Enhance documentation and fix minor typos in MCTWrapper and associated tests

Author: KazunoriSumiya

model_compression_toolkit/wrapper/mct_wrapper.py

@@ -113,7 +113,7 @@ def __init__(self):
            "optimizer", "None", "Optimizer for GPTQ training (low priority)"
            "distance_weighting_method", "None", "Distance weighting method for GPTQ (low priority)"
            "num_of_images", "5", "Number of images for mixed precision"
-           "use_hessian_based_scores", "False", "Use Hessian-based scores for mixed precision"
+           "use_hessian_based_scores", "False", "Use Hessian-based scores for mixed precision (low priority)"
            "save_model_path", "'./qmodel.keras' / './qmodel.onnx'", "Path to save quantized model (Keras/Pytorch)"
 
         """

tests_pytest/keras_tests/e2e_tests/wrapper/test_mct_wrapper_keras_e2e.py

@@ -174,7 +174,7 @@ def PTQ_Keras_mixed_precision(float_model: keras.Model) -> Tuple[bool, keras.Mod
             ['distance_weighting_method', None],  # Distance weighting method for mixed precision (low priority).
             ['num_of_images', 5],  # Number of images for mixed precision.
             ['use_hessian_based_scores', False],  # Use Hessian-based sensitivity scores for layer importance (low priority).
-            ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size.
+            ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size).
             ['save_model_path', './qmodel_PTQ_Keras_mixed_precision.keras']  # Path to save the quantized model.
         ]
 
@@ -244,12 +244,12 @@ def GPTQ_Keras_mixed_precision(float_model: keras.Model) -> Tuple[bool, keras.Mo
             ['z_threshold', float('inf')],  # Threshold for zero-point quantization (low priority).
             ['linear_collapsing', True],  # Enable linear layer collapsing optimization (low priority).
             ['residual_collapsing', True],  # Enable residual connection collapsing (low priority).
-            ['weights_compression_ratio', 0.75],  # Compression ratio for weights.
             ['n_epochs', 5],  # Number of epochs for gradient-based fine-tuning.
             ['optimizer', None],  # Optimizer to use during fine-tuning (low priority).
             ['distance_weighting_method', None],  # Distance weighting method for GPTQ (low priority).
             ['num_of_images', 5],  # Number of images to use for calibration.
-            ['use_hessian_based_scores', False],  # Whether to use Hessian-based scores for layer importance.
+            ['use_hessian_based_scores', False],  # Whether to use Hessian-based scores for layer importance (low priority).
+            ['weights_compression_ratio', 0.75],  # Compression ratio for weights.
             ['save_model_path', './qmodel_GPTQ_Keras_mixed_precision.keras']  # Path to save the quantized model.
         ]
 

tests_pytest/keras_tests/integration_tests/wrapper/test_mct_wrapper_keras_integ.py

@@ -67,7 +67,7 @@ def test_quantize_and_export_PTQ_flow(
         Mocked Components:
             - _get_tpc: TPC configuration
             - _select_method: Framework-specific method selection
-            - _Setting_PTQ: PTQ parameter configuration
+            - _setting_PTQ: PTQ parameter configuration
             - _export_model: Model export functionality
             - _post_training_quantization: Actual quantization process
         
@@ -93,7 +93,7 @@ def test_quantize_and_export_PTQ_flow(
 
         mock_setting_ptq.return_value = {'mock': 'params'}
 
-        param_items = [('n_epochs', 10)]  # Number of epochs
+        param_items = [('sdsp_version', '3.14')]  # SDSP version for TPC
 
         # Call the method
         success, result_model = wrapper.quantize_and_export(

tests_pytest/keras_tests/unit_tests/wrapper/test_mct_wrapper_keras_unit.py

@@ -269,9 +269,9 @@ def test_setting_PTQ_mixed_precision(
     @patch('model_compression_toolkit.core.CoreConfig')
     def test_setting_PTQ(self, mock_core_config: Mock, mock_quant_config: Mock) -> None:
         """
-        Test _Setting_PTQ method for standard Post-Training Quantization.
+        Test _setting_PTQ method for standard Post-Training Quantization.
         
