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P2: real engine-state feed replaces stale shadow counters for migration targeting

Browse Source 
vLLM scheduler publishes real state (running/waiting, KV free, and the
max-in-progress-prefill signal /metrics lacks) to a tmpfs/redis store ~20Hz;
router reads it and avoids GIL-stall (mid-large-prefill) + KV-capacity-wall
targets, using real load over 30s-stale shadow counters. Components:
engine_state.py (canonical+reader), instrument_engine_state.py (scheduler
patch, file/redis writer), migration_target.py (scorer), proxy wiring
(--engine-state-uri, off=unchanged). All unit-tested without GPU; not yet
run live. See P2_ENGINE_STATE.md.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Gahow Wang
2026-05-28 20:01:26 +08:00
parent 19191940e6
commit be948d32b8
5 changed files with 610 additions and 8 deletions
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61
microbench/connector_tax/layerwise/P2_ENGINE_STATE.md Normal file
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@@ -0,0 +1,61 @@
# P2: real engine-state feed for migration target selection
Problem: the router (`cache_aware_proxy.py`) decides migration targets from
**shadow counters** it maintains itself (incremented at dispatch, decremented
at completion) and reconciles to vLLM `/metrics` only every **30 s**
(`_reconcile_loop`). So every routing/migration decision is on stale state.
Worse, the signal that predicts the ~45% control-plane stall — *is the target
mid-large-prefill?* (a big prefill holds the GIL and starves the mooncake
receiver_loop) — isn't visible at all, and `/metrics` doesn't expose it either.
Fix: vLLM publishes **real** per-engine state to a shared store ~20 Hz; the
router reads ground truth and avoids GIL-stall / capacity-wall targets.
## Components (all unit-tested without GPUs)
- `engine_state.py` — canonical `compute_snapshot(scheduler, id)`, `StateWriter`,
`StateReader`. Schema per engine: `ts, num_running, num_waiting,
gpu_blocks_total/free, gpu_kv_used_frac, pending_prefill_tokens,
ongoing_decode_tokens, num_prefilling, max_prefill_remaining`.
- `instrument_engine_state.py` — vLLM `Scheduler` patch (apply/revert markers
`ES_INSTRUMENT_*`): a daemon thread publishes the snapshot every
`AGENTIC_ENGINE_STATE_PERIOD_MS` (50 ms) off the forward hot path. Inlined
writer (engine process needs no repo import). Coexists with MB5.
- `migration_target.py` — pure target scorer: avoid `max_prefill_remaining ≥
es_big_prefill_threshold` (GIL stall) and `gpu_kv_used_frac ≥ es_kv_wall_frac`
(capacity wall), then rank by cache-richness and **real** load.
- `cache_aware_proxy.WRITEMODE.py` — wired: `InstanceState.real_state`,
`_engine_state_poll_loop` (instance i ← `engine_{i}`), `_real_load`/Gate-3 and
Mechanism-B now real-state-aware. `--engine-state-uri` flag; off ⇒ identical
to before (shadow only).
Transport (`AGENTIC_ENGINE_STATE_URI` / `--engine-state-uri`):
`file:///dev/shm/agentic_engine_state` (default, zero-dep, single-node) or
`redis://host:port/0` (multi-node; needs redis-py + server — not installed on
dash0, so file backend is the working default).
## Tests (no GPU)
- `compute_snapshot` field math (mock scheduler): running/waiting,
max_prefill_remaining, pending, decode, kv_used_frac.
- writer→reader round-trip + staleness drop (file backend).
- target scorer: 5 cases incl. *avoid GIL-stall target even when its shadow
load is lower*, *real load beats stale shadow*, *cache-rich wins*,
*avoid KV wall*, *graceful fallback when feed missing*.
- end-to-end: publish 8 engines (one mid-130k-prefill) → proxy inlined reader →
target selection avoids it.
## Enabling in a GPU run (when free)
1. `instrument_engine_state.py --apply` on the dash0 venv.
2. `export AGENTIC_ENGINE_STATE_URI=file:///dev/shm/agentic_engine_state`
before the launcher (vLLM instances inherit it; `AGENTIC_WORKER_ID=engine_{i}`
already set by `b3_isolated_policy.sh` → publishes as `engine_{i}`).
