extract logic for processing a candidate into a method for separation of concerns

[KRRT7]

PR Type

Enhancement

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Description

• Refactor candidate evaluation into helper methods

• Add CandidateEvaluationContext dataclass

• Improve trace ID handling utility

• Streamline ranking and logging flow

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Diagram Walkthrough

flowchart LR
  A["determine_best_candidate"] -- "initializes" --> B["CandidateEvaluationContext"]
  A -- "submits" --> C["line profiler future"]
  A -- "processes via" --> D["process_single_candidate"]
  D -- "on success" --> E["handle_successful_candidate"]
  E -- "collects" --> F["valid_optimizations"]
  A -- "selects best" --> G["select_best_optimization"]
  A -- "logs results" --> H["log_evaluation_results"]

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File Walkthrough

Relevant files

Enhancement

models.py Add evaluation context for candidate processing                    codeflash/models/models.py Import Field and diff_length Add CandidateEvaluationContext dataclass Provide methods for candidate tracking and deduping Use diff_length for shorter-code selection +79/-2    function_optimizer.py Refactor candidate evaluation into modular helpers              codeflash/optimization/function_optimizer.py Extract helper methods for evaluation flow Replace inline logic with process_single_candidate Add get_trace_id, logging and selection helpers Simplify ranking trace_id usage and output trees +376/-276

