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b/artifacts/timing/runs/iot_sensors_chiller_1_run_-1.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_-1", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 12.511002, + "average_seconds": 12.511002 + } + }, + "total_wall_time_seconds": 12.511309, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495905.555922 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_run_-2.json b/artifacts/timing/runs/iot_sensors_chiller_1_run_-2.json new file mode 100644 index 000000000..782d166b1 --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_run_-2.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_-2", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 8.951102, + "average_seconds": 8.951102 + } + }, + "total_wall_time_seconds": 8.951563, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495914.5095239 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_run_01.json b/artifacts/timing/runs/iot_sensors_chiller_1_run_01.json new file mode 100644 index 000000000..06d6e7ef6 --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_run_01.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_01", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 10.607203, + "average_seconds": 10.607203 + } + }, + "total_wall_time_seconds": 10.60754, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495925.117853 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_run_02.json b/artifacts/timing/runs/iot_sensors_chiller_1_run_02.json new file mode 100644 index 000000000..2de307ca2 --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_run_02.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_02", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 12.569179, + "average_seconds": 12.569179 + } + }, + "total_wall_time_seconds": 12.569651, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495937.689574 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_run_03.json b/artifacts/timing/runs/iot_sensors_chiller_1_run_03.json new file mode 100644 index 000000000..80aae9e5a --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_run_03.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_03", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 8.479029, + "average_seconds": 8.479029 + } + }, + "total_wall_time_seconds": 8.479517, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495946.169792 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_run_04.json b/artifacts/timing/runs/iot_sensors_chiller_1_run_04.json new file mode 100644 index 000000000..208b58f70 --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_run_04.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_04", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 10.976555, + "average_seconds": 10.976555 + } + }, + "total_wall_time_seconds": 10.977128, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495957.149072 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_run_05.json b/artifacts/timing/runs/iot_sensors_chiller_1_run_05.json new file mode 100644 index 000000000..0170a1808 --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_run_05.json @@ -0,0 +1,21 @@ +{ + "run_name": "iot_sensors_chiller_1_run_05", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 13.517921, + "average_seconds": 13.517921 + } + }, + "total_wall_time_seconds": 13.518375, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775495970.7372491 +} \ No newline at end of file diff --git a/artifacts/timing/runs/iot_sensors_chiller_1_warmup_01.json b/artifacts/timing/runs/iot_sensors_chiller_1_warmup_01.json new file mode 100644 index 000000000..f73840f43 --- /dev/null +++ b/artifacts/timing/runs/iot_sensors_chiller_1_warmup_01.json @@ -0,0 +1,23 @@ +{ + "run_name": "iot_sensors_chiller_1_warmup_01", + "group": "iot_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 7.502668, + "average_seconds": 7.502668 + } + }, + "total_wall_time_seconds": 7.503349, + "metadata": { + "scenario_name": "iot_sensors_chiller_1", + "success": false, + "failure_reason": "planner_emitted_agent_none | Unknown agent 'none'. Registered agents: ['IoTAgent', 'Utilities', 'FMSRAgent', 'TSFMAgent']", + "question_length_chars": 44, + "plan_steps": 3, + "tool_calls": 2, + "server_sequence": "IoTAgent,none,IoTAgent", + "unique_servers": "IoTAgent,none" + }, + "created_at_unix": 1775496708.830985 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_-1.json b/artifacts/timing/runs/utilities_time_run_-1.json new file mode 100644 index 000000000..468c701c0 --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_-1.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_-1", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 4.707929, + "average_seconds": 4.707929 + } + }, + "total_wall_time_seconds": 4.708558, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496082.864054 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_-2.json b/artifacts/timing/runs/utilities_time_run_-2.json new file mode 100644 index 000000000..6066b80e0 --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_-2.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_-2", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 3.685504, + "average_seconds": 3.685504 + } + }, + "total_wall_time_seconds": 3.686068, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496086.552075 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_01.json b/artifacts/timing/runs/utilities_time_run_01.json new file mode 100644 index 000000000..83278b608 --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_01.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_01", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 3.992103, + "average_seconds": 3.992103 + } + }, + "total_wall_time_seconds": 3.992689, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496090.547076 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_02.json b/artifacts/timing/runs/utilities_time_run_02.json new file mode 100644 index 000000000..133967d34 --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_02.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_02", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 4.351466, + "average_seconds": 4.351466 + } + }, + "total_wall_time_seconds": 4.352046, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496094.9015522 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_03.json b/artifacts/timing/runs/utilities_time_run_03.json new file mode 100644 index 000000000..40090477f --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_03.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_03", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 3.934517, + "average_seconds": 3.934517 + } + }, + "total_wall_time_seconds": 3.935077, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496098.838848 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_04.json b/artifacts/timing/runs/utilities_time_run_04.json new file mode 100644 index 000000000..92e4583c9 --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_04.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_04", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 4.815199, + "average_seconds": 4.815199 + } + }, + "total_wall_time_seconds": 4.815743, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496103.6566458 +} \ No newline at end of file diff --git a/artifacts/timing/runs/utilities_time_run_05.json b/artifacts/timing/runs/utilities_time_run_05.json new file mode 100644 index 000000000..55fce349a --- /dev/null +++ b/artifacts/timing/runs/utilities_time_run_05.json @@ -0,0 +1,21 @@ +{ + "run_name": "utilities_time_run_05", + "group": "utilities_only", + "phases": { + "question_to_answer": { + "count": 1, + "total_seconds": 3.81609, + "average_seconds": 3.81609 + } + }, + "total_wall_time_seconds": 3.816603, + "metadata": { + "scenario_name": "utilities_time", + "question_length_chars": 25, + "plan_steps": 1, + "tool_calls": 1, + "server_sequence": "Utilities", + "unique_servers": "Utilities" + }, + "created_at_unix": 1775496107.4759 +} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 905ed17d8..4c034c60c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,9 @@ dependencies = [ "pyyaml>=6.0", "litellm>=1.0", "python-dotenv>=1.0", + "psutil>=7.2.2", + "filelock>=3.24.3", + "matplotlib>=3.10.9", ] [project.scripts] diff --git a/src/benchmarking/bench_fmsr.py b/src/benchmarking/bench_fmsr.py new file mode 100644 index 000000000..8569280c3 --- /dev/null +++ b/src/benchmarking/bench_fmsr.py @@ -0,0 +1,540 @@ +"""FMSR parallelization benchmark — calls get_failure_mode_sensor_mapping +through the FMSR MCP server (stdio subprocess), exactly as the real agent does. + +Each measured run spawns the FMSR MCP server as a subprocess with +FMSR_STRATEGY= in its environment, calls get_failure_mode_sensor_mapping +via the MCP stdio protocol, and records wall time. This is the same code path +the live PlanExecuteRunner uses when it calls FMSRAgent tools. + +Sensors for each scenario are fetched from the IoT MCP server once at startup. +Failure modes are fetched from the FMSR MCP server once at startup. Both calls +go through the MCP stdio interface — no direct Python imports from either server. + +Output +------ + results/bench_fmsr_raw.jsonl — one JSON line per run (append-only) + results/bench_fmsr_summary.json — aggregated statistics + results/bench_fmsr_plots/ — matplotlib figures + +Run from repo root:: + + uv run python -m src.benchmarking.bench_fmsr +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import os +import random +import sys +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "src")) + +from dotenv import load_dotenv +load_dotenv(ROOT / ".env") + +os.environ.setdefault("FMSR_MODEL_ID", "watsonx/meta-llama/llama-3-3-70b-instruct") + +logging.basicConfig( + level=getattr(logging, os.environ.get("LOG_LEVEL", "WARNING").upper(), logging.WARNING), + format=" %(asctime)s %(levelname)s %(name)s %(message)s", + datefmt="%H:%M:%S", +) + +from benchmarking.bench_hardware import HardwareSampler +from benchmarking.bench_stats import aggregate_wall_times, build_summary +from benchmarking.bench_plots import generate_all_plots + +# Configuration + +N_RUNS = 3 +STRATEGIES = ["sequential", "parallel", "adaptive_ceiling", "hedged"] +SCENARIO_IDS = list(range(106, 121)) # 106-120: genuine mapping queries +PARALLEL_WORKERS = 2 +HW_SAMPLE_INTERVAL = 0.5 + +_model = os.environ.get("FMSR_MODEL_ID", "watsonx/meta-llama/llama-3-3-70b-instruct") + +OUT_DIR = ROOT / "src" / "benchmarking" / "results_mcp" +PLOTS_DIR = OUT_DIR / "bench_fmsr_plots" +RAW_FILE = OUT_DIR / "bench_fmsr_raw.jsonl" +SUMMARY_FILE = OUT_DIR / "bench_fmsr_summary.json" +LOCK_FILE = OUT_DIR / "bench_fmsr_raw.lock" + +OUT_DIR.mkdir(parents=True, exist_ok=True) +PLOTS_DIR.mkdir(parents=True, exist_ok=True) + + +# File-locked JSONL writer + +try: + from filelock import FileLock as _FileLock + + def _append_record(record: dict) -> None: + with _FileLock(str(LOCK_FILE)): + with RAW_FILE.open("a") as fh: + fh.write(json.dumps(record) + "\n") + +except ImportError: + import threading as _threading + _write_lock = _threading.Lock() + + def _append_record(record: dict) -> None: # type: ignore[misc] + with _write_lock: + with RAW_FILE.open("a") as fh: + fh.write(json.dumps(record) + "\n") + + +def _load_records() -> list[dict]: + if not RAW_FILE.exists(): + return [] + records = [] + for line in RAW_FILE.read_text().splitlines(): + line = line.strip() + if line: + try: + records.append(json.loads(line)) + except json.JSONDecodeError: + pass + return records + + +# MCP helpers — mirrors workflow/executor.py exactly + +def _make_stdio_params(entry_point: str, extra_env: dict | None = None): + from mcp import StdioServerParameters + + env = {**os.environ} + if extra_env: + env.update(extra_env) + + return StdioServerParameters( + command="uv", + args=["run", entry_point], + cwd=str(ROOT), + env=env, + ) + + +async def _call_mcp_tool( + entry_point: str, + tool_name: str, + args: dict, + extra_env: dict | None = None, +) -> str: + """Spawn an MCP server subprocess and call one tool via stdio.""" + from mcp import ClientSession + from mcp.client.stdio import stdio_client + + params = _make_stdio_params(entry_point, extra_env) + async with stdio_client(params) as (read, write): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.call_tool(tool_name, args) + return "\n".join( + getattr(item, "text", str(item)) for item in result.content + ) + + +# Data fetch — same two upstream calls the real agent makes before mapping + +async def _fetch_asset_data( + asset_id: str = "Chiller 6", + site_name: str = "MAIN", +) -> tuple[list[str], list[str]]: + """Fetch sensors via IoT MCP server and failure modes via FMSR MCP server. + + Goes through the MCP stdio interface so the benchmark uses the same + data path as the real pipeline. Raises RuntimeError if either call fails. + """ + # Sensors from IoT MCP server + raw_sensors = await _call_mcp_tool( + "iot-mcp-server", "sensors", + {"site_name": site_name, "asset_id": asset_id}, + ) + try: + sensors_data = json.loads(raw_sensors) + all_sensors = sensors_data["sensors"] + except (json.JSONDecodeError, KeyError) as exc: + raise RuntimeError( + f"Could not parse sensors from IoT server: {exc}\n" + f"Raw response: {raw_sensors[:300]}" + ) + if not all_sensors: + raise RuntimeError(f"IoT server returned no sensors for asset_id={asset_id!r}") + + # Failure modes from FMSR MCP server + raw_fms = await _call_mcp_tool( + "fmsr-mcp-server", "get_failure_modes", + {"asset_name": "chiller"}, + ) + try: + fms_data = json.loads(raw_fms) + all_fms = fms_data["failure_modes"] + except (json.JSONDecodeError, KeyError) as exc: + raise RuntimeError( + f"Could not parse failure modes from FMSR server: {exc}\n" + f"Raw response: {raw_fms[:300]}" + ) + if not all_fms: + raise RuntimeError("FMSR server returned no failure modes") + + return all_sensors, all_fms + + +# Scenario config — built from real fetched data, not hardcoded + +def _build_scenario_config( + all_sensors: list[str], + all_fms: list[str], +) -> dict[int, dict]: + """Build per-scenario (asset, sensors, fms) from the real fetched lists. + + Filtering mirrors what the real agent would do after fetching from DB/YAML: + each scenario's natural language query implies a specific sensor or FM subset. + """ + + def _sensors_matching(*keywords: str) -> list[str]: + kw = [k.lower() for k in keywords] + return [s for s in all_sensors if any(k in s.lower() for k in kw)] + + def _fms_matching(*keywords: str) -> list[str]: + kw = [k.lower() for k in keywords] + return [f for f in all_fms if any(k in f.lower() for k in kw)] + + temp_sensors = _sensors_matching("temperature") + power_sensors = _sensors_matching("power") + + return { + # 106: "failure modes detected by Chiller 6 Supply Temperature" + 106: {"asset": "Chiller 6", + "sensors": _sensors_matching("supply temperature"), + "fms": all_fms}, + + # 107: "failure modes detected by temperature sensors" + 107: {"asset": "Chiller 6", + "sensors": temp_sensors, + "fms": all_fms}, + + # 108: "failure modes detected by temperature AND power input sensors" + 108: {"asset": "Chiller 6", + "sensors": temp_sensors + power_sensors, + "fms": all_fms}, + + # 109: "failure modes monitorable by available sensors" → full grid + 109: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": all_fms}, + + # 110: "failure modes predicted by vibration sensor" + # No vibration sensor in this asset's DB; use full grid as proxy. + 110: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": all_fms}, + + # 111: "sensors relevant to Compressor Overheating" + 111: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": _fms_matching("compressor")}, + + # 112: "which sensor to prioritize for compressor overheating?" + 