-        This test verifies that the _Setting_PTQ method correctly configures
+        This test verifies that the _setting_PTQ method correctly configures
         standard Post-Training Quantization parameters without mixed precision,
         focusing on fixed-precision quantization with comprehensive error
         minimization and optimization techniques.

tests_pytest/pytorch_tests/e2e_tests/wrapper/test_mct_wrapper_pytorch_e2e.py

@@ -206,7 +206,7 @@ def PTQ_Pytorch_mixed_precision(float_model):
             ['distance_weighting_method', None],  # Distance weighting method for mixed precision (low priority).
             ['num_of_images', 5],  # Number of images for mixed precision.
             ['use_hessian_based_scores', False],  # Use Hessian-based sensitivity scores for layer importance (low priority).
-            ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size.
+            ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size).
             ['save_model_path', './qmodel_PTQ_Pytorch_mixed_precision.onnx']  # Path to save quantized model as ONNX.
         ]
 
@@ -297,7 +297,7 @@ def GPTQ_Pytorch_mixed_precision(float_model):
             ['optimizer', None],  # Optimizer to use during fine-tuning (low priority).
             ['distance_weighting_method', None],  # Distance weighting method for GPTQ (low priority).
             ['num_of_images', 5],  # Number of images to use for calibration.
-            ['use_hessian_based_scores', False],  # Whether to use Hessian-based scores for layer importance.
+            ['use_hessian_based_scores', False],  # Whether to use Hessian-based scores for layer importance (low priority).
             ['save_model_path', './qmodel_GPTQ_Pytorch_mixed_precision.onnx']  # Path to save quantized model as ONNX.
         ]
 

tests_pytest/pytorch_tests/integration_tests/wrapper/test_mct_wrapper_pytorch_integ.py

@@ -67,7 +67,7 @@ def test_quantize_and_export_PTQ_flow(
         Mocked Components:
             - _get_tpc: TPC configuration
             - _select_method: Framework-specific method selection
-            - _Setting_PTQ: PTQ parameter configuration
+            - _setting_PTQ: PTQ parameter configuration
             - _export_model: Model export functionality
             - _post_training_quantization: Actual quantization process
         
@@ -93,7 +93,7 @@ def test_quantize_and_export_PTQ_flow(
 
         mock_setting_ptq.return_value = {'mock': 'params'}
 
-        param_items = [('n_epochs', 10)]  # Number of epochs
+        param_items = [('sdsp_version', '3.14')]  # SDSP version for TPC
 
         # Call the method
         success, result_model = wrapper.quantize_and_export(

tests_pytest/pytorch_tests/unit_tests/wrapper/test_mct_wrapper_pytorch_unit.py

@@ -269,9 +269,9 @@ def test_setting_PTQ_mixed_precision(
     def test_setting_PTQ(self, mock_core_config: Mock,
                          mock_quant_config: Mock) -> None:
         """
-        Test _Setting_PTQ method for standard Post-Training Quantization.
+        Test _setting_PTQ method for standard Post-Training Quantization.
         
-        This test verifies that the _Setting_PTQ method correctly configures
+        This test verifies that the _setting_PTQ method correctly configures
         standard Post-Training Quantization parameters without mixed precision,
         focusing on fixed-precision quantization with comprehensive error
         minimization and optimization techniques.

tutorials/notebooks/mct_features_notebooks/keras/example_keras_mct_wrapper.ipynb

@@ -391,7 +391,7 @@
     "        ['distance_weighting_method', None],  # Distance weighting method for mixed precision (low priority).\n",
     "        ['num_of_images', 5],  # Number of images for mixed precision.\n",
     "        ['use_hessian_based_scores', False],  # Use Hessian-based sensitivity scores for layer importance (low priority).\n",
-    "        ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size.\n",
+    "        ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size).\n",
     "        ['save_model_path', './qmodel_PTQ_Keras_mixed_precision.keras']  # Path to save the quantized model.\n",
     "    ]\n",
     "\n",

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mct_wrapper.ipynb

@@ -381,7 +381,7 @@
     "        ['distance_weighting_method', None],  # Distance weighting method for mixed precision (low priority).\n",
     "        ['num_of_images', 5],  # Number of images for mixed precision.\n",
     "        ['use_hessian_based_scores', False],  # Use Hessian-based sensitivity scores for layer importance (low priority).\n",
-    "        ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size.\n",
+    "        ['weights_compression_ratio', 0.75],  # Target compression ratio for model weights (75% of original size).\n",
     "        ['save_model_path', './qmodel_PTQ_Pytorch_mixed_precision.onnx']  # Path to save quantized model as ONNX.\n",
     "    ]\n",
     "\n",