fixes based on review comments

Author: kkawa14

model_compression_toolkit/core/common/mixed_precision/mixed_precision_search_manager.py

@@ -189,7 +189,6 @@ def ensure_maxbit_minimal_metric(node_candidates_metrics, max_ind):
             metrics[max_ind] = max_val
             return metrics
 
-        position = int(self.progress_info_controller is not None)
         if self.progress_info_controller is not None:
             self.progress_info_controller.set_description('Research Mixed Precision')
 

model_compression_toolkit/core/common/progress_config/progress_info_controller.py

@@ -64,7 +64,7 @@ def __new__(cls, *args, **kwargs):
 
     def __post_init__(self):
         """Create progress bar after initialization."""
-        # Initial single bar mode (position=0, top of screen)
+        # Initial single bar mode
         self.pbar = tqdm(
             total=self.total_step,
             desc=self.description,
@@ -112,11 +112,9 @@ def close(self):
             self.pbar = None
 
 
-def research_progress_total(
-    core_config: 'CoreConfig',
-    target_resource_utilization: 'ResourceUtilization' = None,
-    gptq_config: 'GradientPTQConfig' = None,
-) -> int:
+def research_progress_total(core_config: 'CoreConfig',
+                            target_resource_utilization: 'ResourceUtilization' = None,
+                            gptq_config: 'GradientPTQConfig' = None) -> int:
     """
     Check whether specific processing will be executed based on input arguments
     and calculate the total number of processing steps.

model_compression_toolkit/core/common/quantization/debug_config.py

@@ -30,6 +30,7 @@ class DebugConfig:
         network_editor (List[EditRule]): A list of rules and actions to edit the network for quantization.
         simulate_scheduler (bool): Simulate scheduler behavior to compute operators' order and cuts.
         bypass (bool): A flag to enable MCT bypass, which skips MCT runner and returns the input model unchanged.
+        progress_info_callback (Callable): A user-defined callback function for retrieving progress information.
     """
 
     analyze_similarity: bool = False

model_compression_toolkit/gptq/runner.py

@@ -58,6 +58,7 @@ def _apply_gptq(gptq_config: GradientPTQConfig,
         fw_info: Information needed for quantization about the specific framework (e.g., kernel channels indices, groups of layers by how they should be quantized, etc.).
         fw_impl: Framework implementation per framework
         hessian_info_service: HessianInfoService to fetch information based on the hessian approximation for the float model.
+        progress_info_controller: ProgressInfoController to display and manage overall progress information.
     Returns:
 
     """
@@ -100,6 +101,7 @@ def gptq_runner(tg: Graph,
         fw_impl: FrameworkImplementation object with a specific framework methods implementation.
         tb_w: A TensorBoardWriter object initialized with the logger dir path if it was set, or None otherwise.
         hessian_info_service: HessianScoresService to fetch approximations of the hessian scores for the float model.
+        progress_info_controller: ProgressInfoController to display and manage overall progress information.
 
     Returns:
         A graph after model weights GPTQ fine-tuning.