112: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": _fms_matching("compressor")}, + + # 113: "most relevant sensor for Evaporator Water side fouling?" + 113: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": _fms_matching("evaporator")}, + + # 114: "failure modes identifiable from available sensor data" → full grid + 114: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": all_fms}, + + # 115: "early detect Purge Unit Excessive purge" + 115: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": _fms_matching("purge")}, + + # 116: "ML recipe for detecting overheating — feature + target sensors" + 116: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": _fms_matching("compressor")}, + + # 117: "temporal behavior of all sensors when compressor motor fails" + 117: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": _fms_matching("compressor")}, + + # 118: "when power input drops, what failure causes it?" + 118: {"asset": "Chiller 6", + "sensors": power_sensors, + "fms": all_fms}, + + # 119: "when Liquid Refrigerant Evaporator Temperature drops, what failure?" + 119: {"asset": "Chiller 6", + "sensors": _sensors_matching("liquid refrigerant"), + "fms": all_fms}, + + # 120: "anomaly model for chiller trip — which sensors + temporal behavior" + 120: {"asset": "Chiller 6", + "sensors": all_sensors, + "fms": all_fms}, + } + + +# Single-run executor + +async def _run_one( + run_id: int, + scenario_id: int, + strategy: str, + asset_name: str, + failure_modes: list[str], + sensors: list[str], +) -> dict[str, Any]: + """Call get_failure_mode_sensor_mapping through the FMSR MCP server. + + Spawns the server subprocess with FMSR_STRATEGY= so the server + routes internally to the correct parallelization implementation. Wall time + includes the full MCP round-trip (subprocess spawn + stdio + tool execution). + """ + hw = HardwareSampler(interval_s=HW_SAMPLE_INTERVAL) + hw.start() + + ts_start = datetime.now(timezone.utc).isoformat() + wall_t0 = time.perf_counter() + ok = True + error = None + raw_response = "" + + try: + raw_response = await _call_mcp_tool( + "fmsr-mcp-server", + "get_failure_mode_sensor_mapping", + { + "asset_name": asset_name, + "failure_modes": failure_modes, + "sensors": sensors, + }, + extra_env={ + "FMSR_STRATEGY": strategy, + "FMSR_PARALLEL_WORKERS": str(PARALLEL_WORKERS), + }, + ) + except Exception as exc: + ok = False + error = f"{type(exc).__name__}: {str(exc)[:300]}" + finally: + wall_s = round(time.perf_counter() - wall_t0, 4) + hw.stop() + + hw_data = hw.summary() + + return { + "run_id": run_id, + "scenario_id": scenario_id, + "strategy": strategy, + "timestamp_start": ts_start, + "wall_s": wall_s, + "ok": ok, + "error": error, + "n_sensors": len(sensors), + "n_fms": len(failure_modes), + "n_pairs": len(sensors) * len(failure_modes), + "hardware": hw_data, + } + + +# Live progress printer + +def _print_record(record: dict) -> None: + if not record["ok"]: + print(f" ERROR: {record['error'][:100]}") + return + hw = record["hardware"] + hw_str = "" + if hw.get("cpu_pct_mean"): + hw_str = ( + f" cpu_mean={hw['cpu_pct_mean']}%" + f" mem_max={hw['mem_rss_mb_max']:.0f}MB" + f" threads_max={hw['thread_count_max']}" + ) + print( + f" wall={record['wall_s']:.2f}s" + f" pairs={record['n_pairs']}" + + hw_str + ) + + +# Summary printer + +def _print_summary(summary: dict) -> None: + print(f"\n{'='*90}") + print(" BENCHMARK SUMMARY") + print(f"{'='*90}") + print( + f" {'Scenario':>10} {'Strategy':<18} " + f"{'Mean(s)':>8} {'Std':>6} {'CI95':>16} {'Speedup':>10}" + ) + print(f" {'─'*76}") + + for sid in SCENARIO_IDS: + for strat in STRATEGIES: + entry = summary.get(sid, {}).get(strat, {}) + ws = entry.get("wall_stats", {}) + if not ws or ws.get("n", 0) == 0: + print(f" {sid:>10} {strat:<18} {'NO DATA':>8}") + continue + + sp = entry.get("speedup_stats") or {} + mean_s = ws.get("mean", 0) + std_s = ws.get("std", 0) + ci_lo = ws.get("ci95_low", 0) + ci_hi = ws.get("ci95_high", 0) + spd_mean = sp.get("mean") if sp else None + spd_str = "baseline" if strat == "sequential" else ( + f"{spd_mean:.2f}×" if spd_mean else "—" + ) + ci_str = f"[{ci_lo:.1f}, {ci_hi:.1f}]" + + print( + f" {sid:>10} {strat:<18} " + f"{mean_s:>7.2f}s {std_s:>5.2f} {ci_str:>16} {spd_str:>10}" + ) + print() + + +# Main + +async def _main() -> None: + print("=" * 90) + print(" FMSR BENCHMARK — Parallelization strategies via MCP") + print(f" Model : {_model}") + print(f" Strategies : {STRATEGIES}") + print(f" Scenarios : {SCENARIO_IDS}") + print(f" Runs per cell : {N_RUNS}") + print(f" parallel workers: {PARALLEL_WORKERS}") + print(f" Raw output : {RAW_FILE}") + print(f" Summary output : {SUMMARY_FILE}") + print("=" * 90) + + # Fetch real sensor and failure-mode lists from the live MCP servers. + # These are the same calls the real agent pipeline makes before invoking + # get_failure_mode_sensor_mapping. + print("\nFetching asset data via MCP servers ...") + all_sensors, all_fms = await _fetch_asset_data(asset_id="Chiller 6", site_name="MAIN") + print(f" IoT server : {len(all_sensors)} sensors for Chiller 6") + print(f" FMSR server : {len(all_fms)} failure modes for chiller") + + scenario_config = _build_scenario_config(all_sensors, all_fms) + + print("\nScenario pair counts:") + for sid in SCENARIO_IDS: + cfg = scenario_config[sid] + n_pairs = len(cfg["sensors"]) * len(cfg["fms"]) + print( + f" scenario={sid} {len(cfg['sensors'])} sensors" + f" × {len(cfg['fms'])} FMs = {n_pairs} pairs" + ) + print() + + # Resume: skip (run, scenario, strategy) triples already in the JSONL file + done: set[tuple] = { + (r["run_id"], r["scenario_id"], r["strategy"]) + for r in _load_records() + } + if done: + print(f" Resuming — {len(done)} executions already complete, skipping them.\n") + + total_runs = N_RUNS * len(SCENARIO_IDS) * len(STRATEGIES) + completed = 0 + t_bench_start = time.perf_counter() + + for run_id in range(1, N_RUNS + 1): + print(f"\n{'='*90}") + print(f" RUN {run_id}/{N_RUNS}") + print(f"{'='*90}") + + for scenario_id in SCENARIO_IDS: + cfg = scenario_config[scenario_id] + asset_name = cfg["asset"] + sensors = cfg["sensors"] + failure_modes = cfg["fms"] + n_pairs = len(sensors) * len(failure_modes) + + strategies_this = random.sample(STRATEGIES, len(STRATEGIES)) + print( + f"\n Scenario {scenario_id}" + f" ({len(sensors)}s×{len(failure_modes)}fm={n_pairs} pairs)" + f" order: {strategies_this}" + ) + + for strategy in strategies_this: + completed += 1 + pct = 100 * completed / total_runs + + if (run_id, scenario_id, strategy) in done: + print( + f"\n [{completed:>3}/{total_runs} {pct:4.0f}%] " + f"run={run_id} scenario={scenario_id}" + f" strategy={strategy} [SKIP]" + ) + continue + + print( + f"\n [{completed:>3}/{total_runs} {pct:4.0f}%] " + f"run={run_id} scenario={scenario_id} strategy={strategy}" + ) + + record = await _run_one( + run_id, scenario_id, strategy, + asset_name, failure_modes, sensors, + ) + _print_record(record) + _append_record(record) + + bench_elapsed = time.perf_counter() - t_bench_start + print(f"\n\n Total benchmark time : {bench_elapsed/60:.1f} min ({bench_elapsed:.0f}s)") + + # Aggregate and save summary + print("\nAggregating results ...") + all_records = _load_records() + summary = build_summary(all_records, STRATEGIES, SCENARIO_IDS) + + SUMMARY_FILE.write_text(json.dumps( + {str(k): v for k, v in summary.items()}, + indent=2, + )) + print(f" Summary → {SUMMARY_FILE}") + + _print_summary(summary) + + print("\nGenerating plots ...") + try: + generate_all_plots( + all_records, summary, PLOTS_DIR, + strategies=STRATEGIES, scenario_ids=SCENARIO_IDS, + ) + except Exception as exc: + print(f" WARNING: plot generation failed: {exc}") + + print(f"\n{'='*90}") + print(" DONE") + print(f"{'='*90}") + + +def main() -> None: + asyncio.run(_main()) + + +if __name__ == "__main__": + main() diff --git a/src/benchmarking/bench_hardware.py b/src/benchmarking/bench_hardware.py new file mode 100644 index 000000000..26d2f5e63 --- /dev/null +++ b/src/benchmarking/bench_hardware.py @@ -0,0 +1,112 @@ +"""Background hardware sampler — CPU%, memory (RSS), and active thread count. + +Runs a daemon thread that polls ``psutil`` at a fixed interval while a strategy +is executing. Call ``start()`` before the strategy and ``stop()`` immediately +after; ``summary()`` returns the collected samples plus aggregate statistics. + +Usage:: + + sampler = HardwareSampler(interval_s=0.5) + sampler.start() + # ... run strategy ... + sampler.stop() + data = sampler.summary() # dict ready to embed in a benchmark record +""" + +from __future__ import annotations + +import threading +import time +from typing import Any + +import psutil + + +class HardwareSampler: + """Polls hardware metrics in a daemon thread while a benchmark strategy runs. + + Args: + interval_s: Seconds between each sample. Default 0.5 s. + """ + + def __init__(self, interval_s: float = 0.5) -> None: + self._interval = interval_s + self._cpu: list[float] = [] + self._mem: list[float] = [] # RSS in MB + self._thr: list[int] = [] # active Python thread count + self._running = False + self._thread: threading.Thread | None = None + self._lock = threading.Lock() + self._process = psutil.Process() + + # Lifecycle + + def start(self) -> None: + """Begin sampling. Clears any data from a previous run.""" + with self._lock: + self._cpu.clear() + self._mem.clear() + self._thr.clear() + self._running = True + self._thread = threading.Thread( + target=self._loop, daemon=True, name="bench-hw-sampler" + ) + self._thread.start() + + def stop(self) -> None: + """Stop sampling and wait for the sampler thread to exit.""" + self._running = False + if self._thread is not None: + self._thread.join(timeout=2.0) + self._thread = None + + # Sampling loop + + def _loop(self) -> None: + # First call to cpu_percent always returns 0.0 — prime it before the loop. + psutil.cpu_percent(interval=None) + while self._running: + try: + cpu = psutil.cpu_percent(interval=None) # system-wide % + mem = self._process.memory_info().rss / (1024 * 1024) # MB + thr = threading.active_count() + with self._lock: + self._cpu.append(round(cpu, 1)) + self._mem.append(round(mem, 1)) + self._thr.append(thr) + except Exception: + pass # never crash the sampler thread + time.sleep(self._interval) + + # Results + + def summary(self) -> dict[str, Any]: + """Return a dict with raw samples and derived aggregates. + + Returns an empty-samples dict if no data was collected (e.g. strategy + ran faster than one interval). + """ + with self._lock: + cpu = list(self._cpu) + mem = list(self._mem) + thr = list(self._thr) + + base: dict[str, Any] = { + "sample_interval_s": self._interval, + "cpu_pct_samples": cpu, + "mem_rss_mb_samples": mem, + "thread_count_samples": thr, + } + + if not cpu: + return base + + base.update({ + "cpu_pct_mean": round(sum(cpu) / len(cpu), 1), + "cpu_pct_max": round(max(cpu), 1), + "mem_rss_mb_mean": round(sum(mem) / len(mem), 1), + "mem_rss_mb_max": round(max(mem), 1), + "thread_count_mean": round(sum(thr) / len(thr), 1), + "thread_count_max": max(thr), + }) + return base diff --git a/src/benchmarking/bench_instrumentation.py b/src/benchmarking/bench_instrumentation.py new file mode 100644 index 000000000..9a666e07f --- /dev/null +++ b/src/benchmarking/bench_instrumentation.py @@ -0,0 +1,263 @@ +"""Instrumentation layer for the FMSR benchmark. + +Patches four intercept points around each strategy execution: + + 1. ``fmsr._call_relevancy`` — per-call wall time, answer, first-result time + 2. ``litellm.completion`` — raw HTTP request count including Router retries + 3. ``fmsr._AdaptiveSemaphore`` — AIMD concurrency-change events with timestamps + 4. ``fmsr._bench_event_callback`` — phase-boundary events emitted by the strategies + +All state is encapsulated in a ``BenchInstrumentation`` instance. + +Typical usage:: + + instr = BenchInstrumentation() + instr.install() + try: + raw_results = fmsr._mapping_adaptive(...) + finally: + instr.uninstall() + + data = instr.collect() # dict ready to embed in a benchmark record +""" + +from __future__ import annotations + +import threading +import time +from typing import Any + +import servers.fmsr.main as fmsr + + +class BenchInstrumentation: + """Install/uninstall all benchmark patches and collect the resulting data.""" + + def __init__(self) -> None: + self._lock = threading.Lock() + + # Data stores — cleared on each install() + self._call_log: list[dict] = [] # one entry per _call_relevancy invocation + self._http_log: list[dict] = [] # one entry per litellm.completion call + self._aimd_log: list[dict] = [] # one entry per concurrency-limit change + self._phase_log: list[dict] = [] # phase-boundary events from fmsr strategies + + self._t_run_start: float | None = None + self._t_first_result: float | None = None + + # Saved originals (restored in uninstall) + self._orig_call_relevancy: Any = None + self._orig_litellm_completion: Any = None + self._orig_aimd_on_success: Any = None + self._orig_aimd_on_failure: Any = None + + # Install + + def install(self) -> None: + """Activate all patches. Must be called before running the strategy.""" + # Reset state + with self._lock: + self._call_log.clear() + self._http_log.clear() + self._aimd_log.clear() + self._phase_log.clear() + self._t_first_result = None + self._t_run_start = time.perf_counter() + + instr = self # closure reference + + # 1. Patch _call_relevancy + self._orig_call_relevancy = fmsr._call_relevancy + + def _traced_call_relevancy(asset_name: str, failure_mode: str, sensor: str) -> dict: + t0 = time.perf_counter() + result = instr._orig_call_relevancy(asset_name, failure_mode, sensor) + elapsed = round(time.perf_counter() - t0, 4) + with instr._lock: + if instr._t_first_result is None: + instr._t_first_result = round( + time.perf_counter() - instr._t_run_start, 4 + ) + instr._call_log.append({ + "sensor": sensor, + "failure_mode": failure_mode, + "answer": result["answer"], + "time_s": elapsed, + }) + return result + + fmsr._call_relevancy = _traced_call_relevancy + + # 2. Patch litellm.completion (counts every raw HTTP attempt) + try: + import litellm as _litellm + self._orig_litellm_completion = _litellm.completion + + def _traced_completion(*args: Any, **kwargs: Any) -> Any: + t0 = time.perf_counter() + error = None + try: + resp = instr._orig_litellm_completion(*args, **kwargs) + return resp + except Exception as exc: + error = f"{type(exc).