feat: integrate parallel evaluator into function optimizer

Wires the parallel evaluation path into _evaluate_candidates:
- Checks --parallel-candidates flag to branch between sequential/parallel
- Batches candidates with dedup/normalization gating
- Dispatches repair and refinement futures from evaluation results
- Calls _run_line_profiler_for_winner after selection

New methods: _evaluate_candidates_parallel, _dispatch_refinement,
_dispatch_repair_if_possible.

Author: KRRT7

codeflash/languages/function_optimizer.py

@@ -78,6 +78,7 @@
 from codeflash.models.models import (
     AdaptiveOptimizedCandidate,
     AIServiceAdaptiveOptimizeRequest,
+    AIServiceBatchRefinerCandidate,
     AIServiceCodeRepairRequest,
     BestOptimization,
     CandidateEvaluationContext,
@@ -1407,37 +1408,52 @@ def determine_best_candidate(
             original_flat_code=code_context.read_writable_code.flat,
         )
         candidate_index = 0
+        parallel_pool_size = getattr(self.args, "parallel_candidates", 0)
 
-        # Process candidates using queue-based approach
-        while not processor.is_done():
-            candidate_node = processor.get_next_candidate()
-            if candidate_node is None:
-                logger.debug("everything done, exiting")
-                break
+        if parallel_pool_size > 1:
+            self._evaluate_candidates_parallel(
+                processor=processor,
+                code_context=code_context,
+                original_code_baseline=original_code_baseline,
+                original_helper_code=original_helper_code,
+                file_path_to_helper_classes=file_path_to_helper_classes,
+                eval_ctx=eval_ctx,
+                exp_type=exp_type,
+                function_references=function_references,
+                normalized_original=normalized_original,
+                pool_size=parallel_pool_size,
+            )
+        else:
+            # Process candidates using queue-based approach (sequential)
+            while not processor.is_done():
+                candidate_node = processor.get_next_candidate()
+                if candidate_node is None:
+                    logger.debug("everything done, exiting")
+                    break
 
-            try:
-                candidate_index += 1
-                self.process_single_candidate(
-                    candidate_node=candidate_node,
-                    candidate_index=candidate_index,
-                    total_candidates=processor.candidate_len,
-                    code_context=code_context,
-                    original_code_baseline=original_code_baseline,
-                    original_helper_code=original_helper_code,
-                    file_path_to_helper_classes=file_path_to_helper_classes,
-                    eval_ctx=eval_ctx,
-                    exp_type=exp_type,
-                    function_references=function_references,
-                    normalized_original=normalized_original,
-                    cached_normalized_code=processor.normalized_cache.get(candidate_node.candidate.optimization_id),
-                )
-            except KeyboardInterrupt as e:
-                logger.exception(f"Optimization interrupted: {e}")
-                raise
-            finally:
-                self.write_code_and_helpers(
-                    self.function_to_optimize_source_code, original_helper_code, self.function_to_optimize.file_path
-                )
+                try:
+                    candidate_index += 1
+                    self.process_single_candidate(
+                        candidate_node=candidate_node,
+                        candidate_index=candidate_index,
+                        total_candidates=processor.candidate_len,
+                        code_context=code_context,
+                        original_code_baseline=original_code_baseline,
+                        original_helper_code=original_helper_code,
+                        file_path_to_helper_classes=file_path_to_helper_classes,
+                        eval_ctx=eval_ctx,
+                        exp_type=exp_type,
+                        function_references=function_references,
+                        normalized_original=normalized_original,
+                        cached_normalized_code=processor.normalized_cache.get(candidate_node.candidate.optimization_id),
+                    )
+                except KeyboardInterrupt as e:
+                    logger.exception(f"Optimization interrupted: {e}")
+                    raise
+                finally:
+                    self.write_code_and_helpers(
+                        self.function_to_optimize_source_code, original_helper_code, self.function_to_optimize.file_path
+                    )
 
         # Select and return the best optimization
         best_optimization = self.select_best_optimization(
@@ -1450,6 +1466,11 @@ def determine_best_candidate(
         )
 
         if best_optimization:
+            if parallel_pool_size > 1:
+                best_optimization = self._run_line_profiler_for_winner(
+                    best_optimization, code_context, original_helper_code, eval_ctx
+                )
+
             self.log_evaluation_results(
                 eval_ctx=eval_ctx,
                 best_optimization=best_optimization,
@@ -1460,6 +1481,231 @@ def determine_best_candidate(
 
