File size: 4,154 Bytes
6461f0c | 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 | """Vanilla scoring pipeline for a vLLM/OpenAI-compatible backend.
Each video uses one model call per scoring dimension; strict per-dim
JSON outputs are aggregated into the final ``scoring`` block. This entry
talks to an OpenAI-compatible ``/v1/chat/completions`` endpoint.
"""
from __future__ import annotations
import argparse
import os
from _core import (
DEFAULT_DATA_PATH,
DEFAULT_RESULTS_DIR,
run_scoring,
)
from tools import VLLMClient, load_pointwise_data
# ---------------------------------------------------------------------------
# Defaults
# ---------------------------------------------------------------------------
DEFAULT_TAG = "infer"
DEFAULT_SCORE_OUTPUT = os.path.join(
DEFAULT_RESULTS_DIR, f"{DEFAULT_TAG}_scores.json"
)
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description=(
"Vanilla video reward scoring pipeline (per-dimension, "
"3 dims = 3 calls/video) — local vLLM OpenAI-compatible "
"backend."
)
)
parser.add_argument("--data", type=str, default=DEFAULT_DATA_PATH)
parser.add_argument("--score_output", type=str, default=DEFAULT_SCORE_OUTPUT)
parser.add_argument(
"--num_samples",
type=str,
default="all",
help="Number of input videos, or 'all'",
)
parser.add_argument(
"--concurrency",
type=int,
default=32,
help="Max concurrent worker threads (videos in flight). Each "
"video issues one model call per scoring dimension; the per-dim "
"calls run sequentially within a video.",
)
# vLLM connection / generation params
parser.add_argument(
"--vllm_base_url",
type=str,
default=os.environ.get("VLLM_BASE_URL", "http://127.0.0.1:8000/v1"),
help="vLLM OpenAI base URL, e.g. http://127.0.0.1:8000/v1",
)
parser.add_argument(
"--model",
type=str,
default=os.environ.get("VLLM_MODEL", "Qwen3-VL-8B-Instruct"),
help="Served model name for vLLM backend",
)
parser.add_argument(
"--api_key",
type=str,
default=os.environ.get("VLLM_API_KEY", "EMPTY"),
help="OpenAI-compatible API key; vLLM usually accepts EMPTY",
)
parser.add_argument("--max_tokens", type=int, default=2048)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument(
"--request_interval",
type=float,
default=0.0,
help="Sleep seconds between successive videos on the same worker.",
)
parser.add_argument("--max_retries", type=int, default=3)
parser.add_argument("--retry_base_delay", type=float, default=2.0)
parser.add_argument("--request_timeout", type=int, default=300)
return parser.parse_args()
def main() -> None:
args = parse_args()
print(f"[vllm] base_url: {args.vllm_base_url}")
print(f"[vllm] model: {args.model}")
print(
f"[vllm] max_tokens={args.max_tokens}, "
f"temperature={args.temperature}, top_p={args.top_p}"
)
_, expanded_data = load_pointwise_data(
data_path=args.data,
num_samples=args.num_samples,
)
client = VLLMClient(
base_url=args.vllm_base_url,
model_name=args.model,
api_key=args.api_key,
max_tokens=args.max_tokens,
temperature=args.temperature,
top_p=args.top_p,
request_interval=args.request_interval,
max_retries=args.max_retries,
retry_base_delay=args.retry_base_delay,
request_timeout=args.request_timeout,
)
run_scoring(
client=client,
expanded_data=expanded_data,
score_path=args.score_output,
concurrency=args.concurrency,
)
print("\n" + "=" * 72)
print("DONE")
print(f"Scores: {args.score_output}")
print("=" * 72)
if __name__ == "__main__":
main()
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