Gahow Wang
5c66f500fc
The cache_gate_ratio=0.3 check blocked 83/112 HEAVY requests (75%) because they were cold (cache_ratio=0). But with direct RDMA read, D reads C's cached blocks via RDMA regardless of cache ratio — the gate was protecting against the OLD flow (C does prefill + push). Also fixed cost model: offload_cost now reflects direct read reality: OLD: P_queue + P_full_prefill + RDMA (P has no cache → expensive) NEW: D_queue + RDMA_read + D_local_prefill(new_tokens) Offload wins when C_s queue > RDMA_overhead (~2s). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
676 lines
27 KiB
Python
676 lines
27 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 collections import OrderedDict
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from contextlib import asynccontextmanager
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from dataclasses import dataclass
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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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@dataclass
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class Settings:
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"""Runtime-tunable knobs. Populated from argparse in __main__.
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All routing/offload code reads from the SETTINGS singleton so that
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CLI overrides survive even when the module is imported as a library
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(e.g. by tests/) and __main__ does not run.
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"""
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heavy_threshold: int = 20000 # new-token cutoff for HEAVY classification
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overload_factor: float = 2.0 # break session affinity above this * avg load
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max_offload_inflight: int = 4 # global cap on concurrent P-role offloads
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cache_gate_ratio: float = 0.3 # min cache_hit/input ratio to allow offload
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prefill_throughput: float = 7000.0 # tokens/s per GPU (H20 measurement)
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rdma_overhead_s: float = 2.0 # RDMA transfer + decode-start overhead
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cache_capacity_blocks: int = 200000 # per-instance LRU cap on shadow cached_blocks
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SETTINGS = Settings()
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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.active_p_offloads = 0 # number of HEAVY prefills this instance is doing for others
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self.engine_id: dict[int, str] = {}
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self.dp_size = 1
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# OrderedDict acts as an LRU keyed by block hash; value is unused.
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self.cached_blocks: OrderedDict[int, None] = OrderedDict()
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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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self.cached_blocks.move_to_end(bh) # LRU touch on hit
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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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bh = hash(tuple(token_ids[i:i + BLOCK_SIZE]))
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if bh in self.cached_blocks:
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self.cached_blocks.move_to_end(bh)
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else:
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self.cached_blocks[bh] = None
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if len(self.cached_blocks) > SETTINGS.cache_capacity_blocks:
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self.cached_blocks.popitem(last=False)
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def _p_offload_penalty(inst: InstanceState) -> int:
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"""Penalty for instances currently doing P-role offloaded prefills.
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When an instance is busy with offloaded HEAVY prefills for other
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instances, we want to steer WARM/MEDIUM requests away from it so
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its GPU is dedicated to prefill (soft PD separation).
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"""
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if inst.active_p_offloads <= 0:
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return 0
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return inst.active_p_offloads * SETTINGS.heavy_threshold
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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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or busy with P-role offloads, in which case pick least-loaded.
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Turn 1: pick instance with best score (load + cache combined).
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Instances doing P-role offloads get a large penalty to steer
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WARM/MEDIUM traffic away.
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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 * SETTINGS.overload_factor
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and inst.active_p_offloads == 0):
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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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score = (inst.ongoing_tokens + _p_offload_penalty(inst)
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- 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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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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Pure per-request load-based routing, no session affinity.
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P = pending_prefill_tokens + (input_length - cache_hit)
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BS = num_requests (current batch size)
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"""
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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
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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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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 is namespace-isolated: combined-mode and pd-sep mode index
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# different instance lists, so a shared dict could mis-route after a mode switch.
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session_affinity_combined: dict[str, int] = {}
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session_affinity_prefill: dict[str, int] = {}
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# Backwards-compat alias used by /stats etc.
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session_affinity = session_affinity_combined
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is_pd_sep = False
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_breakdown_log: list[dict] = []
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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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async def _reconcile_loop():
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"""Periodic safety net for shadow state.
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StreamingResponse generators decrement load counters in their finally
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block, but if a client disconnects before the body is consumed the
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generator is never entered and the decrement is lost. Clamp negative
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drift every minute so router scores stay sane. This does not replace
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proper exact-state syncing with vLLM (see TODO.md item 6).
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"""
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while True:
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try:
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await asyncio.sleep(60)
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except asyncio.CancelledError:
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return
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for inst in combined_instances + prefill_instances + decode_instances:
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if inst.ongoing_tokens < 0:
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inst.ongoing_tokens = 0
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if inst.ongoing_decode_tokens < 0:
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inst.ongoing_decode_tokens = 0
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if inst.pending_prefill_tokens < 0:
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inst.pending_prefill_tokens = 0
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if inst.num_requests < 0:
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inst.num_requests = 0
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if inst.active_p_offloads < 0:
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inst.active_p_offloads = 0
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def _verify_vllm_patch():
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"""Startup self-check for patches/0001-fix-kv-transfer-abort-race.patch.
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The patch turns an `assert req_id in self.requests` into a soft warn so
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that engines do not crash on the KV-transfer abort race (see REPORT
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§3.x). If somebody upgrades vLLM without re-applying the patch, the
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assert returns and elastic mode dies under load. Print a loud warning
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so we catch the regression before the first HEAVY request.
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"""
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try:
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import inspect
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from vllm.v1.core.sched.scheduler import Scheduler
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src = inspect.getsource(Scheduler)
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if "assert req_id in self.requests" in src:
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print("WARNING: vLLM scheduler still contains the unpatched "
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"`assert req_id in self.requests` line; expect engine "
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"death on KV-transfer abort race. Apply "
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"patches/0001-fix-kv-transfer-abort-race.patch.")
