5c21a485
 tangwang
qwen3-reranker-0....
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"""
from __future__ import annotations
import argparse
import json
import os
import random
import statistics
import subprocess
import sys
import time
from pathlib import Path
from typing import Any
DEFAULT_TITLES = Path("/home/ubuntu/rerank_test/titles.1.8w")
def load_titles(path: Path) -> list[str]:
items: list[str] = []
with path.open(encoding="utf-8", errors="replace") as fh:
for line in fh:
text = line.strip()
if text:
items.append(text)
return items
def gpu_mem_for_pid(pid: int) -> int:
try:
out = subprocess.check_output(
[
"nvidia-smi",
"--query-compute-apps=pid,used_gpu_memory",
"--format=csv,noheader,nounits",
],
text=True,
)
except Exception:
return -1
for raw in out.splitlines():
parts = [p.strip() for p in raw.split(",")]
if len(parts) != 2:
continue
try:
row_pid = int(parts[0])
row_mem = int(parts[1])
except ValueError:
continue
if row_pid == pid:
return row_mem
return -1
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--backend-name", type=str, default="qwen3_gguf")
parser.add_argument("--titles-file", type=Path, default=DEFAULT_TITLES)
parser.add_argument("--query", type=str, default="白色oversized T-shirt")
parser.add_argument("--docs", type=int, default=160)
parser.add_argument("--repeat", type=int, default=1)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument(
"--configs-json",
type=str,
default="",
help="JSON array of config objects; when omitted, uses built-in scan set.",
)
args = parser.parse_args()
if not args.titles_file.is_file():
print(f"missing titles file: {args.titles_file}", file=sys.stderr)
return 2
titles = load_titles(args.titles_file)
if len(titles) < args.docs:
print(f"not enough titles: need {args.docs}, got {len(titles)}", file=sys.stderr)
return 2
random.seed(args.seed)
docs = random.sample(titles, args.docs)
if args.configs_json:
configs = json.loads(args.configs_json)
elif args.backend_name == "qwen3_gguf_06b":
configs = [
{"name": "gguf_06b_full_256", "n_ctx": 256, "n_batch": 256, "n_ubatch": 256, "n_gpu_layers": 999},
{"name": "gguf_06b_full_320", "n_ctx": 320, "n_batch": 320, "n_ubatch": 320, "n_gpu_layers": 999},
{"name": "gguf_06b_full_384", "n_ctx": 384, "n_batch": 384, "n_ubatch": 384, "n_gpu_layers": 999},
{"name": "gguf_06b_full_512", "n_ctx": 512, "n_batch": 512, "n_ubatch": 512, "n_gpu_layers": 999},
]
else:
configs = [
{"name": "gguf_t4_24g", "n_ctx": 384, "n_batch": 384, "n_ubatch": 128, "n_gpu_layers": 24},
{"name": "gguf_t4_40g", "n_ctx": 384, "n_batch": 384, "n_ubatch": 128, "n_gpu_layers": 40},
{"name": "gguf_t4_full", "n_ctx": 384, "n_batch": 384, "n_ubatch": 128, "n_gpu_layers": 999},
{"name": "gguf_t4_full_512", "n_ctx": 512, "n_batch": 512, "n_ubatch": 256, "n_gpu_layers": 999},
{"name": "gguf_t4_full_512_u512", "n_ctx": 512, "n_batch": 512, "n_ubatch": 512, "n_gpu_layers": 999},
{"name": "gguf_t4_full_768", "n_ctx": 768, "n_batch": 768, "n_ubatch": 256, "n_gpu_layers": 999},
]
from reranker.backends.qwen3_gguf import Qwen3GGUFRerankerBackend
default_cfg_by_backend: dict[str, dict[str, Any]] = {
"qwen3_gguf": {
"_backend_name": "qwen3_gguf",
"repo_id": "DevQuasar/Qwen.Qwen3-Reranker-4B-GGUF",
"filename": "*Q8_0.gguf",
"local_dir": "./models/reranker/qwen3-reranker-4b-gguf",
"infer_batch_size": 8,
},
"qwen3_gguf_06b": {
"_backend_name": "qwen3_gguf_06b",
"repo_id": "ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF",
"filename": "qwen3-reranker-0.6b-q8_0.gguf",
"local_dir": "./models/reranker/qwen3-reranker-0.6b-q8_0-gguf",
"infer_batch_size": 32,
},
}
if args.backend_name not in default_cfg_by_backend:
print(f"unsupported backend: {args.backend_name}", file=sys.stderr)
return 2
base_cfg: dict[str, Any] = {
**default_cfg_by_backend[args.backend_name],
"instruction": "Rank products by query with category & style match prioritized",
"cache_dir": "./model_cache",
"main_gpu": 0,
"n_threads": 2,
"n_threads_batch": 4,
"flash_attn": True,
"offload_kqv": True,
"use_mmap": True,
"use_mlock": False,
"sort_by_doc_length": True,
"length_sort_mode": "char",
"enable_warmup": True,
"verbose": False,
"reuse_query_state": True,
}
all_results: list[dict[str, Any]] = []
for cfg in configs:
merged = dict(base_cfg)
merged.update(cfg)
name = str(merged.pop("name"))
t0 = time.perf_counter()
backend = Qwen3GGUFRerankerBackend(merged)
load_ms = (time.perf_counter() - t0) * 1000.0
gpu_mem_mib = gpu_mem_for_pid(os.getpid())
runs: list[float] = []
last_meta: dict[str, Any] = {}
for _ in range(args.repeat):
t1 = time.perf_counter()
_scores, meta = backend.score_with_meta(args.query, docs, normalize=True)
runs.append((time.perf_counter() - t1) * 1000.0)
last_meta = dict(meta)
result = {
"name": name,
"config": merged,
"load_ms": round(load_ms, 2),
"gpu_mem_mib": gpu_mem_mib,
"latency_ms_min": round(min(runs), 2),
"latency_ms_avg": round(statistics.mean(runs), 2),
"latency_ms_max": round(max(runs), 2),
"meta": last_meta,
}
all_results.append(result)
print(json.dumps(result, ensure_ascii=False))
del backend
print("SUMMARY")
for item in sorted(all_results, key=lambda x: x["latency_ms_avg"]):
print(
f'{item["name"]}: avg={item["latency_ms_avg"]}ms '
f'gpu={item["gpu_mem_mib"]}MiB load={item["load_ms"]}ms'
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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