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import argparse
import threading
import time
from pathlib import Path
from typing import Any, Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from .constants import (
DEFAULT_DOCUMENT_MAX_LENGTH,
DEFAULT_ENCODER_CHUNK_SIZE,
DEFAULT_MAX_MODEL_LEN,
DEFAULT_QUERY_MAX_LENGTH,
MODEL_ID,
SUPPORTED_ENCODER_CHUNK_SIZES,
parse_encoder_chunk_size,
)
from .reranker import KaLMVLLMReranker
class RerankRequest(BaseModel):
query: str
documents: list[str] = Field(min_length=1)
instruction: Optional[str] = None
top_k: Optional[int] = None
return_margin: bool = False
class ScorePair(BaseModel):
query: str
document: str
id: Optional[Any] = None
class ScoreRequest(BaseModel):
pairs: list[ScorePair] = Field(min_length=1)
instruction: Optional[str] = None
return_margin: bool = False
class OnlineRerankerService:
def __init__(self, args: argparse.Namespace) -> None:
self.model = str(args.model)
self.query_max_length = int(args.query_max_length)
self.document_max_length = int(args.document_max_length)
self.encoder_chunk_size = parse_encoder_chunk_size(args.encoder_chunk_size)
self.max_model_len = int(args.max_model_len)
self.batch_size = int(args.batch_size)
self.dtype = str(args.dtype)
self.gpu_memory_utilization = float(args.gpu_memory_utilization)
self.started_at = time.time()
self.lock = threading.Lock()
self.reranker = KaLMVLLMReranker(
self.model,
query_max_length=self.query_max_length,
document_max_length=self.document_max_length,
encoder_chunk_size=self.encoder_chunk_size,
max_model_len=self.max_model_len,
batch_size=self.batch_size,
dtype=self.dtype,
gpu_memory_utilization=self.gpu_memory_utilization,
tensor_parallel_size=args.tensor_parallel_size,
)
def config(self) -> dict[str, Any]:
return {
"model": self.model,
"query_max_length": self.query_max_length,
"document_max_length": self.document_max_length,
"encoder_chunk_size": self.encoder_chunk_size,
"max_model_len": self.max_model_len,
"batch_size": self.batch_size,
"dtype": self.dtype,
"gpu_memory_utilization": self.gpu_memory_utilization,
"tensor_parallel_size": 1,
"supported_encoder_chunk_sizes": sorted(
SUPPORTED_ENCODER_CHUNK_SIZES
),
}
def health(self) -> dict[str, Any]:
return {
"status": "ok",
"uptime_seconds": round(time.time() - self.started_at, 3),
**self.config(),
}
def close(self) -> None:
self.reranker.close()
def rerank(self, request: RerankRequest) -> dict[str, Any]:
if request.top_k is not None and request.top_k < 0:
raise ValueError("top_k must be non-negative or null.")
with self.lock:
rankings = self.reranker.rank(
request.query,
request.documents,
instruction=request.instruction,
top_k=request.top_k,
return_margin=request.return_margin,
)
results: list[dict[str, Any]] = []
for item in rankings:
result = {
"index": int(item["corpus_id"]),
"score": float(item["score"]),
}
if request.return_margin:
result["margin"] = float(item["margin"])
results.append(result)
return {"object": "rerank", "results": results}
def score(self, request: ScoreRequest) -> dict[str, Any]:
pairs = [(item.query, item.document) for item in request.pairs]
with self.lock:
predictions = self.reranker.predict(
pairs,
instruction=request.instruction,
return_margin=True,
)
results: list[dict[str, Any]] = []
for index, (pair, prediction) in enumerate(zip(request.pairs, predictions)):
assert isinstance(prediction, dict)
result = {
"index": index,
"score": float(prediction["score"]),
}
if pair.id is not None:
result["id"] = pair.id
if request.return_margin:
result["margin"] = float(prediction["margin"])
results.append(result)
return {"object": "score", "results": results}
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="FastAPI server for the KaLM vLLM adapter.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--host", default="0.0.0.0")
parser.add_argument("--port", type=int, default=8000)
parser.add_argument("--model", default=MODEL_ID)
parser.add_argument("--query-max-length", type=int, default=DEFAULT_QUERY_MAX_LENGTH)
parser.add_argument(
"--document-max-length", type=int, default=DEFAULT_DOCUMENT_MAX_LENGTH
)
parser.add_argument(
"--encoder-chunk-size", default=str(DEFAULT_ENCODER_CHUNK_SIZE)
)
parser.add_argument("--max-model-len", type=int, default=DEFAULT_MAX_MODEL_LEN)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--dtype", default="bfloat16")
parser.add_argument("--gpu-memory-utilization", type=float, default=0.85)
parser.add_argument("--tensor-parallel-size", type=int, default=1)
parser.add_argument("--log-level", default="info")
return parser
def create_app(service: OnlineRerankerService) -> FastAPI:
app = FastAPI(title="KaLM vLLM Online Reranker", version="0.1.0")
app.state.service = service
app.add_event_handler("shutdown", service.close)
@app.get("/health")
def health():
return app.state.service.health()
@app.post("/rerank")
def rerank(request: RerankRequest):
try:
return app.state.service.rerank(request)
except (TypeError, ValueError) as error:
raise HTTPException(status_code=400, detail=str(error)) from error
@app.post("/score")
def score(request: ScoreRequest):
try:
return app.state.service.score(request)
except (TypeError, ValueError) as error:
raise HTTPException(status_code=400, detail=str(error)) from error
return app
def main() -> int:
args = build_parser().parse_args()
args.encoder_chunk_size = parse_encoder_chunk_size(args.encoder_chunk_size)
print("=== KaLM vLLM Online Reranker ===", flush=True)
print(f"model: {args.model}", flush=True)
print(f"query_max_length: {args.query_max_length}", flush=True)
print(f"document_max_length: {args.document_max_length}", flush=True)
print(f"encoder_chunk_size: {args.encoder_chunk_size}", flush=True)
print(f"max_model_len: {args.max_model_len}", flush=True)
print(f"listen: http://{args.host}:{args.port}", flush=True)
service = OnlineRerankerService(args)
app = create_app(service)
import uvicorn
uvicorn.run(
app,
host=args.host,
port=args.port,
log_level=args.log_level,
workers=1,
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
|