add tuning result table

[xin3he]

PR Type

Enhancement

---

Description

• Added tuning result table printing

• Updated max_trials based on available configurations

---

Diagram Walkthrough

flowchart LR
  A["Add _print_trial_results_table method"] -- "Prints formatted table" --> B["add_trial_result method"]
  C["Update max_trials in init_tuning"] -- "Based on available configs" --> D["init_tuning function"]

Loading

File Walkthrough

	Relevant files
Enhancement	base_tuning.py Enhance tuning results and configuration handling                neural_compressor/common/base_tuning.py Added _print_trial_results_table method for formatted table output Updated add_trial_result to call _print_trial_results_table Modified init_tuning to adjust max_trials based on available configurations +48/-1

---

[PRAgent4INC]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Possible Issue The baseline check in _print_trial_results_table could lead to incorrect calculations if baseline is None. Ensure that all calculations handle None values correctly. 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() Performance Concern Printing a table after every trial could impact performance, especially for large numbers of trials. Consider adding a flag to enable/disable this feature or batching the prints. 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()

[PRAgent4INC]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Handle empty history Handle the case where tuning_history might be empty to avoid errors. neural_compressor/common/base_tuning.py [445] -best_result = max(record.trial_result for record in self.tuning_history) +best_result = max((record.trial_result for record in self.tuning_history), default=float('-inf')) Suggestion importance[1-10]: 8 __ Why: Handles the edge case where tuning_history might be empty, preventing potential errors. This is a significant improvement.	Medium
	Prevent division by zero Ensure that baseline_val is not zero to avoid division by zero errors. neural_compressor/common/base_tuning.py [441-444] relative_loss_val = 0.0 relative_loss_str = "N/A" -if self.baseline is not None: +if self.baseline is not None and baseline_val != 0: relative_loss_val = (baseline_val - trial_result) / baseline_val relative_loss_str = f"{relative_loss_val*100:.2f}%" Suggestion importance[1-10]: 7 __ Why: Ensures that division by zero is avoided, which is important for robustness. However, this is a minor improvement.	Medium
General	Improve table formatting Ensure consistent formatting and alignment in the table. neural_compressor/common/base_tuning.py [454-460] -field_names = ["📊 Metric", "📈 Value"] +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}"], ] Suggestion importance[1-10]: 5 __ Why: Improves the formatting and alignment of the table, enhancing readability. This is a minor improvement.	Low

[xin3he]