#!/usr/bin/env python3 from __future__ import annotations import importlib.util import math from pathlib import Path from types import SimpleNamespace HERE = Path(__file__).resolve().parent def load_module(): spec = importlib.util.spec_from_file_location( "intervention_response_phase_aware_v2", HERE / "analyze_existing.py" ) module = importlib.util.module_from_spec(spec) assert spec.loader is not None spec.loader.exec_module(module) return module def load_prepare_module(): spec = importlib.util.spec_from_file_location( "intervention_response_phase_aware_prepare", HERE / "prepare_pilot.py" ) module = importlib.util.module_from_spec(spec) assert spec.loader is not None spec.loader.exec_module(module) return module def load_controller_module(): spec = importlib.util.spec_from_file_location( "intervention_response_phase_aware_controller", HERE / "pilot_controller.py" ) module = importlib.util.module_from_spec(spec) assert spec.loader is not None spec.loader.exec_module(module) return module def load_pilot_analysis_module(): spec = importlib.util.spec_from_file_location( "intervention_response_phase_aware_pilot_analysis", HERE / "analyze_pilot.py" ) module = importlib.util.module_from_spec(spec) assert spec.loader is not None spec.loader.exec_module(module) return module def main() -> None: module = load_module() assert module.common_decile_fractions( trace_duration_s=60.0, minimum_elapsed_s=19.448 ) == (0.1, 0.2, 0.3) assert module.common_decile_fractions( trace_duration_s=60.0, minimum_elapsed_s=60.0 )[-1] == 1.0 stats = module.numeric([0.0, 1.0, 2.0]) assert stats == { "n": 3, "min": 0.0, "max": 2.0, "distinct_n": 3, "median": 1.0, } assert math.isclose(module._pearson([1.0, 2.0], [2.0, 4.0]), 1.0) assert module._pearson([1.0, 1.0], [2.0, 3.0]) is None prepare = load_prepare_module() requests = [ SimpleNamespace( sampling_u=index / 10.0, row_id=f"r{index}", arrival_s=float(index), prompt_tokens_hint=100 + index, ) for index in range(1, 6) ] _anchor, selected = prepare.attainable_anchor(requests, 3) assert len(selected) == 3 record = prepare.selection_record(selected, duration_s=3.0) assert record["selected_count"] == 3 assert record["offered_req_s_per_gpu"] == 0.25 assert len(prepare.SESSION_ORDER) == 6 assert {mns for _replicate, mns in prepare.SESSION_ORDER} == {16, 64} first_id = prepare.private_request_id( source_sha256="a" * 64, line_number=1, original_id="1" ) assert first_id == prepare.private_request_id( source_sha256="a" * 64, line_number=1, original_id="1" ) assert first_id != prepare.private_request_id( source_sha256="b" * 64, line_number=1, original_id="1" ) controller = load_controller_module() assert math.isclose(controller.remaining_projection(6, 0), 7.7) assert math.isclose(controller.remaining_projection(6, 5), 1.45) parsed = controller.parser().parse_args( [ "--manifest", "/tmp/manifest.json", "--run-root", "/tmp/run", "--aituner-root", "/tmp/aituner", "--vllm-source", "/tmp/vllm", "--venv", "/tmp/venv", "--model", "/tmp/model", "--client", "/tmp/client.py", "--dry-run", ] ) assert parsed.dry_run is True pilot_analysis = load_pilot_analysis_module() stable = pilot_analysis.stable_adjacent_features( [ {"end_fraction": 0.1, "qualifying_response_features": ["queue"]}, { "end_fraction": 0.25, "qualifying_response_features": ["kv", "queue"], }, {"end_fraction": 0.5, "qualifying_response_features": ["queue"]}, ] ) assert stable == {"0.10->0.25": ["queue"], "0.25->0.50": ["queue"]} load_consistency = { "0.10->0.25:queue": {"passes_two_regimes": True}, "0.25->0.50:queue": {"passes_two_regimes": True}, } mechanism = pilot_analysis.mechanism_gate(stable, load_consistency) assert mechanism["passes"] is False stable["0.25->0.50"].append("kv") load_consistency["0.25->0.50:kv"] = {"passes_two_regimes": True} mechanism = pilot_analysis.mechanism_gate(stable, load_consistency) assert mechanism["passes"] is True assert mechanism["passing_transitions"] == ["0.25->0.50"] efficacy = pilot_analysis.stable_adjacent_efficacy_features( [ { "end_fraction": 0.1, "efficacy": {"telemetry_qualifying_features": ["early"]}, }, { "end_fraction": 0.25, "efficacy": {"telemetry_qualifying_features": ["queue"]}, }, { "end_fraction": 0.5, "efficacy": {"telemetry_qualifying_features": ["kv", "queue"]}, }, ] ) assert efficacy == {"0.25->0.50": ["queue"]} coverage = pilot_analysis.telemetry_coverage( [ {"step_index": 1, "submit_mono_ns": 100_000_000}, {"step_index": 2, "submit_mono_ns": 200_000_000}, ], start_ns=0, end_ns=300_000_000, ) assert coverage == { "start_gap_s": 0.1, "end_gap_s": 0.1, "max_internal_gap_s": 0.1, } coverage_gate = pilot_analysis.cumulative_coverage_gate( [ { "trial_sanity": [ { "trial_id": "a", "admitted_fraction": 0.25, "completed_fraction": 0.2, } ] }, { "trial_sanity": [ { "trial_id": "a", "admitted_fraction": 0.5, "completed_fraction": 0.4, } ] }, ] ) assert coverage_gate["red_flags"] == [] print("phase-aware intervention response v2 analysis: PASS") if __name__ == "__main__": main()