add tuning result table

neural_compressor/common/base_tuning.py

@@ -18,7 +18,7 @@
 from typing import Any, Callable, Dict, Generator, Iterator, List, Optional, Sized, Tuple, Union
 
 from neural_compressor.common.base_config import BaseConfig
-from neural_compressor.common.utils import TuningLogger, logger
+from neural_compressor.common.utils import Statistics, TuningLogger, logger
 
 __all__ = [
     "Evaluator",
@@ -423,6 +423,47 @@ def add_trial_result(self, trial_index: int, trial_result: Union[int, float], qu
         trial_record = _TrialRecord(trial_index, trial_result, quant_config)
         self.tuning_history.append(trial_record)
 
+        # Print tuning results table
+        self._print_trial_results_table(trial_index, trial_result)
+
+    def _print_trial_results_table(self, trial_index: int, trial_result: Union[int, float]) -> None:
+        """Print trial results in a formatted table using Statistics class."""
+        baseline_val = self.baseline if self.baseline is not None else 0.0
+        baseline_str = f"{baseline_val:.4f}" if self.baseline is not None else "N/A"
+        target_threshold_str = (
+            f"{baseline_val * (1 - self.tuning_config.tolerable_loss):.4f}" if self.baseline is not None else "N/A"
+        )
+
+        # Calculate relative loss if baseline is available
+        relative_loss_val = 0.0
+        relative_loss_str = "N/A"
+        if self.baseline is not None:
+            relative_loss_val = (baseline_val - trial_result) / baseline_val
+            relative_loss_str = f"{relative_loss_val*100:.2f}%"
+
+        # Get best result so far
+        best_result = max(record.trial_result for record in self.tuning_history)
+
+        # Status indicator with emoji
+        if self.baseline is not None and trial_result >= (baseline_val * (1 - self.tuning_config.tolerable_loss)):
+            status = "✅ PASSED"
+        else:
+            status = "❌ FAILED"
+
+        # Prepare data for Statistics table with combined fields
+        field_names = ["📊 Metric", "📈 Value"]
+        output_data = [
+            ["Trial / Progress", f"{len(self.tuning_history)}/{self.tuning_config.max_trials}"],
+            ["Baseline / Target", f"{baseline_str} / {target_threshold_str}"],
+            ["Current / Status", f"{trial_result:.4f} | {status}"],
+            ["Best / Relative Loss", f"{best_result:.4f} / {relative_loss_str}"],
+        ]
+
+        # Use Statistics class to print the table
+        Statistics(
+            output_data, header=f"🎯 Auto-Tune Trial #{trial_index} Results", field_names=field_names
+        ).print_stat()
+
     def set_baseline(self, baseline: float):
         """Set the baseline value for auto-tune.
 
@@ -488,4 +529,10 @@ def init_tuning(tuning_config: TuningConfig) -> Tuple[ConfigLoader, TuningLogger
     config_loader = ConfigLoader(config_set=tuning_config.config_set, sampler=tuning_config.sampler)
     tuning_logger = TuningLogger()
     tuning_monitor = TuningMonitor(tuning_config)
+
+    # Update max_trials based on actual number of available configurations
+    actual_config_count = len(config_loader.config_set)
+    if tuning_config.max_trials > actual_config_count:
+        tuning_config.max_trials = actual_config_count
+
     return config_loader, tuning_logger, tuning_monitor