add support for custom metric function for mixed precision

model_compression_toolkit/core/common/mixed_precision/mixed_precision_quantization_config.py

@@ -27,6 +27,7 @@ class MixedPrecisionQuantizationConfig:
     Args:
         compute_distance_fn (Callable): Function to compute a distance between two tensors. If None, using pre-defined distance methods based on the layer type for each layer.
         distance_weighting_method (MpDistanceWeighting): MpDistanceWeighting enum value that provides a function to use when weighting the distances among different layers when computing the sensitivity metric.
+        custom_metric_fn (Callable): Function to compute a custom metric. As input gets the model_mp and returns a float value for metric. If None, uses interest point metric.
         num_of_images (int): Number of images to use to evaluate the sensitivity of a mixed-precision model comparing to the float model.
         configuration_overwrite (List[int]): A list of integers that enables overwrite of mixed precision with a predefined one.
         num_interest_points_factor (float): A multiplication factor between zero and one (represents percentage) to reduce the number of interest points used to calculate the distance metric.
@@ -39,6 +40,7 @@ class MixedPrecisionQuantizationConfig:
 
     compute_distance_fn: Optional[Callable] = None
     distance_weighting_method: MpDistanceWeighting = MpDistanceWeighting.AVG
+    custom_metric_fn: Optional[Callable] = None
     num_of_images: int = MP_DEFAULT_NUM_SAMPLES
     configuration_overwrite: Optional[List[int]] = None
     num_interest_points_factor: float = field(default=1.0, metadata={"description": "Should be between 0.0 and 1.0"})

model_compression_toolkit/core/common/mixed_precision/mixed_precision_search_manager.py

@@ -165,6 +165,7 @@ def compute_metric(cfg, node_idx=None, baseline_cfg=None):
             return self.sensitivity_evaluator.compute_metric(topo_cfg(cfg),
                                                              node_idx,
                                                              topo_cfg(baseline_cfg) if baseline_cfg else None)
+
         if self.using_virtual_graph:
             origin_max_config = self.config_reconstruction_helper.reconstruct_config_from_virtual_graph(
                 self.max_ru_config)

model_compression_toolkit/core/common/mixed_precision/sensitivity_evaluation.py

@@ -89,13 +89,19 @@ def __init__(self,
         self.interest_points = get_mp_interest_points(graph,
                                                       fw_impl.count_node_for_mixed_precision_interest_points,
                                                       quant_config.num_interest_points_factor)
+        # If using a custom metric - return only model outputs
+        if self.quant_config.custom_metric_fn is not None:
+            self.interest_points = []
 
         # We use normalized MSE when not running hessian-based. For Hessian-based normalized MSE is not needed
         # because hessian weights already do normalization.
         use_normalized_mse = self.quant_config.use_hessian_based_scores is False
         self.ips_distance_fns, self.ips_axis = self._init_metric_points_lists(self.interest_points, use_normalized_mse)
 
         self.output_points = get_output_nodes_for_metric(graph)
+        # If using a custom metric - return all model outputs
+        if self.quant_config.custom_metric_fn is not None:
+            self.output_points = [n.node for n in graph.get_outputs()]
         self.out_ps_distance_fns, self.out_ps_axis = self._init_metric_points_lists(self.output_points,
                                                                                     use_normalized_mse)
 
@@ -160,7 +166,7 @@ def compute_metric(self,
         """
         Compute the sensitivity metric of the MP model for a given configuration (the sensitivity
         is computed based on the similarity of the interest points' outputs between the MP model
-        and the float model).
+        and the float model or a custom metric if given).
 
         Args:
             mp_model_configuration: Bitwidth configuration to use to configure the MP model.
@@ -177,15 +183,21 @@ def compute_metric(self,
                                         node_idx)
 
         # Compute the distance metric
-        ipts_distances, out_pts_distances = self._compute_distance()
+        if self.quant_config.custom_metric_fn is None:
+            ipts_distances, out_pts_distances = self._compute_distance()
+            sensitivity_metric = self._compute_mp_distance_measure(ipts_distances, out_pts_distances,
+                                              self.quant_config.distance_weighting_method)
+        else:
+            sensitivity_metric = self.quant_config.custom_metric_fn(self.model_mp)
+            if not isinstance(sensitivity_metric, (float, np.floating)):
+                raise TypeError(f'The custom_metric_fn is expected to return float or numpy float, got {type(sensitivity_metric).__name__}')
 
         # Configure MP model back to the same configuration as the baseline model if baseline provided
         if baseline_mp_configuration is not None:
             self._configure_bitwidths_model(baseline_mp_configuration,
                                             node_idx)
 
