Gahow Wang
e4fa56cb1e
Implement LMetric (P_tokens × BS multiplication score) from "Simple is
Better" (Zhang et al., OSDI'26) as alternative routing policy for
combined mode. Key changes:
- cache_aware_proxy.py: add --policy {linear,lmetric} flag, track
pending_prefill_tokens and num_requests per instance, /stats endpoint
- run_lmetric_ab.sh: automated A/B script for fair comparison
Results (200 req, fresh restart, same trace):
Linear: TTFT50=1.086 TPOT90=0.077 E2E50=5.423
LMetric: TTFT50=1.099 TPOT90=0.073 E2E50=5.205
Delta: TTFT +1.2% TPOT -5.9% E2E -4.0%
LMetric improves TPOT/E2E modestly through better load balancing, but
routing policy headroom is limited vs elastic P2P offload (-44% E2E).
TODO: vLLM → Redis → router pipeline for exact state ablation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
596 lines
24 KiB
Python
596 lines
24 KiB
Python
"""Unified cache-aware + token-level load-balanced global scheduler.
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Supports two modes:
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--combined URL [URL ...]: PD co-located instances (normal vLLM, no KV transfer)
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--prefill URL BP --decode URL: PD disaggregated instances (Mooncake KV transfer)
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Routing policies (--policy):
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linear (default): score = ongoing_tokens - ALPHA * cache_hit_tokens
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lmetric: score = P_tokens * BS (LMetric, OSDI'26)
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P_tokens = pending_prefill_tokens + new_uncached_tokens
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BS = num_requests (waiting + running)
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Session affinity: multi-turn sessions stick to same instance (all policies).
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"""
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import argparse
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import asyncio
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import os
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import time as _time
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import urllib.parse
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import uuid
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from contextlib import asynccontextmanager
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import httpx
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import uvicorn
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.responses import StreamingResponse
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BLOCK_SIZE = 512
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CACHE_HIT_ALPHA = 1.0
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HEAVY_THRESHOLD = 20000 # default; overridden by --heavy-threshold
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OVERLOAD_FACTOR = 2.0
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class InstanceState:
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def __init__(self, url: str, bootstrap_port: int | None = None):
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self.url = url
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self.bootstrap_port = bootstrap_port
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self.client = httpx.AsyncClient(
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timeout=None, base_url=url,
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limits=httpx.Limits(max_connections=None, max_keepalive_connections=None),
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)
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self.ongoing_tokens = 0
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self.ongoing_decode_tokens = 0 # subset: tokens in decode phase
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self.pending_prefill_tokens = 0 # tokens for requests still in prefill
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self.num_requests = 0 # total in-flight requests (waiting + running)
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self.engine_id: dict[int, str] = {}
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self.dp_size = 1
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self.cached_blocks: set[int] = set()
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def estimate_cache_hit(self, token_ids: list[int] | None) -> int:
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if not token_ids or len(token_ids) < BLOCK_SIZE:
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return 0
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hit = 0
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for i in range(0, len(token_ids) - BLOCK_SIZE + 1, BLOCK_SIZE):
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bh = hash(tuple(token_ids[i:i + BLOCK_SIZE]))
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if bh in self.cached_blocks:
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hit += BLOCK_SIZE
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else:
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break
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return hit
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def record_prefix(self, token_ids: list[int] | None):
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if not token_ids:
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return
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for i in range(0, len(token_ids) - BLOCK_SIZE + 1, BLOCK_SIZE):
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self.cached_blocks.add(hash(tuple(token_ids[i:i + BLOCK_SIZE])))
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if len(self.cached_blocks) > 200000:
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self.cached_blocks = set(list(self.cached_blocks)[-100000:])
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# Cumulative token load per instance (for balanced session placement)
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_inst_cumulative_tokens: list[int] = []
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def pick_instance(instances: list[InstanceState], token_ids: list[int] | None,
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session_id: str | None, input_length: int,
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affinity: dict[str, int]) -> tuple[InstanceState, int]:
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"""Session-sticky with load-aware override.