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else:
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print("vLLM patch self-check: kv-transfer-abort assert is patched.")
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except Exception as exc:
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print(f"vLLM patch self-check skipped: {exc!r}")
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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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_verify_vllm_patch()
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reconcile_task = asyncio.create_task(_reconcile_loop())
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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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# Bootstrap combined instances for offload (need engine_ids for KV transfer)
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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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reconcile_task.cancel()
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try:
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await reconcile_task
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except asyncio.CancelledError:
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pass
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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 V2 P2P offload.
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WARM/MEDIUM: route to best instance, co-located P+D (no KV transfer).
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HEAVY: C_s (session-sticky, has cache) does FAST prefill,
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D (least-loaded C, D != C_s) pulls KV via Mooncake and decodes.
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Offload only when D is meaningfully less loaded than C_s.
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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_combined)
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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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# Runtime cost-model offload gate: compare co-located vs offload latency
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# Co-located = queue(C_s) + prefill(new_tokens)
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# Offload = queue(P) + prefill(P_new_tokens) + RDMA_overhead
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offload_enabled = getattr(global_args, 'offload', False) and len(combined_instances) >= 2
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use_offload = False
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offload_reason = "offload_disabled"
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if estimated_new >= SETTINGS.heavy_threshold and offload_enabled:
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cache_ratio = cache_hit / max(input_length, 1)
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current_offloads = sum(c.active_p_offloads for c in combined_instances)
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# P candidate: least-loaded instance excluding C_s, preferring instances
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# not already shouldering an active P-role offload.
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def _p_pick_score(c: InstanceState) -> int:
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return c.ongoing_tokens + c.active_p_offloads * SETTINGS.heavy_threshold
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p_candidate = min(
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(c for c in combined_instances if c is not best_inst),
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key=_p_pick_score,
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)
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# D candidate: least-loaded excluding both C_s and P
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remaining = [c for c in combined_instances if c is not best_inst and c is not p_candidate]
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d_candidate = min(remaining, key=lambda c: c.ongoing_tokens) if remaining else p_candidate
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# Cost model: compare co-located vs direct-RDMA-read offload
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# Co-located: queue on C_s + prefill new tokens on C_s
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cs_queue = best_inst.pending_prefill_tokens / SETTINGS.prefill_throughput
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colocated_cost = cs_queue + estimated_new / SETTINGS.prefill_throughput
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# Direct RDMA read: D reads C_s's cached blocks via RDMA + D prefills new tokens locally
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# D's queue + RDMA read time + D local prefill of new tokens only
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d_queue = d_candidate.pending_prefill_tokens / SETTINGS.prefill_throughput
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offload_cost = d_queue + SETTINGS.rdma_overhead_s + estimated_new / SETTINGS.prefill_throughput
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breakdown["cache_ratio"] = cache_ratio
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breakdown["colocated_cost"] = round(colocated_cost, 2)
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breakdown["offload_cost"] = round(offload_cost, 2)
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if current_offloads >= SETTINGS.max_offload_inflight:
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offload_reason = "cap_reached_%d" % current_offloads
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elif offload_cost < colocated_cost:
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use_offload = True
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offload_reason = "cost_model_%.1fvs%.1f" % (offload_cost, colocated_cost)
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else:
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offload_reason = "colocated_cheaper_%.1fvs%.1f" % (colocated_cost, offload_cost)
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if use_offload:
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# Direct RDMA read: D reads cached KV from C_s's GPU, no request to C_s
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c_inst = best_inst # has cache (not doing any work)
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d_inst = d_candidate
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d_idx = combined_instances.index(d_inst)
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d_inst.ongoing_tokens += input_length
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d_inst.pending_prefill_tokens += estimated_new
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d_inst.num_requests += 1
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c_inst.active_p_offloads += 1
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breakdown["route_class"] = "HEAVY_OFFLOAD"
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breakdown["offload_reason"] = offload_reason
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breakdown["c_inst"] = c_inst.url
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breakdown["d_inst"] = d_inst.url
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breakdown["cache_hit_tokens"] = cache_hit
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if session_id:
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session_affinity_combined[session_id] = d_idx
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return await _handle_direct_read_offload(
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api, req_data, headers, token_ids, input_length,
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c_inst, d_inst, cache_hit, estimated_new, breakdown)
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else:
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if estimated_new >= SETTINGS.heavy_threshold:
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breakdown["route_class"] = "HEAVY_COLO"
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breakdown["offload_reason"] = offload_reason
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elif estimated_new < 5000:
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breakdown["route_class"] = "WARM"
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else:
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breakdown["route_class"] = "MEDIUM"
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inst = best_inst
|
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breakdown["routed_to"] = inst.url
|
||
breakdown["policy"] = policy
|
||
inst.ongoing_tokens += input_length
|
||
inst.pending_prefill_tokens += estimated_new
|
||
inst.num_requests += 1
|
||
|
||
async def generate():
|
||
prefill_done = False
|
||
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)
|
||
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()
|
||
SETTINGS.heavy_threshold = global_args.heavy_threshold
|
||
SETTINGS.overload_factor = global_args.overload_factor
|
||
SETTINGS.max_offload_inflight = global_args.max_offload_inflight
|
||
SETTINGS.cache_gate_ratio = global_args.cache_gate_ratio
|
||
print(
|
||
"SETTINGS: heavy=%d overload=%.1f max_offload=%d cache_gate=%.2f"
|
||
% (SETTINGS.heavy_threshold, SETTINGS.overload_factor,
|
||
SETTINGS.max_offload_inflight, SETTINGS.cache_gate_ratio)
|
||
)
|
||
uvicorn.run(app, host=global_args.host, port=global_args.port)
|