         return best_optimization
 
+    def _evaluate_candidates_parallel(
+        self,
+        processor: CandidateProcessor,
+        code_context: CodeOptimizationContext,
+        original_code_baseline: OriginalCodeBaseline,
+        original_helper_code: dict[Path, str],
+        file_path_to_helper_classes: dict[Path, set[str]],
+        eval_ctx: CandidateEvaluationContext,
+        exp_type: str,
+        function_references: str,
+        normalized_original: str,
+        pool_size: int,
+    ) -> None:
+        """Evaluate candidates in parallel using git worktrees and async subprocess execution."""
+        from codeflash.optimization.parallel_evaluator import run_parallel_evaluation
+
+        ai_service_client = self.aiservice_client if exp_type == "EXP0" else self.local_aiservice_client
+        assert ai_service_client is not None
+
+        candidate_index = 0
+
+        while not processor.is_done():
+            batch: list[tuple[CandidateNode, int, str | None]] = []
+            while len(batch) < pool_size:
+                candidate_node = processor.get_next_candidate()
+                if candidate_node is None:
+                    break
+                candidate_index += 1
+                cached = processor.normalized_cache.get(candidate_node.candidate.optimization_id)
+
+                normalized_code = cached or self.language_support.normalize_code(
+                    candidate_node.candidate.source_code.flat.strip()
+                )
+                if normalized_code == normalized_original:
+                    logger.info(f"h3|Candidate {candidate_index}: Identical to original code, skipping.")
+                    continue
+                if normalized_code in eval_ctx.ast_code_to_id:
+                    logger.info(f"h3|Candidate {candidate_index}: Duplicate of a previous candidate, skipping.")
+                    eval_ctx.handle_duplicate_candidate(
+                        candidate_node.candidate, normalized_code, code_context.read_writable_code.flat
+                    )
+                    continue
+
+                eval_ctx.register_new_candidate(
+                    normalized_code, candidate_node.candidate, code_context.read_writable_code.flat
+                )
+                batch.append((candidate_node, candidate_index, cached))
+
+            if not batch:
+                break
+
+            logger.info(f"Evaluating batch of {len(batch)} candidates in parallel…")
+
+            results, _, _ = run_parallel_evaluation(
+                optimizer=self,
+                candidates=batch,
+                code_context=code_context,
+                original_code_baseline=original_code_baseline,
+                original_helper_code=original_helper_code,
+                file_path_to_helper_classes=file_path_to_helper_classes,
+                eval_ctx=eval_ctx,
+                exp_type=exp_type,
+                pool_size=pool_size,
+            )
+
+            # Process results and dispatch refinement/repair futures immediately
+            batch_refiner_candidates: list[AIServiceBatchRefinerCandidate] = []
+            for (candidate_node, _idx, _), (_, run_result) in zip(batch, results):
+                candidate = candidate_node.candidate
+
+                if run_result is None or not is_successful(run_result):
+                    eval_ctx.record_failed_candidate(candidate.optimization_id)
+                    if run_result is not None and isinstance(run_result, Failure):
+                        eval_failure = run_result.failure()
+                        repair_future = self._dispatch_repair_if_possible(
+                            candidate,
+                            eval_ctx,
+                            code_context,
+                            exp_type,
+                            ai_service_client,
+                            test_diffs=eval_failure.diffs,
+                        )
+                        if repair_future is not None:
+                            self.future_all_code_repair.append(repair_future)
+                    continue
+
+                candidate_result = run_result.unwrap()
+                perf_gain = performance_gain(
+                    original_runtime_ns=original_code_baseline.runtime,
+                    optimized_runtime_ns=candidate_result.best_test_runtime,
+                )
+                eval_ctx.record_successful_candidate(
+                    candidate.optimization_id, candidate_result.best_test_runtime, perf_gain
+                )
+
+                is_successful_opt = speedup_critic(
+                    candidate_result,
+                    original_code_baseline.runtime,
+                    best_runtime_until_now=None,
+                    original_async_throughput=original_code_baseline.async_throughput,
+                    best_throughput_until_now=None,
+                    original_concurrency_metrics=original_code_baseline.concurrency_metrics,
+                    best_concurrency_ratio_until_now=None,
+                ) and quantity_of_tests_critic(candidate_result)
+
+                if is_successful_opt:
+                    empty_lp = {"timings": {}, "unit": 0, "str_out": ""}
+                    best_optimization = BestOptimization(
+                        candidate=candidate,
+                        helper_functions=code_context.helper_functions,
+                        code_context=code_context,
+                        runtime=candidate_result.best_test_runtime,
+                        line_profiler_test_results=empty_lp,
+                        winning_behavior_test_results=candidate_result.behavior_test_results,
+                        winning_benchmarking_test_results=candidate_result.benchmarking_test_results,