3. Proxy: `EXTRA_PROXY_ARGS="--engine-state-uri file:///dev/shm/agentic_engine_state ..."`.
4. Revert the patch + `rm -rf /dev/shm/agentic_engine_state` after.
## Status / scope
- Built + unit-tested; NOT yet run against live engines (GPU busy).
- Scoped to **migration target selection** (the P2 ask). The same real-load
signal could also de-stale the base `pick_instance_unified_hybrid` LMetric
fallback (the 8007-hotspot class from UNIFIED_ABLATION) — follow-up.
- TP=1 only (one EngineCore/instance → one publisher/engine_id). TP>1 needs
per-rank ids.

104
microbench/connector_tax/layerwise/cache_aware_proxy.WRITEMODE.py
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@@ -111,6 +111,13 @@ class Settings:
v3_recent_mig_weight: float = 1.0 # how many "virtual requests" each
# recent migration counts as
# P2: real engine-state feed (replaces 30s-stale shadow counters for
# migration target selection). Empty = disabled (use shadow only).
engine_state_uri: str = "" # file:///dev/shm/... or redis://...
engine_state_period_ms: int = 50 # router poll period
es_big_prefill_threshold: int = 16000 # target mid-prefill >= this => avoid (GIL stall)
es_kv_wall_frac: float = 0.90 # target KV usage >= this => avoid (capacity wall)
# Direction B knob: LMetric fallback adds decode-token penalty to score.
# score = (pending_prefill + new + lmetric_decode_weight * ongoing_decode_tok) * num_req
# Empirical iter-time slope on H100 + Qwen3-30B-A3B: each decode token in
@@ -206,6 +213,9 @@ class InstanceState:
# recent-migration count over a sliding window, preventing back-to-back
# decisions from clustering on the same dst.
self.recent_mig_targeted_at: deque[float] = deque(maxlen=64)
# P2: latest real engine state (from the engine-state feed), or None
# when the feed is disabled/stale. Set by _engine_state_poll_loop.
self.real_state: dict | None = None
def estimate_cache_hit(self, token_ids: list[int] | None) -> int:
if not token_ids or len(token_ids) < BLOCK_SIZE:
@@ -672,20 +682,29 @@ def pick_instance_unified_v3(
now_mono = _time.monotonic()
cutoff = now_mono - SETTINGS.v3_recent_mig_window_s
def _real_load(inst):
# P2: prefer REAL engine state (running+waiting) over the proxy's
# 30s-stale shadow num_requests, when the engine-state feed is fresh.
rs = getattr(inst, "real_state", None)
if rs is not None:
return rs.get("num_running", 0) + rs.get("num_waiting", 0)
return inst.num_requests
def effective_load(inst):
# Drop expired entries lazily.
while inst.recent_mig_targeted_at and inst.recent_mig_targeted_at[0] < cutoff:
inst.recent_mig_targeted_at.popleft()
recent = len(inst.recent_mig_targeted_at)
return inst.num_requests + recent * SETTINGS.v3_recent_mig_weight
return _real_load(inst) + recent * SETTINGS.v3_recent_mig_weight
ph_load = _real_load(prefill_host)
threshold_loaded = max(1,
int(prefill_host.num_requests * SETTINGS.v3_target_load_ratio))
int(ph_load * SETTINGS.v3_target_load_ratio))
candidates = [
(i, inst) for i, inst in enumerate(instances)
if i != prefill_idx
and effective_load(inst) < threshold_loaded
and effective_load(inst) <= prefill_host.num_requests - SETTINGS.v3_min_load_gap
and effective_load(inst) <= ph_load - SETTINGS.v3_min_load_gap
]
if not candidates:
decision["v3_reason"] = (
@@ -700,11 +719,23 @@ def pick_instance_unified_v3(
# cache_hit DESC (more cache = less KV to transfer), then by effective_load
# (which includes recent-migration penalty), then by ongoing_tokens.
if SETTINGS.v3_prefer_cache_target:
decode_target_idx, decode_target = min(
candidates,
key=lambda x: (-x[1].estimate_cache_hit(token_ids),
effective_load(x[1]),
x[1].ongoing_tokens))
def _tgt_key(x):
# P2: avoid a target that is mid-large-prefill (holds the GIL,
# stalls the mooncake receiver_loop = the ~45% control-plane
# residual layer-wise can't fix) or near the KV capacity wall,
# before ranking by cache-richness and real load.
inst = x[1]
ch = inst.estimate_cache_hit(token_ids)
rs = getattr(inst, "real_state", None)
stalls = near_wall = 0
if rs is not None:
if int(rs.get("max_prefill_remaining", 0)) >= SETTINGS.es_big_prefill_threshold:
stalls = 1
f = rs.get("gpu_kv_used_frac", 0.0) or 0.0
if float(f) >= SETTINGS.es_kv_wall_frac:
near_wall = 1
return (stalls, near_wall, -ch, effective_load(inst), inst.ongoing_tokens)
decode_target_idx, decode_target = min(candidates, key=_tgt_key)
else:
decode_target_idx, decode_target = min(
candidates, key=lambda x: (effective_load(x[1]), x[1].ongoing_tokens))
@@ -857,6 +888,57 @@ async def _fetch_vllm_inflight(inst: "InstanceState") -> tuple[int, int] | None:
return running, waiting
def _engine_state_read_all(uri: str, max_age_s: float = 2.0) -> dict:
"""P2 reader (inlined; mirrors engine_state.StateReader). Returns
{engine_id: state}, dropping records older than max_age_s."""
now = _time.time()
out: dict = {}
try:
if uri.startswith("file://"):
import glob
d = uri[len("file://"):]
for p in glob.glob(os.path.join(d, "*.json")):
try:
s = json.load(open(p))
except Exception:
continue
if now - s.get("ts", 0) <= max_age_s:
out[s.get("engine_id", os.path.basename(p)[:-5])] = s
elif uri.startswith("redis://"):
import redis
r = redis.Redis.from_url(uri)
for k in r.scan_iter("engine_state:*"):
v = r.get(k)
if not v:
continue
s = json.loads(v)
if now - s.get("ts", 0) <= max_age_s:
out[s.get("engine_id")] = s
except Exception:
pass
return out
async def _engine_state_poll_loop():
"""P2: poll the engine-state feed and attach real_state to each instance.
Instance i is keyed engine_{i} (matches AGENTIC_WORKER_ID in the launcher).
"""
uri = SETTINGS.engine_state_uri
if not uri:
return
period = max(0.01, SETTINGS.engine_state_period_ms / 1000.0)
insts = combined_instances or (prefill_instances + decode_instances)
print(f"[engine-state] polling {uri} every {period*1000:.0f}ms for {len(insts)} instances")
while True:
try:
await asyncio.sleep(period)
except asyncio.CancelledError:
return
states = await asyncio.to_thread(_engine_state_read_all, uri)
for i, inst in enumerate(insts):
inst.real_state = states.get(f"engine_{i}")
async def _reconcile_loop():
"""Periodic shadow-state reconciliation against vLLM /metrics truth.
@@ -960,6 +1042,7 @@ async def lifespan(app: FastAPI):
_verify_vllm_patch()
reconcile_task = asyncio.create_task(_reconcile_loop())
engine_state_task = asyncio.create_task(_engine_state_poll_loop())
if global_args.combined:
is_pd_sep = False
@@ -1720,6 +1803,10 @@ def parse_args():
" penalised. 0 = original behavior; 0.01 is a reasonable start.")
p.add_argument("--overload-factor", type=float, default=2.0,
help="Break session affinity when instance load > factor * avg")
p.add_argument("--engine-state-uri", type=str, default="",
help="P2: real engine-state feed for migration target "
"selection (file:///dev/shm/... or redis://...). "
"Empty=disabled (shadow counters only).")