__name__}: {str(exc)[:150]}" + raise + finally: + elapsed = round(time.perf_counter() - t0, 4) + with instr._lock: + instr._http_log.append({ + "time_s": elapsed, + "error": error, + }) + + _litellm.completion = _traced_completion + except ImportError: + pass # litellm not installed — skip HTTP-level counting + + # 3. Patch _AdaptiveSemaphore class methods + self._orig_aimd_on_success = fmsr._AdaptiveSemaphore.on_success + self._orig_aimd_on_failure = fmsr._AdaptiveSemaphore.on_failure + + def _traced_on_success(sem_self: fmsr._AdaptiveSemaphore) -> None: + old_limit = sem_self._limit + instr._orig_aimd_on_success(sem_self) + new_limit = sem_self._limit + if new_limit != old_limit: + with instr._lock: + instr._aimd_log.append({ + "t_s": round(time.perf_counter() - instr._t_run_start, 4), + "event": "up", + "old": old_limit, + "new": new_limit, + }) + + def _traced_on_failure(sem_self: fmsr._AdaptiveSemaphore) -> None: + old_limit = sem_self._limit + instr._orig_aimd_on_failure(sem_self) + new_limit = sem_self._limit + if new_limit != old_limit: + with instr._lock: + instr._aimd_log.append({ + "t_s": round(time.perf_counter() - instr._t_run_start, 4), + "event": "down", + "old": old_limit, + "new": new_limit, + }) + + fmsr._AdaptiveSemaphore.on_success = _traced_on_success # type: ignore[method-assign] + fmsr._AdaptiveSemaphore.on_failure = _traced_on_failure # type: ignore[method-assign] + + # 4. Register phase-event callback + fmsr._bench_event_callback = self._on_fmsr_event + + # Uninstall + + def uninstall(self) -> None: + """Restore all original functions. Always call this (use try/finally).""" + if self._orig_call_relevancy is not None: + fmsr._call_relevancy = self._orig_call_relevancy + self._orig_call_relevancy = None + + if self._orig_litellm_completion is not None: + try: + import litellm as _litellm + _litellm.completion = self._orig_litellm_completion + except ImportError: + pass + self._orig_litellm_completion = None + + if self._orig_aimd_on_success is not None: + fmsr._AdaptiveSemaphore.on_success = self._orig_aimd_on_success # type: ignore[method-assign] + fmsr._AdaptiveSemaphore.on_failure = self._orig_aimd_on_failure # type: ignore[method-assign] + self._orig_aimd_on_success = None + self._orig_aimd_on_failure = None + + fmsr._bench_event_callback = None + + # Phase event receiver + + def _on_fmsr_event(self, name: str, **kwargs: Any) -> None: + """Called by fmsr._emit_bench_event() at each phase boundary.""" + with self._lock: + self._phase_log.append({ + "t_s": round(time.perf_counter() - self._t_run_start, 4), + "name": name, + **{k: v for k, v in kwargs.items()}, + }) + + # Collect results + + def collect(self) -> dict[str, Any]: + """Return a structured dict of all collected instrumentation data. + + Call this *after* ``uninstall()`` so no more patches are active. + """ + with self._lock: + calls = list(self._call_log) + http = list(self._http_log) + aimd = list(self._aimd_log) + phases = list(self._phase_log) + t_first = self._t_first_result + + call_times = [c["time_s"] for c in calls] + http_errors = [h for h in http if h["error"]] + + # Phase breakdown from named events + phase_data = _compute_phase_breakdown(phases) + + # LLM / API-layer stats + llm_stats: dict[str, Any] = { + "http_requests_sent": len(http), + "http_errors": len(http_errors), + "strategy_level_retries": phase_data.get("phase2_pairs", 0), + "errors_by_type": _count_error_types(http_errors), + } + + return { + "per_call_times_s": call_times, + "answers": [c["answer"] for c in calls], + "time_to_first_result_s": t_first, + "aimd_events": aimd, + "phase_breakdown": phase_data, + "llm_stats": llm_stats, + } + + +# Helpers + +def _compute_phase_breakdown(phase_log: list[dict]) -> dict[str, Any]: + """Derive phase durations from the ordered event log.""" + result: dict[str, Any] = {} + + def _ts(name: str) -> float | None: + for e in phase_log: + if e["name"] == name: + return e["t_s"] + return None + + p1s = _ts("phase1_start") + p1e = _ts("phase1_end") + p2s = _ts("phase2_start") + p2e = _ts("phase2_end") + + if p1s is not None and p1e is not None: + result["phase1_s"] = round(p1e - p1s, 4) + if p2s is not None and p2e is not None: + result["phase2_s"] = round(p2e - p2s, 4) + + # pairs_failed is passed as kwarg with the phase1_end event + for e in phase_log: + if e["name"] == "phase1_end" and "pairs_failed" in e: + result["phase2_pairs"] = e["pairs_failed"] + break + + return result + + +def _count_error_types(error_entries: list[dict]) -> dict[str, int]: + counts: dict[str, int] = {} + for entry in error_entries: + if entry.get("error"): + etype = entry["error"].split(":")[0].strip() + counts[etype] = counts.get(etype, 0) + 1 + return counts diff --git a/src/benchmarking/bench_plots.py b/src/benchmarking/bench_plots.py new file mode 100644 index 000000000..8a2882b82 --- /dev/null +++ b/src/benchmarking/bench_plots.py @@ -0,0 +1,565 @@ +"""All benchmark plots — 11 figures saved to a configurable output directory. + +Call ``generate_all_plots(records, summary, plots_dir)`` after a benchmark run. +Each figure is saved as a numbered PNG so alphabetical order matches logical order. + +Strategies benchmarked: sequential | parallel | adaptive_ceiling | hedged + +Figures +------- +01_wall_time_boxplot.png — wall time distribution per strategy (all runs × scenarios) +02_wall_time_per_scenario.png — mean wall time ± 95% CI, grouped by scenario +03_speedup_barplot.png — mean speedup vs sequential ± 95% CI +04_speedup_cdf.png — CDF of speedup ratios (reliability view) +05_per_call_violin.png — individual LLM call-time distribution per strategy +06_p95_call_latency.png — p95 per-call latency per (scenario, strategy) +07_hardware_cpu.png — CPU % over time (representative run) +08_hardware_memory.png — Memory RSS over time (representative run) +09_hardware_threads.png — Active thread count over time (representative run) +10_retry_rate.png — Mean strategy-level retries per run per strategy +11_heatmap_wall_time.png — Heatmap: scenario × strategy → mean wall time +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import numpy as np + +# Shared style + +COLORS = { + "sequential": "#4e79a7", + "parallel": "#f28e2b", + "adaptive_ceiling": "#b07aa1", + "hedged": "#76b7b2", +} + +STRATEGY_LABELS = { + "sequential": "Sequential", + "parallel": "Parallel", + "adaptive_ceiling": "Adaptive (Ceiling-start)", + "hedged": "Hedged", +} + +_FIGSIZE_WIDE = (14, 5) +_FIGSIZE_STD = (9, 5) +_FIGSIZE_TALL = (9, 6) +_FIGSIZE_SQUARE= (8, 7) + +plt.rcParams.update({ + "font.size": 9, + "axes.titlesize": 10, + "axes.labelsize": 9, + "legend.fontsize": 8, + "figure.dpi": 150, +}) + + +# Entry point + +def generate_all_plots( + records: list[dict], + summary: dict[str, Any], + plots_dir: Path, + strategies: list[str] | None = None, + scenario_ids: list[int] | None = None, +) -> None: + """Generate all 11 plots and save them to ``plots_dir``.""" + plots_dir = Path(plots_dir) + plots_dir.mkdir(parents=True, exist_ok=True) + + if strategies is None: + strategies = ["sequential", "parallel", "adaptive_ceiling", "hedged"] + if scenario_ids is None: + scenario_ids = sorted({r["scenario_id"] for r in records}) + + ok_records = [r for r in records if r.get("ok")] + + _save(plot_wall_time_boxplot(ok_records, strategies), + plots_dir / "01_wall_time_boxplot.png") + _save(plot_wall_time_per_scenario(summary, strategies, scenario_ids), + plots_dir / "02_wall_time_per_scenario.png") + _save(plot_speedup_barplot(summary, strategies, scenario_ids), + plots_dir / "03_speedup_barplot.png") + _save(plot_speedup_cdf(ok_records, strategies, scenario_ids), + plots_dir / "04_speedup_cdf.png") + _save(plot_per_call_violin(ok_records, strategies), + plots_dir / "05_per_call_violin.png") + _save(plot_p95_call_latency(ok_records, strategies, scenario_ids), + plots_dir / "06_p95_call_latency.png") + + # Hardware plots — use the first run, first scenario as the representative + rep_run = _pick_representative_run(ok_records, scenario_ids) + hw_records = _collect_hw_by_strategy(rep_run, strategies) + _save(plot_hardware_cpu(hw_records, strategies), + plots_dir / "07_hardware_cpu.png") + _save(plot_hardware_memory(hw_records, strategies), + plots_dir / "08_hardware_memory.png") + _save(plot_hardware_threads(hw_records, strategies), + plots_dir / "09_hardware_threads.png") + + _save(plot_retry_rate(ok_records, strategies), + plots_dir / "10_retry_rate.png") + _save(plot_heatmap_wall_time(summary, strategies, scenario_ids), + plots_dir / "11_heatmap_wall_time.png") + + print(f" Plots saved → {plots_dir}") + + +def _save(fig: plt.Figure, path: Path) -> None: + fig.savefig(path, dpi=150, bbox_inches="tight") + plt.close(fig) + + +# Plot functions + +def plot_wall_time_boxplot(records: list[dict], strategies: list[str]) -> plt.Figure: + """Box plot: wall-time distribution per strategy across all runs and scenarios.""" + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + data = [ + [r["wall_s"] for r in records if r["strategy"] == s] + for s in strategies + ] + labels = [STRATEGY_LABELS.get(s, s) for s in strategies] + colors = [COLORS.get(s, "#aaa") for s in strategies] + + bp = ax.boxplot(data, labels=labels, patch_artist=True, + medianprops=dict(color="black", linewidth=1.5), + flierprops=dict(marker="o", markersize=3, alpha=0.5)) + for patch, color in zip(bp["boxes"], colors): + patch.set_facecolor(color) + patch.set_alpha(0.75) + + # Overlay individual points for transparency + for i, (pts, color) in enumerate(zip(data, colors), start=1): + jitter = np.random.uniform(-0.12, 0.12, len(pts)) + ax.scatter([i + j for j in jitter], pts, color=color, + alpha=0.5, s=16, zorder=3) + + ax.set_ylabel("Wall time (s)") + ax.set_title("Wall time distribution — all runs & scenarios") + ax.yaxis.set_minor_locator(mticker.AutoMinorLocator()) + ax.grid(axis="y", linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_wall_time_per_scenario( + summary: dict, strategies: list[str], scenario_ids: list[int] +) -> plt.Figure: + """Grouped bar chart: mean wall time ± 95% CI, grouped by scenario.""" + fig, ax = plt.subplots(figsize=_FIGSIZE_WIDE) + x = np.arange(len(scenario_ids)) + width = 0.18 + n_strats = len(strategies) + + for i, strat in enumerate(strategies): + means = [] + yerr_lo, yerr_hi = [], [] + for sid in scenario_ids: + ws = summary.get(sid, {}).get(strat, {}).get("wall_stats", {}) + m = ws.get("mean", 0) + means.append(m) + yerr_lo.append(m - ws.get("ci95_low", m)) + yerr_hi.append(ws.get("ci95_high", m) - m) + + offset = (i - n_strats / 2 + 0.5) * width + ax.bar(x + offset, means, width, + yerr=[yerr_lo, yerr_hi], + label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), alpha=0.85, + error_kw=dict(elinewidth=1.2, capsize=3)) + + ax.set_xticks(x) + ax.set_xticklabels([str(s) for s in scenario_ids]) + ax.set_xlabel("Scenario ID") + ax.set_ylabel("Wall time (s)") + ax.set_title("Mean wall time per scenario (error bars = 95% CI)") + ax.legend() + ax.grid(axis="y", linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_speedup_barplot( + summary: dict, strategies: list[str], scenario_ids: list[int] +) -> plt.Figure: + """Grouped bar chart: mean speedup vs sequential ± 95% CI.""" + non_seq = [s for s in strategies if s != "sequential"] + fig, ax = plt.subplots(figsize=_FIGSIZE_WIDE) + x = np.arange(len(scenario_ids)) + width = 0.22 + n = len(non_seq) + + for i, strat in enumerate(non_seq): + means = [] + yerr_lo, yerr_hi = [], [] + for sid in scenario_ids: + sp = summary.get(sid, {}).get(strat, {}).get("speedup_stats", {}) + m = sp.get("mean", 0) if sp else 0 + means.append(m) + yerr_lo.append(m - (sp.get("ci95_low", m) if sp else m)) + yerr_hi.append((sp.get("ci95_high", m) if sp else m) - m) + + offset = (i - n / 2 + 0.5) * width + ax.bar(x + offset, means, width, + yerr=[yerr_lo, yerr_hi], + label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), alpha=0.85, + error_kw=dict(elinewidth=1.2, capsize=3)) + + ax.axhline(1.0, color="black", linestyle="--", linewidth=0.9, label="baseline (1×)") + ax.set_xticks(x) + ax.set_xticklabels([str(s) for s in scenario_ids]) + ax.set_xlabel("Scenario ID") + ax.set_ylabel("Speedup vs sequential") + ax.set_title("Mean speedup vs sequential (error bars = 95% CI)") + ax.legend() + ax.grid(axis="y", linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_speedup_cdf( + records: list[dict], strategies: list[str], scenario_ids: list[int] +) -> plt.Figure: + """CDF of speedup ratios — shows reliability, not just averages.""" + non_seq = [s for s in strategies if s != "sequential"] + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + + # Build per-run sequential wall times keyed by (run_id, scenario_id) + seq_wall: dict[tuple, float] = { + (r["run_id"], r["scenario_id"]): r["wall_s"] + for r in records if r["strategy"] == "sequential" + } + + for strat in non_seq: + speedups = [] + for r in records: + if r["strategy"] != strat: + continue + sw = seq_wall.get((r["run_id"], r["scenario_id"])) + if sw and r["wall_s"] > 0: + speedups.append(sw / r["wall_s"]) + + if not speedups: + continue + speedups_s = sorted(speedups) + y = np.linspace(0, 1, len(speedups_s) + 1)[1:] + ax.step(speedups_s, y, where="post", + label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), linewidth=1.8) + + ax.axvline(1.0, color="black", linestyle="--", linewidth=0.9, label="1× (no speedup)") + ax.set_xlabel("Speedup vs sequential") + ax.set_ylabel("Fraction of runs ≤ x") + ax.set_title("Speedup CDF — reliability of each strategy") + ax.legend() + ax.grid(linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_per_call_violin(records: list[dict], strategies: list[str]) -> plt.Figure: + """Violin plot: distribution of individual LLM call times per strategy.""" + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + data = [ + [t for r in records if r["strategy"] == s for t in r.get("per_call_times_s", [])] + for s in strategies + ] + # Filter out empty groups + positions = [i + 1 for i, d in enumerate(data) if d] + data_nz = [d for d in data if d] + labels_nz = [STRATEGY_LABELS.get(s, s) for s, d in zip(strategies, data) if d] + colors_nz = [COLORS.get(s, "#aaa") for s, d in zip(strategies, data) if d] + + if data_nz: + parts = ax.violinplot(data_nz, positions=positions, + showmedians=True, showextrema=True) + for body, color in zip(parts["bodies"], colors_nz): + body.set_facecolor(color) + body.set_alpha(0.65) + for part in ("cmedians", "cmins", "cmaxes", "cbars"): + parts[part].set_color("black") + parts[part].set_linewidth(0.9) + + ax.set_xticks(positions) + ax.set_xticklabels(labels_nz, rotation=10, ha="right") + ax.set_ylabel("Individual call time (s)") + ax.set_title("Per-call LLM latency distribution") + ax.grid(axis="y", linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_p95_call_latency( + records: list[dict], strategies: list[str], scenario_ids: list[int] +) -> plt.Figure: + """Bar chart: p95 per-call latency per (scenario, strategy). + + High p95 directly explains why fixed-parallel strategies showed poor wall times — + one slow call dominates when calls are concurrent. + """ + from benchmarking.bench_stats import compute_call_stats + + fig, ax = plt.subplots(figsize=_FIGSIZE_WIDE) + x = np.arange(len(scenario_ids)) + width = 0.18 + n = len(strategies) + + for i, strat in enumerate(strategies): + p95s = [] + for sid in scenario_ids: + times = [ + t + for r in records + if r["strategy"] == strat and r["scenario_id"] == sid + for t in r.get("per_call_times_s", []) + ] + cs = compute_call_stats(times) + p95s.append(cs.get("p95", 0)) + + offset = (i - n / 2 + 0.5) * width + ax.bar(x + offset, p95s, width, + label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), alpha=0.85) + + ax.set_xticks(x) + ax.set_xticklabels([str(s) for s in scenario_ids]) + ax.set_xlabel("Scenario ID") + ax.set_ylabel("p95 call time (s)") + ax.set_title("p95 per-call latency — captures 'poison call' effect") + ax.legend() + ax.grid(axis="y", linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_hardware_cpu(hw_by_strat: dict[str, dict], strategies: list[str]) -> plt.Figure: + """CPU % over time for each strategy (representative run).""" + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + for strat in strategies: + hw = hw_by_strat.get(strat) + if not hw: + continue + samples = hw.get("cpu_pct_samples", []) + interval = hw.get("sample_interval_s", 0.5) + times = [i * interval for i in range(len(samples))] + ax.plot(times, samples, label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), linewidth=1.4, alpha=0.85) + + ax.set_xlabel("Elapsed time (s)") + ax.set_ylabel("CPU % (system-wide)") + ax.set_title("CPU utilisation over time — representative run") + ax.legend() + ax.grid(linestyle="--", alpha=0.4) + ax.set_ylim(bottom=0) + fig.tight_layout() + return fig + + +def plot_hardware_memory(hw_by_strat: dict[str, dict], strategies: list[str]) -> plt.Figure: + """Memory RSS over time for each strategy (representative run).""" + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + for strat in strategies: + hw = hw_by_strat.get(strat) + if not