv2dMetatype, nncf_graph_cls=PTNNCFGraph, const_metatype=PTConstNoopMetatype
+        ).nncf_graph
+
+
+class TestTorchFXGetChannelAxes(TemplateTestGetChannelAxes, TestTorchFXMinMaxAlgorithm):
+    @property
+    def depthwiseconv_metatype(self):
+        return PTDepthwiseConv2dSubtype
+
+    @property
+    def matmul_metatype(self):
+        return PTLinearMetatype
+
+    @staticmethod
+    def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes:
+        # This method isn't needed for Torch FX backend
+        return None
+
+    @staticmethod
+    def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes:
+        # This method isn't needed for Torch FX backend
+        return None
+
+    @staticmethod
+    def get_matmul_node_attrs(
+        weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int]
+    ) -> BaseLayerAttributes:
+        # This method isn't needed for Torch FX backend
+        return None
+
+    def test_get_channel_axes_matmul_node_ov_onnx(self):
+        pytest.skip("Test is not applied for Torch FX backend.")
+
+    def test_get_channel_axes_deptwiseconv_node_ov(self):
+        pytest.skip("Test is not applied for Torch FX backend.")

tests/torch/ptq/test_min_max.py

@@ -13,9 +13,7 @@
 import pytest
 
 from nncf.common.graph.graph import NNCFGraph
-from nncf.common.graph.layer_attributes import ConstantLayerAttributes
-from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes
-from nncf.common.graph.layer_attributes import LinearLayerAttributes
+from nncf.common.graph.layer_attributes import BaseLayerAttributes
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.quantization.algorithms.min_max.backend import MinMaxAlgoBackend
 from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend
@@ -48,23 +46,8 @@ def create_target_point(self, target_point_type: TargetType, name: str, port_id:
 
 class TestTorchGetTargetPointShape(TemplateTestGetTargetPointShape, TestTorchMinMaxAlgorithm):
     def get_nncf_graph(self, weight_port_id: int, weight_shape: Tuple[int]) -> NNCFGraph:
-        conv_layer_attrs = ConvolutionLayerAttributes(
-            weight_requires_grad=True,
-            in_channels=weight_shape[1],
-            out_channels=weight_shape[0],
-            kernel_size=weight_shape[2:],
-            stride=1,
-            dilations=1,
-            groups=1,
-            transpose=False,
-            padding_values=[],
-        )
         return NNCFGraphToTest(
-            PTConv2dMetatype,
-            conv_layer_attrs,
-            PTNNCFGraph,
-            const_metatype=PTConstNoopMetatype,
-            const_layer_attrs=ConstantLayerAttributes("w", shape=weight_shape),
+            conv_metatype=PTConv2dMetatype, nncf_graph_cls=PTNNCFGraph, const_metatype=PTConstNoopMetatype
         ).nncf_graph
 
 
@@ -78,36 +61,21 @@ def matmul_metatype(self):
         return PTLinearMetatype
 
     @staticmethod
-    def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> ConvolutionLayerAttributes:
-        return ConvolutionLayerAttributes(
-            weight_requires_grad=False,
-            in_channels=weight_shape[0],
-            out_channels=weight_shape[1],
-            kernel_size=weight_shape[2:],
-            stride=1,
-            dilations=1,
-            groups=1,
-            transpose=False,
-            padding_values=[],
-        )
+    def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes:
+        # This method isn't needed for Torch backend
+        return None
 
     @staticmethod
-    def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> ConvolutionLayerAttributes:
-        return ConvolutionLayerAttributes(
-            weight_requires_grad=False,
-            in_channels=weight_shape[1],
-            out_channels=weight_shape[2],
-            kernel_size=weight_shape[3:],
-            stride=1,
-            dilations=1,
-            groups=weight_shape[0],
-            transpose=False,
-            padding_values=[],
-        )
+    def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes:
+        # This method isn't needed for Torch backend
+        return None
 
     @staticmethod
-    def get_matmul_node_attrs(weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int]):
-        return LinearLayerAttributes(False, in_features=weight_shape[0], out_features=weight_shape[1])
+    def get_matmul_node_attrs(
+        weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int]
+    ) -> BaseLayerAttributes:
+        # This method isn't needed for Torch backend
+        return None
 
     def test_get_channel_axes_matmul_node_ov_onnx(self):
         pytest.skip("Test is not applied for Torch backend.")