Optimization effort

codeflash/api/aiservice.py

@@ -12,7 +12,6 @@
 from codeflash.cli_cmds.console import console, logger
 from codeflash.code_utils.code_replacer import is_zero_diff
 from codeflash.code_utils.code_utils import unified_diff_strings
-from codeflash.code_utils.config_consts import N_CANDIDATES_EFFECTIVE, N_CANDIDATES_LP_EFFECTIVE
 from codeflash.code_utils.env_utils import get_codeflash_api_key
 from codeflash.code_utils.git_utils import get_last_commit_author_if_pr_exists, get_repo_owner_and_name
 from codeflash.code_utils.time_utils import humanize_runtime
@@ -141,15 +140,14 @@ def optimize_python_code(  # noqa: D417
         payload = {
             "source_code": source_code,
             "dependency_code": dependency_code,
-            "num_variants": num_candidates,
+            "n_candidates": num_candidates,
             "trace_id": trace_id,
             "python_version": platform.python_version(),
             "experiment_metadata": experiment_metadata,
             "codeflash_version": codeflash_version,
             "current_username": get_last_commit_author_if_pr_exists(None),
             "repo_owner": git_repo_owner,
             "repo_name": git_repo_name,
-            "n_candidates": N_CANDIDATES_EFFECTIVE,
             "is_async": is_async,
         }
 
@@ -183,7 +181,7 @@ def optimize_python_code_line_profiler(  # noqa: D417
         dependency_code: str,
         trace_id: str,
         line_profiler_results: str,
-        num_candidates: int = 10,
+        num_candidates: int = 8,
         experiment_metadata: ExperimentMetadata | None = None,
     ) -> list[OptimizedCandidate]:
         """Optimize the given python code for performance by making a request to the Django endpoint.
@@ -204,14 +202,13 @@ def optimize_python_code_line_profiler(  # noqa: D417
         payload = {
             "source_code": source_code,
             "dependency_code": dependency_code,
-            "num_variants": num_candidates,
+            "n_candidates_lp": num_candidates,
             "line_profiler_results": line_profiler_results,
             "trace_id": trace_id,
             "python_version": platform.python_version(),
             "experiment_metadata": experiment_metadata,
             "codeflash_version": codeflash_version,
             "lsp_mode": is_LSP_enabled(),
-            "n_candidates_lp": N_CANDIDATES_LP_EFFECTIVE,
         }
 
         console.rule()

codeflash/cli_cmds/cli.py

@@ -104,6 +104,9 @@ def parse_args() -> Namespace:
         action="store_true",
         help="(Deprecated) Async function optimization is now enabled by default. This flag is ignored.",
     )
+    parser.add_argument(
+        "--effort", type=str, help="Effort level for optimization", choices=["low", "medium", "high"], default="medium"
+    )
 
     args, unknown_args = parser.parse_known_args()
     sys.argv[:] = [sys.argv[0], *unknown_args]

codeflash/code_utils/config_consts.py

@@ -1,36 +1,22 @@
+from enum import StrEnum, auto
+
 MAX_TEST_RUN_ITERATIONS = 5
 INDIVIDUAL_TESTCASE_TIMEOUT = 15
 MAX_FUNCTION_TEST_SECONDS = 60
-N_CANDIDATES = 5
 MIN_IMPROVEMENT_THRESHOLD = 0.05
 MIN_THROUGHPUT_IMPROVEMENT_THRESHOLD = 0.10  # 10% minimum improvement for async throughput
 MAX_TEST_FUNCTION_RUNS = 50
 MAX_CUMULATIVE_TEST_RUNTIME_NANOSECONDS = 100e6  # 100ms
-N_TESTS_TO_GENERATE = 2
 TOTAL_LOOPING_TIME = 10.0  # 10 second candidate benchmarking budget
 COVERAGE_THRESHOLD = 60.0
 MIN_TESTCASE_PASSED_THRESHOLD = 6
 REPEAT_OPTIMIZATION_PROBABILITY = 0.1
 DEFAULT_IMPORTANCE_THRESHOLD = 0.001
-N_CANDIDATES_LP = 6
 
-# Refinement
-REFINE_ALL_THRESHOLD = 2  # when valid optimizations count is 2 or less, refine all optimizations
 REFINED_CANDIDATE_RANKING_WEIGHTS = (2, 1)  # (runtime, diff), runtime is more important than diff by a factor of 2
-TOP_N_REFINEMENTS = 0.45  # top 45% of valid optimizations (based on the weighted score) are refined
 
