agentic-kvc/scripts/cache_aware_proxy.py

"""Unified cache-aware + token-level load-balanced global scheduler.

Supports two modes:
  --combined URL [URL ...]: PD co-located instances (normal vLLM, no KV transfer)
  --prefill URL BP --decode URL: PD disaggregated instances (Mooncake KV transfer)

Routing policy (same for both modes):
  score = ongoing_tokens / avg_ongoing  -  ALPHA * cache_hit_ratio
  Normalized load prevents "rich get richer"; cache bonus gives affinity.
  Session affinity: multi-turn sessions stick to same instance.
"""

import argparse
import asyncio
import os
import time as _time
import urllib.parse
import uuid
from contextlib import asynccontextmanager

import httpx
import uvicorn
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import StreamingResponse

BLOCK_SIZE = 512
CACHE_HIT_ALPHA = 1.0
HEAVY_THRESHOLD = 20000  # default; overridden by --heavy-threshold


class InstanceState:
    def __init__(self, url: str, bootstrap_port: int | None = None):
        self.url = url
        self.bootstrap_port = bootstrap_port
        self.client = httpx.AsyncClient(
            timeout=None, base_url=url,
            limits=httpx.Limits(max_connections=None, max_keepalive_connections=None),
        )
        self.ongoing_tokens = 0
        self.ongoing_decode_tokens = 0  # subset: tokens in decode phase
        self.engine_id: dict[int, str] = {}
        self.dp_size = 1
        self.cached_blocks: set[int] = set()

    def estimate_cache_hit(self, token_ids: list[int] | None) -> int:
        if not token_ids or len(token_ids) < BLOCK_SIZE:
            return 0
        hit = 0
        for i in range(0, len(token_ids) - BLOCK_SIZE + 1, BLOCK_SIZE):
            bh = hash(tuple(token_ids[i:i + BLOCK_SIZE]))
            if bh in self.cached_blocks:
                hit += BLOCK_SIZE
            else:
                break
        return hit

    def record_prefix(self, token_ids: list[int] | None):
        if not token_ids:
            return
        for i in range(0, len(token_ids) - BLOCK_SIZE + 1, BLOCK_SIZE):
            self.cached_blocks.add(hash(tuple(token_ids[i:i + BLOCK_SIZE])))
        if len(self.cached_blocks) > 200000:
            self.cached_blocks = set(list(self.cached_blocks)[-100000:])


# Cumulative token load per instance (for balanced session placement)
_inst_cumulative_tokens: list[int] = []


def pick_instance(instances: list[InstanceState], token_ids: list[int] | None,
                  session_id: str | None, input_length: int,
                  affinity: dict[str, int]) -> tuple[InstanceState, int]:
    """Session-sticky with load-aware override.

    Turn 2+: use session affinity UNLESS pinned instance is overloaded
    (ongoing_tokens > 2x average), in which case pick least-loaded.
    Turn 1: pick instance with best score (load + cache combined).
    """
    global _inst_cumulative_tokens
    if not _inst_cumulative_tokens:
        _inst_cumulative_tokens = [0] * len(instances)

    avg_load = max(sum(i.ongoing_tokens for i in instances) / len(instances), 1.0)
    OVERLOAD_FACTOR = 2.0

    # Session affinity for turn 2+ (with load override)
    if session_id and session_id in affinity:
        idx = affinity[session_id]
        if idx < len(instances):
            inst = instances[idx]
            # Stick if not overloaded
            if inst.ongoing_tokens <= avg_load * OVERLOAD_FACTOR:
                return inst, idx
            # Overloaded: fall through to score-based selection

    # Score = ongoing_tokens - ALPHA * cache_hit_tokens
    # Balances load (lower is better) with cache affinity (higher hit is better)
    best_idx, best_score = 0, float("inf")
    for i, inst in enumerate(instances):
        cache_hit = inst.estimate_cache_hit(token_ids)
        score = inst.ongoing_tokens - CACHE_HIT_ALPHA * cache_hit
        if score < best_score:
            best_score = score
            best_idx = i

    _inst_cumulative_tokens[best_idx] += input_length
    if session_id:
        affinity[session_id] = best_idx
    return instances[best_idx], best_idx


global_args = None
combined_instances: list[InstanceState] = []
prefill_instances: list[InstanceState] = []
decode_instances: list[InstanceState] = []
session_affinity: dict[str, int] = {}
is_pd_sep = False
_breakdown_log: list[dict] = []


async def init_prefill_bootstrap(instances: list[InstanceState], ready: asyncio.Event):
    for inst in instances:
        if inst.bootstrap_port is None:
            continue
        while True:
            try:
                await inst.client.get("/health")
            except Exception:
                await asyncio.sleep(1)
                continue
            parsed = urllib.parse.urlparse(str(inst.client.base_url))
            url = f"http://{parsed.hostname}:{inst.bootstrap_port}/query"
            resp = await inst.client.get(url)
            resp.raise_for_status()
            data = resp.json()
            for dp_rank, dp_entry in data.items():
                inst.engine_id[int(dp_rank)] = dp_entry["engine_id"]
            inst.dp_size = len(data)
            print(f"Inited {inst.url} engine_ids={inst.engine_id}")
            break
    ready.set()


