File size: 13,784 Bytes
3774d79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 | """
KVInfer β FastAPI Backend v4.1
2 vCPU Β· 16 GB RAM HuggingFace Space ke liye optimize kiya hua
RAM estimate:
2 engines Γ 4 GB (Llama 1B float32) = 8.0 GB
2 engines Γ 8 sess Γ ~48 MB KV = 0.8 GB
Python + FastAPI + tokenizer = ~0.7 GB
ββββββββββββββββββββββββββββββββββββββββββββ
TOTAL β 9.5 GB β (16 GB mein safe)
"""
import asyncio, json, os, time, uuid
from contextlib import asynccontextmanager
from pathlib import Path
import psutil
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse, StreamingResponse
from pydantic import BaseModel, Field
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
# ββ Config βββββββββββββββββββββββββββββββββββββββββββββββ
BASE_DIR = Path(__file__).parent
INFERENCE_EXE = BASE_DIR / "inference"
MODEL_BIN = BASE_DIR / "model_llama.bin"
HF_REPO_ID = os.environ.get("HF_REPO_ID", "YOUR_HF_USERNAME/YOUR_REPO")
BLOCK_SIZE = 2048
MAX_GEN_CEILING = 500
SAFETY_MARGIN = 50
MAX_SESS_TOKENS = BLOCK_SIZE - MAX_GEN_CEILING - SAFETY_MARGIN # 1498
# 2 vCPU β 2 engines, each pinned to 1 thread
N_ENGINES = int(os.environ.get("N_ENGINES", "2"))
# Llama 3 special tokens
EOS_IDS = [128001, 128009] # <|end_of_text|> <|eot_id|>
EOT_STR = "<|eot_id|>"
SYS_H = "<|start_header_id|>system<|end_header_id|>\n\n"
USR_H = "<|start_header_id|>user<|end_header_id|>\n\n"
AST_H = "<|start_header_id|>assistant<|end_header_id|>\n\n"
STOP_STR = ["<|eot_id|>", "<|start_header_id|>user", "<|start_header_id|>system"]
tokenizer = None
def load_tokenizer():
global tokenizer
local = BASE_DIR / "tokenizer_files"
src = str(local) if local.exists() else "unsloth/Llama-3.2-1B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(src)
print(f"[tok] vocab={tokenizer.vocab_size}")
def enc(text: str) -> list[int]:
return tokenizer.encode(text, add_special_tokens=False)
def dec(ids: list[int]) -> str:
return tokenizer.decode(ids, skip_special_tokens=False)
# ββ Engine βββββββββββββββββββββββββββββββββββββββββββββββ
class Engine:
def __init__(self, eid):
self.eid = eid; self._proc = None; self._ready = False
async def start(self):
if not INFERENCE_EXE.exists(): raise RuntimeError("Binary not found")
if not MODEL_BIN.exists(): raise RuntimeError("model_llama.bin not found")
env = os.environ.copy()
env["OMP_NUM_THREADS"] = "1" # 1 thread per engine = 2 threads total on 2vCPU
self._proc = await asyncio.create_subprocess_exec(
str(INFERENCE_EXE),
stdin=asyncio.subprocess.PIPE,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.DEVNULL,
cwd=str(BASE_DIR), env=env,
)
while True:
line = (await self._proc.stdout.readline()).decode().strip()
if line.startswith("[engine]"): print(f"[E{self.eid}] {line}")
elif line == "READY":
self._ready = True
print(f"[E{self.eid}] READY pid={self._proc.pid}")
break
elif line.startswith("ERROR"): raise RuntimeError(line)
async def stop(self):
if not self._proc: return
try:
self._proc.stdin.write(b"QUIT\n"); await self._proc.stdin.drain()
await asyncio.wait_for(self._proc.wait(), 3.0)
except: self._proc.kill()
async def reset(self, sid):
self._proc.stdin.write(f"RESET|{sid}\n".encode())
await self._proc.stdin.drain()
while True:
raw = await self._proc.stdout.readline()
if not raw or raw.decode().strip() == "RESET_OK": break
async def generate(self, sid, tokens, max_new, temp, top_k):
if not self._ready: yield {"type":"error","message":"not ready"}; return
cmd = f"REQUEST|{sid}|{','.join(map(str,tokens))}|{max_new}|{temp}|{top_k}|{','.join(map(str,EOS_IDS))}\n"
self._proc.stdin.write(cmd.encode()); await self._proc.stdin.drain()
try:
while True:
raw = await self._proc.stdout.readline()
if not raw: break
line = raw.decode("utf-8","replace").strip()
if not line: continue
if line.startswith("TOKEN"):