# The four flags below are accepted for bench.sh backward compatibility but
# have no effect after the PD-sep offload path was retired (REPORT §3.9,
# commits 4c583f2 / cc6e562). Removing them would break scripts/bench.sh and
@@ -1752,6 +1839,7 @@ if __name__ == "__main__":
global_args = parse_args()
SETTINGS.heavy_threshold = global_args.heavy_threshold
SETTINGS.overload_factor = global_args.overload_factor
SETTINGS.engine_state_uri = getattr(global_args, 'engine_state_uri', '') or ''
SETTINGS.max_offload_inflight = global_args.max_offload_inflight
SETTINGS.cache_gate_ratio = global_args.cache_gate_ratio
SETTINGS.decode_iteration_s = getattr(global_args, 'decode_iteration_s', 0.05)

140
microbench/connector_tax/layerwise/engine_state.py Normal file
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@@ -0,0 +1,140 @@
#!/usr/bin/env python3
"""Engine-state store: canonical snapshot + writer/reader, shared schema.
The vLLM scheduler patch (instrument_engine_state.py) inlines a faithful copy
of `compute_snapshot` + the file/redis writer (engine process needs no repo
import). The router (cache_aware_proxy) imports `StateReader` here to read the
real per-engine state instead of its stale shadow counters.
Schema (one record per engine, key = engine_id):
ts, engine_id, num_running, num_waiting, gpu_blocks_total, gpu_blocks_free,
gpu_kv_used_frac, pending_prefill_tokens, ongoing_decode_tokens,
num_prefilling, max_prefill_remaining
Transport URIs:
file:///dev/shm/agentic_engine_state (default; atomic temp+rename)
redis://host:port/0 (optional; needs redis-py)
"""
from __future__ import annotations
import json
import os
import time
def compute_snapshot(scheduler, engine_id: str) -> dict:
"""Cheap O(batch) read of routing-relevant real state from a live
vLLM V1 Scheduler (duck-typed for testability)."""
try:
pool = scheduler.kv_cache_manager.block_pool
total = int(pool.num_gpu_blocks)
free = int(pool.get_num_free_blocks())
except Exception:
total = free = -1
n_run = pend = dec = n_pref = max_pref = 0
try:
for r in scheduler.running:
n_run += 1
npr = int(getattr(r, "num_prompt_tokens", 0))
nct = int(getattr(r, "num_computed_tokens", 0))
if nct < npr:
rem = npr - nct
pend += rem
n_pref += 1
max_pref = max(max_pref, rem)
else:
dec += int(getattr(r, "num_tokens", 0))
except Exception:
pass
n_wait = 0
try:
n_wait = len(scheduler.waiting) + len(getattr(scheduler, "skipped_waiting", []))
for r in list(scheduler.waiting):
pend += max(0, int(getattr(r, "num_prompt_tokens", 0))
- int(getattr(r, "num_computed_tokens", 0)))
except Exception:
pass
used = ((total - free) / total) if (total and total > 0) else -1.0
return {
"ts": time.time(),
"engine_id": engine_id,
"num_running": n_run,
"num_waiting": int(n_wait),
"gpu_blocks_total": total,
"gpu_blocks_free": free,
"gpu_kv_used_frac": used,
"pending_prefill_tokens": int(pend),
"ongoing_decode_tokens": int(dec),
"num_prefilling": n_pref,
"max_prefill_remaining": int(max_pref),
}
class StateWriter:
def __init__(self, uri: str, engine_id: str):
self.engine_id = engine_id
self.kind = None
if uri.startswith("file://"):
self.kind = "file"
self.dir = uri[len("file://"):]
os.makedirs(self.dir, exist_ok=True)
self.path = os.path.join(self.dir, f"{engine_id}.json")
self.tmp = self.path + f".tmp.{os.getpid()}"
elif uri.startswith("redis://"):
self.kind = "redis"
import redis
self.r = redis.Redis.from_url(uri)
self.key = f"engine_state:{engine_id}"
else:
raise ValueError(f"unsupported engine-state URI: {uri}")
def publish(self, state: dict):
if self.kind == "file":
with open(self.tmp, "w") as f:
f.write(json.dumps(state))
os.replace(self.tmp, self.path)
elif self.kind == "redis":
self.r.set(self.key, json.dumps(state), ex=5)
class StateReader:
"""Router-side reader. read_all() returns {engine_id: state}, dropping
records older than max_age_s (so a dead/hung engine is ignored)."""
