Set default shift negative in quantization config (#1157)

* Enable shift negative by default in quantization config
sony · Aug 12, 2024 · 27b0742 · 27b0742
1 parent 77406ae
commit 27b0742
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Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/model_compression_toolkit/core/common/quantization/quantization_config.py b/model_compression_toolkit/core/common/quantization/quantization_config.py
@@ -56,7 +56,7 @@ def __init__(self,
                  weights_second_moment_correction: bool = False,
                  input_scaling: bool = False,
                  softmax_shift: bool = False,
-                 shift_negative_activation_correction: bool = False,
+                 shift_negative_activation_correction: bool = True,
                  activation_channel_equalization: bool = False,
                  z_threshold: float = math.inf,
                  min_threshold: float = MIN_THRESHOLD,

diff --git a/tests/pytorch_tests/model_tests/feature_models/qat_test.py b/tests/pytorch_tests/model_tests/feature_models/qat_test.py
@@ -261,7 +261,7 @@ def get_tpc(self):
     def run_test(self):
         self._gen_fixed_input()
         model_float = self.create_networks()
-        config = mct.core.CoreConfig()
+        config = mct.core.CoreConfig(mct.core.QuantizationConfig(shift_negative_activation_correction=False))
         ru = mct.core.ResourceUtilization(np.inf, 47)  # inf memory
         qat_ready_model, quantization_info = mct.qat.pytorch_quantization_aware_training_init_experimental(model_float,
                                                                                                            self.representative_data_gen_experimental,
@@ -306,7 +306,7 @@ def get_tpc(self):
     def run_test(self):
         self._gen_fixed_input()
         model_float = self.create_networks()
-        config = mct.core.CoreConfig()
+        config = mct.core.CoreConfig(mct.core.QuantizationConfig(shift_negative_activation_correction=False))
         ru = mct.core.ResourceUtilization(weights_memory=50, activation_memory=40)
         qat_ready_model, quantization_info = mct.qat.pytorch_quantization_aware_training_init_experimental(model_float,
                                                                                                            self.representative_data_gen_experimental,