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clean previous optimizations when using --file mode optimizations #163

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Apr 21, 2025
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clean previous optimizations when using --file mode optimizations #163

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48 changes: 33 additions & 15 deletions codeflash/optimization/function_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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,
Expand All @@ -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)
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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()
Expand All @@ -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,
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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,
)
Expand Down
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