 # LSP-specific
-N_CANDIDATES_LSP = 3
-N_TESTS_TO_GENERATE_LSP = 2
 TOTAL_LOOPING_TIME_LSP = 10.0  # Kept same timing for LSP mode to avoid in increase in performance reporting
-N_CANDIDATES_LP_LSP = 3
-
-# Code repair
-REPAIR_UNMATCHED_PERCENTAGE_LIMIT = 0.4  # if the percentage of unmatched tests is greater than this, we won't fix it (lowering this value makes the repair more stricted)
-MAX_REPAIRS_PER_TRACE = 4  # maximum number of repairs we will do for each function
-
-MAX_N_CANDIDATES = 5
-MAX_N_CANDIDATES_LP = 6
 
 try:
     from codeflash.lsp.helpers import is_LSP_enabled
@@ -39,9 +25,54 @@
 except ImportError:
     _IS_LSP_ENABLED = False
 
-N_CANDIDATES_EFFECTIVE = min(N_CANDIDATES_LSP if _IS_LSP_ENABLED else N_CANDIDATES, MAX_N_CANDIDATES)
-N_CANDIDATES_LP_EFFECTIVE = min(N_CANDIDATES_LP_LSP if _IS_LSP_ENABLED else N_CANDIDATES_LP, MAX_N_CANDIDATES_LP)
-N_TESTS_TO_GENERATE_EFFECTIVE = N_TESTS_TO_GENERATE_LSP if _IS_LSP_ENABLED else N_TESTS_TO_GENERATE
 TOTAL_LOOPING_TIME_EFFECTIVE = TOTAL_LOOPING_TIME_LSP if _IS_LSP_ENABLED else TOTAL_LOOPING_TIME
 
 MAX_CONTEXT_LEN_REVIEW = 1000
+
+
+class EffortLevel(StrEnum):
+    LOW = auto()
+    MEDIUM = auto()
+    HIGH = auto()
+
+
+class EffortKeys(StrEnum):
+    N_OPTIMIZER_CANDIDATES = auto()
+    N_OPTIMIZER_LP_CANDIDATES = auto()
+    N_GENERATED_TESTS = auto()
+    MAX_CODE_REPAIRS_PER_TRACE = auto()
+    REPAIR_UNMATCHED_PERCENTAGE_LIMIT = auto()
+    REFINE_ALL_THRESHOLD = auto()
+    TOP_VALID_CANDIDATES_FOR_REFINEMENT = auto()
+
+
+EFFORT_VALUES: dict[str, dict[EffortLevel, any]] = {
+    EffortKeys.N_OPTIMIZER_CANDIDATES.value: {EffortLevel.LOW: 3, EffortLevel.MEDIUM: 4, EffortLevel.HIGH: 5},
+    EffortKeys.N_OPTIMIZER_LP_CANDIDATES.value: {EffortLevel.LOW: 3, EffortLevel.MEDIUM: 5, EffortLevel.HIGH: 6},
+    # we don't use effort with generated tests for now
+    EffortKeys.N_GENERATED_TESTS.value: {EffortLevel.LOW: 2, EffortLevel.MEDIUM: 2, EffortLevel.HIGH: 2},
+    # maximum number of repairs we will do for each function
+    EffortKeys.MAX_CODE_REPAIRS_PER_TRACE.value: {EffortLevel.LOW: 2, EffortLevel.MEDIUM: 4, EffortLevel.HIGH: 5},
+    # if the percentage of unmatched tests is greater than this, we won't fix it (lowering this value makes the repair more stricted)
+    # on the low effort we lower the limit to 20% to be more strict (less repairs)
+    EffortKeys.REPAIR_UNMATCHED_PERCENTAGE_LIMIT.value: {
+        EffortLevel.LOW: 0.2,
+        EffortLevel.MEDIUM: 0.4,
+        EffortLevel.HIGH: 0.5,
+    },
+    # when valid optimizations count is N or less, refine all optimizations
+    EffortKeys.REFINE_ALL_THRESHOLD.value: {EffortLevel.LOW: 2, EffortLevel.MEDIUM: 3, EffortLevel.HIGH: 4},
+    # Top valid candidates for refinements
+    EffortKeys.TOP_VALID_CANDIDATES_FOR_REFINEMENT: {EffortLevel.LOW: 2, EffortLevel.MEDIUM: 3, EffortLevel.HIGH: 4},
+}
+
+
+def get_effort_value(key: EffortKeys, effort: EffortLevel) -> any:
+    key_str = key.value
+    if key_str in EFFORT_VALUES:
+        if effort in EFFORT_VALUES[key_str]:
+            return EFFORT_VALUES[key_str][effort]
+        msg = f"Invalid effort level: {effort}"
+        raise ValueError(msg)
+    msg = f"Invalid key: {key_str}"
+    raise ValueError(msg)

codeflash/code_utils/git_utils.py

@@ -1,10 +1,7 @@
 from __future__ import annotations
 
 import os
-import shutil
-import subprocess
 import sys
-import tempfile
 import time
 from functools import cache
 from io import StringIO
@@ -16,7 +13,6 @@
 from unidiff import PatchSet
 
 from codeflash.cli_cmds.console import logger
-from codeflash.code_utils.config_consts import N_CANDIDATES_EFFECTIVE
 
 if TYPE_CHECKING:
     from git import Repo
@@ -153,36 +149,6 @@ def check_and_push_branch(repo: git.Repo, git_remote: str | None = "origin", *,
     return True
 
