"""Unified cache-aware + token-level load-balanced global scheduler. Supports two modes: --combined URL [URL ...]: PD co-located instances (normal vLLM, no KV transfer) --prefill URL BP --decode URL: PD disaggregated instances (Mooncake KV transfer) Routing policies (--policy): linear (default): score = ongoing_tokens - ALPHA * cache_hit_tokens lmetric: score = P_tokens * BS (LMetric, OSDI'26) P_tokens = pending_prefill_tokens + new_uncached_tokens BS = num_requests (waiting + running) Session affinity: multi-turn sessions stick to same instance (all policies). """ import argparse import asyncio import os import time as _time import urllib.parse import uuid from collections import OrderedDict from contextlib import asynccontextmanager from dataclasses import dataclass import httpx MAX_STREAM_RETRIES = 3 RETRY_DELAY_S = 0.5 import uvicorn from fastapi import FastAPI, HTTPException, Request from fastapi.responses import StreamingResponse BLOCK_SIZE = 512 CACHE_HIT_ALPHA = 1.0 @dataclass class Settings: """Runtime-tunable knobs. Populated from argparse in __main__. All routing/offload code reads from the SETTINGS singleton so that CLI overrides survive even when the module is imported as a library (e.g. by tests/) and __main__ does not run. """ prefill_throughput: float = 7000.0 # tokens/s per GPU (measured on H20) rdma_overhead_s: float = 0.1 # RDMA PUSH overhead (~10-50ms measured) cache_capacity_blocks: int = 200000 # per-instance LRU cap on shadow cached_blocks SETTINGS = Settings() class InstanceState: def __init__(self, url: str, bootstrap_port: int | None = None): self.url = url self.bootstrap_port = bootstrap_port self.client = httpx.AsyncClient( timeout=None, base_url=url, limits=httpx.Limits(max_connections=None, max_keepalive_connections=None), ) self.ongoing_tokens = 0 self.ongoing_decode_tokens = 0 # subset: tokens in decode phase self.pending_prefill_tokens = 0 # tokens for requests still in prefill self.num_requests = 0 # total in-flight requests (waiting + running) self.active_p_offloads = 0 # number of HEAVY prefills this instance is doing for others self.engine_id: dict[int, str] = {} self.dp_size = 1 # OrderedDict acts as an LRU keyed by block hash; value is unused. self.cached_blocks: OrderedDict[int, None] = OrderedDict() def estimate_cache_hit(self, token_ids: list[int] | None) -> int: if not token_ids or len(token_ids) < BLOCK_SIZE: return 0 hit = 0 for i in range(0, len(token_ids) - BLOCK_SIZE + 1, BLOCK_SIZE): bh = hash(tuple(token_ids[i:i + BLOCK_SIZE])) if bh in self.cached_blocks: self.cached_blocks.move_to_end(bh) # LRU touch on hit hit += BLOCK_SIZE else: break return hit def record_prefix(self, token_ids: list[int] | None): if not token_ids: return for i in range(0, len(token_ids) - BLOCK_SIZE + 1, BLOCK_SIZE): bh = hash(tuple(token_ids[i:i + BLOCK_SIZE])) if bh in self.cached_blocks: self.cached_blocks.move_to_end(bh) else: self.cached_blocks[bh] = None if len(self.cached_blocks) > SETTINGS.cache_capacity_blocks: self.cached_blocks.popitem(last=False) def _p_offload_penalty(inst: InstanceState) -> int: """Penalty for PD-sep mode routing (legacy).""" if inst.active_p_offloads <= 0: return 0 return inst.active_p_offloads * 20000 def pick_instance(instances: list[InstanceState], token_ids: list[int] | None, session_id: str | None, input_length: int, affinity: dict[str, int]) -> tuple[InstanceState, int]: """Session-sticky with load-aware override. Turn 2+: use session affinity UNLESS pinned instance is overloaded or busy with P-role offloads, in which case pick least-loaded. Turn 1: pick instance with best score (load + cache combined). Instances doing P-role offloads