p = line.split(); yield {"type":"token","id":int(p[1]),"text":dec([int(p[1])]),"elapsed_ms":float(p[2])}
elif line.startswith("DONE"):
p = line.split(); t=int(p[1]); ms=float(p[2])
yield {"type":"done","total_tokens":t,"total_ms":ms,
"tps": round(t/(ms/1000),2) if ms>0 else 0}; break
elif line.startswith("ERROR"):
yield {"type":"error","message":line}; break
except asyncio.CancelledError:
while True:
raw = await self._proc.stdout.readline()
if not raw or raw.decode().strip().startswith(("DONE","ERROR")): break
raise
@property
def pid(self): return self._proc.pid if self._proc else None
# ββ Pool βββββββββββββββββββββββββββββββββββββββββββββββββ
class Pool:
def __init__(self, n):
self.n=n; self.engines=[Engine(i) for i in range(n)]
self._locks=[]; self._smap={}; self._load=[]; self._ml=None
async def start(self):
self._ml=asyncio.Lock(); self._locks=[asyncio.Lock() for _ in range(self.n)]
self._load=[0]*self.n
await asyncio.gather(*(e.start() for e in self.engines))
print(f"[pool] {self.n} engines up")
async def stop(self):
await asyncio.gather(*(e.stop() for e in self.engines),return_exceptions=True)
async def _assign(self, sid):
async with self._ml:
if sid not in self._smap:
idx=min(range(self.n),key=lambda i:self._load[i])
self._smap[sid]=idx; self._load[idx]+=1
return self._smap[sid]
async def _drop(self, sid):
async with self._ml:
if sid in self._smap:
idx=self._smap.pop(sid); self._load[idx]=max(0,self._load[idx]-1)
async def generate(self, sid, tokens, max_new, temp, top_k):
idx=await self._assign(sid)
async with self._locks[idx]:
async for c in self.engines[idx].generate(sid,tokens,max_new,temp,top_k): yield c
async def reset(self, sid):
async with self._ml: idx=self._smap.get(sid)
if idx is not None:
async with self._locks[idx]: await self.engines[idx].reset(sid)
await self._drop(sid)
def pids(self): return [e.pid for e in self.engines if e.pid]
def status(self):
return [{"id":i,"pid":self.engines[i].pid,"sessions":self._load[i],
"busy":self._locks[i].locked(),"ready":self.engines[i]._ready}
for i in range(self.n)]
pool = Pool(N_ENGINES)
# ββ Session ββββββββββββββββββββββββββββββββββββββββββββββ
class Sess:
def __init__(self, sys_p):
self.sys_p=sys_p; self.history=[]; self.n_cached=0
def push_user(self, m): self.history.append({"role":"user","content":m})
def push_asst(self, m): self.history.append({"role":"assistant","content":m})
def new_tokens(self, msg):
if self.n_cached == 0:
text = f"<|begin_of_text|>{SYS_H}{self.sys_p}{EOT_STR}{USR_H}{msg}{EOT_STR}{AST_H}"
else:
text = f"{USR_H}{msg}{EOT_STR}{AST_H}"
return enc(text)
sessions: dict[str, Sess] = {}
metrics = {"req":0,"tok":0,"ms":0.0,"err":0,"t0":time.time()}
def total_ram():
try:
mb=psutil.Process(os.getpid()).memory_info().rss
for p in pool.pids():
try: mb+=psutil.Process(p).memory_info().rss
except: pass
return round(mb/1e6,1)
except: return 0.0
# ββ Lifespan βββββββββββββββββββββββββββββββββββββββββββββ
@asynccontextmanager
async def lifespan(app):
print("[start] Loading tokenizerβ¦")
load_tokenizer()
if not MODEL_BIN.exists():
try:
print("[start] Downloading model_llama.bin from HFβ¦")
hf_hub_download(repo_id=HF_REPO_ID,filename="model_llama.bin",local_dir=str(BASE_DIR))
except Exception as e: print(f"[warn] download failed: {e}")
try: await pool.start()
except Exception as e: print(f"[error] pool start: {e}")
yield
await pool.stop()
app = FastAPI(title="KVInfer",version="4.1",lifespan=lifespan)
app.add_middleware(CORSMiddleware,allow_origins=["*"],allow_methods=["*"],allow_headers=["*"])
# ββ Models βββββββββββββββββββββββββββββββββββββββββββββββ
class ChatReq(BaseModel):
message: str
session_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
system_prompt: str = "You are a helpful, concise assistant."