 
-def create_worktree_root_dir(module_root: Path) -> tuple[Path | None, Path | None]:
-    git_root = git_root_dir() if check_running_in_git_repo(module_root) else None
-    worktree_root_dir = Path(tempfile.mkdtemp()) if git_root else None
-    return git_root, worktree_root_dir
-
-
-def create_git_worktrees(
-    git_root: Path | None, worktree_root_dir: Path | None, module_root: Path
-) -> tuple[Path | None, list[Path]]:
-    if git_root and worktree_root_dir:
-        worktree_root = Path(tempfile.mkdtemp(dir=worktree_root_dir))
-        worktrees = [Path(tempfile.mkdtemp(dir=worktree_root)) for _ in range(N_CANDIDATES_EFFECTIVE + 1)]
-        for worktree in worktrees:
-            subprocess.run(["git", "worktree", "add", "-d", worktree], cwd=module_root, check=True)
-    else:
-        worktree_root = None
-        worktrees = []
-    return worktree_root, worktrees
-
-
-def remove_git_worktrees(worktree_root: Path | None, worktrees: list[Path]) -> None:
-    try:
-        for worktree in worktrees:
-            subprocess.run(["git", "worktree", "remove", "-f", worktree], check=True)
-    except subprocess.CalledProcessError as e:
-        logger.warning(f"Error removing worktrees: {e}")
-    if worktree_root:
-        shutil.rmtree(worktree_root)
-
-
 def get_last_commit_author_if_pr_exists(repo: Repo | None = None) -> str | None:
     """Return the author's name of the last commit in the current branch if PR_NUMBER is set.
 

codeflash/optimization/function_optimizer.py

@@ -45,16 +45,11 @@
 from codeflash.code_utils.config_consts import (
     COVERAGE_THRESHOLD,
     INDIVIDUAL_TESTCASE_TIMEOUT,
-    MAX_REPAIRS_PER_TRACE,
-    N_CANDIDATES_EFFECTIVE,
-    N_CANDIDATES_LP_EFFECTIVE,
-    N_TESTS_TO_GENERATE_EFFECTIVE,
-    REFINE_ALL_THRESHOLD,
     REFINED_CANDIDATE_RANKING_WEIGHTS,
-    REPAIR_UNMATCHED_PERCENTAGE_LIMIT,
     REPEAT_OPTIMIZATION_PROBABILITY,
-    TOP_N_REFINEMENTS,
     TOTAL_LOOPING_TIME_EFFECTIVE,
+    EffortKeys,
+    get_effort_value,
 )
 from codeflash.code_utils.deduplicate_code import normalize_code
 from codeflash.code_utils.edit_generated_tests import (
@@ -139,13 +134,15 @@ def __init__(
         ai_service_client: AiServiceClient,
         executor: concurrent.futures.ThreadPoolExecutor,
         future_all_code_repair: list[concurrent.futures.Future],
+        effort: str,
     ) -> None:
         self.candidate_queue = queue.Queue()
         self.line_profiler_done = False
         self.refinement_done = False
         self.candidate_len = len(initial_candidates)
         self.ai_service_client = ai_service_client
         self.executor = executor
+        self.effort = effort
 
         # Initialize queue with initial candidates
         for candidate in initial_candidates:
@@ -193,8 +190,16 @@ def refine_optimizations(self, request: list[AIServiceRefinerRequest]) -> concur
     def _process_refinement_results(self) -> OptimizedCandidate | None:
         """Process refinement results and add to queue. We generate a weighted ranking based on the runtime and diff lines and select the best (round of 45%) of valid optimizations to be refined."""
         future_refinements: list[concurrent.futures.Future] = []
+        top_n_candidates = int(
+            min(
+                get_effort_value(EffortKeys.TOP_VALID_CANDIDATES_FOR_REFINEMENT, self.effort),
+                len(self.all_refinements_data),
+            )
+        )
 