get a large penalty to steer WARM/MEDIUM traffic away. """ avg_load = max(sum(i.ongoing_tokens for i in instances) / len(instances), 1.0) if session_id and session_id in affinity: idx = affinity[session_id] if idx < len(instances): inst = instances[idx] if (inst.ongoing_tokens <= avg_load * 2.0 and inst.active_p_offloads == 0): return inst, idx best_idx, best_score = 0, float("inf") for i, inst in enumerate(instances): cache_hit = inst.estimate_cache_hit(token_ids) score = (inst.ongoing_tokens + _p_offload_penalty(inst) - CACHE_HIT_ALPHA * cache_hit) if score < best_score: best_score = score best_idx = i if session_id: affinity[session_id] = best_idx return instances[best_idx], best_idx def pick_instance_lmetric(instances: list[InstanceState], token_ids: list[int] | None, session_id: str | None, input_length: int, affinity: dict[str, int]) -> tuple[InstanceState, int]: """LMetric routing: score = P_tokens × BS (OSDI'26). Pure per-request load-based routing, no session affinity. P = pending_prefill_tokens + (input_length - cache_hit) BS = num_requests (current batch size) """ best_idx, best_score = 0, float("inf") for i, inst in enumerate(instances): cache_hit = inst.estimate_cache_hit(token_ids) new_prefill = max(0, input_length - cache_hit) p_tokens = inst.pending_prefill_tokens + new_prefill bs = inst.num_requests score = p_tokens * bs if score < best_score: best_score = score best_idx = i return instances[best_idx], best_idx global_args = None combined_instances: list[InstanceState] = [] prefill_instances: list[InstanceState] = [] decode_instances: list[InstanceState] = [] # Session affinity is namespace-isolated: combined-mode and pd-sep mode index # different instance lists, so a shared dict could mis-route after a mode switch. session_affinity_combined: dict[str, int] = {} session_affinity_prefill: dict[str, int] = {} # Backwards-compat alias used by /stats etc. session_affinity = session_affinity_combined is_pd_sep = False _breakdown_log: list[dict] = [] async def init_prefill_bootstrap(instances: list[InstanceState], ready: asyncio.Event): for inst in instances: if inst.bootstrap_port is None: continue while True: try: await inst.client.get("/health") except Exception: await asyncio.sleep(1) continue parsed = urllib.parse.urlparse(str(inst.client.base_url)) url = f"http://{parsed.hostname}:{inst.bootstrap_port}/query" resp = await inst.client.get(url) resp.raise_for_status() data = resp.json() for dp_rank, dp_entry in data.items(): inst.engine_id[int(dp_rank)] = dp_entry["engine_id"] inst.dp_size = len(data) print(f"Inited {inst.url} engine_ids={inst.engine_id}") break ready.set() async def _reconcile_loop(): """Periodic safety net for shadow state. StreamingResponse generators decrement load counters in their finally block, but if a client disconnects before the body is consumed the generator is never entered and the decrement is lost. Clamp negative drift every minute so router scores stay sane. This does not replace proper exact-state syncing with vLLM (see TODO.md item 6). """ while True: try: await asyncio.sleep(60) except asyncio.CancelledError: return for inst in combined_instances + prefill_instances + decode_instances: if inst.ongoing_tokens < 0: inst.ongoing_tokens = 0 if inst.ongoing_decode_tokens < 0: inst.ongoing_decode_tokens = 0 if inst.pending_prefill_tokens < 0: inst.pending_prefill_tokens = 0 if inst.num_requests < 0: inst.num_requests = 0 if inst.active_p_offloads < 0: inst.active_p_offloads = 0 def _verify_vllm_patch(): """Startup self-check for patches/0001-fix-kv-transfer-abort-race.patch. The patch turns an `assert req_id in self.requests` into a soft warn so that