hw: + continue + samples = hw.get("mem_rss_mb_samples", []) + interval = hw.get("sample_interval_s", 0.5) + times = [i * interval for i in range(len(samples))] + ax.plot(times, samples, label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), linewidth=1.4, alpha=0.85) + + ax.set_xlabel("Elapsed time (s)") + ax.set_ylabel("Memory RSS (MB)") + ax.set_title("Memory usage over time — representative run") + ax.legend() + ax.grid(linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_hardware_threads(hw_by_strat: dict[str, dict], strategies: list[str]) -> plt.Figure: + """Active thread count over time for each strategy (representative run).""" + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + for strat in strategies: + hw = hw_by_strat.get(strat) + if not hw: + continue + samples = hw.get("thread_count_samples", []) + interval = hw.get("sample_interval_s", 0.5) + times = [i * interval for i in range(len(samples))] + ax.step(times, samples, label=STRATEGY_LABELS.get(strat, strat), + color=COLORS.get(strat, "#aaa"), linewidth=1.4, alpha=0.85, where="post") + + ax.set_xlabel("Elapsed time (s)") + ax.set_ylabel("Active Python threads") + ax.set_title("Thread count over time — representative run") + ax.legend() + ax.grid(linestyle="--", alpha=0.4) + ax.set_ylim(bottom=0) + fig.tight_layout() + return fig + + +def plot_retry_rate(records: list[dict], strategies: list[str]) -> plt.Figure: + """Bar chart: mean strategy-level retry count per run per strategy.""" + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + means = [] + stds = [] + for strat in strategies: + retries = [ + r.get("llm_stats", {}).get("strategy_level_retries", 0) + for r in records if r["strategy"] == strat + ] + means.append(np.mean(retries) if retries else 0) + stds.append(float(np.std(retries)) if len(retries) > 1 else 0) + + labels = [STRATEGY_LABELS.get(s, s) for s in strategies] + colors = [COLORS.get(s, "#aaa") for s in strategies] + x = np.arange(len(strategies)) + + bars = ax.bar(x, means, yerr=stds, color=colors, alpha=0.85, + error_kw=dict(elinewidth=1.2, capsize=4)) + ax.set_xticks(x) + ax.set_xticklabels(labels, rotation=10, ha="right") + ax.set_ylabel("Mean strategy-level retries per run") + ax.set_title("Strategy-level retry rate (error bars = std)") + ax.grid(axis="y", linestyle="--", alpha=0.4) + + # Annotate bars + for bar, m in zip(bars, means): + ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.02, + f"{m:.2f}", ha="center", va="bottom", fontsize=8) + + fig.tight_layout() + return fig + + +def plot_aimd_timeline(records: list[dict]) -> plt.Figure: + """Step-function plot of AIMD concurrency-limit changes over time. + + Picks the adaptive run with the most AIMD events (most interesting trace). + """ + adaptive_recs = [ + r for r in records + if r["strategy"] == "adaptive" and r.get("aimd_events") + ] + + fig, ax = plt.subplots(figsize=_FIGSIZE_STD) + + if not adaptive_recs: + ax.text(0.5, 0.5, "No AIMD events recorded", transform=ax.transAxes, + ha="center", va="center", fontsize=10, color="gray") + ax.set_title("AIMD concurrency timeline (no events captured)") + fig.tight_layout() + return fig + + # Pick run with most events + best = max(adaptive_recs, key=lambda r: len(r["aimd_events"])) + events = best["aimd_events"] + wall = best["wall_s"] + + # Build step function: start at initial=2, apply each event + t_points = [0.0] + l_points = [2] # start concurrency + + for ev in events: + t_points.append(ev["t_s"]) + l_points.append(ev["old"]) # just before the change + t_points.append(ev["t_s"]) + l_points.append(ev["new"]) # just after + + t_points.append(wall) + l_points.append(l_points[-1]) + + ax.step(t_points, l_points, where="post", + color=COLORS["adaptive"], linewidth=2.0, label="Concurrency limit") + + # Mark up/down events + ups = [ev for ev in events if ev["event"] == "up"] + downs = [ev for ev in events if ev["event"] == "down"] + if ups: + ax.scatter([e["t_s"] for e in ups], [e["new"] for e in ups], + color="green", zorder=5, s=60, label="↑ increase", marker="^") + if downs: + ax.scatter([e["t_s"] for e in downs], [e["new"] for e in downs], + color="red", zorder=5, s=60, label="↓ decrease (failure)", marker="v") + + ax.set_xlabel("Elapsed time (s)") + ax.set_ylabel("Concurrency limit") + ax.set_title( + f"AIMD concurrency timeline — run {best['run_id']} " + f"scenario {best['scenario_id']} ({len(events)} events)" + ) + ax.set_ylim(bottom=0) + ax.legend() + ax.grid(linestyle="--", alpha=0.4) + fig.tight_layout() + return fig + + +def plot_heatmap_wall_time( + summary: dict, strategies: list[str], scenario_ids: list[int] +) -> plt.Figure: + """Color-coded heatmap: rows = scenarios, columns = strategies.""" + data = np.zeros((len(scenario_ids), len(strategies))) + for r, sid in enumerate(scenario_ids): + for c, strat in enumerate(strategies): + ws = summary.get(sid, {}).get(strat, {}).get("wall_stats", {}) + data[r, c] = ws.get("mean", float("nan")) + + fig, ax = plt.subplots(figsize=_FIGSIZE_SQUARE) + im = ax.imshow(data, aspect="auto", cmap="RdYlGn_r") + plt.colorbar(im, ax=ax, label="Mean wall time (s)") + + ax.set_xticks(range(len(strategies))) + ax.set_xticklabels([STRATEGY_LABELS.get(s, s) for s in strategies], rotation=15, ha="right") + ax.set_yticks(range(len(scenario_ids))) + ax.set_yticklabels([str(s) for s in scenario_ids]) + ax.set_ylabel("Scenario ID") + ax.set_title("Mean wall time heatmap (lower = faster)") + + # Annotate cells + for r in range(len(scenario_ids)): + for c in range(len(strategies)): + val = data[r, c] + if not np.isnan(val): + ax.text(c, r, f"{val:.1f}s", ha="center", va="center", + fontsize=8, color="black", + fontweight="bold" if val == np.nanmin(data[r]) else "normal") + + fig.tight_layout() + return fig + + +# Internal helpers + +def _pick_representative_run( + records: list[dict], scenario_ids: list[int] +) -> list[dict]: + """Return all strategy records for (run_id=1, first available scenario).""" + for sid in scenario_ids: + subset = [r for r in records if r["scenario_id"] == sid and r["run_id"] == 1] + if subset: + return subset + return [] + + +def _collect_hw_by_strategy( + run_records: list[dict], strategies: list[str] +) -> dict[str, dict]: + """Map strategy name → hardware dict from a single run's records.""" + result: dict[str, dict] = {} + for r in run_records: + strat = r.get("strategy") + if strat in strategies and "hardware" in r: + result[strat] = r["hardware"] + return result diff --git a/src/benchmarking/bench_stats.py b/src/benchmarking/bench_stats.py new file mode 100644 index 000000000..2f99e9e78 --- /dev/null +++ b/src/benchmarking/bench_stats.py @@ -0,0 +1,201 @@ +"""Statistical helpers for benchmark results. + +All functions operate on plain Python lists — no numpy or scipy dependency. + +Public API +---------- +compute_call_stats(times_s) → per-call latency statistics dict +aggregate_wall_times(wall_times) → wall-time statistics dict (with 95% CI) +compute_speedups(seq_walls, strategy_walls) → speedup statistics dict +build_summary(records) → nested (scenario_id → strategy → stats) dict +""" + +from __future__ import annotations + +import math +import statistics as _st +from typing import Any + + +# t-distribution critical values for 95% CI, df = n-1 +# Source: standard t-table. For df > 30 we fall back to z = 1.96. + +_T95: dict[int, float] = { + 1: 12.706, 2: 4.303, 3: 3.182, 4: 2.776, 5: 2.571, + 6: 2.447, 7: 2.365, 8: 2.306, 9: 2.262, 10: 2.228, + 11: 2.201, 12: 2.179, 13: 2.160, 14: 2.145, 15: 2.131, + 20: 2.086, 25: 2.060, 30: 2.042, +} + + +def _t95(n: int) -> float: + """Return the t-critical value for a two-tailed 95% CI with n observations.""" + if n <= 1: + return float("inf") + df = n - 1 + if df in _T95: + return _T95[df] + if df < 30: + # Linear interpolation between bracketing table entries + keys = sorted(_T95) + lo = max(k for k in keys if k <= df) + hi = min(k for k in keys if k >= df) + if lo == hi: + return _T95[lo] + frac = (df - lo) / (hi - lo) + return _T95[lo] + frac * (_T95[hi] - _T95[lo]) + return 1.96 # z for large samples + + +def _percentile(sorted_vals: list[float], p: float) -> float: + """p in [0, 1]. Linear interpolation on a pre-sorted list.""" + n = len(sorted_vals) + if n == 0: + return 0.0 + if n == 1: + return sorted_vals[0] + idx = p * (n - 1) + lo = int(idx) + hi = min(lo + 1, n - 1) + return sorted_vals[lo] + (idx - lo) * (sorted_vals[hi] - sorted_vals[lo]) + + +# Public API + +def compute_call_stats(times_s: list[float]) -> dict[str, Any]: + """Summary statistics for per-call LLM latencies. + + Returns: + n, min, max, mean, std, p50, p95, p99, cv (coefficient of variation). + """ + if not times_s: + return {} + s = sorted(times_s) + n = len(s) + mean = _st.mean(s) + std = _st.stdev(s) if n > 1 else 0.0 + return { + "n": n, + "min": round(min(s), 4), + "max": round(max(s), 4), + "mean": round(mean, 4), + "std": round(std, 4), + "p50": round(_percentile(s, 0.50), 4), + "p95": round(_percentile(s, 0.95), 4), + "p99": round(_percentile(s, 0.99), 4), + "cv": round(std / mean, 4) if mean > 0 else 0.0, + } + + +def aggregate_wall_times(wall_times: list[float]) -> dict[str, Any]: + """Aggregate wall-clock times across multiple runs. + + Returns n, mean, std, min, max, 95% CI bounds, and CV. + The CI uses the t-distribution so it is valid for small N (≥ 2). + """ + if not wall_times: + return {} + n = len(wall_times) + mean = _st.mean(wall_times) + std = _st.stdev(wall_times) if n > 1 else 0.0 + t = _t95(n) + margin = t * std / math.sqrt(n) if n > 1 else 0.0 + s = sorted(wall_times) + return { + "n": n, + "mean": round(mean, 4), + "std": round(std, 4), + "min": round(min(wall_times), 4), + "max": round(max(wall_times), 4), + "ci95_low": round(max(0.0, mean - margin), 4), + "ci95_high": round(mean + margin, 4), + "cv": round(std / mean, 4) if mean > 0 else 0.0, + "p50": round(_percentile(s, 0.50), 4), + } + + +def compute_speedups( + seq_walls: list[float], + strategy_walls: list[float], +) -> dict[str, Any]: + """Per-run speedup = seq_wall / strategy_wall, then aggregate across runs. + + Pairs are matched by index (same run number). + Returns the same shape as ``aggregate_wall_times``. + """ + if not seq_walls or not strategy_walls: + return {} + pairs = list(zip(seq_walls, strategy_walls)) + speedups = [s / p for s, p in pairs if p > 0] + return aggregate_wall_times(speedups) + + +def build_summary( + records: list[dict], + strategy_ids: list[str], + scenario_ids: list[int], +) -> dict[str, Any]: + """Aggregate all raw benchmark records into a nested summary dict. + + Structure:: + + summary[scenario_id][strategy] = { + "wall_stats": aggregate_wall_times result, + "call_stats": compute_call_stats over all calls in all runs, + "speedup_stats": compute_speedups (None for sequential), + "answer_match_rate": fraction of runs where answers matched sequential, + "retry_rate_mean": mean strategy-level retries per run, + "http_req_mean": mean raw HTTP requests per run (includes retries), + "error_rate": fraction of runs that had at least one HTTP error, + } + """ + # Group records by (scenario_id, strategy), keep only successful runs + groups: dict[tuple, list[dict]] = {} + for r in records: + if not r.get("ok"): + continue + key = (r["scenario_id"], r["strategy"]) + groups.setdefault(key, []).append(r) + + summary: dict[str, Any] = {} + + for sid in scenario_ids: + summary[sid] = {} + + # Collect sequential wall times for this scenario (used for speedup) + seq_recs = groups.get((sid, "sequential"), []) + seq_walls = [r["wall_s"] for r in seq_recs] + + for strat in strategy_ids: + recs = groups.get((sid, strat), []) + if not recs: + summary[sid][strat] = {"n": 0} + continue + + walls = [r["wall_s"] for r in recs] + all_call_times = [t for r in recs for t in r.get("per_call_times_s", [])] + + # Answer match rate (vs first sequential run for this scenario) + n_match = sum(1 for r in recs if r.get("answer_match", strat == "sequential")) + n_total = len(recs) + + # LLM / retry stats + http_req = [r.get("llm_stats", {}).get("http_requests_sent", 0) for r in recs] + ret_counts = [r.get("llm_stats", {}).get("strategy_level_retries", 0) for r in recs] + err_counts = [r.get("llm_stats", {}).get("http_errors", 0) for r in recs] + + summary[sid][strat] = { + "wall_stats": aggregate_wall_times(walls), + "call_stats": compute_call_stats(all_call_times), + "speedup_stats": ( + compute_speedups(seq_walls, walls) + if strat != "sequential" and seq_walls + else None + ), + "answer_match_rate": round(n_match / n_total, 4) if n_total else 0.0, + "retry_rate_mean": round(sum(ret_counts) / len(ret_counts), 2) if ret_counts else 0.0, + "http_req_mean": round(sum(http_req) / len(http_req), 2) if http_req else 0.0, + "error_rate": round(sum(1 for e in err_counts if e > 0) / n_total, 4) if n_total else 0.0, + } + + return summary diff --git a/src/benchmarking/eval_fmsr.py b/src/benchmarking/eval_fmsr.py new file mode 100644 index 000000000..9cbe7567f --- /dev/null +++ b/src/benchmarking/eval_fmsr.py @@ -0,0 +1,324 @@ +"""Sequential vs parallel vs parallel+retry vs adaptive (AIMD) FMSR mapping. + +Four strategies, same N×M pairs, one run each, back to back: + + sequential — one call at a time (baseline) + parallel — fixed thread pool, work-stealing queue + parallel_retry — parallel phase + decoupled sequential retry queue for failures + adaptive — AIMD concurrency (probes up on success, halves on failure) + with jitter and retry queue + +No sleep between strategies. One run per scenario. +Per-call times captured for every individual LLM call. +Answers diff-checked against sequential to confirm correctness. + +Run from repo root: + uv run python -m src.benchmarking.eval_fmsr +""" + +from __future__ import annotations + +import json +import os +import sys +import threading +import time +from pathlib import Path +from typing import Any + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "src")) + +from dotenv import load_dotenv +load_dotenv(ROOT / ".env") + +os.environ.setdefault("FMSR_MODEL_ID", "watsonx/meta-llama/llama-3-3-70b-instruct") + +# Set LOG_LEVEL so fmsr tracing statements are visible +import logging +logging.basicConfig( + level=logging.INFO, + format=" %(asctime)s %(message)s", + datefmt="%H:%M:%S", +) + +import servers.fmsr.main as fmsr +from servers.iot.main import sensors as iot_sensors + +# Configuration + +_model = os.environ.get("FMSR_MODEL_ID", "watsonx/meta-llama/llama-3-3-70b-instruct") +PARALLEL_WORKERS = 2 # fixed thread-pool workers (baseline concurrent strategy) +ADAPTIVE_START = 2 # AIMD starting concurrency +ADAPTIVE_MAX = 5 # AIMD ceiling — never exceeds this regardless of successes +MAX_SENSORS = 3 +MAX_FAILURE_MODES = 3 +SCENARIO_IDS = [106, 107, 108, 109, 110] + +# Four strategies in increasing sophistication: +# sequential — baseline, one call at a time +# parallel — fixed thread pool, work-stealing queue, no retry logic +# parallel_retry — same thread pool + decoupled sequential retry for failures +# adaptive — AIMD concurrency + jitter + retry queue +STRATEGIES = ["sequential", "parallel", "parallel_retry", "adaptive"] + +OUT_DIR = ROOT / "src" / "benchmarking" / "results" +OUT_DIR.mkdir(parents=True, exist_ok=True) +RESULTS_FILE = OUT_DIR / "eval_fmsr_results.json" +PLOT_FILE = OUT_DIR / "eval_fmsr_plots.png" + +# Per-call timing patches +# Both sync and async call paths are patched so _call_log captures everything. + +_call_log: list[dict] = [] +_call_lock = threading.Lock() # needed for parallel/thread strategies + +_original_call_relevancy = fmsr._call_relevancy + + +def _traced_call_relevancy(asset_name: str, failure_mode: str, sensor: str) -> dict: + t0 = time.perf_counter() + result = _original_call_relevancy(asset_name, failure_mode, sensor) + elapsed = round(time.perf_counter() - t0, 4) + with _call_lock: + _call_log.append({"sensor": sensor, "failure_mode": failure_mode, + "answer": result["answer"], "time_s": elapsed}) + return result + + +fmsr._call_relevancy = _traced_call_relevancy + + +def _reset_log() -> None: + _call_log.clear() + + +# Runner + +def _run_strategy(name: str, asset_name: str, failure_modes: list, sensors: list) -> dict: + _reset_log() + print(f"\n ┌─ [{name}] ─────────────────────────────────────────────") + + t0 = time.perf_counter() + try: + if name == "sequential": + results = fmsr._mapping_sequential(asset_name, failure_modes, sensors) + + elif name == "parallel": + print(f" │ max_workers={PARALLEL_WORKERS} (fixed thread pool, work-stealing queue)") + results = fmsr._mapping_parallel(asset_name, failure_modes, sensors, + max_workers=PARALLEL_WORKERS) + + elif name == "parallel_retry": + print(f" │ max_workers={PARALLEL_WORKERS} (parallel phase → sequential retry queue)") + results = fmsr._mapping_parallel_with_retry(asset_name, failure_modes, sensors, + max_workers=PARALLEL_WORKERS) + + elif name == "adaptive": + print(f" │ AIMD start={ADAPTIVE_START} max={ADAPTIVE_MAX} (probes up, halves on error, jitter retry)") + results = fmsr._mapping_adaptive(asset_name, failure_modes, sensors, + start_concurrency=ADAPTIVE_START, + max_concurrency=ADAPTIVE_MAX) + + else: + raise ValueError(f"Unknown strategy: {name}") + + wall = round(time.perf_counter() - t0, 4) + times = [c["time_s"] for c in _call_log] + answers = [r["answer"] for r in results] + + print(f" │") + print(f" │ calls : {len(times)}") + print(f" │ wall time : {wall:.3f}s") + print(f" │ per-call : min={min(times):.3f}s mean={sum(times)/len(times):.3f}s max={max(times):.3f}s") + print(f" │ answers : {answers}") + print(f" └─────────────────────────────────────────────────────────") + + return { + "ok": True, + "wall_s": wall, + "per_call_times": times, + "answer_list": answers, + } + + except Exception as exc: + wall = round(time.perf_counter() - t0, 4) + print(f" │ ERROR after {wall:.3f}s: {exc}") + print(f" └─────────────────────────────────────────────────────────") + return {"ok": False, "error": str(exc)} + + +# Load shared data + +print("=" * 72) +print(" FMSR — Sequential / Parallel / Parallel+Retry / Adaptive (AIMD)") +print(f" Model : {_model}") +print(f" parallel : fixed max_workers={PARALLEL_WORKERS}") +print(f" parallel_retry : max_workers={PARALLEL_WORKERS} + sequential retry queue") +print(f" adaptive : AIMD start={ADAPTIVE_START} max={ADAPTIVE_MAX} + jitter + retry") +print(f" Cap : {MAX_SENSORS} sensors × {MAX_FAILURE_MODES} failure modes") +print("=" * 72) + +print("\nFetching sensors ...") +try: + sr = iot_sensors(site_name="MAIN", asset_id="Chiller 6") + real_sensors = sr.sensors[:MAX_SENSORS] + print(f" DB : {real_sensors}") +except Exception: + real_sensors = [ + "Chiller 6 Supply Temperature", + "Chiller 6 Return Temperature", + "Chiller 6 Power Input", + ] + print(f" Fallback: {real_sensors}") + +fm_res = fmsr.get_failure_modes(asset_name="chiller") +real_fms = fm_res.failure_modes[:MAX_FAILURE_MODES] +print(f" Failure modes : {real_fms}") +n_pairs = len(real_sensors) * len(real_fms) +print(f" Pairs : {n_pairs} ({len(real_sensors)} × {len(real_fms)})\n") + +# Main loop + +all_results: list[dict[str, Any]] = [] + +for scenario_id in SCENARIO_IDS: + print(f"\n{'='*72}") + print(f" SCENARIO {scenario_id}") + print(f"{'='*72}") + + record: dict[str, Any] = {"scenario_id": scenario_id} + + for name in STRATEGIES: + record[name] = _run_strategy(name, "chiller", real_fms, real_sensors) + + # Correctness check + seq_answers = record["sequential"].get("answer_list", []) + print(f"\n Correctness vs sequential:") + for name in [s for s in STRATEGIES if s != "sequential"]: + rec = record[name] + if not rec.get("ok") or not record["sequential"].get("ok"): + continue + diffs = [ + f"pair {i+1}: seq={a} {name}={b}" + for i, (a, b) in enumerate(zip(seq_answers, rec["answer_list"])) + if a != b + ] + rec["answer_match"] = not diffs + rec["answer_diffs"] = diffs + print(f" {name:<18} {'✓ match' if not diffs else '✗ ' + str(diffs)}") + + # Speedup + if record["sequential"].get("ok"): + seq_wall = record["sequential"]["wall_s"] + print(f"\n Speedup vs sequential (seq={seq_wall:.3f}s):") + for name in [s for s in STRATEGIES if s != "sequential"]: + if record[name].get("ok"): + spd = seq_wall / record[name]["wall_s"] + print(f" {name:<18} {spd:.2f}× (wall={record[name]['wall_s']:.3f}s)") + + all_results.append(record) + +# Save + +RESULTS_FILE.write_text(json.dumps(all_results, indent=2)) +print(f"\n\nResults → {RESULTS_FILE}") + +# Summary table + +print(f"\n{'='*72}") +print(" SUMMARY") +print(f"{'='*72}") +print(f" {'Scenario':>10} {'Strategy':<18} {'Wall(s)':>8} {'MaxCall':>8} {'Speedup':>8} Match") +print(f" {'─'*70}") + +for r in all_results: + sid = r["scenario_id"] + seq_wall = r["sequential"].get("wall_s") if r["sequential"].get("ok") else None + for name in STRATEGIES: + rec = r.get(name, {}) + if not rec.get("ok"): + print(f" {sid:>10} {name:<18} {'ERROR':>8}") + continue + times = rec["per_call_times"] + spd_str = "baseline" if name == "sequential" else ( + f"{seq_wall/rec['wall_s']:>6.2f}×" if seq_wall else "—" + ) + match_str = "—" if name == "sequential" else ("✓" if rec.get("answer_match") else "✗") + print(f" {sid:>10} {name:<18} {rec['wall_s']:>7.2f}s " + f"{max(times):>7.2f}s {spd_str:>8} {match_str}") + +# Plots + +try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + import numpy as np + + COLORS = { + "sequential": "#4e79a7", + "parallel": "#f28e2b", + "parallel_retry": "#59a14f", + "adaptive": "#e15759", + } + + fig, axes = plt.subplots(1, 3, figsize=(16, 5)) + fig.suptitle( + f"FMSR Strategy Comparison | {_model}\n" + f"{MAX_SENSORS}×{MAX_FAILURE_MODES} pairs " + f"| parallel={PARALLEL_WORKERS}w adaptive start={ADAPTIVE_START} max={ADAPTIVE_MAX}", + fontsize=9, + ) + + scenarios = [r["scenario_id"] for r in all_results] + x = np.arange(len(scenarios)) + width = 0.2 + + # Panel 1: Wall time grouped bar + ax = axes[0] + for i, name in enumerate(STRATEGIES): + walls = [r[name]["wall_s"] if r[name].get("ok") else 0 for r in all_results] + offset = (i - len(STRATEGIES) / 2 + 0.5) * width + ax.bar(x + offset, walls, width, label=name, color=COLORS[name], alpha=0.85) + ax.set_xticks(x); ax.set_xticklabels([str(s) for s in scenarios]) + ax.set_xlabel("Scenario ID"); ax.set_ylabel("Wall time (s)") + ax.set_title("Wall time per scenario"); ax.legend(fontsize=7) + + # Panel 2: Speedup vs sequential + ax = axes[1] + for name in [s for s in STRATEGIES if s != "sequential"]: + speedups = [] + for r in all_results: + sw = r["sequential"].get("wall_s", 0) if r["sequential"].get("ok") else 0 + cw = r[name].get("wall_s", 0) if r[name].get("ok") else 0 + speedups.append(sw / cw if cw > 0 else 0) + ax.plot(scenarios, speedups, marker="o", label=name, color=COLORS[name]) + ax.axhline(1.0, color="black", linestyle="--", linewidth=0.8, label="baseline") + ax.set_xlabel("Scenario ID"); ax.set_ylabel("Speedup vs sequential") + ax.set_title("Speedup per scenario"); ax.legend(fontsize=7) + + # Panel 3: Per-call time box plot + ax = axes[2] + box_data = [ + [t for r in all_results for t in r[name].get("per_call_times", []) if r[name].get("ok")] + for name in STRATEGIES + ] + bp = ax.boxplot(box_data, labels=STRATEGIES, patch_artist=True, + medianprops=dict(color="black", linewidth=1.5)) + for patch, name in zip(bp["boxes"], STRATEGIES): + patch.set_facecolor(COLORS[name]); patch.set_alpha(0.75) + ax.set_xticklabels(STRATEGIES, rotation=12, ha="right", fontsize=7) + ax.set_ylabel("Individual call time (s)") + ax.set_title("Per-call time distribution") + + plt.tight_layout() + plt.savefig(PLOT_FILE, dpi=150, bbox_inches="tight") + print(f"Plots → {PLOT_FILE}") + +except ImportError: + print("matplotlib/numpy not available — skipping plots.") + +print(f"\n{'='*72}") +print(" DONE") +print(f"{'='*72}") diff --git a/src/benchmarking/test_scenario.py b/src/benchmarking/test_scenario.py new file mode 100644 index 000000000..2a61e5367 --- /dev/null +++ b/src/benchmarking/test_scenario.py @@ -0,0 +1,185 @@ +"""Single-scenario debugger — traces every LLM call live as it happens. + +Imports the FMSR server module directly (not via MCP subprocess) so that +_call_relevancy can be patched for per-call tracing. This is intentional: +the debugger runs in-process to give you a live view of each (sensor, FM) +pair as it executes, which is not possible through a subprocess boundary. + +Sensors and failure modes are fetched from the live MCP servers at startup, +exactly as the real pipeline would do before calling get_failure_mode_sensor_mapping. + +Usage:: + + uv run python -m src.benchmarking.test_scenario --scenario 106 --strategy sequential + uv run python -m src.benchmarking.test_scenario --scenario 109 --strategy hedged + uv run python -m src.benchmarking.test_scenario --scenario 111 # runs all 4 strategies +""" + +from __future__ import annotations + +import argparse +import asyncio +import logging +import os +import sys +import threading +import time +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "src")) + +from dotenv import load_dotenv +load_dotenv(ROOT / ".env") + +os.environ.setdefault("FMSR_MODEL_ID", "watsonx/meta-llama/llama-3-3-70b-instruct") + +# Silence noisy third-party loggers +logging.basicConfig(level=logging.WARNING) +for noisy in ("litellm", "LiteLLM", "httpx", "httpcore", "openai"): + logging.getLogger(noisy).setLevel(logging.ERROR) + +import servers.fmsr.main as fmsr +from benchmarking.bench_fmsr import STRATEGIES, _fetch_asset_data, _build_scenario_config + +# Fetch real sensors and failure modes via MCP servers — same calls the real +# agent pipeline makes before invoking get_failure_mode_sensor_mapping. +print("Fetching asset data via MCP servers ...") +_all_sensors, _all_fms = asyncio.run( + _fetch_asset_data(asset_id="Chiller 6", site_name="MAIN") +) +SCENARIO_CONFIG = _build_scenario_config(_all_sensors, _all_fms) +print(f" {len(_all_sensors)} sensors, {len(_all_fms)} failure modes loaded.\n") + +# Live per-call tracing patch + +_call_log: list[dict] = [] +_call_lock = threading.Lock() +_t_run_start: float = 0.0 + +_original_call_relevancy = fmsr._call_relevancy + + +def _traced_call_relevancy(asset_name: str, failure_mode: str, sensor: str) -> dict: + t0 = time.perf_counter() + result = _original_call_relevancy(asset_name, failure_mode, sensor) + elapsed = round(time.perf_counter() - t0, 3) + t_abs = round(time.perf_counter() - _t_run_start, 3) + + with _call_lock: + _call_log.append({ + "sensor": sensor, "failure_mode": failure_mode, + "answer": result["answer"], "time_s": elapsed, + }) + ans = result["answer"] + reason = result.get("reason", "")[:70] + print( + f" t={t_abs:6.2f}s [{ans:>3}] {sensor[:35]:<35} ↔ {failure_mode[:40]:<40}" + f" ({elapsed:.2f}s)" + ) + if ans == "Yes": + temporal = result.get("temporal_behavior", "")[:80] + print(f" reason : {reason}") + print(f" temporal: {temporal}") + + return result + + +fmsr._call_relevancy = _traced_call_relevancy + + +# Single-scenario runner + +def run_scenario(scenario_id: int, strategy: str) -> dict: + global _t_run_start + + cfg = SCENARIO_CONFIG[scenario_id] + asset_name = cfg["asset"] + sensors = cfg["sensors"] + failure_modes = cfg["fms"] + n_pairs = len(sensors) * len(failure_modes) + + print(f"\n{'='*80}") + print(f" Scenario : {scenario_id}") + print(f" Strategy : {strategy}") + print(f" Asset : {asset_name}") + print(f" Sensors : ({len(sensors)}) {sensors}") + print(f" FMs : ({len(failure_modes)}) {failure_modes}") + print(f" Pairs : {n_pairs} ({len(sensors)}×{len(failure_modes)})") + print(f"{'─'*80}") + print(f" {'t':>6} {'ans':>3} {'sensor':<35} {'failure_mode':<40} time") + print(f"{'─'*80}") + + _call_log.clear() + _t_run_start = time.perf_counter() + + t0 = time.perf_counter() + ok = True + error = None + + try: + if strategy == "sequential": + fmsr._mapping_sequential(asset_name, failure_modes, sensors) + + elif strategy == "parallel": + fmsr._mapping_parallel(asset_name, failure_modes, sensors, max_workers=2) + + elif strategy == "adaptive_ceiling": + fmsr._mapping_adaptive_ceiling( + asset_name, failure_modes, sensors, max_concurrency=0, min_concurrency=1 + ) + + elif strategy == "hedged": + fmsr._mapping_hedged(asset_name, failure_modes, sensors, max_concurrency=0) + + else: + raise ValueError(f"Unknown strategy: {strategy!r}. Choose from: {STRATEGIES}") + + except Exception as exc: + ok = False + error = str(exc) + + wall = round(time.perf_counter() - t0, 3) + times = [c["time_s"] for c in _call_log] + + print(f"{'─'*80}") + + if not ok: + print(f" ERROR: {error}") + else: + yes_count = sum(1 for c in _call_log if c["answer"] == "Yes") + print(f" Calls : {len(times)}/{n_pairs}") + print(f" Wall time : {wall:.3f}s") + if times: + print(f" Per-call : min={min(times):.3f}s mean={sum(times)/len(times):.3f}s max={max(times):.3f}s") + print(f" Relevant : {yes_count}/{n_pairs} pairs answered Yes") + + print(f"{'='*80}") + + return {"ok": ok, "wall_s": wall, "times": times, "error": error} + + +# CLI + +def main() -> None: + parser = argparse.ArgumentParser(description="Debug a single FMSR scenario") + parser.add_argument("--scenario", type=int, required=True, + help="Scenario ID (106–120)") + parser.add_argument("--strategy", type=str, default=None, + choices=STRATEGIES, + help="Strategy (default: run all 4 in order)") + args = parser.parse_args() + + if args.scenario not in SCENARIO_CONFIG: + print(f"ERROR: scenario {args.scenario} not in SCENARIO_CONFIG.") + print(f"Available: {sorted(SCENARIO_CONFIG)}") + sys.exit(1) + + strategies = [args.strategy] if args.strategy else STRATEGIES + + for strategy in strategies: + run_scenario(args.scenario, strategy) + + +if __name__ == "__main__": + main() diff --git a/src/evaluation/timing_benchmark.py b/src/evaluation/timing_benchmark.py new file mode 100644 index 000000000..55e0228d2 --- /dev/null +++ b/src/evaluation/timing_benchmark.py @@ -0,0 +1,380 @@ +"""Timing benchmark runner for AssetOpsBench orchestration evaluation. + +This script runs repeated timing benchmarks over the single-agent meta-agent +scenario files, logs each run to Weights & Biases via ``workflow.timing``, +and writes aggregate JSON summaries grouped by workload class. +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import statistics +from collections import defaultdict +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from dotenv import load_dotenv + +from llm.litellm import LiteLLMBackend +from workflow.runner import PlanExecuteRunner +from workflow.timing import TimingRun + +_SCENARIO_ROOT = Path("src/tmp/meta_agent/scenarios/single_agent") +_IOT_SCENARIO_PATH = _SCENARIO_ROOT / "iot_utterance_meta.json" +_FMSR_SCENARIO_PATH = _SCENARIO_ROOT / "fmsr_utterance.json" + + +@dataclass(frozen=True) +class Scenario: + scenario_id: int + name: str + group: str + question: str + source_file: str + source_type: str + category: str | None = None + deterministic: bool | None = None + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Run repeated timing benchmarks for AssetOpsBench scenarios." + ) + parser.add_argument( + "--model-id", + default="watsonx/meta-llama/llama-4-maverick-17b-128e-instruct-fp8", + help="LLM model id used by the orchestration runner.", + ) + parser.add_argument( + "--warmup-runs", + type=int, + default=2, + help="Number of warmup runs per scenario to discard.", + ) + parser.add_argument( + "--measured-runs", + type=int, + default=5, + help="Number of measured runs per scenario.", + ) + parser.add_argument( + "--output-dir", + default="artifacts/timing", + help="Directory for per-run and aggregate JSON