max_new_tokens: int = Field(default=256, ge=1, le=500)
temperature: float = Field(default=0.7, ge=0.01, le=2.0)
top_k: int = Field(default=40, ge=1, le=200)
class ResetReq(BaseModel):
session_id: str
# ββ Routes βββββββββββββββββββββββββββββββββββββββββββββββ
@app.get("/")
async def ui(): return FileResponse(BASE_DIR/"index.html")
@app.get("/health")
async def health():
mem=psutil.virtual_memory()
return {"status":"ok" if any(e._ready for e in pool.engines) else "starting",
"engines_ready":sum(1 for e in pool.engines if e._ready),
"engines_total":N_ENGINES,"active_sessions":len(sessions),
"process_ram_mb":total_ram(),"system_ram_used_pct":mem.percent,
"uptime_seconds":round(time.time()-metrics["t0"],1)}
@app.get("/metrics")
async def get_metrics():
n,tok,ms=metrics["req"],metrics["tok"],metrics["ms"]
mem=psutil.virtual_memory()
return {"total_requests":n,"total_tokens":tok,"total_errors":metrics["err"],
"avg_tps":round(tok/(ms/1000),2) if ms>0 else 0,
"active_sessions":len(sessions),"n_engines":N_ENGINES,
"engines_ready":sum(1 for e in pool.engines if e._ready),
"engines_busy":sum(1 for lk in pool._locks if lk.locked()),
"process_ram_mb":total_ram(),"system_ram_used_pct":mem.percent,
"uptime_s":round(time.time()-metrics["t0"],1)}
@app.post("/chat")
async def chat(req: ChatReq):
if not any(e._ready for e in pool.engines):
raise HTTPException(503,"No engines ready yet β please wait a moment.")
sess=sessions.setdefault(req.session_id, Sess(req.system_prompt))
toks=sess.new_tokens(req.message)
if sess.n_cached+len(toks)+req.max_new_tokens > MAX_SESS_TOKENS:
await pool.reset(req.session_id); sess.n_cached=0; toks=sess.new_tokens(req.message)
sess.push_user(req.message); metrics["req"]+=1
async def stream():
parts=[]; t0=time.time(); stopped=False
try:
async for c in pool.generate(req.session_id,toks,req.max_new_tokens,req.temperature,req.top_k):
if c["type"]=="token" and not stopped:
parts.append(c["text"]); joined="".join(parts)
for s in STOP_STR:
if s in joined: parts=[joined[:joined.find(s)]]; stopped=True; break
if not stopped: yield f"data:{json.dumps(c)}\n\n"
elif c["type"]=="done":
reply="".join(parts).strip()
for s in STOP_STR: reply=reply.split(s)[0]
reply=reply.strip()
sess.push_asst(reply)
sess.n_cached+=len(toks)+c["total_tokens"]
metrics["tok"]+=c["total_tokens"]; metrics["ms"]+=(time.time()-t0)*1000
yield f"data:{json.dumps({**c,'session_id':req.session_id,'full_response':reply})}\n\n"
elif c["type"]=="error":
metrics["err"]+=1; yield f"data:{json.dumps(c)}\n\n"
except Exception as e:
metrics["err"]+=1; yield f"data:{json.dumps({'type':'error','message':str(e)})}\n\n"
finally: yield "data:[DONE]\n\n"
return StreamingResponse(stream(),media_type="text/event-stream",
headers={"Cache-Control":"no-cache","X-Accel-Buffering":"no"})
@app.post("/chat/reset")
async def reset(req: ResetReq):
sessions.pop(req.session_id, None)
await pool.reset(req.session_id)
return {"status":"ok","session_id":req.session_id}
@app.get("/chat/history")
async def history(session_id: str):
s=sessions.get(session_id)
if not s: return {"session_id":session_id,"turns":0,"history":[]}
return {"session_id":session_id,"tokens_in_engine":s.n_cached,
"turns":sum(1 for m in s.history if m["role"]=="user"),"history":s.history}
@app.get("/pool/status")
async def pool_status(): return {"n_engines":N_ENGINES,"engines":pool.status(),"sessions":len(sessions)}
if __name__=="__main__":
import uvicorn; uvicorn.run("main:app",host="0.0.0.0",port=7860,reload=False)
|