-        if len(self.all_refinements_data) <= REFINE_ALL_THRESHOLD:
+        if top_n_candidates == len(self.all_refinements_data) or len(self.all_refinements_data) <= get_effort_value(
+            EffortKeys.REFINE_ALL_THRESHOLD, self.effort
+        ):
             for data in self.all_refinements_data:
                 future_refinements.append(self.refine_optimizations([data]))  # noqa: PERF401
         else:
@@ -211,7 +216,6 @@ def _process_refinement_results(self) -> OptimizedCandidate | None:
             diffs_norm = normalize_by_max(diff_lens_list)
             # the lower the better
             score_dict = create_score_dictionary_from_metrics(weights, runtime_norm, diffs_norm)
-            top_n_candidates = int((TOP_N_REFINEMENTS * len(runtimes_list)) + 0.5)
             top_indecies = sorted(score_dict, key=score_dict.get)[:top_n_candidates]
 
             for idx in top_indecies:
@@ -312,7 +316,7 @@ def __init__(
         self.function_benchmark_timings = function_benchmark_timings if function_benchmark_timings else {}
         self.total_benchmark_timings = total_benchmark_timings if total_benchmark_timings else {}
         self.replay_tests_dir = replay_tests_dir if replay_tests_dir else None
-        n_tests = N_TESTS_TO_GENERATE_EFFECTIVE
+        n_tests = get_effort_value(EffortKeys.N_GENERATED_TESTS, args.effort)
         self.executor = concurrent.futures.ThreadPoolExecutor(
             max_workers=n_tests + 3 if self.experiment_id is None else n_tests + 4
         )
@@ -362,7 +366,7 @@ def generate_and_instrument_tests(
         str,
     ]:
         """Generate and instrument tests for the function."""
-        n_tests = N_TESTS_TO_GENERATE_EFFECTIVE
+        n_tests = get_effort_value(EffortKeys.N_GENERATED_TESTS, self.args.effort)
         generated_test_paths = [
             get_test_file_path(
                 self.test_cfg.tests_root, self.function_to_optimize.function_name, test_index, test_type="unit"
@@ -927,7 +931,7 @@ def determine_best_candidate(
             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,
+            num_candidates=get_effort_value(EffortKeys.N_OPTIMIZER_LP_CANDIDATES, self.args.effort),
             experiment_metadata=ExperimentMetadata(
                 id=self.experiment_id, group="control" if exp_type == "EXP0" else "experiment"
             )
@@ -942,6 +946,7 @@ def determine_best_candidate(
             self.aiservice_client,
             self.executor,
             self.future_all_code_repair,
+            self.args.effort,
         )
         candidate_index = 0
 
@@ -1292,7 +1297,7 @@ def generate_tests(
         generated_perf_test_paths: list[Path],
     ) -> Result[tuple[int, GeneratedTestsList, dict[str, set[FunctionCalledInTest]], str], str]:
         """Generate unit tests and concolic tests for the function."""
-        n_tests = N_TESTS_TO_GENERATE_EFFECTIVE
+        n_tests = get_effort_value(EffortKeys.N_GENERATED_TESTS, self.args.effort)
         assert len(generated_test_paths) == n_tests
 
         # Submit test generation tasks
@@ -1354,7 +1359,7 @@ def generate_optimizations(
         run_experiment: bool = False,  # noqa: FBT001, FBT002
     ) -> Result[tuple[OptimizationSet, str], str]:
         """Generate optimization candidates for the function."""
-        n_candidates = N_CANDIDATES_EFFECTIVE
+        n_candidates = get_effort_value(EffortKeys.N_OPTIMIZER_CANDIDATES, self.args.effort)
 
         future_optimization_candidates = self.executor.submit(
             self.aiservice_client.optimize_python_code,
@@ -1921,8 +1926,9 @@ def repair_if_possible(
         test_results_count: int,
         exp_type: str,
     ) -> None:
-        if self.repair_counter >= MAX_REPAIRS_PER_TRACE:
-            logger.debug(f"Repair counter reached {MAX_REPAIRS_PER_TRACE}, skipping repair")
+        max_repairs = get_effort_value(EffortKeys.MAX_CODE_REPAIRS_PER_TRACE, self.args.effort)
+        if self.repair_counter >= max_repairs:
+            logger.debug(f"Repair counter reached {max_repairs}, skipping repair")
             return
         if candidate.source not in (OptimizedCandidateSource.OPTIMIZE, OptimizedCandidateSource.OPTIMIZE_LP):
             # only repair the first pass of the candidates for now
@@ -1932,7 +1938,7 @@ def repair_if_possible(
             logger.debug("No diffs found, skipping repair")
             return
         result_unmatched_perc = len(diffs) / test_results_count
-        if result_unmatched_perc > REPAIR_UNMATCHED_PERCENTAGE_LIMIT:
+        if result_unmatched_perc > get_effort_value(EffortKeys.REPAIR_UNMATCHED_PERCENTAGE_LIMIT, self.args.effort):
             logger.debug(f"Result unmatched percentage is {result_unmatched_perc * 100}%, skipping repair")
             return