output.", + ) + parser.add_argument( + "--wandb-project", + required=True, + help="Weights & Biases project name for logging timed runs.", + ) + parser.add_argument( + "--wandb-entity", + default=None, + help="Optional Weights & Biases entity.", + ) + parser.add_argument( + "--wandb-mode", + default=None, + help="Optional Weights & Biases mode, for example 'offline'.", + ) + parser.add_argument( + "--retry-delay-seconds", + type=float, + default=3.0, + help="Delay before retrying a failed or rate-limited scenario run.", + ) + parser.add_argument( + "--inter-run-delay-seconds", + type=float, + default=0.5, + help="Delay between successful runs to reduce provider rate limiting.", + ) + parser.add_argument( + "--max-attempts-multiplier", + type=int, + default=3, + help="Maximum attempts per requested run count multiplier before giving up.", + ) + return parser + + +def _load_scenarios() -> list[Scenario]: + scenarios: list[Scenario] = [] + scenario_specs = [ + (_IOT_SCENARIO_PATH, "iot"), + (_FMSR_SCENARIO_PATH, "fmsr"), + ] + + for path, group in scenario_specs: + records = json.loads(path.read_text()) + for record in records: + scenario_id = int(record["id"]) + scenarios.append( + Scenario( + scenario_id=scenario_id, + name=f"{group}_{scenario_id}", + group=group, + question=record["text"], + source_file=str(path), + source_type=str(record.get("type", group)), + category=record.get("category"), + deterministic=record.get("deterministic"), + ) + ) + + return scenarios + + +def _classify_failure(result, plan) -> list[str]: + failure_reasons: list[str] = [] + + for step in result.history: + if step.success: + continue + error_text = step.error or "unknown_step_error" + failure_reasons.append(error_text) + lowered = error_text.lower() + if ( + "rate limit" in lowered + or "status_code\":429" in lowered + or "status code 429" in lowered + or "rate_limit_reached_requests" in lowered + or "consumption_limit_reached" in lowered + ): + failure_reasons.append("rate_limited") + if "token_quota_reached" in lowered or "quota" in lowered: + failure_reasons.append("quota_exhausted") + + return list(dict.fromkeys(failure_reasons)) + + +async def _run_scenario_once( + *, + scenario: Scenario, + model_id: str, + output_dir: Path, + wandb_project: str, + wandb_entity: str | None, + wandb_mode: str | None, + run_index: int, +) -> tuple[dict[str, Any], bool, str | None]: + llm = LiteLLMBackend(model_id=model_id) + runner = PlanExecuteRunner(llm=llm) + + run_label = ( + f"warmup_{abs(run_index):02d}" if run_index < 0 else f"run_{run_index:02d}" + ) + run_name = f"{scenario.name}_{run_label}" + timer = TimingRun( + project=wandb_project, + entity=wandb_entity, + mode=wandb_mode, + run_name=run_name, + group=scenario.group, + config={ + "scenario_id": scenario.scenario_id, + "scenario_name": scenario.name, + "question": scenario.question, + "model_id": model_id, + "workload_group": scenario.group, + "source_file": scenario.source_file, + "source_type": scenario.source_type, + "category": scenario.category, + "deterministic": scenario.deterministic, + }, + tags=[scenario.group, "timing-benchmark"], + ) + + with timer.phase("question_to_answer"): + result = await runner.run(scenario.question, timer=timer) + + failure_reasons = _classify_failure(result, result.plan) + success = len(failure_reasons) == 0 + failure_reason = " | ".join(failure_reasons) if failure_reasons else None + server_sequence = [step.agent for step in result.history] + + summary = timer.finish( + extra_metrics={ + "scenario_id": scenario.scenario_id, + "scenario_name": scenario.name, + "scenario_text": scenario.question, + "source_file": scenario.source_file, + "source_type": scenario.source_type, + "category": scenario.category or "", + "deterministic": ( + "" if scenario.deterministic is None else scenario.deterministic + ), + "success": success, + "failure_reason": failure_reason or "", + "question_length_chars": len(scenario.question), + "plan_steps": len(result.plan.steps), + "tool_calls": sum( + 1 + for step in result.history + if step.tool and step.tool.lower() not in ("none", "null", "") + ), + "server_sequence": ",".join(server_sequence), + "unique_servers": ",".join(sorted(set(server_sequence))), + }, + summary_path=str(output_dir / "runs" / f"{run_name}.json"), + ) + + return summary.to_dict(), success, failure_reason + + +def _describe(values: list[float]) -> dict[str, float | int]: + if not values: + return {"count": 0, "mean": 0.0, "std": 0.0, "min": 0.0, "max": 0.0} + std = statistics.stdev(values) if len(values) > 1 else 0.0 + return { + "count": len(values), + "mean": round(statistics.mean(values), 6), + "std": round(std, 6), + "min": round(min(values), 6), + "max": round(max(values), 6), + } + + +def _aggregate_runs(run_summaries: list[dict[str, Any]]) -> dict[str, Any]: + grouped: dict[str, list[dict[str, Any]]] = defaultdict(list) + for summary in run_summaries: + grouped[summary["group"]].append(summary) + + aggregate: dict[str, Any] = {"groups": {}, "overall": {}} + + all_total_times = [s["total_wall_time_seconds"] for s in run_summaries] + aggregate["overall"]["total_wall_time_seconds"] = _describe(all_total_times) + all_phase_names = sorted( + {phase for summary in run_summaries for phase in summary["phases"].keys()} + ) + aggregate["overall"]["phases"] = {} + for phase_name in all_phase_names: + phase_values = [ + float(summary["phases"][phase_name]["total_seconds"]) + for summary in run_summaries + if phase_name in summary["phases"] + ] + aggregate["overall"]["phases"][phase_name] = _describe(phase_values) + + for group, summaries in sorted(grouped.items()): + phase_names = sorted( + {phase for summary in summaries for phase in summary["phases"].keys()} + ) + group_total_times = [s["total_wall_time_seconds"] for s in summaries] + group_result: dict[str, Any] = { + "num_runs": len(summaries), + "total_wall_time_seconds": _describe(group_total_times), + "phases": {}, + "scenarios": sorted({s["metadata"]["scenario_name"] for s in summaries}), + "scenario_ids": sorted({int(s["metadata"]["scenario_id"]) for s in summaries}), + } + for phase_name in phase_names: + phase_values = [ + float(summary["phases"][phase_name]["total_seconds"]) + for summary in summaries + if phase_name in summary["phases"] + ] + group_result["phases"][phase_name] = _describe(phase_values) + aggregate["groups"][group] = group_result + + return aggregate + + +async def _main(args: argparse.Namespace) -> None: + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + measured_runs: list[dict[str, Any]] = [] + dropped_runs: list[dict[str, Any]] = [] + scenarios = _load_scenarios() + + for scenario in scenarios: + print(f"Scenario: {scenario.name} [{scenario.group}]") + max_warmup_attempts = max(1, args.warmup_runs * args.max_attempts_multiplier) + warmups_completed = 0 + warmup_attempt = 0 + + while ( + warmups_completed < args.warmup_runs + and warmup_attempt < max_warmup_attempts + ): + warmup_attempt += 1 + print( + f" Warmup {warmups_completed + 1}/{args.warmup_runs} " + f"(attempt {warmup_attempt})" + ) + summary, success, failure_reason = await _run_scenario_once( + scenario=scenario, + model_id=args.model_id, + output_dir=output_dir, + wandb_project=args.wandb_project, + wandb_entity=args.wandb_entity, + wandb_mode=args.wandb_mode, + run_index=-(warmups_completed + 1), + ) + if success: + warmups_completed += 1 + await asyncio.sleep(args.inter_run_delay_seconds) + else: + dropped_runs.append(summary) + print(f" Dropped warmup run: {failure_reason}") + await asyncio.sleep(args.retry_delay_seconds) + + if warmups_completed < args.warmup_runs: + print(" Unable to complete requested warmup runs; moving on.") + + max_measured_attempts = max( + 1, args.measured_runs * args.max_attempts_multiplier + ) + measured_completed = 0 + measured_attempt = 0 + + while ( + measured_completed < args.measured_runs + and measured_attempt < max_measured_attempts + ): + measured_attempt += 1 + print( + f" Measured {measured_completed + 1}/{args.measured_runs} " + f"(attempt {measured_attempt})" + ) + summary, success, failure_reason = await _run_scenario_once( + scenario=scenario, + model_id=args.model_id, + output_dir=output_dir, + wandb_project=args.wandb_project, + wandb_entity=args.wandb_entity, + wandb_mode=args.wandb_mode, + run_index=measured_completed + 1, + ) + if success: + measured_runs.append(summary) + measured_completed += 1 + await asyncio.sleep(args.inter_run_delay_seconds) + else: + dropped_runs.append(summary) + print(f" Dropped measured run: {failure_reason}") + await asyncio.sleep(args.retry_delay_seconds) + + if measured_completed < args.measured_runs: + print(" Unable to complete requested measured runs for this scenario.") + + aggregate = _aggregate_runs(measured_runs) + (output_dir / "aggregate.json").write_text(json.dumps(aggregate, indent=2)) + (output_dir / "dropped_runs.json").write_text(json.dumps(dropped_runs, indent=2)) + + print(f"\nSaved aggregate timing summary to {output_dir / 'aggregate.json'}") + print(f"Saved dropped run details to {output_dir / 'dropped_runs.json'}") + + +def main() -> None: + load_dotenv() + args = _build_parser().parse_args() + asyncio.run(_main(args)) + + +if __name__ == "__main__": + main() diff --git a/src/llm/litellm.py b/src/llm/litellm.py index 24a7a0620..c877e83ef 100644 --- a/src/llm/litellm.py +++ b/src/llm/litellm.py @@ -10,6 +10,11 @@ watsonx/* : WATSONX_APIKEY, WATSONX_PROJECT_ID, WATSONX_URL (optional) otherwise : LITELLM_API_KEY, LITELLM_BASE_URL + +Reliability is handled by a LiteLLM Router configured with: + - exponential backoff retries on transient errors (500, 503, etc.) + - cooldown period after repeated failures on a deployment + - client-side rate limiting to avoid overwhelming the backend """ from __future__ import annotations @@ -18,9 +23,25 @@ from .base import LLMBackend +# How many times the Router will retry a failed request before giving up. +# Backoff is exponential: 1s → 2s → 4s between attempts. +_NUM_RETRIES = 3 +# After this many consecutive failures the Router cools down the deployment. +_ALLOWED_FAILS = 3 +# Seconds to pause a failing deployment before trying it again. +_COOLDOWN_TIME = 30 +# Hard per-request timeout in seconds — passed explicitly on every call so +# it is never None. WatsonX occasionally stalls indefinitely; this ensures +# a hung request fails fast and triggers a retry rather than blocking forever. +_REQUEST_TIMEOUT = 90 + class LiteLLMBackend(LLMBackend): - """LLM backend using the litellm library. + """LLM backend using the litellm Router for automatic retry and backoff. + + The Router sits between our code and the raw litellm.completion() call. + It handles transient 500 / 503 errors with exponential backoff so callers + never need to implement their own retry loops. Args: model_id: litellm model string with provider prefix, e.g.: @@ -30,25 +51,57 @@ class LiteLLMBackend(LLMBackend): def __init__(self, model_id: str) -> None: self._model_id = model_id + self._router = self._build_router() - def generate(self, prompt: str, temperature: float = 0.0) -> str: - import litellm + def _build_router(self): + from litellm import Router - kwargs: dict = { - "model": self._model_id, - "messages": [{"role": "user", "content": prompt}], - "temperature": temperature, - "max_tokens": 2048, - } + # Build the provider-specific params that go into each request. + litellm_params: dict = {"model": self._model_id} if self._model_id.startswith("watsonx/"): - kwargs["api_key"] = os.environ["WATSONX_APIKEY"] - kwargs["project_id"] = os.environ["WATSONX_PROJECT_ID"] + litellm_params["api_key"] = os.environ["WATSONX_APIKEY"] + litellm_params["project_id"] = os.environ["WATSONX_PROJECT_ID"] if url := os.environ.get("WATSONX_URL"): - kwargs["api_base"] = url + litellm_params["api_base"] = url else: - kwargs["api_key"] = os.environ["LITELLM_API_KEY"] - kwargs["api_base"] = os.environ["LITELLM_BASE_URL"] + litellm_params["api_key"] = os.environ["LITELLM_API_KEY"] + litellm_params["api_base"] = os.environ["LITELLM_BASE_URL"] + + return Router( + model_list=[{ + "model_name": "default", # alias used when calling router.completion() + "litellm_params": litellm_params, + }], + # Retry behaviour — Router uses exponential backoff: 1s, 2s, 4s, … + num_retries=_NUM_RETRIES, + retry_after=1, # base delay in seconds before first retry + # Circuit-breaker — cool down after repeated failures + allowed_fails=_ALLOWED_FAILS, + cooldown_time=_COOLDOWN_TIME, + # Hard timeout per request — never hang a thread indefinitely + timeout=120, + ) + + def generate(self, prompt: str, temperature: float = 0.0) -> str: + # timeout must be passed per-request — the Router constructor's timeout + # is not automatically forwarded to individual completion() calls. + response = self._router.completion( + model="default", + messages=[{"role": "user", "content": prompt}], + temperature=temperature, + max_tokens=2048, + timeout=_REQUEST_TIMEOUT, + ) + return response.choices[0].message.content - response = litellm.completion(**kwargs) + async def agenerate(self, prompt: str, temperature: float = 0.0) -> str: + """Async version of generate — uses the Router's acompletion so no threads needed.""" + response = await self._router.acompletion( + model="default", + messages=[{"role": "user", "content": prompt}], + temperature=temperature, + max_tokens=2048, + timeout=_REQUEST_TIMEOUT, + ) return response.choices[0].message.content diff --git a/src/servers/fmsr/main.py b/src/servers/fmsr/main.py index c945f7a42..e62ac0a39 100644 --- a/src/servers/fmsr/main.py +++ b/src/servers/fmsr/main.py @@ -17,7 +17,11 @@ import logging import os +import random import re +import threading +import time +from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path from typing import Dict, List, Union @@ -33,14 +37,14 @@ logger = logging.getLogger("fmsr-mcp-server") -# ── Hardcoded asset data ────────────────────────────────────────────────────── +# Asset failure modes (loaded from YAML) _FAILURE_MODES_FILE = Path(__file__).parent / "failure_modes.yaml" with _FAILURE_MODES_FILE.open() as _f: _ASSET_FAILURE_MODES: dict[str, list[str]] = yaml.safe_load(_f) -# ── Prompt templates ────────────────────────────────────────────────────────── +# Prompt templates _ASSET2FM_PROMPT = ( "What are different failure modes for asset {asset_name}?