test_find_groups_of_quantizers_to_rank is presented

tests/common/accuracy_control/backend.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2023 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 Any, List, Optional
+
+from nncf.common.graph.graph import NNCFGraph
+from nncf.common.graph.graph import NNCFNode
+from nncf.common.graph.operator_metatypes import OperatorMetatype
+from nncf.quantization.algorithms.accuracy_control.backend import AccuracyControlAlgoBackend
+from nncf.quantization.algorithms.accuracy_control.backend import TModel
+from tests.common.quantization.metatypes import CONSTANT_METATYPES
+from tests.common.quantization.metatypes import QUANTIZABLE_METATYPES
+from tests.common.quantization.metatypes import QUANTIZE_AGNOSTIC_METATYPES
+from tests.common.quantization.metatypes import QUANTIZER_METATYPES
+from tests.common.quantization.metatypes import ShapeOfTestMetatype
+
+
+class AABackendForTests(AccuracyControlAlgoBackend):
+    @staticmethod
+    def get_quantizer_metatypes() -> List[OperatorMetatype]:
+        return QUANTIZER_METATYPES
+
+    @staticmethod
+    def get_const_metatypes() -> List[OperatorMetatype]:
+        return CONSTANT_METATYPES
+
+    @staticmethod
+    def get_quantizable_metatypes() -> List[OperatorMetatype]:
+        return QUANTIZABLE_METATYPES
+
+    @staticmethod
+    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
+        return nncf_graph.get_input_nodes()
+
+    @staticmethod
+    def get_quantize_agnostic_metatypes() -> List[OperatorMetatype]:
+        return QUANTIZE_AGNOSTIC_METATYPES
+
+    @staticmethod
+    def get_shapeof_metatypes() -> List[OperatorMetatype]:
+        return [ShapeOfTestMetatype]
+
+    @staticmethod
+    def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool:
+        return False
+
+    @staticmethod
+    def is_node_with_weight(node: NNCFNode) -> bool:
+        return False
+
+    @staticmethod
+    def get_bias_value(node_with_bias: NNCFNode, nncf_graph: NNCFGraph, model: TModel) -> Any:
+        return None
+
+    @staticmethod
+    def get_weight_value(node_with_weight: NNCFNode, model: TModel, port_id: int) -> Any:
+        return None
+
+    @staticmethod
+    def get_weight_tensor_port_ids(node: NNCFNode) -> List[Optional[int]]:
+        return None
+
+    @staticmethod
+    def get_model_size(model: TModel) -> int:
+        return 0
+
+    @staticmethod
+    def prepare_for_inference(model: TModel) -> TModel:
+        return model

tests/common/accuracy_control/test_ranking.py

@@ -9,13 +9,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
 from typing import List
 
 import numpy as np
 import pytest
 
+from nncf.common.graph.graph import NNCFGraph
 from nncf.quantization.algorithms.accuracy_control.rank_functions import normalized_mse
+from nncf.quantization.algorithms.accuracy_control.ranker import GroupToRank
+from nncf.quantization.algorithms.accuracy_control.ranker import Ranker
 from nncf.quantization.algorithms.accuracy_control.subset_selection import get_subset_indices
+from tests.common.accuracy_control.backend import AABackendForTests
+from tests.common.quantization.test_quantizer_removal import GRAPHS as AA_GRAPHS_DESCR
+from tests.common.quantization.test_quantizer_removal import create_nncf_graph as aa_create_nncf_graph
 
 
 def create_fp32_tensor_1d(items):
@@ -77,3 +84,41 @@ def test_normalized_mse(x_ref: np.ndarray, x_approx: np.ndarray, expected_nmse:
 def test_get_subset_indices(errors: List[float], subset_size: int, expected_indices: List[int]):
     actual_indices = get_subset_indices(errors, subset_size)
     assert expected_indices == actual_indices
+
+
+@pytest.mark.parametrize(
+    "nncf_graph_name,ref_groups",
+    [
+        (
+            "simple_graph",
+            [
+                GroupToRank(["quantizer_139", "quantizer_162", "quantizer_119"], ["add_117", "conv2d_161"]),
+                GroupToRank(["quantizer_153", "quantizer_147"], ["conv2d_146"]),
+                GroupToRank(["quantizer_134", "quantizer_128"], ["conv2d_127"]),
+            ],
+        ),
+        (
+            "graph_with_shapeof",
+            [
+                GroupToRank(["quantizer_105"], ["interpolate_115"]),
+                GroupToRank(["quantizer_710", "quantizer_93"], ["multiply_99"]),
+                GroupToRank(["quantizer_82"], ["power_87"]),
+            ],
+        ),
+    ],
+)
+def test_find_groups_of_quantizers_to_rank(nncf_graph_name: NNCFGraph, ref_groups: List[GroupToRank]):
+    ranker = Ranker(1, tuple(), AABackendForTests, None)
+    nncf_graph = aa_create_nncf_graph(AA_GRAPHS_DESCR[nncf_graph_name])
+    ret_val = ranker.find_groups_of_quantizers_to_rank(nncf_graph)
+    assert len(ret_val) == len(ref_groups)
+    # Can zip as qauantizers are topologically sorted
+    for actual_group, ref_group in zip(ret_val, ref_groups):
+        for attr in ["quantizers", "operations"]:
+            acutal_attr_value = getattr(actual_group, attr)
+            ref_attr_value = getattr(ref_group, attr)
+
+            assert len(acutal_attr_value) == len(ref_attr_value)
+            actual_node_names = [n.node_name for n in acutal_attr_value]
+            for ref_node_name in ref_attr_value:
+                assert ref_node_name in actual_node_names