+                        winning_replay_benchmarking_test_results=None,
+                        async_throughput=candidate_result.async_throughput,
+                        concurrency_metrics=candidate_result.concurrency_metrics,
+                    )
+                    eval_ctx.valid_optimizations.append(best_optimization)
+
+                    batch_refiner_candidates.append(
+                        AIServiceBatchRefinerCandidate(
+                            optimization_id=candidate.optimization_id,
+                            optimized_source_code=candidate.source_code.markdown,
+                            optimized_explanation=candidate.explanation,
+                            optimized_code_runtime=candidate_result.best_test_runtime,
+                            original_code_runtime=original_code_baseline.runtime,
+                            speedup=f"{int(perf_gain * 100)}%",
+                            optimized_line_profiler_results="",
+                        )
+                    )
+
+            # Dispatch refinement immediately so CandidateProcessor sees it
+            if batch_refiner_candidates:
+                self._dispatch_refinement(
+                    batch_refiner_candidates,
+                    code_context,
+                    original_code_baseline,
+                    exp_type,
+                    function_references,
+                    ai_service_client,
+                )
+
+    def _dispatch_refinement(
+        self,
+        batch_refiner_candidates: list[AIServiceBatchRefinerCandidate],
+        code_context: CodeOptimizationContext,
+        original_code_baseline: OriginalCodeBaseline,
+        exp_type: str,
+        function_references: str,
+        ai_service_client: AiServiceClient,
+    ) -> None:
+        """Submit refinement request to thread pool so CandidateProcessor can consume results."""
+        if len(batch_refiner_candidates) > 1:
+            future = self.executor.submit(
+                ai_service_client.optimize_code_refinement_batch,
+                original_source_code=code_context.read_writable_code.markdown,
+                read_only_dependency_code=code_context.read_only_context_code,
+                original_line_profiler_results=original_code_baseline.line_profile_results["str_out"],
+                trace_id=self.get_trace_id(exp_type),
+                language=self.function_to_optimize.language,
+                language_version=self.language_support.language_version,
+                function_references=function_references,
+                candidates=batch_refiner_candidates,
+                rerun_trace_id=self.rerun_trace_id,
+            )
+        else:
+            c = batch_refiner_candidates[0]
+            future = self.executor.submit(
+                ai_service_client.optimize_code_refinement,
+                request=[
+                    AIServiceRefinerRequest(
+                        optimization_id=c.optimization_id,
+                        original_source_code=code_context.read_writable_code.markdown,
+                        read_only_dependency_code=code_context.read_only_context_code,
+                        original_code_runtime=c.original_code_runtime,
+                        optimized_source_code=c.optimized_source_code,
+                        optimized_explanation=c.optimized_explanation,
+                        optimized_code_runtime=c.optimized_code_runtime,
+                        speedup=c.speedup,
+                        trace_id=self.get_trace_id(exp_type),
+                        original_line_profiler_results=original_code_baseline.line_profile_results["str_out"],
+                        optimized_line_profiler_results=c.optimized_line_profiler_results,
+                        function_references=function_references,
+                        language=self.function_to_optimize.language,
+                        language_version=self.language_support.language_version,
+                    )
+                ],
+                rerun_trace_id=self.rerun_trace_id,
+            )
+        self.future_all_refinements.append(future)
+
+    def _dispatch_repair_if_possible(
+        self,
+        candidate: OptimizedCandidate,
+        eval_ctx: CandidateEvaluationContext,
+        code_context: CodeOptimizationContext,
+        exp_type: str,
+        ai_service_client: AiServiceClient,
+        test_diffs: list[TestDiff] | None = None,
+    ) -> concurrent.futures.Future | None:
+        """Submit a code repair request if the candidate is eligible."""
+        max_repairs = get_effort_value(EffortKeys.MAX_CODE_REPAIRS_PER_TRACE, self.effort)
+        if self.repair_counter >= max_repairs:
+            return None
+
+        successful_candidates_count = sum(1 for is_correct in eval_ctx.is_correct.values() if is_correct)
+        if successful_candidates_count >= MIN_CORRECT_CANDIDATES:
+            return None
+
+        if candidate.source not in (OptimizedCandidateSource.OPTIMIZE, OptimizedCandidateSource.OPTIMIZE_LP):
+            return None
+
+        self.repair_counter += 1
+        request = AIServiceCodeRepairRequest(
+            optimization_id=candidate.optimization_id,
+            original_source_code=code_context.read_writable_code.markdown,
+            modified_source_code=candidate.source_code.markdown,
+            test_diffs=test_diffs or [],
+            trace_id=self.function_trace_id[:-4] + exp_type if self.experiment_id else self.function_trace_id,
+            language=self.function_to_optimize.language,
+        )
+        return self.executor.submit(ai_service_client.code_repair, request=request, rerun_trace_id=self.rerun_trace_id)
+
     def call_adaptive_optimize(
         self,
         trace_id: str,