def __init__(self, uri: str, max_age_s: float = 2.0):
self.uri = uri
self.max_age_s = max_age_s
self.kind = None
if uri.startswith("file://"):
self.kind = "file"
self.dir = uri[len("file://"):]
elif uri.startswith("redis://"):
self.kind = "redis"
import redis
self.r = redis.Redis.from_url(uri)
else:
raise ValueError(f"unsupported engine-state URI: {uri}")
def read_all(self) -> dict[str, dict]:
now = time.time()
out: dict[str, dict] = {}
try:
if self.kind == "file":
import glob
for p in glob.glob(os.path.join(self.dir, "*.json")):
try:
s = json.load(open(p))
except Exception:
continue
if now - s.get("ts", 0) <= self.max_age_s:
out[s.get("engine_id", os.path.basename(p)[:-5])] = s
elif self.kind == "redis":
for k in self.r.scan_iter("engine_state:*"):
v = self.r.get(k)
if not v:
continue
s = json.loads(v)
if now - s.get("ts", 0) <= self.max_age_s:
out[s.get("engine_id")] = s
except Exception:
pass
return out

234
microbench/connector_tax/layerwise/instrument_engine_state.py Normal file
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@@ -0,0 +1,234 @@
#!/usr/bin/env python3
"""Patch vLLM V1 scheduler to publish REAL engine state to a shared store,
so the global router reads ground truth instead of its own stale shadow
counters (reconciled only every 30s).
Published per engine (key = AGENTIC_ENGINE_ID), throttled ~20 Hz from a
daemon thread (off the forward hot path):
{ts, num_running, num_waiting, gpu_blocks_total, gpu_blocks_free,
gpu_kv_used_frac, pending_prefill_tokens, ongoing_decode_tokens,
num_prefilling, max_prefill_remaining}
`max_prefill_remaining` is the key signal /metrics does NOT expose: the
largest in-progress prefill on the engine. A big in-progress prefill holds
the GIL and stalls the mooncake receiver_loop — so the router should avoid
migrating KV to such an instance (P2).
Transport (env AGENTIC_ENGINE_STATE_URI):
file:///dev/shm/agentic_engine_state (default; atomic temp+rename)
redis://host:port/0 (optional; needs redis-py + server)
Self-contained (inlined writer) so the engine process needs no repo import.
Apply/revert markers: # ES_INSTRUMENT_START / # ES_INSTRUMENT_END.
Usage:
python instrument_engine_state.py --apply [--venv PATH]
python instrument_engine_state.py --revert [--venv PATH]
python instrument_engine_state.py --check [--venv PATH]
"""
from __future__ import annotations
import argparse
import re
from pathlib import Path
DEFAULT_VENV = Path("/home/admin/cpfs/wjh/agentic-kv/.venv")
TARGET_REL = "lib/python3.12/site-packages/vllm/v1/core/sched/scheduler.py"
START = "# ES_INSTRUMENT_START"
END = "# ES_INSTRUMENT_END"
# ---- Patch 1: header (writer + publisher thread), before class Scheduler ----
HEADER_ANCHOR = "class Scheduler(SchedulerInterface):"
HEADER = f'''{START}
import json as _es_json
import os as _es_os
import threading as _es_threading
import time as _es_time
_ES_URI = _es_os.environ.get("AGENTIC_ENGINE_STATE_URI", "")
_ES_ID = _es_os.environ.get("AGENTIC_ENGINE_ID") or _es_os.environ.get(
"AGENTIC_WORKER_ID", f"engine_{{_es_os.getpid()}}")
_ES_PERIOD_S = float(_es_os.environ.get("AGENTIC_ENGINE_STATE_PERIOD_MS", "50")) / 1000.0
class _ESWriter:
"""Pluggable state writer: file:// (atomic temp+rename) or redis://."""