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Dataclass/Pydantic Mixing Fields in the dataclass use pydantic Field and type hints with dict/list but no type parameters for some entries; ensure runtime behavior is intended and serialization/validation works given pydantic.dataclasses plus BaseModel elsewhere. @dataclass class CandidateEvaluationContext: """Holds tracking state during candidate evaluation in determine_best_candidate.""" speedup_ratios: dict[str, float | None] = Field(default_factory=dict) optimized_runtimes: dict[str, float | None] = Field(default_factory=dict) is_correct: dict[str, bool] = Field(default_factory=dict) optimized_line_profiler_results: dict[str, str] = Field(default_factory=dict) ast_code_to_id: dict = Field(default_factory=dict) optimizations_post: dict[str, str] = Field(default_factory=dict) valid_optimizations: list = Field(default_factory=list) def record_failed_candidate(self, optimization_id: str) -> None: """Record results for a failed candidate.""" self.optimized_runtimes[optimization_id] = None self.is_correct[optimization_id] = False self.speedup_ratios[optimization_id] = None def record_successful_candidate( self, optimization_id: str, runtime: float, speedup: float ) -> None: """Record results for a successful candidate.""" self.optimized_runtimes[optimization_id] = runtime self.is_correct[optimization_id] = True self.speedup_ratios[optimization_id] = speedup def record_line_profiler_result(self, optimization_id: str, result: str) -> None: """Record line profiler results for a candidate.""" self.optimized_line_profiler_results[optimization_id] = result def handle_duplicate_candidate( self, candidate: OptimizedCandidate, normalized_code: str, code_context: CodeOptimizationContext, ) -> None: """Handle a candidate that has been seen before.""" past_opt_id = self.ast_code_to_id[normalized_code]["optimization_id"] # Copy results from the previous evaluation self.speedup_ratios[candidate.optimization_id] = self.speedup_ratios[past_opt_id] self.is_correct[candidate.optimization_id] = self.is_correct[past_opt_id] self.optimized_runtimes[candidate.optimization_id] = self.optimized_runtimes[past_opt_id] # Line profiler results only available for successful runs if past_opt_id in self.optimized_line_profiler_results: self.optimized_line_profiler_results[candidate.optimization_id] = ( self.optimized_line_profiler_results[past_opt_id] ) self.optimizations_post[candidate.optimization_id] = ( self.ast_code_to_id[normalized_code]["shorter_source_code"].markdown ) self.optimizations_post[past_opt_id] = ( self.ast_code_to_id[normalized_code]["shorter_source_code"].markdown ) # Update to shorter code if this candidate has a shorter diff new_diff_len = diff_length(candidate.source_code.flat, code_context.read_writable_code.flat) if new_diff_len < self.ast_code_to_id[normalized_code]["diff_len"]: self.ast_code_to_id[normalized_code]["shorter_source_code"] = candidate.source_code self.ast_code_to_id[normalized_code]["diff_len"] = new_diff_len def register_new_candidate( self, normalized_code: str, candidate: OptimizedCandidate, code_context: CodeOptimizationContext, ) -> None: """Register a new candidate that hasn't been seen before.""" self.ast_code_to_id[normalized_code] = { "optimization_id": candidate.optimization_id, "shorter_source_code": candidate.source_code, "diff_len": diff_length(candidate.source_code.flat, code_context.read_writable_code.flat), } Trace ID Logic get_trace_id trims last 4 chars of function_trace_id; verify all IDs follow that suffix pattern to avoid malformed IDs and collisions. def get_trace_id(self, exp_type: str) -> str: """Get the trace ID for the current experiment type.""" if self.experiment_id: return self.function_trace_id[:-4] + exp_type return self.function_trace_id None Return Paths select_best_optimization can return None silently when there are 0 candidates or ranking paths fail; ensure callers handle None and that logging communicates why no selection was made. def select_best_optimization( self, eval_ctx: CandidateEvaluationContext, code_context: CodeOptimizationContext, original_code_baseline: OriginalCodeBaseline, ai_service_client: AiServiceClient, exp_type: str, function_references: str, ) -> BestOptimization | None: """Select the best optimization from valid candidates.""" if not eval_ctx.valid_optimizations: return None valid_candidates_with_shorter_code = [] diff_lens_list = [] # character level diff speedups_list = [] optimization_ids = [] diff_strs = [] runtimes_list = [] for valid_opt in eval_ctx.valid_optimizations: valid_opt_normalized_code = normalize_code(valid_opt.candidate.source_code.flat.strip()) new_candidate_with_shorter_code = OptimizedCandidate( source_code=eval_ctx.ast_code_to_id[valid_opt_normalized_code]["shorter_source_code"], optimization_id=valid_opt.candidate.optimization_id, explanation=valid_opt.candidate.explanation, ) new_best_opt = BestOptimization( candidate=new_candidate_with_shorter_code, helper_functions=valid_opt.helper_functions, code_context=valid_opt.code_context, runtime=valid_opt.runtime, line_profiler_test_results=valid_opt.line_profiler_test_results, winning_behavior_test_results=valid_opt.winning_behavior_test_results, replay_performance_gain=valid_opt.replay_performance_gain, winning_benchmarking_test_results=valid_opt.winning_benchmarking_test_results, winning_replay_benchmarking_test_results=valid_opt.winning_replay_benchmarking_test_results, async_throughput=valid_opt.async_throughput, ) valid_candidates_with_shorter_code.append(new_best_opt) diff_lens_list.append( diff_length(new_best_opt.candidate.source_code.flat, code_context.read_writable_code.flat) ) diff_strs.append( unified_diff_strings(code_context.read_writable_code.flat, new_best_opt.candidate.source_code.flat) ) speedups_list.append( 1 + performance_gain( original_runtime_ns=original_code_baseline.runtime, optimized_runtime_ns=new_best_opt.runtime ) ) optimization_ids.append(new_best_opt.candidate.optimization_id) runtimes_list.append(new_best_opt.runtime) if len(optimization_ids) > 1: future_ranking = self.executor.submit( ai_service_client.generate_ranking, diffs=diff_strs, optimization_ids=optimization_ids, speedups=speedups_list, trace_id=self.get_trace_id(exp_type), function_references=function_references, ) concurrent.futures.wait([future_ranking]) ranking = future_ranking.result() if ranking: min_key = ranking[0] else: diff_lens_ranking = create_rank_dictionary_compact(diff_lens_list) runtimes_ranking = create_rank_dictionary_compact(runtimes_list) overall_ranking = {key: diff_lens_ranking[key] + runtimes_ranking[key] for key in diff_lens_ranking} min_key = min(overall_ranking, key=overall_ranking.get) elif len(optimization_ids) == 1: min_key = 0 else: return None return valid_candidates_with_shorter_code[min_key]