engines do not crash on the KV-transfer abort race (see REPORT §3.x). If somebody upgrades vLLM without re-applying the patch, the assert returns and elastic mode dies under load. Print a loud warning so we catch the regression before the first HEAVY request. """ try: import inspect from vllm.v1.core.sched.scheduler import Scheduler src = inspect.getsource(Scheduler) if "assert req_id in self.requests" in src: print("WARNING: vLLM scheduler still contains the unpatched " "`assert req_id in self.requests` line; expect engine " "death on KV-transfer abort race. Apply " "patches/0001-fix-kv-transfer-abort-race.patch.") else: print("vLLM patch self-check: kv-transfer-abort assert is patched.") except Exception as exc: print(f"vLLM patch self-check skipped: {exc!r}") @asynccontextmanager async def lifespan(app: FastAPI): global is_pd_sep app.state.ready = asyncio.Event() _verify_vllm_patch() reconcile_task = asyncio.create_task(_reconcile_loop()) if global_args.combined: is_pd_sep = False bp_list = [int(p) for p in global_args.bootstrap_ports.split(",") if p.strip()] if global_args.bootstrap_ports else [] for i, url in enumerate(global_args.combined): bp = bp_list[i] if i < len(bp_list) else None combined_instances.append(InstanceState(url, bp)) # Bootstrap combined instances for offload (need engine_ids for KV transfer) if global_args.offload and bp_list: await init_prefill_bootstrap(combined_instances, app.state.ready) else: app.state.ready.set() policy = getattr(global_args, 'policy', 'linear') print(f"Combined mode: {len(combined_instances)} instances, policy={policy}, offload={'ON' if global_args.offload else 'OFF'}") else: is_pd_sep = True for url, bp in global_args.prefill: prefill_instances.append(InstanceState(url, bp)) for url in global_args.decode: decode_instances.append(InstanceState(url)) await init_prefill_bootstrap(prefill_instances, app.state.ready) print(f"PD-Sep mode: {len(prefill_instances)}P + {len(decode_instances)}D") yield reconcile_task.cancel() try: await reconcile_task except asyncio.CancelledError: pass for inst in combined_instances + prefill_instances + decode_instances: await inst.client.aclose() app = FastAPI(lifespan=lifespan) @app.post("/v1/completions") async def handle_completions(request: Request): return await _handle(request, "/v1/completions") @app.post("/v1/chat/completions") async def handle_chat(request: Request): return await _handle(request, "/v1/chat/completions") async def _handle(request: Request, api: str): if not app.state.ready.is_set(): raise HTTPException(status_code=503, detail="Service Unavailable") req_data = await request.json() request_id = str(uuid.uuid4()) prompt = req_data.get("prompt") token_ids = prompt if isinstance(prompt, list) else None input_length = len(token_ids) if token_ids else 0 session_id = request.headers.get("X-Session-Id") headers = {"X-Request-Id": request_id} api_key = os.environ.get("OPENAI_API_KEY") if api_key: headers["Authorization"] = f"Bearer {api_key}" if is_pd_sep: return await _handle_pd_sep(api, req_data, request_id, token_ids, input_length, session_id, headers) else: return await _handle_combined(api, req_data, token_ids, input_length, session_id, headers) async def _handle_combined(api, req_data, token_ids, input_length, session_id, headers): """Unified routing: pick the instance with lowest expected latency. For each instance, estimate: latency = queue_time + prefill_time + transfer_cost where prefill_time depends on whether the instance has cache (local), can receive cache via PUSH (remote), or must do cold prefill. """ offload_enabled = getattr(global_args, 'offload', False) and len(combined_instances) >= 2 