""" Evaluation harness for RouterBench-inspired policies in InferRoute. This script evaluates various routing strategies (Always Cloud, Always Local, Zero Router, Rule Router, KNN Router, MLP Router, Oracle Router, and Cascade Router) against the workload dataset. It sweeps parameters (mixture ratio p, willingness to pay lambda) to trace cost-quality curves. Inspired by: "ROUTERBENCH: A Benchmark for Multi-LLM Routing System" (withmartian/routerbench) """ import os import sys import json import time import asyncio from typing import Any, List from httpx import AsyncClient # Add project root to path to resolve imports correctly sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from inferroute.main import app from benchmarks.evaluate_quality import evaluate_response_quality DATASET_PATH = os.path.join(os.path.dirname(__file__), "datasets", "workload.json") RESULTS_DIR = os.path.join(os.path.dirname(__file__), "results") RESULTS_PATH = os.path.join(RESULTS_DIR, "eval_results.json") # Ensure results directory exists os.makedirs(RESULTS_DIR, exist_ok=True) # API Token for acme_corp tenant HEADERS = {"Authorization": "Bearer sk-inferroute-demo"} # Defined evaluation scenarios SCENARIOS = [ { "name": "always-openai", "payload_patch": {"model": "gpt-4o-mini", "routing": {"allow_local": False}} }, { "name": "always-gemini", "payload_patch": {"model": "gemini-1.5-flash", "routing": {"allow_local": False}} }, { "name": "always-vllm", "payload_patch": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "routing": {"allow_cloud": False}} }, { "name": "always-ollama", "payload_patch": {"model": "llama3", "routing": {"allow_cloud": False}} }, { "name": "rule-router", "payload_patch": {"model": "edge/auto", "routing": {"policy": "rule"}} }, { "name": "oracle-router", "payload_patch": {"model": "edge/auto", "routing": {"policy": "oracle"}} } ] # Sweep Zero Router (baseline mixture ratios p from 0.0 to 1.0) for p in [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]: SCENARIOS.append({ "name": f"zero-router_p{p:.1f}", "payload_patch": {"model": "edge/auto", "routing": {"policy": "zero", "mixture_ratio": p}} }) # Sweep KNN Router (willingness to pay lambdas) for lam in [0.0, 0.25, 0.5, 1.0, 2.0, 5.0]: SCENARIOS.append({ "name": f"knn-router_l{lam:.2f}", "payload_patch": {"model": "edge/auto", "routing": {"policy": "knn", "lambda": lam}} }) # Sweep MLP Router (willingness to pay lambdas) for lam in [0.0, 0.25, 0.5, 1.0, 2.0, 5.0]: SCENARIOS.append({ "name": f"mlp-router_l{lam:.2f}", "payload_patch": {"model": "edge/auto", "routing": {"policy": "mlp", "lambda": lam}} }) # Sweep Cascade Router (acceptance thresholds tau from 0.0 to 1.0) for tau in [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]: SCENARIOS.append({ "name": f"cascade-router_t{tau:.2f}", "payload_patch": {"model": "edge/auto", "routing": {"policy": "cascade", "acceptance_threshold": tau}} }) async def execute_request(client: AsyncClient, payload: dict) -> dict[str, Any]: """Issues POST completion to the gateway and measures latencies.""" start_time = time.time() response = await client.post( "/v1/chat/completions", headers=HEADERS, json=payload, timeout=10.0 ) latency = (time.time() - start_time) * 1000.0 if response.status_code != 200: return { "success": False, "error": response.text, "latency_ms": latency, "ttft_ms": latency, "cost_usd": 0.0, "content": "", "fallback_triggered": False } data = response.json() choices = data.get("choices", []) content = choices[0].get("message", {}).get("content", "") if choices else "" timing = data.get("timing", {}) usage = data.get("usage", {}) route = data.get("route", {}) fallback_count = route.get("fallback_count", 0) return { "success": True, "content": content, "latency_ms": timing.get("latency_ms", latency), "ttft_ms": timing.get("ttft_ms", latency / 2.0), "cost_usd": usage.get("estimated_cost_usd", 0.0), "model_used": data.get("model", ""), "fallback_triggered": fallback_count > 0 } async def run_evaluation(): print("=============================================================") print("Starting RouterBench-driven Routing Policy Sweeping Harness") print("=============================================================") # Load dataset with open(DATASET_PATH, "r", encoding="utf-8") as f: prompts = json.load(f) print(f"Loaded {len(prompts)} prompts across {len(set(p['category'] for p in prompts))} categories.") results = [] from httpx import ASGITransport transport = ASGITransport(app=app) async with AsyncClient(transport=transport, base_url="http://testserver") as client: # Give lifespan a millisecond to boot databases await asyncio.sleep(0.5) for scenario in SCENARIOS: name = scenario["name"] print(f"\n---> Running scenario: {name}") for item in prompts: prompt_id = item["id"] category = item["category"] prompt_text = item["prompt"] requires_json = item["requires_json"] expected_keys = item.get("expected_keys") ref_keywords = item.get("reference_keywords") # Base request body payload = { "messages": [{"role": "user", "content": prompt_text}], "temperature": 0.2, "max_output_tokens": 128 } payload.update(scenario["payload_patch"]) # Regular execution (cascade fallback is handled on server side) res = await execute_request(client, payload) fallback_triggered = res.get("fallback_triggered", False) if res["success"]: q_score = evaluate_response_quality( category, res["content"], requires_json, expected_keys, ref_keywords ) else: q_score = 0.0 # Check SLO compliance (target: p95 latency < 2000ms, success=True) slo_compliant = res["success"] and (res["latency_ms"] < 2000.0) results.append({ "scenario": name, "prompt_id": prompt_id, "category": category, "success": res["success"], "model_used": res.get("model_used", "unknown"), "latency_ms": res["latency_ms"], "ttft_ms": res["ttft_ms"], "cost_usd": res["cost_usd"], "quality_score": q_score, "fallback_triggered": fallback_triggered, "slo_compliant": slo_compliant }) print(f" ID={prompt_id:<14} Model={res.get('model_used', 'None'):<14} Latency={res['latency_ms']:>6.1f}ms Cost=${res['cost_usd']:.6f} Quality={q_score:.2f}") # Write evaluation results to file with open(RESULTS_PATH, "w", encoding="utf-8") as f: json.dump(results, f, indent=2) print(f"\nEvaluation finished. Results saved to {RESULTS_PATH}") if __name__ == "__main__": asyncio.run(run_evaluation())