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Use proper dataclass default factories Remove the use of pydantic.Field in a @dataclass class; Field won't apply and can mislead defaults. Use plain dataclass field defaults via dataclasses.field(default_factory=...) to ensure correct initialization. This prevents subtle bugs where attributes may not be initialized as intended. codeflash/models/models.py [349-360] +from dataclasses import dataclass, field + @dataclass class CandidateEvaluationContext: """Holds tracking state during candidate evaluation in determine_best_candidate.""" - speedup_ratios: dict[str, float | None] = Field(default_factory=dict) - optimized_runtimes: dict[str, float | None] = Field(default_factory=dict) - is_correct: dict[str, bool] = Field(default_factory=dict) - optimized_line_profiler_results: dict[str, str] = Field(default_factory=dict) - ast_code_to_id: dict = Field(default_factory=dict) - optimizations_post: dict[str, str] = Field(default_factory=dict) - valid_optimizations: list = Field(default_factory=list) + speedup_ratios: dict[str, float | None] = field(default_factory=dict) + optimized_runtimes: dict[str, float | None] = field(default_factory=dict) + is_correct: dict[str, bool] = field(default_factory=dict) + optimized_line_profiler_results: dict[str, str] = field(default_factory=dict) + ast_code_to_id: dict = field(default_factory=dict) + optimizations_post: dict[str, str] = field(default_factory=dict) + valid_optimizations: list = field(default_factory=list) Suggestion importance[1-10]: 7 __ Why: Correct: pydantic.Field has no effect in a standard @dataclass; using dataclasses.field avoids misleading defaults and potential init bugs. Moderate impact as it improves correctness and maintainability of state tracking.	Medium
General	Avoid hard assert crash path Replace the hard assert with a controlled early return and log to avoid crashing production code. If the client is unexpectedly None, log an error and skip line profiler submission, preventing an exception that aborts the entire optimization flow. codeflash/optimization/function_optimizer.py [838-853] -assert ai_service_client is not None, "AI service client must be set for optimization" +if ai_service_client is None: + logger.error("AI service client is not available; skipping line profiler optimization submission.") + return None future_line_profile_results = self.executor.submit( ai_service_client.optimize_python_code_line_profiler, source_code=code_context.read_writable_code.markdown, dependency_code=code_context.read_only_context_code, trace_id=self.get_trace_id(exp_type), line_profiler_results=original_code_baseline.line_profile_results["str_out"], num_candidates=N_CANDIDATES_LP_EFFECTIVE, experiment_metadata=ExperimentMetadata( id=self.experiment_id, group="control" if exp_type == "EXP0" else "experiment" ) if self.experiment_id else None, ) Suggestion importance[1-10]: 6 __ Why: Replacing a hard assert with a logged early return improves resiliency in production flows; however, it changes failure semantics and may hide configuration errors, so impact is moderate.	Low
	Make trace ID slicing safe Guard against None or short function_trace_id when slicing; self.function_trace_id[:-4] can produce incorrect IDs or empty strings. Use a safer conditional that only slices when the suffix exists and fall back otherwise. This avoids malformed trace IDs in logs and downstream services. codeflash/optimization/function_optimizer.py [460-465] def get_trace_id(self, exp_type: str) -> str: """Get the trace ID for the current experiment type.""" - if self.experiment_id: - return self.function_trace_id[:-4] + exp_type - return self.function_trace_id + base = self.function_trace_id or "" + if self.experiment_id and len(base) >= 4: + return base[:-4] + exp_type + return base Suggestion importance[1-10]: 5 __ Why: Reasonable defensive change; while the code likely expects a suffix, guarding against short or None IDs prevents malformed trace IDs. It's a minor robustness improvement.	Low

[misrasaurabh1]

lets not remove comments unless have a good reason

[misrasaurabh1]

its a large diff - the logic seems sound. but ensure that you change no behavior what so ever, otherwise we won't catch it until 2 months later. verify functional equivalence deeply by reading the previous and new and comparing them

[KRRT7]

its a large diff - the logic seems sound. but ensure that you change no behavior what so ever, otherwise we won't catch it until 2 months later. verify functional equivalence deeply by reading the previous and new and comparing them

yeah agreed, I'm only doing these refactors in anticipation of the agentic workflows, the currently implementations makes it very hard