config_consts.py

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

# pytest loop stability
# For now, we use strict thresholds (large windows and low tolerances), since this is still experimental.
STABILITY_WINDOW_SIZE = 0.35  # 35% of total window
STABILITY_CENTER_TOLERANCE = 0.0025  # ±0.25% around median
STABILITY_SPREAD_TOLERANCE = 0.0025  # 0.25% window spread

# 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

# Adaptive optimization
# TODO (ali): make this configurable with effort arg once the PR is merged
ADAPTIVE_OPTIMIZATION_THRESHOLD = 2  # Max adaptive optimizations per single candidate tree (for example : optimize -> refine -> adaptive -> another adaptive).
# MAX_ADAPTIVE_OPTIMIZATIONS_PER_TRACE = 4  # maximum number of adaptive optimizations we will do for each function (this can be 2 adaptive optimizations for 2 candidates for example)
MAX_ADAPTIVE_OPTIMIZATIONS_PER_TRACE = (
    0  # disable adaptive optimizations until we have this value controlled by the effort arg
)

MAX_N_CANDIDATES = 5
MAX_N_CANDIDATES_LP = 6

try:
    from codeflash.lsp.helpers import is_LSP_enabled

    _IS_LSP_ENABLED = is_LSP_enabled()
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