def __init__(self, uri: str, engine_id: str):
self.engine_id = engine_id
self.kind = None
if uri.startswith("file://"):
self.kind = "file"
self.dir = uri[len("file://"):]
_es_os.makedirs(self.dir, exist_ok=True)
self.path = _es_os.path.join(self.dir, f"{{engine_id}}.json")
self.tmp = self.path + f".tmp.{{_es_os.getpid()}}"
elif uri.startswith("redis://"):
self.kind = "redis"
import redis # lazy
self.r = redis.Redis.from_url(uri)
self.key = f"engine_state:{{engine_id}}"
def publish(self, state: dict):
try:
if self.kind == "file":
with open(self.tmp, "w") as f:
f.write(_es_json.dumps(state))
_es_os.replace(self.tmp, self.path) # atomic
elif self.kind == "redis":
self.r.set(self.key, _es_json.dumps(state), ex=5)
except Exception:
pass
def _es_compute_snapshot(scheduler) -> dict:
"""Cheap O(batch) state read from the live scheduler."""
try:
kvm = scheduler.kv_cache_manager
pool = kvm.block_pool
total = int(pool.num_gpu_blocks)
free = int(pool.get_num_free_blocks())
except Exception:
total = free = -1
n_run = 0
pend = 0
dec = 0
n_pref = 0
max_pref = 0
try:
for r in scheduler.running:
n_run += 1
npr = int(getattr(r, "num_prompt_tokens", 0))
nct = int(getattr(r, "num_computed_tokens", 0))
if nct < npr: # still prefilling
rem = npr - nct
pend += rem
n_pref += 1
if rem > max_pref:
max_pref = rem
else: # decoding
dec += int(getattr(r, "num_tokens", 0))
except Exception:
pass
n_wait = 0
try:
n_wait = len(scheduler.waiting) + len(getattr(scheduler, "skipped_waiting", []))
for r in list(scheduler.waiting):
pend += max(0, int(getattr(r, "num_prompt_tokens", 0))
- int(getattr(r, "num_computed_tokens", 0)))
except Exception:
pass
used_frac = ((total - free) / total) if (total and total > 0) else -1.0
return {{
"ts": _es_time.time(),
"engine_id": _ES_ID,
"num_running": n_run,
"num_waiting": int(n_wait),
"gpu_blocks_total": total,
"gpu_blocks_free": free,
"gpu_kv_used_frac": used_frac,
"pending_prefill_tokens": int(pend),
"ongoing_decode_tokens": int(dec),
"num_prefilling": n_pref,
"max_prefill_remaining": int(max_pref),
}}
class _ESPublisher:
def __init__(self, scheduler):
self._sched = scheduler
self._writer = _ESWriter(_ES_URI, _ES_ID)
self._stop = _es_threading.Event()
self._t = _es_threading.Thread(target=self._loop, daemon=True)
self._t.start()
def _loop(self):
while not self._stop.is_set():
try:
self._writer.publish(_es_compute_snapshot(self._sched))
except Exception:
pass
_es_time.sleep(_ES_PERIOD_S)
{END}
'''
# ---- Patch 2: start the publisher at the end of Scheduler.__init__ ----------
# Anchor on the existing agentic step-log block tail in __init__.
INIT_ANCHOR = """ _step_path = _os.environ.get("AGENTIC_STEP_LOG_PATH")"""
INIT_INSERT = f""" {START}
if _ES_URI:
try:
self._es_publisher = _ESPublisher(self)
logger.info("agentic engine-state publisher: uri=%s id=%s",
_ES_URI, _ES_ID)
except Exception as _e:
logger.warning("engine-state publisher disabled (%r)", _e)
{END}
_step_path = _os.environ.get("AGENTIC_STEP_LOG_PATH")"""
PATCHES = [
("header", HEADER_ANCHOR, HEADER + HEADER_ANCHOR),
("init", INIT_ANCHOR, INIT_INSERT),
]
def find_target(venv: Path) -> Path:
for c in (venv / TARGET_REL, DEFAULT_VENV / TARGET_REL):
if c.is_file():
return c
raise FileNotFoundError(f"cannot find {TARGET_REL} under {venv}")
def is_patched(t: str) -> bool:
return START in t
def apply(target: Path):
text = target.read_text()
if is_patched(text):
print(f"[es-instr] already patched: {target}")
return
new = text
for name, src, dst in PATCHES:
if src not in new:
raise RuntimeError(f"patch {name!r}: anchor not found in {target}")
new = new.replace(src, dst, 1)
target.write_text(new)
