clean previous optimizations when using --file mode optimizations

Author: misrasaurabh1

codeflash/optimization/function_optimizer.py

@@ -94,7 +94,7 @@ def __init__(
         function_benchmark_timings: dict[BenchmarkKey, int] | None = None,
         total_benchmark_timings: dict[BenchmarkKey, int] | None = None,
         args: Namespace | None = None,
-        replay_tests_dir: Path|None = None
+        replay_tests_dir: Path | None = None,
     ) -> None:
         self.project_root = test_cfg.project_root_path
         self.test_cfg = test_cfg
@@ -273,7 +273,7 @@ def optimize_function(self) -> Result[BestOptimization, str]:
                     processed_benchmark_info = process_benchmark_data(
                         replay_performance_gain=best_optimization.replay_performance_gain,
                         fto_benchmark_timings=self.function_benchmark_timings,
-                        total_benchmark_timings=self.total_benchmark_timings
+                        total_benchmark_timings=self.total_benchmark_timings,
                     )
                 explanation = Explanation(
                     raw_explanation_message=best_optimization.candidate.explanation,
@@ -283,7 +283,7 @@ def optimize_function(self) -> Result[BestOptimization, str]:
                     best_runtime_ns=best_optimization.runtime,
                     function_name=function_to_optimize_qualified_name,
                     file_path=self.function_to_optimize.file_path,
-                    benchmark_details=processed_benchmark_info.benchmark_details if processed_benchmark_info else None
+                    benchmark_details=processed_benchmark_info.benchmark_details if processed_benchmark_info else None,
                 )
 
                 self.log_successful_optimization(explanation, generated_tests)
@@ -328,7 +328,7 @@ def optimize_function(self) -> Result[BestOptimization, str]:
                         coverage_message=coverage_message,
                         git_remote=self.args.git_remote,
                     )
-                    if self.args.all or env_utils.get_pr_number():
+                    if self.args.all or env_utils.get_pr_number() or (self.args.file and not self.args.function):
                         self.write_code_and_helpers(
                             self.function_to_optimize_source_code,
                             original_helper_code,
@@ -397,8 +397,10 @@ def determine_best_candidate(
                     if done and (future_line_profile_results is not None):
                         line_profile_results = future_line_profile_results.result()
                         candidates.extend(line_profile_results)
-                        original_len+= len(candidates)
-                        logger.info(f"Added results from line profiler to candidates, total candidates now: {original_len}")
+                        original_len += len(candidates)
+                        logger.info(
+                            f"Added results from line profiler to candidates, total candidates now: {original_len}"
+                        )
                         future_line_profile_results = None
                     candidate_index += 1
                     candidate = candidates.popleft()
@@ -425,7 +427,6 @@ def determine_best_candidate(
                         )
                         continue
 
-
                     run_results = self.run_optimized_candidate(
                         optimization_candidate_index=candidate_index,
                         baseline_results=original_code_baseline,
@@ -464,12 +465,19 @@ def determine_best_candidate(
                             tree.add(f"Speedup ratio: {perf_gain + 1:.1f}X")
                             replay_perf_gain = {}
                             if self.args.benchmark:
-                                test_results_by_benchmark = candidate_result.benchmarking_test_results.group_by_benchmarks(self.total_benchmark_timings.keys(), self.replay_tests_dir, self.project_root)
+                                test_results_by_benchmark = (
+                                    candidate_result.benchmarking_test_results.group_by_benchmarks(
+                                        self.total_benchmark_timings.keys(), self.replay_tests_dir, self.project_root
+                                    )
+                                )
                                 if len(test_results_by_benchmark) > 0:
                                     benchmark_tree = Tree("Speedup percentage on benchmarks:")
                                 for benchmark_key, candidate_test_results in test_results_by_benchmark.items():
-
-                                    original_code_replay_runtime = original_code_baseline.replay_benchmarking_test_results[benchmark_key].total_passed_runtime()
+                                    original_code_replay_runtime = (
+                                        original_code_baseline.replay_benchmarking_test_results[
+                                            benchmark_key
+                                        ].total_passed_runtime()
+                                    )
                                     candidate_replay_runtime = candidate_test_results.total_passed_runtime()
                                     replay_perf_gain[benchmark_key] = performance_gain(
                                         original_runtime_ns=original_code_replay_runtime,
@@ -958,13 +966,17 @@ def establish_original_code_baseline(
             logger.debug(f"Total original code runtime (ns): {total_timing}")
 
             if self.args.benchmark:
-                replay_benchmarking_test_results = benchmarking_results.group_by_benchmarks(self.total_benchmark_timings.keys(), self.replay_tests_dir, self.project_root)
+                replay_benchmarking_test_results = benchmarking_results.group_by_benchmarks(
+                    self.total_benchmark_timings.keys(), self.replay_tests_dir, self.project_root
+                )
             return Success(
                 (
                     OriginalCodeBaseline(
                         behavioral_test_results=behavioral_results,
                         benchmarking_test_results=benchmarking_results,
-                        replay_benchmarking_test_results = replay_benchmarking_test_results if self.args.benchmark else None,
+                        replay_benchmarking_test_results=replay_benchmarking_test_results
+                        if self.args.benchmark
+                        else None,
                         runtime=total_timing,
                         coverage_results=coverage_results,
                         line_profile_results=line_profile_results,
@@ -1077,16 +1089,22 @@ def run_optimized_candidate(
 
             logger.debug(f"Total optimized code {optimization_candidate_index} runtime (ns): {total_candidate_timing}")
             if self.args.benchmark:
-                candidate_replay_benchmarking_results = candidate_benchmarking_results.group_by_benchmarks(self.total_benchmark_timings.keys(), self.replay_tests_dir, self.project_root)
+                candidate_replay_benchmarking_results = candidate_benchmarking_results.group_by_benchmarks(
+                    self.total_benchmark_timings.keys(), self.replay_tests_dir, self.project_root
+                )
                 for benchmark_name, benchmark_results in candidate_replay_benchmarking_results.items():
-                    logger.debug(f"Benchmark {benchmark_name} runtime (ns): {humanize_runtime(benchmark_results.total_passed_runtime())}")
+                    logger.debug(
+                        f"Benchmark {benchmark_name} runtime (ns): {humanize_runtime(benchmark_results.total_passed_runtime())}"
+                    )
             return Success(
                 OptimizedCandidateResult(
                     max_loop_count=loop_count,
                     best_test_runtime=total_candidate_timing,
                     behavior_test_results=candidate_behavior_results,
                     benchmarking_test_results=candidate_benchmarking_results,
-                    replay_benchmarking_test_results = candidate_replay_benchmarking_results if self.args.benchmark else None,
+                    replay_benchmarking_test_results=candidate_replay_benchmarking_results
+                    if self.args.benchmark
+                    else None,
                     optimization_candidate_index=optimization_candidate_index,
                     total_candidate_timing=total_candidate_timing,
                 )