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Turn 2+: use session affinity UNLESS pinned instance is overloaded
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(ongoing_tokens > 2x average), in which case pick least-loaded.
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Turn 1: pick instance with best score (load + cache combined).
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"""
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global _inst_cumulative_tokens
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if not _inst_cumulative_tokens:
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_inst_cumulative_tokens = [0] * len(instances)
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avg_load = max(sum(i.ongoing_tokens for i in instances) / len(instances), 1.0)
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# Session affinity for turn 2+ (with load override)
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if session_id and session_id in affinity:
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idx = affinity[session_id]
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if idx < len(instances):
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inst = instances[idx]
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# Stick if not overloaded
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if inst.ongoing_tokens <= avg_load * OVERLOAD_FACTOR:
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return inst, idx
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# Overloaded: fall through to score-based selection
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# Score = ongoing_tokens - ALPHA * cache_hit_tokens
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# Balances load (lower is better) with cache affinity (higher hit is better)
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best_idx, best_score = 0, float("inf")
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for i, inst in enumerate(instances):
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cache_hit = inst.estimate_cache_hit(token_ids)
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score = inst.ongoing_tokens - CACHE_HIT_ALPHA * cache_hit
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if score < best_score:
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best_score = score
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best_idx = i
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_inst_cumulative_tokens[best_idx] += input_length
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if session_id:
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affinity[session_id] = best_idx
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return instances[best_idx], best_idx
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def pick_instance_lmetric(instances: list[InstanceState], token_ids: list[int] | None,
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session_id: str | None, input_length: int,
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affinity: dict[str, int]) -> tuple[InstanceState, int]:
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"""LMetric routing: score = P_tokens × BS (OSDI'26).
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P_tokens = pending_prefill_tokens on instance + new request's uncached tokens.
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BS = num_requests on instance + 1 (counting the new request).
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"""
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avg_load = max(sum(i.ongoing_tokens for i in instances) / len(instances), 1.0)
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if session_id and session_id in affinity:
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idx = affinity[session_id]
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if idx < len(instances):
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inst = instances[idx]
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if inst.ongoing_tokens <= avg_load * OVERLOAD_FACTOR:
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return inst, idx
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best_idx, best_score = 0, float("inf")
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for i, inst in enumerate(instances):
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cache_hit = inst.estimate_cache_hit(token_ids)
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new_prefill = max(0, input_length - cache_hit)
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p_tokens = inst.pending_prefill_tokens + new_prefill
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bs = inst.num_requests + 1
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score = p_tokens * bs
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if score < best_score:
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best_score = score
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best_idx = i
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if session_id:
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affinity[session_id] = best_idx
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return instances[best_idx], best_idx
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global_args = None
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combined_instances: list[InstanceState] = []
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prefill_instances: list[InstanceState] = []
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decode_instances: list[InstanceState] = []
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session_affinity: dict[str, int] = {}
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is_pd_sep = False
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_breakdown_log: list[dict] = []
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_offload_inflight = 0 # number of currently in-flight offloaded HEAVY requests
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MAX_OFFLOAD_INFLIGHT = 4 # cap concurrent offloads to prevent P overload
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_p_round_robin_idx = 0 # round-robin counter for P-instance selection
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async def init_prefill_bootstrap(instances: list[InstanceState], ready: asyncio.Event):
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for inst in instances:
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if inst.bootstrap_port is None:
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continue
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while True:
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try:
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await inst.client.get("/health")
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except Exception:
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await asyncio.sleep(1)
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continue
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parsed = urllib.parse.urlparse(str(inst.client.base_url))
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url = f"http://{parsed.hostname}:{inst.bootstrap_port}/query"
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resp = await inst.client.get(url)
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resp.raise_for_status()
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data = resp.json()
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for dp_rank, dp_entry in data.items():
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inst.engine_id[int(dp_rank)] = dp_entry["engine_id"]
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inst.dp_size = len(data)
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print(f"Inited {inst.url} engine_ids={inst.engine_id}")
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break
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ready.set()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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global is_pd_sep
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app.state.ready = asyncio.Event()
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if global_args.combined:
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is_pd_sep = False
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bp_list = [int(p) for p in global_args.bootstrap_ports.split(",") if p.strip()] if global_args.bootstrap_ports else []
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for i, url in enumerate(global_args.combined):
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bp = bp_list[i] if i < len(bp_list) else None
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combined_instances.append(InstanceState(url, bp))
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if global_args.offload and bp_list:
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await init_prefill_bootstrap(combined_instances, app.state.ready)
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else:
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app.state.ready.set()
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policy = getattr(global_args, 'policy', 'linear')
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print(f"Combined mode: {len(combined_instances)} instances, policy={policy}, offload={'ON' if global_args.offload else 'OFF'}")
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else:
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is_pd_sep = True
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for url, bp in global_args.prefill:
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prefill_instances.append(InstanceState(url, bp))
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for url in global_args.decode:
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decode_instances.append(InstanceState(url))
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await init_prefill_bootstrap(prefill_instances, app.state.ready)
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print(f"PD-Sep mode: {len(prefill_instances)}P + {len(decode_instances)}D")
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yield
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for inst in combined_instances + prefill_instances + decode_instances:
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await inst.client.aclose()
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app = FastAPI(lifespan=lifespan)
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@app.post("/v1/completions")
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async def handle_completions(request: Request):
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return await _handle(request, "/v1/completions")
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@app.post("/v1/chat/completions")
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async def handle_chat(request: Request):
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return await _handle(request, "/v1/chat/completions")
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async def _handle(request: Request, api: str):
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if not app.state.ready.is_set():
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raise HTTPException(status_code=503, detail="Service Unavailable")
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req_data = await request.json()
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request_id = str(uuid.uuid4())
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prompt = req_data.get("prompt")
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token_ids = prompt if isinstance(prompt, list) else None
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input_length = len(token_ids) if token_ids else 0
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session_id = request.headers.get("X-Session-Id")
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headers = {"X-Request-Id": request_id}
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api_key = os.environ.get("OPENAI_API_KEY")
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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if is_pd_sep:
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return await _handle_pd_sep(api, req_data, request_id, token_ids,
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input_length, session_id, headers)
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else:
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return await _handle_combined(api, req_data, token_ids,
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input_length, session_id, headers)
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async def _handle_combined(api, req_data, token_ids, input_length, session_id, headers):
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"""Combined mode with adaptive prefill offload (v2).
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WARM/MEDIUM: route to best instance, co-located P+D (no KV transfer).
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HEAVY (kv_both mode): P on least-loaded instance, KV via Mooncake, D on
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session-sticky instance. Only works if instances have kv_role=kv_both.
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Falls back to co-located if --no-offload or instances lack Mooncake.