@asynccontextmanager
async def lifespan(app: FastAPI):
    global is_pd_sep
    app.state.ready = asyncio.Event()

    if global_args.combined:
        is_pd_sep = False
        for url in global_args.combined:
            combined_instances.append(InstanceState(url))
        app.state.ready.set()
        print(f"Combined mode: {len(combined_instances)} instances")
    else:
        is_pd_sep = True
        for url, bp in global_args.prefill:
            prefill_instances.append(InstanceState(url, bp))
        for url in global_args.decode:
            decode_instances.append(InstanceState(url))
        await init_prefill_bootstrap(prefill_instances, app.state.ready)
        print(f"PD-Sep mode: {len(prefill_instances)}P + {len(decode_instances)}D")

    yield
    for inst in combined_instances + prefill_instances + decode_instances:
        await inst.client.aclose()


app = FastAPI(lifespan=lifespan)


@app.post("/v1/completions")
async def handle_completions(request: Request):
    return await _handle(request, "/v1/completions")


@app.post("/v1/chat/completions")
async def handle_chat(request: Request):
    return await _handle(request, "/v1/chat/completions")


async def _handle(request: Request, api: str):
    if not app.state.ready.is_set():
        raise HTTPException(status_code=503, detail="Service Unavailable")

    req_data = await request.json()
    request_id = str(uuid.uuid4())
    prompt = req_data.get("prompt")
    token_ids = prompt if isinstance(prompt, list) else None
    input_length = len(token_ids) if token_ids else 0
    session_id = request.headers.get("X-Session-Id")

    headers = {"X-Request-Id": request_id}
    api_key = os.environ.get("OPENAI_API_KEY")
    if api_key:
        headers["Authorization"] = f"Bearer {api_key}"

    if is_pd_sep:
        return await _handle_pd_sep(api, req_data, request_id, token_ids,
                                     input_length, session_id, headers)
    else:
        return await _handle_combined(api, req_data, token_ids,
                                       input_length, session_id, headers)


async def _handle_combined(api, req_data, token_ids, input_length, session_id, headers):
    """Combined mode with adaptive prefill offload.

    WARM/MEDIUM: route by cache-hit + load balance (co-located P+D).
    HEAVY: route to instance with least decode load, avoiding decode disruption.
    """
    # Estimate new tokens after cache
    best_inst, best_idx = pick_instance(combined_instances, token_ids, session_id,
                                         input_length, session_affinity)
    cache_hit = best_inst.estimate_cache_hit(token_ids)
    estimated_new = max(0, input_length - cache_hit)

    breakdown = {
        "request_id": headers.get("X-Request-Id", ""),
        "input_length": input_length,
        "estimated_new_tokens": estimated_new,
        "cache_hit": cache_hit,
        "t_proxy_recv": _time.monotonic(),
    }

    if estimated_new >= HEAVY_THRESHOLD:
        # HEAVY: pick instance with least ongoing_decode_tokens
        # This avoids sending heavy prefill to an instance busy decoding
        inst = min(combined_instances, key=lambda x: x.ongoing_decode_tokens)
        idx = combined_instances.index(inst)
        breakdown["route_class"] = "HEAVY"
        if session_id:
            session_affinity[session_id] = idx
    else:
        inst = best_inst
        idx = best_idx
        breakdown["route_class"] = "WARM" if estimated_new < 5000 else "MEDIUM"

    breakdown["routed_to"] = inst.url
    inst.ongoing_tokens += input_length

    async def generate():
        first_token = True
        try:
            async with inst.client.stream("POST", api, json=req_data, headers=headers) as resp:
                resp.raise_for_status()
                # Once streaming starts, this instance is in "decode phase"
                inst.ongoing_decode_tokens += input_length
                async for chunk in resp.aiter_bytes():
                    if first_token:
                        breakdown["t_first_token"] = _time.monotonic()
                        first_token = False
                    yield chunk
            inst.record_prefix(token_ids)
        finally:
            inst.ongoing_tokens -= input_length
            inst.ongoing_decode_tokens -= input_length
            breakdown["t_done"] = _time.monotonic()
            _breakdown_log.append(breakdown)

    return StreamingResponse(generate(), media_type="text/event-stream")


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


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)")
    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)