print(f"[es-instr] applied {len(PATCHES)} patches -> {target}")
def revert(target: Path):
text = target.read_text()
if not is_patched(text):
print(f"[es-instr] not patched: {target}")
return
pat = re.compile(r"[ \t]*" + re.escape(START) + r".*?" + re.escape(END) + r"\n",
flags=re.DOTALL)
new = pat.sub("", text)
new = re.sub(r"\n{3,}class Scheduler\(", "\n\nclass Scheduler(", new)
target.write_text(new)
print(f"[es-instr] reverted: {target}")
def main():
p = argparse.ArgumentParser()
p.add_argument("--apply", action="store_true")
p.add_argument("--revert", action="store_true")
p.add_argument("--check", action="store_true")
p.add_argument("--venv", type=Path, default=DEFAULT_VENV)
a = p.parse_args()
t = find_target(a.venv)
if a.apply:
apply(t)
elif a.revert:
revert(t)
elif a.check:
print(f"[es-instr] {'PATCHED' if is_patched(t.read_text()) else 'CLEAN'}: {t}")
else:
p.error("specify --apply/--revert/--check")
if __name__ == "__main__":
main()

79
microbench/connector_tax/layerwise/migration_target.py Normal file
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#!/usr/bin/env python3
"""P2: real-state-aware migration target selection.
Pure helpers (no proxy deps) so they're unit-testable. The router calls
`rank_migration_targets` to pick the decode target, using REAL engine state
(from the engine-state store) when available, falling back to shadow counters.
Key fix over the shadow-only Mechanism B: deprioritise targets that are
mid-large-prefill (`max_prefill_remaining` high) — those hold the GIL and
stall the mooncake receiver_loop, which is the ~45% control-plane residual
that layer-wise transfer does NOT fix. Also avoid targets near the KV
capacity wall (`gpu_kv_used_frac` high).
"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class TargetCandidate:
idx: int
cache_hit: int # estimated transfer bytes saved (tokens)
shadow_num_req: int # proxy shadow counter (fallback)
ongoing_tokens: int # shadow tertiary
real_state: dict | None = None # engine-state record, or None if stale/missing
def real_load(c: TargetCandidate) -> float:
"""Effective load: prefer real (running + waiting); else shadow."""
rs = c.real_state
if rs is not None:
return float(rs.get("num_running", 0) + rs.get("num_waiting", 0))
return float(c.shadow_num_req)
def big_prefill_remaining(c: TargetCandidate) -> int:
"""Largest in-progress prefill on the candidate (GIL-stall predictor).
0 when unknown (no real state) so we don't over-penalise blind."""
rs = c.real_state
return int(rs.get("max_prefill_remaining", 0)) if rs is not None else 0
def kv_used_frac(c: TargetCandidate) -> float:
rs = c.real_state
if rs is not None:
f = rs.get("gpu_kv_used_frac", -1.0)
return float(f) if f is not None and f >= 0 else 0.0
return 0.0
def target_sort_key(
c: TargetCandidate,
big_prefill_threshold: int = 16000,
kv_wall_frac: float = 0.90,
):
"""Sort key (lower = better). Ordering of concerns:
1. NOT mid-large-prefill (avoid the GIL-stall dst) [bool]
2. NOT near the KV capacity wall [bool]
3. most cache-rich (fewest transfer bytes) -> -cache_hit
4. lowest real load
5. lowest ongoing_tokens (shadow tertiary tie-break)
"""
stalls = 1 if big_prefill_remaining(c) >= big_prefill_threshold else 0
near_wall = 1 if kv_used_frac(c) >= kv_wall_frac else 0
return (stalls, near_wall, -c.cache_hit, real_load(c), c.ongoing_tokens)
def rank_migration_targets(
candidates: list[TargetCandidate],
big_prefill_threshold: int = 16000,
kv_wall_frac: float = 0.90,
) -> TargetCandidate | None:
"""Return the best candidate, or None if the list is empty."""
if not candidates:
return None
return min(
candidates,
key=lambda c: target_sort_key(c, big_prefill_threshold, kv_wall_frac),
)