throughput = SETTINGS.prefill_throughput # Compute cache hits for all instances cache_hits = [inst.estimate_cache_hit(token_ids) for inst in combined_instances] best_cache_idx = max(range(len(combined_instances)), key=lambda i: cache_hits[i]) best_cache_hit = cache_hits[best_cache_idx] def _instance_cost(i: int) -> tuple[float, bool]: """Expected latency if this request goes to instance i.""" inst = combined_instances[i] queue = inst.pending_prefill_tokens / throughput local_hit = cache_hits[i] local_new = max(0, input_length - local_hit) local_cost = queue + local_new / throughput if offload_enabled and best_cache_hit > 0 and i != best_cache_idx and local_hit < best_cache_hit: push_new = max(0, input_length - best_cache_hit) push_cost = queue + push_new / throughput + SETTINGS.rdma_overhead_s if push_cost < local_cost: return push_cost, True return local_cost, False # Session affinity: prefer the last-used instance if its cost is reasonable affinity_idx = session_affinity_combined.get(session_id) if session_id else None if affinity_idx is not None and affinity_idx < len(combined_instances): affinity_cost, affinity_push = _instance_cost(affinity_idx) # Compare with the globally best option all_costs = [_instance_cost(i) for i in range(len(combined_instances))] global_best_cost = min(c for c, _ in all_costs) # Use affinity if it's within 2x of the best option if affinity_cost <= global_best_cost * 2.0: best_idx = affinity_idx best_cost = affinity_cost best_needs_push = affinity_push else: best_idx = min(range(len(combined_instances)), key=lambda i: all_costs[i][0]) best_cost, best_needs_push = all_costs[best_idx] else: all_costs = [_instance_cost(i) for i in range(len(combined_instances))] best_idx = min(range(len(combined_instances)), key=lambda i: all_costs[i][0]) best_cost, best_needs_push = all_costs[best_idx] chosen = combined_instances[best_idx] cache_hit = cache_hits[best_idx] estimated_new = max(0, input_length - cache_hit) breakdown = { "request_id": headers.get("X-Request-Id", ""), "input_length": input_length, "cache_hit": cache_hit, "estimated_new_tokens": estimated_new, "t_proxy_recv": _time.monotonic(), "chosen_cost": round(best_cost, 2), } if session_id: session_affinity_combined[session_id] = best_idx if best_needs_push: c_inst = combined_instances[best_cache_idx] d_inst = chosen push_cache_hit = best_cache_hit push_new = max(0, input_length - push_cache_hit) d_inst.ongoing_tokens += input_length d_inst.pending_prefill_tokens += push_new d_inst.num_requests += 1 c_inst.active_p_offloads += 1 breakdown["route_class"] = "PUSH_MIGRATE" breakdown["c_inst"] = c_inst.url breakdown["d_inst"] = d_inst.url breakdown["push_cache_hit"] = push_cache_hit return await _handle_direct_read_offload( api, req_data, headers, token_ids, input_length, c_inst, d_inst, push_cache_hit, push_new, breakdown) else: breakdown["route_class"] = "LOCAL" breakdown["routed_to"] = chosen.url chosen.ongoing_tokens += input_length chosen.pending_prefill_tokens += estimated_new chosen.num_requests += 1 async def generate(): prefill_done = False try: for attempt in range(MAX_STREAM_RETRIES): try: async with inst.client.stream("POST", api, json=req_data, headers=headers) as resp: resp.raise_for_status() async for chunk in resp.aiter_bytes(): if not prefill_done: inst.pending_prefill_tokens -= estimated_new inst.ongoing_decode_tokens += input_length breakdown["t_first_token"] = _time.monotonic() prefill_done = True yield chunk inst.record_prefix(token_ids) break except (httpx.ConnectError, httpx.RemoteProtocolError): if prefill_done or attempt >= MAX_STREAM_RETRIES - 1: raise await asyncio.sleep(RETRY_DELAY_S) finally: if not prefill_done: inst.pending_prefill_tokens -= estimated_new else: inst.ongoing_decode_tokens -= input_length inst.ongoing_tokens -= input_length inst.num_requests -= 1 breakdown["t_done"] = _time.monotonic() _breakdown_log.append(breakdown) return StreamingResponse(generate(), media_type="text/event-stream") PREFILL_TIMEOUT_S = 120 # max seconds to wait for P-instance prefill async def _handle_direct_read_offload(api, req_data, headers, token_ids, input_length, c_inst, d_inst, cache_hit, estimated_new, breakdown): """HEAVY request: D direct-RDMA-reads cached KV from C_s, then does local prefill for new tokens + decode. C_s's scheduler is NOT involved. """ request_id = headers.get("X-Request-Id", "") # Align cache_hit to block boundary for remote_num_tokens cached_tokens = (cache_hit // BLOCK_SIZE) * BLOCK_SIZE breakdown["t_offload_sent"] = _time.monotonic() parsed = urllib.parse.urlparse(str(c_inst.client.base_url)) bootstrap_addr = "http://%s:%s" % (parsed.hostname, c_inst.bootstrap_port) # Send full prompt to D with direct_read flag decode_data = req_data.copy() decode_data["kv_transfer_params"] = { "do_remote_decode": False, "do_remote_prefill": True, "direct_read": True, "remote_bootstrap_addr": bootstrap_addr, "remote_engine_id": c_inst.engine_id.get(0, ""), "transfer_id": "xfer-" + request_id, "remote_num_tokens": cached_tokens, } async def generate(): first_token = True try: async with d_inst.client.stream("POST", api, json=decode_data, headers=headers) as resp: resp.raise_for_status() async for chunk in resp.aiter_bytes(): if first_token: d_inst.pending_prefill_tokens -= estimated_new d_inst.ongoing_decode_tokens += input_length breakdown["t_first_token"] = _time.monotonic() first_token = False yield chunk d_inst.record_prefix(token_ids) finally: if first_token: d_inst.pending_prefill_tokens -= estimated_new else: d_inst.ongoing_decode_tokens -= input_length d_inst.ongoing_tokens -= input_length d_inst.num_requests -= 1 c_inst.active_p_offloads = max(0, c_inst.active_p_offloads - 1) breakdown["t_done"] = _time.monotonic() _breakdown_log.append(breakdown) return StreamingResponse(generate(), media_type="text/event-stream") async def _handle_pd_sep(api, req_data, request_id, token_ids, input_length, session_id, headers): """PD-Sep mode with per-stage breakdown profiling.""" breakdown = { "request_id": request_id, "input_length": input_length, "t_proxy_recv": _time.monotonic(), } p_inst, _ = pick_instance(prefill_instances, token_ids, session_id, input_length, session_affinity_prefill) d_inst = min(decode_instances, key=lambda x: x.ongoing_tokens) breakdown["p_inst"] = p_inst.url breakdown["d_inst"] = d_inst.url prefill_data = req_data.copy() prefill_data["kv_transfer_params"] = { "do_remote_decode": True, "do_remote_prefill": False, "transfer_id": f"xfer-{request_id}", } prefill_data["stream"] = False prefill_data["max_tokens"] = 1 prefill_data.pop("max_completion_tokens", None) prefill_data.pop("stream_options", None) p_headers = {**headers, "X-data-parallel-rank": "0"} p_inst.ongoing_tokens += input_length breakdown["t_prefill_sent"] = _time.monotonic() try: resp = await p_inst.client.post(api, json=prefill_data, headers=p_headers) breakdown["t_prefill_done"] = _time.monotonic() resp.raise_for_status() await resp.aclose() p_inst.record_prefix(token_ids) except Exception as e: breakdown["t_prefill_done"] = _time.monotonic() breakdown["prefill_error"] = True _breakdown_log.append(breakdown) raise HTTPException(status_code=502, detail=f"Prefill failed: {e}") finally: p_inst.ongoing_tokens -= input_length # Send decode d_inst.ongoing_tokens += input_length parsed = urllib.parse.urlparse(str(p_inst.client.base_url)) bootstrap_addr = f"http://{parsed.hostname}:{p_inst.bootstrap_port}" decode_data = req_data.copy() decode_data["kv_transfer_params"] = { "do_remote_decode": False, "do_remote_prefill": True, "remote_bootstrap_addr": bootstrap_addr, "remote_engine_id": p_inst.engine_id.get(0, ""), "transfer_id": f"xfer-{request_id}", } breakdown["t_decode_sent"] = _time.monotonic() async def generate(): first_token = True try: async with d_inst.client.stream("POST", api, json=decode_data, headers=headers) as resp: resp.raise_for_status() async for chunk in resp.aiter_bytes(): if first_token: breakdown["t_first_token"] = _time.monotonic() first_token = False yield chunk finally: breakdown["t_done"] = _time.monotonic() d_inst.ongoing_tokens -= input_length _breakdown_log.append(breakdown) return StreamingResponse(generate(), media_type="application/json") @app.get("/breakdown") async def get_breakdown(): """Return per-request breakdown data for analysis.""" return _breakdown_log @app.get("/stats") async def get_stats(): """Return per-instance live state for debugging.""" instances = combined_instances or prefill_instances + decode_instances return [{ "url": inst.url, "role": "combined", "ongoing_tokens": inst.ongoing_tokens, "pending_prefill_tokens": inst.pending_prefill_tokens, "ongoing_decode_tokens": inst.ongoing_decode_tokens, "num_requests": inst.num_requests, "active_p_offloads": inst.active_p_offloads, "cached_blocks": len(inst.cached_blocks), } for inst in instances] def parse_args(): p = argparse.ArgumentParser(description="Unified cache-aware global scheduler") p.add_argument("--port", type=int, default=8000) p.add_argument("--host", type=str, default="0.0.0.0") p.add_argument("--combined", nargs="+", help="Combined mode: list of instance URLs") p.add_argument("--prefill", nargs="+", action="append", dest="prefill_raw", help="PD-Sep prefill: URL [bootstrap_port]") p.add_argument("--decode", nargs=1, action="append", dest="decode_raw", help="PD-Sep decode: URL") p.add_argument("--heavy-threshold", type=int, default=20000, help="New tokens threshold for HEAVY classification (adaptive offload)") p.add_argument("--offload", action="store_true", help="Enable Mooncake KV offload for HEAVY requests (requires kv_both instances)") p.add_argument("--bootstrap-ports", type=str, default="", help="Comma-separated bootstrap ports for combined instances (for offload mode)") p.add_argument("--policy", type=str, default="linear", choices=["linear", "lmetric"], help="Routing policy: linear (default) or lmetric (P_tokens × BS, OSDI'26)") p.add_argument("--overload-factor", type=float, default=2.0, help="Break session affinity when instance load > factor * avg") p.add_argument("--max-offload-inflight", type=int, default=4, help="Global cap on concurrent P-role offloads (M3)") p.add_argument("--cache-gate-ratio", type=float, default=0.3, help="Min cache_hit/input ratio to allow offload " "(0.0 disables gate, 1.0 disables offload entirely)") args = p.parse_args() args.prefill = [] if args.prefill_raw: for entry in args.prefill_raw: url = entry[0] bp = int(entry[1]) if len(entry) > 1 and entry[1].lower() != "none" else None args.prefill.append((url, bp)) args.decode = [e[0] for e in (args.decode_raw or [])] if not args.combined and not args.prefill: p.error("Must specify either --combined or --prefill/--decode") return args if __name__ == "__main__": global_args = parse_args() print("SETTINGS: throughput=%.0f rdma_overhead=%.2f offload=%s" % ( SETTINGS.prefill_throughput, SETTINGS.rdma_overhead_s, getattr(global_args, 'offload', False))) uvicorn.run(app, host=global_args.host, port=global_args.port)