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"""
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policy = getattr(global_args, 'policy', 'linear') if global_args else 'linear'
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picker = pick_instance_lmetric if policy == 'lmetric' else pick_instance
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best_inst, best_idx = picker(combined_instances, token_ids, session_id,
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input_length, session_affinity)
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cache_hit = best_inst.estimate_cache_hit(token_ids)
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estimated_new = max(0, input_length - cache_hit)
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breakdown = {
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"request_id": headers.get("X-Request-Id", ""),
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"input_length": input_length,
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"estimated_new_tokens": estimated_new,
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"cache_hit": cache_hit,
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"t_proxy_recv": _time.monotonic(),
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}
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offload_enabled = getattr(global_args, 'offload', False) if global_args else False
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has_bootstrap = any(inst.bootstrap_port for inst in combined_instances)
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# Elastic offload decision: offload only when it helps
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use_offload = False
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offload_reason = "disabled"
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if estimated_new >= HEAVY_THRESHOLD and offload_enabled and has_bootstrap and len(combined_instances) >= 2:
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d_inst = best_inst
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p_candidates = [(i, inst) for i, inst in enumerate(combined_instances) if inst is not d_inst]
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avg_load = max(sum(i.ongoing_tokens for i in combined_instances) / len(combined_instances), 1.0)
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# Round-robin P selection with overload skip (spreads P-role evenly)
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global _offload_inflight, _p_round_robin_idx
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p_inst = None
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for _ in range(len(p_candidates)):
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_p_round_robin_idx = (_p_round_robin_idx + 1) % len(p_candidates)
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candidate = p_candidates[_p_round_robin_idx][1]
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if candidate.ongoing_tokens < avg_load * OVERLOAD_FACTOR:
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p_inst = candidate
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break
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if p_inst is None:
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p_inst = min(p_candidates, key=lambda x: x[1].ongoing_tokens)[1]
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if _offload_inflight >= MAX_OFFLOAD_INFLIGHT:
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offload_reason = "max_concurrent_reached"
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elif p_inst.ongoing_tokens >= HEAVY_THRESHOLD * 2:
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offload_reason = "p_saturated"
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else:
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use_offload = True
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offload_reason = "offload_accepted"
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_offload_inflight += 1
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if use_offload:
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d_idx = best_idx
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p_inst.ongoing_tokens += input_length # reserve immediately
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p_inst.pending_prefill_tokens += estimated_new
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p_inst.num_requests += 1
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breakdown["route_class"] = "HEAVY_P2P"
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breakdown["offload_reason"] = offload_reason
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breakdown["p_inst"] = p_inst.url
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breakdown["d_inst"] = d_inst.url
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breakdown["p_load"] = p_inst.ongoing_tokens
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breakdown["d_load"] = d_inst.ongoing_tokens
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if session_id:
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session_affinity[session_id] = d_idx
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return await _handle_heavy_offload(api, req_data, headers, token_ids,
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input_length, p_inst, d_inst, breakdown)
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else:
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if estimated_new >= HEAVY_THRESHOLD:
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breakdown["route_class"] = "HEAVY_COLO"
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breakdown["offload_reason"] = offload_reason
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else:
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breakdown["route_class"] = "WARM" if estimated_new < 5000 else "MEDIUM"
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inst = best_inst
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breakdown["routed_to"] = inst.url
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breakdown["policy"] = policy
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inst.ongoing_tokens += input_length
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inst.pending_prefill_tokens += estimated_new
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inst.num_requests += 1
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async def generate():
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prefill_done = False
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try:
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async with inst.client.stream("POST", api, json=req_data, headers=headers) as resp:
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resp.raise_for_status()
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async for chunk in resp.aiter_bytes():
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if not prefill_done:
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inst.pending_prefill_tokens -= estimated_new
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inst.ongoing_decode_tokens += input_length
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breakdown["t_first_token"] = _time.monotonic()
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prefill_done = True
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yield chunk
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inst.record_prefix(token_ids)
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finally:
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if not prefill_done:
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inst.pending_prefill_tokens -= estimated_new
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else:
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inst.ongoing_decode_tokens -= input_length
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inst.ongoing_tokens -= input_length
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inst.num_requests -= 1
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breakdown["t_done"] = _time.monotonic()
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_breakdown_log.append(breakdown)
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return StreamingResponse(generate(), media_type="text/event-stream")
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async def _handle_heavy_offload(api, req_data, headers, token_ids, input_length,
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p_inst, d_inst, breakdown):
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"""HEAVY request: prefill on p_inst, KV via Mooncake, decode on d_inst."""