-        return self._compute_mp_distance_measure(ipts_distances, out_pts_distances,
-                                                 self.quant_config.distance_weighting_method)
+        return sensitivity_metric
 
     def _init_baseline_tensors_list(self):
         """

tests_pytest/pytorch_tests/unit_tests/core/mixed_precision/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2025 Sony Semiconductor Israel, Inc. All rights reserved.
+#
+# 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.
+# ==============================================================================

tests_pytest/pytorch_tests/unit_tests/core/mixed_precision/test_custom_metric_function.py

@@ -0,0 +1,113 @@
+# Copyright 2025 Sony Semiconductor Israel, Inc. All rights reserved.
+#
+# 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.
+# ==============================================================================
+import pytest
+import numpy as np
+
+from unittest.mock import Mock
+
+from model_compression_toolkit.core import MixedPrecisionQuantizationConfig
+from model_compression_toolkit.core.common.mixed_precision.sensitivity_evaluation import SensitivityEvaluation
+from model_compression_toolkit.core.common.model_builder_mode import ModelBuilderMode
+from model_compression_toolkit.core.pytorch.utils import to_torch_tensor
+
+
+def custom_float_metric(model_mp) -> float:
+    return 100.0
+
+
+def custom_np_float_metric(model_mp) -> np.floating:
+    return np.float64(100.0)
+
+
+def custom_str_metric(model_mp) -> str:
+    return 'test'
+
+
+def custom_none_metric(model_mp):
+    return None
+
+
+@pytest.fixture
+def mock_model_configuration():
+    return Mock()
+
+
+@pytest.fixture
+def sensitivity_evaluator_factory():
+    def _create_sensitivity_evaluator(custom_metric_fn):
+        mp_cfg = MixedPrecisionQuantizationConfig(custom_metric_fn=custom_metric_fn)
+        return get_sensitivity_evaluator(mp_cfg=mp_cfg)
+    return _create_sensitivity_evaluator
+
+
+def get_sensitivity_evaluator(mp_cfg):
+    mock_graph = Mock()
+    mock_graph.get_topo_sorted_nodes.return_value = ['test', 'this', 'is', 'reset']
+    mock_graph.get_outputs.return_value = []
+
+    def representative_data_gen() -> list:
+        for _ in range(5):
+            yield np.random.randn(2, 3, 248, 248)
+
+    mock_fw_info = Mock()
+
+    def custom_model_builder_return_value(*args, **kwargs):
+        mode = kwargs.get('mode')
+        if mode == ModelBuilderMode.FLOAT:
+            return (None, None)
+        else:
+            return (None, None, None)
+
+    def custom_to_tensor(img):
+        return to_torch_tensor(img)
+
+    mock_fw_impl = Mock()
+    mock_fw_impl.model_builder.side_effect = custom_model_builder_return_value
+    mock_fw_impl.to_tensor.side_effect = custom_to_tensor
+
+    mock_set_layer_to_bitwidth = Mock()
+
+    sensitivity_eval = SensitivityEvaluation(graph=mock_graph,
+                                             quant_config=mp_cfg,
+                                             representative_data_gen=representative_data_gen,
+                                             fw_info=mock_fw_info,
+                                             fw_impl=mock_fw_impl,
+                                             set_layer_to_bitwidth=mock_set_layer_to_bitwidth
+                                             )
+    sensitivity_eval._configure_bitwidths_model = lambda *args, **kwargs: None  # Method does nothing
+    sensitivity_eval.model_mp = Mock()
+    return sensitivity_eval
+
+
+class TestMPCustomMetricFunction:
+
+    @pytest.mark.parametrize("metric_fn, expected", [
+        (custom_float_metric, 100.0),
+        (custom_np_float_metric, np.float64(100.0)),
+    ])
+    def test_valid_metric_function(self, sensitivity_evaluator_factory, mock_model_configuration, metric_fn, expected):
+        sensitivity_eval = sensitivity_evaluator_factory(metric_fn)
+        assert len(sensitivity_eval.interest_points) == 0
+        assert sensitivity_eval.compute_metric(mock_model_configuration) == expected
+
+    @pytest.mark.parametrize("metric_fn, expected", [
+        (custom_str_metric, str.__name__),
+        (custom_none_metric, type(None).__name__),
+    ])
+    def test_type_invalid_metric_function(self, sensitivity_evaluator_factory, mock_model_configuration, metric_fn, expected):
+        sensitivity_eval = sensitivity_evaluator_factory(metric_fn)
+        assert len(sensitivity_eval.interest_points) == 0
+        with pytest.raises(TypeError, match=f'The custom_metric_fn is expected to return float or numpy float, got {expected}'):
+            sensitivity_metric = sensitivity_eval.compute_metric(mock_model_configuration)