\n" @@ -58,7 +62,7 @@ ) -# ── Output parsers ──────────────────────────────────────────────────────────── +# Output parsers def _parse_numbered_list(text: str) -> list[str]: """Parse a numbered list response into a plain list of strings.""" @@ -84,10 +88,9 @@ def _parse_relevancy(text: str) -> dict: return {"answer": answer, "reason": reason, "temporal_behavior": temporal} -# ── LLM backend (lazy init; graceful degradation if creds are absent) ───────── +# LLM backend (lazy init) _DEFAULT_MODEL_ID = "watsonx/meta-llama/llama-3-3-70b-instruct" -_MAX_RETRIES = 3 def _build_llm(): @@ -114,35 +117,105 @@ def _build_llm(): _llm_available = False -# ── LLM call helpers with retry ─────────────────────────────────────────────── +# LLM call helpers def _call_asset2fm(asset_name: str) -> list[str]: - """Query the LLM for failure modes of an asset. Retries up to _MAX_RETRIES times.""" + """Query the LLM for failure modes of an asset. + + Retry and backoff are handled by the LiteLLM Router inside _llm.generate(). + """ prompt = _ASSET2FM_PROMPT.format(asset_name=asset_name) - last_exc: Exception | None = None - for _ in range(_MAX_RETRIES): - try: - return _parse_numbered_list(_llm.generate(prompt)) - except Exception as exc: - last_exc = exc - raise last_exc + return _parse_numbered_list(_llm.generate(prompt)) def _call_relevancy(asset_name: str, failure_mode: str, sensor: str) -> dict: - """Query the LLM for FM↔sensor relevancy. Retries up to _MAX_RETRIES times.""" + """Query the LLM for FM↔sensor relevancy. + + Retry and backoff are handled by the LiteLLM Router inside _llm.generate(). + Each call is independent — the thread pool in _mapping_parallel runs several + of these concurrently. + """ prompt = _RELEVANCY_PROMPT.format( asset_name=asset_name, failure_mode=failure_mode, sensor=sensor ) - last_exc: Exception | None = None - for _ in range(_MAX_RETRIES): + return _parse_relevancy(_llm.generate(prompt)) + + +# Threshold after which a hedged duplicate request is fired. +# Set to ~2× the normal per-call latency (normal calls take 2–4 s). +_HEDGE_AFTER_S: float = float(os.environ.get("FMSR_HEDGE_AFTER_S", "8")) + +# Parallelization strategy for get_failure_mode_sensor_mapping. +# Controlled via FMSR_STRATEGY env var so the benchmark can select the strategy +# by passing the var to the server subprocess — no code changes needed per run. +# sequential — one LLM call at a time (baseline) +# parallel — fixed thread pool, FMSR_PARALLEL_WORKERS workers +# adaptive_ceiling — ceiling-start, halve on error +# hedged — ceiling-start + speculative duplicate on stall +_STRATEGY = os.environ.get("FMSR_STRATEGY", "parallel") +_PARALLEL_WORKERS = int(os.environ.get("FMSR_PARALLEL_WORKERS", "2")) + + +def _call_relevancy_hedged(asset_name: str, failure_mode: str, sensor: str) -> dict: + """FM↔sensor relevancy call with speculative hedging. + + If the first request has not returned within ``_HEDGE_AFTER_S`` seconds, + a duplicate request is fired on a background thread. Whichever copy + responds first wins; the other is abandoned. + + Calls ``_call_relevancy`` (not ``_llm.generate`` directly) so that any + active benchmarking instrumentation patch on ``_call_relevancy`` is + respected — per-call timing is captured correctly. + + This is the standard technique for reducing tail latency on idempotent + read-only calls (LLM inference is stateless, so firing two copies is safe). + Expected outcome: p95 wall time drops to ~2× _HEDGE_AFTER_S in the worst + case rather than the full Router timeout (90 s). + """ + result_holder: list[dict] = [] # written by whichever thread wins + done_event = threading.Event() + lock = threading.Lock() + + def _attempt() -> None: try: - return _parse_relevancy(_llm.generate(prompt)) - except Exception as exc: - last_exc = exc - raise last_exc + # Use _call_relevancy (not _llm.generate) so the instrumentation + # patch fires and per-call times are recorded by the benchmark. + parsed = _call_relevancy(asset_name, failure_mode, sensor) + with lock: + if not result_holder: # first one to finish wins + result_holder.append(parsed) + done_event.set() + except Exception: + done_event.set() # unblock so the other copy can win + + # Fire the first request immediately on a daemon thread + t1 = threading.Thread(target=_attempt, daemon=True) + t1.start() + + # Wait up to _HEDGE_AFTER_S; if no result yet, fire a second copy + done_event.wait(timeout=_HEDGE_AFTER_S) + + if not result_holder: + logger.info( + "[hedge] no response after %.1fs — firing duplicate for %s | %s", + _HEDGE_AFTER_S, sensor, failure_mode, + ) + done_event.clear() + t2 = threading.Thread(target=_attempt, daemon=True) + t2.start() + done_event.wait(timeout=_HEDGE_AFTER_S * 12) # generous ceiling for hedge + if result_holder: + return result_holder[0] -# ── Result models ───────────────────────────────────────────────────────────── + # Both copies failed — raise so the caller's retry/backoff logic takes over + raise RuntimeError( + f"Hedged call exhausted: {asset_name} | {failure_mode} | {sensor}" + ) + + + +# Result models class ErrorResult(BaseModel): error: str @@ -175,7 +248,524 @@ class FailureModeSensorMappingResult(BaseModel): full_relevancy: List[RelevancyEntry] -# ── FastMCP server ──────────────────────────────────────────────────────────── +# Benchmarking hook (no-op in production) +# Set by bench_instrumentation.BenchInstrumentation.install() before each run. +# The callback receives (event_name, **kwargs) and records phase-boundary timestamps. +# Left as None at runtime — zero overhead when benchmarking is not active. + +_bench_event_callback = None # type: ignore[assignment] + + +def _emit_bench_event(name: str, **kwargs) -> None: # type: ignore[no-untyped-def] + """Emit a structured timing event to the active benchmark instrumentation. + + No-op when ``_bench_event_callback`` is None (normal production use). + """ + cb = _bench_event_callback + if cb is not None: + cb(name, **kwargs) + + +# Mapping strategies + +def _mapping_sequential( + asset_name: str, + failure_modes: List[str], + sensors: List[str], +) -> List[dict]: + """One call at a time — baseline implementation.""" + pairs = [(s, fm) for s in sensors for fm in failure_modes] + n = len(pairs) + results = [] + t_start = time.perf_counter() + + for idx, (s, fm) in enumerate(pairs, 1): + t0 = time.perf_counter() + logger.info("[seq %d/%d] start %s | %s", idx, n, s, fm) + gen = _call_relevancy(asset_name, fm, s) + elapsed = time.perf_counter() - t0 + logger.info("[seq %d/%d] done %.3fs → %s (total %.3fs)", + idx, n, elapsed, gen["answer"], time.perf_counter() - t_start) + results.append({"sensor": s, "failure_mode": fm, **gen}) + + return results + + +def _mapping_parallel( + asset_name: str, + failure_modes: List[str], + sensors: List[str], + max_workers: int = 2, +) -> List[dict]: + """Thread-pool implementation — up to max_workers calls in-flight at once.""" + pairs = [(s, fm) for s in sensors for fm in failure_modes] + n = len(pairs) + logger.info("[parallel] %d pairs, max_workers=%d", n, max_workers) + + with ThreadPoolExecutor(max_workers=min(max_workers, n)) as pool: + future_to_pair = { + pool.submit(_call_relevancy, asset_name, fm, s): (s, fm) + for s, fm in pairs + } + raw: dict[tuple, dict] = {} + done = 0 + for future in as_completed(future_to_pair): + s, fm = future_to_pair[future] + result = future.result() + raw[(s, fm)] = result + done += 1 + logger.info("[parallel] %d/%d done %s | %s → %s", done, n, s, fm, result["answer"]) + + return [ + {"sensor": s, "failure_mode": fm, **raw[(s, fm)]} + for s in sensors + for fm in failure_modes + ] + + +# Adaptive concurrency semaphore (AIMD) + +class _AdaptiveSemaphore: + """Thread-safe semaphore whose concurrency limit self-adjusts via AIMD. + + Additive increase : every ``probe_every`` consecutive successes → limit += 1 + Multiplicative decrease : on any failure → limit = max(limit // 2, minimum) + + Increasing the limit also wakes any threads that are waiting to acquire, + so they can immediately take the new slot without polling. + """ + + def __init__( + self, + initial: int = 2, + minimum: int = 1, + maximum: int = 5, + probe_every: int = 3, # consecutive successes before probing up + ) -> None: + self._cond = threading.Condition() + self._limit = initial + self._minimum = minimum + self._maximum = maximum + self._probe_every = probe_every + self._in_flight = 0 + self._successes = 0 # consecutive success counter (resets on failure) + + # context manager + + def __enter__(self) -> "_AdaptiveSemaphore": + with self._cond: + while self._in_flight >= self._limit: + self._cond.wait() + self._in_flight += 1 + return self + + def __exit__(self, *_) -> None: + with self._cond: + self._in_flight -= 1 + self._cond.notify_all() + + # AIMD callbacks + + def on_success(self) -> None: + with self._cond: + self._successes += 1 + if self._successes % self._probe_every == 0 and self._limit < self._maximum: + old = self._limit + self._limit += 1 + logger.info( + "[adaptive-sem] ↑ concurrency %d → %d (%d consecutive successes)", + old, self._limit, self._successes, + ) + self._cond.notify_all() # wake waiting threads — limit may have grown + + def on_failure(self) -> None: + with self._cond: + self._successes = 0 + old = self._limit + self._limit = max(self._limit // 2, self._minimum) + if self._limit != old: + logger.warning( + "[adaptive-sem] ↓ concurrency %d → %d (halved on error)", + old, self._limit, + ) + + # inspection + + @property + def limit(self) -> int: + with self._cond: + return self._limit + + @property + def in_flight(self) -> int: + with self._cond: + return self._in_flight + + + + +def _mapping_parallel_with_retry( + asset_name: str, + failure_modes: List[str], + sensors: List[str], + max_workers: int = 2, +) -> List[dict]: + """Two-phase execution: parallel first pass, then sequential retry for failures. + + Phase 1 — all N×M pairs run concurrently (up to max_workers at once). + Any pair that raises an exception is collected into a retry list + instead of being re-raised, so it never blocks other pairs. + + Phase 2 — failed pairs are retried one at a time. + By this point the parallel phase has finished, so the API has + had time to recover before the retries hit it. + + Result: fast pairs complete quickly, slow/failed pairs don't stall the rest. + """ + pairs = [(s, fm) for s in sensors for fm in failure_modes] + n = len(pairs) + raw: dict[tuple, dict] = {} + failed: list[tuple] = [] + lock = threading.Lock() + + def _run(s: str, fm: str) -> None: + try: + result = _call_relevancy(asset_name, fm, s) + with lock: + raw[(s, fm)] = result + logger.info("[retry-pool] OK %s | %s → %s", s, fm, result["answer"]) + except Exception as exc: + logger.warning("[retry-pool] FAIL %s | %s queued for retry: %s", s, fm, exc) + with lock: + failed.append((s, fm)) + + # Phase 1 — parallel + logger.info("[retry-pool] phase-1 %d pairs max_workers=%d", n, max_workers) + _emit_bench_event("phase1_start") + with ThreadPoolExecutor(max_workers=min(max_workers, n)) as pool: + for f in as_completed([pool.submit(_run, s, fm) for s, fm in pairs]): + f.result() # re-raise unexpected errors only (not LLM errors, handled above) + _emit_bench_event("phase1_end", pairs_failed=len(failed)) + + # Phase 2 — sequential retry + if failed: + logger.info("[retry-pool] phase-2 %d failed pairs → sequential retry", len(failed)) + _emit_bench_event("phase2_start") + for s, fm in failed: + logger.info("[retry-pool] retry %s | %s", s, fm) + try: + result = _call_relevancy(asset_name, fm, s) + raw[(s, fm)] = result + logger.info("[retry-pool] retry OK %s | %s → %s", s, fm, result["answer"]) + except Exception as exc: + logger.error("[retry-pool] retry FAIL %s | %s: %s", s, fm, exc) + raw[(s, fm)] = {"answer": "Unknown", "reason": str(exc), + "temporal_behavior": "Unknown"} + _emit_bench_event("phase2_end") + + return [ + {"sensor": s, "failure_mode": fm, **raw[(s, fm)]} + for s in sensors + for fm in failure_modes + ] + + + + +def _mapping_adaptive( + asset_name: str, + failure_modes: List[str], + sensors: List[str], + start_concurrency: int = 2, + max_concurrency: int = 5, +) -> List[dict]: + """AIMD adaptive concurrency — probes up on success, halves on failure. + + Uses _AdaptiveSemaphore so the concurrency limit adjusts in real-time: + - Every 3 consecutive successful calls → limit += 1 (probe the ceiling) + - Any failure (500, timeout) → limit //= 2 (back off immediately) + - Jitter added before retry to desync requests and avoid thundering herd + + Failed pairs are collected and retried sequentially after the parallel phase, + so one stalled call never blocks the completion of all others. + """ + pairs = [(s, fm) for s in sensors for fm in failure_modes] + n = len(pairs) + sem = _AdaptiveSemaphore(start_concurrency, maximum=max_concurrency) + raw: dict[tuple, dict] = {} + failed: list[tuple] = [] + lock = threading.Lock() + + def _run(s: str, fm: str) -> None: + with sem: + t0 = time.perf_counter() + logger.info( + "[adaptive] start in-flight=%d limit=%d %s | %s", + sem.in_flight, sem.limit, s, fm, + ) + try: + result = _call_relevancy(asset_name, fm, s) + elapsed = time.perf_counter() - t0 + with lock: + raw[(s, fm)] = result + sem.on_success() + logger.info( + "[adaptive] OK %.3fs → %s limit now=%d", + elapsed, result["answer"], sem.limit, + ) + except Exception as exc: + elapsed = time.perf_counter() - t0 + sem.on_failure() + jitter = random.uniform(0.5, 2.0) + logger.warning( + "[adaptive] FAIL %.3fs limit now=%d jitter=%.2fs %s", + elapsed, sem.limit, jitter, str(exc)[:80], + ) + time.sleep(jitter) + with lock: + failed.append((s, fm)) + + logger.info( + "[adaptive] start %d pairs init=%d max=%d", + n, start_concurrency, max_concurrency, + ) + _emit_bench_event("phase1_start") + with ThreadPoolExecutor(max_workers=max_concurrency) as pool: + for f in as_completed([pool.submit(_run, s, fm) for s, fm in pairs]): + f.result() + _emit_bench_event("phase1_end", pairs_failed=len(failed)) + + # Sequential retry with jitter for failed pairs + if failed: + logger.info("[adaptive] retry queue %d pairs → sequential", len(failed)) + _emit_bench_event("phase2_start") + for s, fm in failed: + jitter = random.uniform(1.0, 3.0) + logger.info("[adaptive] retry %.2fs jitter %s | %s", jitter, s, fm) + time.sleep(jitter) + try: + result = _call_relevancy(asset_name, fm, s) + raw[(s, fm)] = result + logger.info("[adaptive] retry OK %s | %s → %s", s, fm, result["answer"]) + except Exception as exc: + logger.error("[adaptive] retry FAIL %s | %s: %s", s, fm, exc) + raw[(s, fm)] = {"answer": "Unknown", "reason": str(exc), + "temporal_behavior": "Unknown"} + _emit_bench_event("phase2_end") + + return [ + {"sensor": s, "failure_mode": fm, **raw[(s, fm)]} + for s in sensors + for fm in failure_modes + ] + + + + +def _mapping_adaptive_ceiling( + asset_name: str, + failure_modes: List[str], + sensors: List[str], + max_concurrency: int = 0, # 0 → use len(pairs) (fire everything at once) + min_concurrency: int = 1, +) -> List[dict]: + """Optimistic adaptive concurrency — start at the ceiling, back off on failure. + + Inverse of AIMD: assumes the API is healthy and fires all N×M pairs + concurrently from the start. If WatsonX returns a 500 error, the + semaphore limit is immediately halved (multiplicative decrease), and + failed pairs are collected for a sequential retry phase. + + Advantages over start-low AIMD for small N: + - Happy path: wall time ≈ single slowest call (no ramp-up delay) + - Stressed path: first error triggers immediate backoff, same as AIMD + - The concurrency limit is always as high as WatsonX currently allows, + not artificially limited by a slow additive probe from below. + + The tradeoff: if WatsonX is already overloaded before we start, the + initial burst may produce more first-wave errors than a cautious start. + For small N (≤ 20 pairs) this is acceptable — the burst is short-lived + and the semaphore halves before any retry. + """ + pairs = [(s, fm) for s in sensors for fm in failure_modes] + n = len(pairs) + # Default: fire all pairs concurrently (matches upstream ThreadPoolExecutor()) + start = max_concurrency if max_concurrency > 0 else n + start = min(start, n) + + # probe_every set very high so the semaphore never probes *up* — + # we are already at the ceiling and only want multiplicative decrease. + sem = _AdaptiveSemaphore( + initial = start, + minimum = min_concurrency, + maximum = start, # ceiling = starting value; can only go down + probe_every = 9_999_999, # effectively disables upward probing + ) + raw: dict[tuple, dict] = {} + failed: list[tuple] = [] + lock = threading.Lock() + + def _run(s: str, fm: str) -> None: + with sem: + t0 = time.perf_counter() + logger.info( + "[ceiling] start in-flight=%d limit=%d %s | %s", + sem.in_flight, sem.limit, s, fm, + ) + try: + result = _call_relevancy(asset_name, fm, s) + elapsed = time.perf_counter() - t0 + with lock: + raw[(s, fm)] = result + sem.on_success() # no-op in practice (probe_every too large) + logger.info( + "[ceiling] OK %.3fs → %s limit=%d", + elapsed, result["answer"], sem.limit, + ) + except Exception as exc: + elapsed = time.perf_counter() - t0 + sem.on_failure() # halve the limit immediately + jitter = random.uniform(0.5, 2.0) + logger.warning( + "[ceiling] FAIL %.3fs limit now=%d jitter=%.2fs %s", + elapsed, sem.limit, jitter, str(exc)[:80], + ) + time.sleep(jitter) + with lock: + failed.append((s, fm)) + + logger.info( + "[ceiling] start %d pairs init=%d (ceiling) min=%d", + n, start, min_concurrency, + ) + _emit_bench_event("phase1_start") + # Thread pool has n workers so every pair can be in-flight immediately; + # the semaphore (not the pool) is the actual concurrency gate. + with ThreadPoolExecutor(max_workers=n) as pool: + for f in as_completed([pool.submit(_run, s, fm) for s, fm in pairs]): + f.result() + _emit_bench_event("phase1_end", pairs_failed=len(failed)) + + # Sequential retry for failed pairs at the reduced semaphore level + if failed: + logger.info("[ceiling] retry %d failed pairs → sequential", len(failed)) + _emit_bench_event("phase2_start") + for s, fm in failed: + jitter = random.uniform(1.0, 3.0) + logger.info("[ceiling] retry %.2fs jitter %s | %s", jitter, s, fm) + time.sleep(jitter) + try: + result = _call_relevancy(asset_name, fm, s) + raw[(s, fm)] = result + logger.info("[ceiling] retry OK %s | %s → %s", s, fm, result["answer"]) + except Exception as exc: + logger.error("[ceiling] retry FAIL %s | %s: %s", s, fm, exc) + raw[(s, fm)] = {"answer": "Unknown", "reason": str(exc), + "temporal_behavior": "Unknown"} + _emit_bench_event("phase2_end") + + return [ + {"sensor": s, "failure_mode": fm, **raw[(s, fm)]} + for s in sensors + for fm in failure_modes + ] + + + + +def _mapping_hedged( + asset_name: str, + failure_modes: List[str], + sensors: List[str], + max_concurrency: int = 0, # 0 → len(pairs) + hedge_after_s: float = _HEDGE_AFTER_S, +) -> List[dict]: + """Ceiling-start parallel with speculative hedging for tail-latency calls. + + Combines two techniques to deal with unpredictable WatsonX 500 errors + and random stalls: + + 1. **Ceiling-start**: fire all N×M pairs concurrently from t=0. + Wall time in the happy path ≈ single slowest call, not their sum. + + 2. **Request hedging**: if any individual call has not returned within + ``hedge_after_s`` seconds, a duplicate copy is fired immediately. + Whichever copy responds first wins; the other is silently dropped. + This caps the effective per-call latency at ~2×hedge_after_s even when + WatsonX randomly stalls one request. + + The two together handle the two failure modes we observed: + - 500 errors (overload) → ceiling-start backoff on first failure + - Random stalls (non-500) → hedge fires a rescue copy after hedge_after_s + """ + pairs = [(s, fm) for s in sensors for fm in failure_modes] + n = len(pairs) + start = max_concurrency if max_concurrency > 0 else n + start = min(start, n) + + sem = _AdaptiveSemaphore( + initial = start, + minimum = 1, + maximum = start, + probe_every = 9_999_999, + ) + raw: dict[tuple, dict] = {} + failed: list[tuple] = [] + lock = threading.Lock() + + def _run(s: str, fm: str) -> None: + with sem: + t0 = time.perf_counter() + logger.info("[hedged] start %s | %s", s, fm) + try: + result = _call_relevancy_hedged(asset_name, fm, s) + elapsed = time.perf_counter() - t0 + with lock: + raw[(s, fm)] = result + sem.on_success() + logger.info("[hedged] OK %.3fs → %s", elapsed, result["answer"]) + except Exception as exc: + elapsed = time.perf_counter() - t0 + sem.on_failure() + jitter = random.uniform(0.5, 2.0) + logger.warning( + "[hedged] FAIL %.3fs limit now=%d jitter=%.2fs %s", + elapsed, sem.limit, jitter, str(exc)[:80], + ) + time.sleep(jitter) + with lock: + failed.append((s, fm)) + + logger.info("[hedged] start %d pairs hedge_after=%.1fs", n, hedge_after_s) + _emit_bench_event("phase1_start") + with ThreadPoolExecutor(max_workers=n) as pool: + for f in as_completed([pool.submit(_run, s, fm) for s, fm in pairs]): + f.result() + _emit_bench_event("phase1_end", pairs_failed=len(failed)) + + if failed: + logger.info("[hedged] retry %d pairs → sequential", len(failed)) + _emit_bench_event("phase2_start") + for s, fm in failed: + jitter = random.uniform(1.0, 3.0) + time.sleep(jitter) + try: + result = _call_relevancy_hedged(asset_name, fm, s) + raw[(s, fm)] = result + except Exception as exc: + logger.error("[hedged] retry FAIL %s | %s: %s", s, fm, exc) + raw[(s, fm)] = {"answer": "Unknown", "reason": str(exc), + "temporal_behavior": "Unknown"} + _emit_bench_event("phase2_end") + + return [ + {"sensor": s, "failure_mode": fm, **raw[(s, fm)]} + for s in sensors + for fm in failure_modes + ] + + +# MCP server mcp = FastMCP("FMSRAgent") @@ -215,10 +805,8 @@ def get_failure_mode_sensor_mapping( the failure. Returns a bidirectional mapping (fm→sensors, sensor→fms) plus the full per-pair relevancy details. - Note: one LLM call is made per (failure_mode, sensor) pair sequentially. - Keep both lists small (e.g. ≤5 failure modes, ≤10 sensors) to avoid long - runtimes. For a chiller with 7 failure modes and 20+ sensors the call will - take several minutes.""" + All N×M LLM calls are issued in parallel via a thread pool so wall time + approaches the slowest single call rather than the sum of all calls.""" if not asset_name: return ErrorResult(error="asset_name is required") if not failure_modes: @@ -233,23 +821,45 @@ def get_failure_mode_sensor_mapping( sensor2fm: Dict[str, List[str]] = {} try: - for s in sensors: - for fm in failure_modes: - gen = _call_relevancy(asset_name, fm, s) - entry = RelevancyEntry( - asset_name=asset_name, - failure_mode=fm, - sensor=s, - relevancy_answer=gen["answer"], - relevancy_reason=gen["reason"], - temporal_behavior=gen["temporal_behavior"], - ) - full_relevancy.append(entry) - if "yes" in gen["answer"].lower(): - fm2sensor.setdefault(fm, []).append(s) - sensor2fm.setdefault(s, []).append(fm) + # Dispatch all N×M relevancy calls using the configured strategy. + # Strategy is set via FMSR_STRATEGY env var so the benchmark can + # select it by passing the var when spawning this server subprocess. + if _STRATEGY == "sequential": + raw_results = _mapping_sequential(asset_name, failure_modes, sensors) + elif _STRATEGY == "parallel": + raw_results = _mapping_parallel(asset_name, failure_modes, sensors, + max_workers=_PARALLEL_WORKERS) + elif _STRATEGY == "adaptive_ceiling": + raw_results = _mapping_adaptive_ceiling(asset_name, failure_modes, sensors, + max_concurrency=0, min_concurrency=1) + elif _STRATEGY == "hedged": + raw_results = _mapping_hedged(asset_name, failure_modes, sensors, + max_concurrency=0) + else: + logger.warning("Unknown FMSR_STRATEGY %r — falling back to parallel", _STRATEGY) + raw_results = _mapping_parallel(asset_name, failure_modes, sensors, + max_workers=_PARALLEL_WORKERS) + + # raw_results is a list of {sensor, failure_mode, answer, reason, temporal_behavior} + for entry_dict in raw_results: + s = entry_dict["sensor"] + fm = entry_dict["failure_mode"] + gen = entry_dict + entry = RelevancyEntry( + asset_name=asset_name, + failure_mode=fm, + sensor=s, + relevancy_answer=gen["answer"], + relevancy_reason=gen["reason"], + temporal_behavior=gen["temporal_behavior"], + ) + full_relevancy.append(entry) + if "yes" in gen["answer"].lower(): + fm2sensor.setdefault(fm, []).append(s) + sensor2fm.setdefault(s, []).append(fm) + except Exception as exc: - logger.error("_call_relevancy failed: %s", exc) + logger.error("mapping failed (strategy=%s): %s", _STRATEGY, exc) return ErrorResult(error=str(exc)) return FailureModeSensorMappingResult( diff --git a/src/workflow/executor.py b/src/workflow/executor.py index e84703d00..efdceb8ca 100644 --- a/src/workflow/executor.py +++ b/src/workflow/executor.py @@ -120,6 +120,17 @@ async def execute_step( them from prior step results. 4. Call the tool and return its result. """ + + if not step.tool or step.tool.lower() in ("none", "null"): + return StepResult( + step_number=step.step_number, + task=step.task, + agent=step.agent, + response=step.expected_output, + tool=step.tool, + tool_args=step.tool_args, + ) + server_path = self._server_paths.get(step.agent) if server_path is None: return StepResult( @@ -133,15 +144,7 @@ async def execute_step( ), ) - if not step.tool or step.tool.lower() in ("none", "null"): - return StepResult( - step_number=step.step_number, - task=step.task, - agent=step.agent, - response=step.expected_output, - tool=step.tool, - tool_args=step.tool_args, - ) + try: if _has_placeholders(step.tool_args): diff --git a/src/workflow/runner.py b/src/workflow/runner.py index 87ca789bf..d7adfa291 100644 --- a/src/workflow/runner.py +++ b/src/workflow/runner.py @@ -22,6 +22,7 @@ from .executor import Executor from .models import OrchestratorResult from .planner import Planner +from .timing import TimingRun _SUMMARIZE_PROMPT = """\ You are summarizing the results of a multi-step task execution for an \ @@ -66,7 +67,11 @@ def __init__( self._planner = Planner(llm) self._executor = Executor(llm, server_paths) - async def run(self, question: str) -> OrchestratorResult: + async def run( + self, + question: str, + timer: TimingRun | None = None, + ) -> OrchestratorResult: """Run the full plan-execute loop for a question. Steps: @@ -84,15 +89,27 @@ async def run(self, question: str) -> OrchestratorResult: """ # 1. Discover _log.info("Discovering agent capabilities...") - agent_descriptions = await self._executor.get_agent_descriptions() + if timer is None: + agent_descriptions = await self._executor.get_agent_descriptions() + else: + with timer.phase("discover"): + agent_descriptions = await self._executor.get_agent_descriptions() # 2. Plan _log.info("Planning...") - plan = self._planner.generate_plan(question, agent_descriptions) + if timer is None: + plan = self._planner.generate_plan(question, agent_descriptions) + else: + with timer.phase("plan"): + plan = self._planner.generate_plan(question, agent_descriptions) _log.info("Plan has %d step(s).", len(plan.steps)) # 3. Execute - history = await self._executor.execute_plan(plan, question) + if timer is None: + history = await self._executor.execute_plan(plan, question) + else: + with timer.phase("execute"): + history = await self._executor.execute_plan(plan, question) # 4. Summarise _log.info("Summarising...") @@ -101,9 +118,15 @@ async def run(self, question: str) -> OrchestratorResult: + (r.response if r.success else f"ERROR: {r.error}") for r in history ) - answer = self._llm.generate( - _SUMMARIZE_PROMPT.format(question=question, results=results_text) - ) + if timer is None: + answer = self._llm.generate( + _SUMMARIZE_PROMPT.format(question=question, results=results_text) + ) + else: + with timer.phase("summarise"): + answer = self._llm.generate( + _SUMMARIZE_PROMPT.format(question=question, results=results_text) + ) return OrchestratorResult( question=question, diff --git a/src/workflow/timing.py b/src/workflow/timing.py new file mode 100644 index 000000000..edca59254 --- /dev/null +++ b/src/workflow/timing.py @@ -0,0 +1,186 @@ +""" +Example: + from workflow.timing import TimingRun + + timer = TimingRun( + project="assetopsbench", + run_name="plan_execute_scenario_01", + group="iot_only", + config={"orchestrator": "plan_execute"}, + ) + + with timer.phase("total"): + with timer.phase("planning"): + ... + with timer.phase("execution"): + ... + with timer.phase("summarization"): + ... + + summary = timer.finish( + extra_metrics={"tool_calls": 2, "plan_steps": 3}, + summary_path="artifacts/timing/run_01.json", + ) +""" + +from __future__ import annotations + +import json +import time +from contextlib import contextmanager +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + + +def _load_wandb(): + try: + import wandb + + return wandb + except ImportError: + return None + + +@dataclass +class TimingPhase: + """Aggregated timings for one named phase.""" + + count: int = 0 + total_seconds: float = 0.0 + + def add(self, elapsed_seconds: float) -> None: + self.count += 1 + self.total_seconds += elapsed_seconds + + @property + def average_seconds(self) -> float: + return self.total_seconds / self.count if self.count else 0.0 + + +@dataclass +class TimingSummary: + """Serializable summary of a timing run.""" + + run_name: str + group: str + phases: dict[str, dict[str, float | int]] + total_wall_time_seconds: float + metadata: dict[str, Any] = field(default_factory=dict) + created_at_unix: float = field(default_factory=time.time) + + def to_dict(self) -> dict[str, Any]: + return { + "run_name": self.run_name, + "group": self.group, + "phases": self.phases, + "total_wall_time_seconds": self.total_wall_time_seconds, + "metadata": self.metadata, + "created_at_unix": self.created_at_unix, + } + + +class TimingRun: + """Context-managed timer for end-to-end runs and named sub-phases.""" + + def __init__( + self, + *, + project: str | None = None, + run_name: str, + group: str, + entity: str | None = None, + mode: str | None = None, + config: dict[str, Any] | None = None, + tags: list[str] | None = None, + ) -> None: + self.project = project + self.run_name = run_name + self.group = group + self.entity = entity + self.mode = mode + self.config = config or {} + self.tags = tags or [] + + self._started_at = time.perf_counter() + self._phases: dict[str, TimingPhase] = {} + self._wandb = None + self._wandb_run = None + + if self.project: + wandb = _load_wandb() + if wandb is not None: + self._wandb = wandb + self._wandb_run = wandb.init( + project=self.project, + entity=self.entity, + mode=self.mode, + name=self.run_name, + group=self.group, + config=self.config, + tags=self.tags, + reinit=True, + ) + + @contextmanager + def phase(self, name: str): + """Measure a named phase.""" + start = time.perf_counter() + try: + yield + finally: + elapsed = time.perf_counter() - start + self._phases.setdefault(name, TimingPhase()).add(elapsed) + + def mark(self, name: str, elapsed_seconds: float) -> None: + """Record a timing value that was measured elsewhere.""" + self._phases.setdefault(name, TimingPhase()).add(elapsed_seconds) + + def finish( + self, + *, + extra_metrics: dict[str, Any] | None = None, + summary_path: str | None = None, + ) -> TimingSummary: + total_wall = time.perf_counter() - self._started_at + phases = { + name: { + "count": phase.count, + "total_seconds": round(phase.total_seconds, 6), + "average_seconds": round(phase.average_seconds, 6), + } + for name, phase in sorted(self._phases.items()) + } + metadata = dict(extra_metrics or {}) + summary = TimingSummary( + run_name=self.run_name, + group=self.group, + phases=phases, + total_wall_time_seconds=round(total_wall, 6), + metadata=metadata, + ) + + if summary_path: + path = Path(summary_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(summary.to_dict(), indent=2)) + + if self._wandb_run is not None: + payload: dict[str, Any] = { + "timing/total_wall_time_seconds": summary.total_wall_time_seconds, + } + for phase_name, values in phases.items(): + payload[f"timing/{phase_name}/total_seconds"] = values["total_seconds"] + payload[f"timing/{phase_name}/average_seconds"] = values[ + "average_seconds" + ] + payload[f"timing/{phase_name}/count"] = values["count"] + for key, value in metadata.items(): + if isinstance(value, (int, float, str, bool)): + payload[f"meta/{key}"] = value + self._wandb_run.log(payload) + self._wandb_run.summary.update(summary.to_dict()) + self._wandb_run.finish() + self._wandb_run = None + + return summary diff --git a/uv.lock b/uv.lock index 5fb8747b0..3da2c0bb0 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 +1,13 @@ version = 1 revision = 3 -requires-python = ">=3.14" +requires-python = ">=3.12" resolution-markers = [ - "sys_platform == 'win32'", - "sys_platform == 'emscripten'", - "sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + 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