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request_id = headers.get("X-Request-Id", "")
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# Step 1: Await prefill on p_inst (ongoing_tokens already reserved by caller)
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breakdown["t_prefill_sent"] = _time.monotonic()
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try:
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prefill_data = req_data.copy()
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prefill_data["kv_transfer_params"] = {
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"do_remote_decode": True,
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"do_remote_prefill": False,
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"transfer_id": "xfer-" + request_id,
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}
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prefill_data["stream"] = False
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prefill_data["max_tokens"] = 1
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prefill_data.pop("max_completion_tokens", None)
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prefill_data.pop("stream_options", None)
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p_headers = {**headers, "X-data-parallel-rank": "0"}
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resp = await p_inst.client.post(api, json=prefill_data, headers=p_headers)
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resp.raise_for_status()
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await resp.aclose()
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p_inst.record_prefix(token_ids)
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breakdown["t_prefill_done"] = _time.monotonic()
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except Exception as e:
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breakdown["t_prefill_done"] = _time.monotonic()
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breakdown["error"] = str(e)
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_breakdown_log.append(breakdown)
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global _offload_inflight
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_offload_inflight = max(0, _offload_inflight - 1)
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p_inst.num_requests -= 1
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raise HTTPException(status_code=502, detail="Prefill failed: %s" % e)
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finally:
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p_inst.ongoing_tokens -= input_length
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p_inst.pending_prefill_tokens -= breakdown.get("estimated_new_tokens", 0)
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_offload_inflight = max(0, _offload_inflight - 1)
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p_inst.num_requests -= 1
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# Step 2: Stream decode on d_inst (pulls KV from Mooncake)
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d_inst.ongoing_tokens += input_length
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d_inst.ongoing_decode_tokens += input_length
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d_inst.num_requests += 1
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breakdown["t_decode_sent"] = _time.monotonic()
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parsed = urllib.parse.urlparse(str(p_inst.client.base_url))
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bootstrap_addr = "http://%s:%s" % (parsed.hostname, p_inst.bootstrap_port)
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decode_data = req_data.copy()
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decode_data["kv_transfer_params"] = {
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"do_remote_decode": False,
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"do_remote_prefill": True,
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"remote_bootstrap_addr": bootstrap_addr,
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"remote_engine_id": p_inst.engine_id.get(0, ""),
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"transfer_id": "xfer-" + request_id,
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}
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async def generate():
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first_token = True
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try:
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async with d_inst.client.stream("POST", api, json=decode_data, headers=headers) as resp:
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resp.raise_for_status()
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async for chunk in resp.aiter_bytes():
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if first_token:
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breakdown["t_first_token"] = _time.monotonic()
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first_token = False
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yield chunk
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finally:
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d_inst.ongoing_tokens -= input_length
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d_inst.ongoing_decode_tokens -= input_length
|
||
d_inst.num_requests -= 1
|
||
breakdown["t_done"] = _time.monotonic()
|
||
_breakdown_log.append(breakdown)
|
||
|
||
return StreamingResponse(generate(), media_type="application/json")
|
||
|
||
|
||
async def _send_prefill_async(p_inst, api, prefill_data, p_headers, token_ids,
|
||
input_length, breakdown):
|
||
"""Fire-and-forget prefill: send and don't block caller."""
|
||
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:
|
||
breakdown["t_prefill_done"] = _time.monotonic()
|
||
breakdown["prefill_error"] = True
|
||
finally:
|
||
p_inst.ongoing_tokens -= input_length
|
||
|
||
|
||
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)
|
||
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()
|
||
|
||
if global_args.fire_and_forget:
|
||
asyncio.create_task(_send_prefill_async(
|
||
p_inst, api, prefill_data, p_headers, token_ids, input_length, breakdown))
|
||
else:
|
||
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,
|
||
"ongoing_tokens": inst.ongoing_tokens,
|
||
"pending_prefill_tokens": inst.pending_prefill_tokens,
|
||
"ongoing_decode_tokens": inst.ongoing_decode_tokens,
|
||
"num_requests": inst.num_requests,
|
||
"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("--fire-and-forget", action="store_true",
|
||
help="Send prefill async, don't await before decode")
|
||
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)")
|
||
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()
|
||
HEAVY_THRESHOLD = global_args.heavy_threshold
|
||
uvicorn.run(app, host=global_args.host, port=global_args.port)
|