code/rl_inference/run_inference_vllm_server.py

import argparse
import json
import os
import re
from datetime import datetime
from typing import Any, Dict, List, Optional

import pandas as pd
import requests
from tqdm import tqdm
from transformers import AutoTokenizer


DEFAULT_MODEL_PATH = "Qwen/Qwen3-4B-Instruct-2507"
DEFAULT_DATASET_PATH = (
    "/home/mshahidul/readctrl/code/text_classifier/data/verified_combined_0-80_clean200.json"
)
DEFAULT_OUTPUT_DIR = "/home/mshahidul/readctrl/code/RL_model/inference_data"
DEFAULT_BASE_URL = "http://127.0.0.1:8001/v1"
DEFAULT_SERVED_MODEL_NAME = "inference"
DEFAULT_PROMPT_LOW_PATH = (
    "/home/mshahidul/readctrl/code/RL_model/verl/verl_train/dataset/prompt_low"
)
DEFAULT_PROMPT_INTERMEDIATE_PATH = (
    "/home/mshahidul/readctrl/code/RL_model/verl/verl_train/dataset/prompt_intermediate"
)
DEFAULT_PROMPT_PROFICIENT_PATH = (
    "/home/mshahidul/readctrl/code/RL_model/verl/verl_train/dataset/prompt_proficient"
)
VALID_LABELS = {
    "low_health_literacy",
    "intermediate_health_literacy",
    "proficient_health_literacy",
}


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Run batched inference via vLLM OpenAI-compatible server.")
    parser.add_argument("--model_path", type=str, default=DEFAULT_MODEL_PATH, help="Local path for tokenizer/chat template.")
    parser.add_argument("--dataset_path", type=str, default=DEFAULT_DATASET_PATH)
    parser.add_argument("--prompt-low-path", type=str, default=DEFAULT_PROMPT_LOW_PATH)
    parser.add_argument("--prompt-intermediate-path", type=str, default=DEFAULT_PROMPT_INTERMEDIATE_PATH)
    parser.add_argument("--prompt-proficient-path", type=str, default=DEFAULT_PROMPT_PROFICIENT_PATH)
    parser.add_argument("--output_dir", type=str, default=DEFAULT_OUTPUT_DIR)
    parser.add_argument("--base_url", type=str, default=DEFAULT_BASE_URL, help="vLLM OpenAI base URL, e.g. http://127.0.0.1:8000/v1")
    parser.add_argument("--served_model_name", type=str, default=DEFAULT_SERVED_MODEL_NAME, help="Model name exposed by vLLM server.")
    parser.add_argument("--batch_size", type=int, default=8)
    parser.add_argument("--max_samples", type=int, default=-1, help="Use -1 for full dataset.")
    parser.add_argument("--max_tokens", type=int, default=1024)
    parser.add_argument("--temperature", type=float, default=0.7)
    parser.add_argument("--top_p", type=float, default=0.8)
    parser.add_argument("--api_key", type=str, default="EMPTY")
    parser.add_argument("--timeout_sec", type=int, default=300)
    return parser.parse_args()


def load_prompt_templates(args: argparse.Namespace) -> Dict[str, str]:
    prompt_path_by_label = {
        "low_health_literacy": args.prompt_low_path,
        "intermediate_health_literacy": args.prompt_intermediate_path,
        "proficient_health_literacy": args.prompt_proficient_path,
    }
    templates: Dict[str, str] = {}
    for label, path in prompt_path_by_label.items():
        if not os.path.exists(path):
            raise FileNotFoundError(f"Prompt file not found: {path}")
        with open(path, "r", encoding="utf-8") as f:
            templates[label] = f.read()
    return templates


def load_verified_rows(path: str) -> List[Dict[str, Any]]:
    if not os.path.exists(path):
        raise FileNotFoundError(f"Input file not found: {path}")
    with open(path, "r", encoding="utf-8") as f:
        parsed = json.load(f)
    if not isinstance(parsed, list):
        raise ValueError(f"Expected top-level JSON array in {path}")
    return [row for row in parsed if isinstance(row, dict)]


def infer_source_lang(fulltext: str) -> str:
    if fulltext and any("a" <= ch.lower() <= "z" for ch in fulltext):
        return "English"
    return "Unknown"


def build_prompt(template: str, fulltext: str, summary: str, source_lang: str) -> str:
    return (
        template.replace("{source_lang}", source_lang)
        .replace("{gold_summary}", summary)
        .replace("{full_text}", fulltext)
    )


def _clean_json_block(text: str) -> str:
    cleaned = text.strip()
    if "```json" in cleaned:
        cleaned = cleaned.split("```json", 1)[1].split("```", 1)[0].strip()
    elif "```" in cleaned:
        cleaned = cleaned.split("```", 1)[1].split("```", 1)[0].strip()
    return cleaned


def extract_generated_text(raw_response: str, expected_label: str) -> str:
    cleaned = _clean_json_block(raw_response)
    try:
        parsed = json.loads(cleaned)
    except json.JSONDecodeError:
        return raw_response.strip()

    if isinstance(parsed, dict):
        value = parsed.get(expected_label)
        if isinstance(value, str) and value.strip():
            return value.strip()
    return raw_response.strip()


def _normalize_messages(prompt_obj: Any) -> List[Dict[str, str]]:
    if hasattr(prompt_obj, "tolist"):
        prompt_obj = prompt_obj.tolist()

    if isinstance(prompt_obj, dict):
        if "role" in prompt_obj and "content" in prompt_obj:
            return [{"role": str(prompt_obj["role"]), "content": str(prompt_obj["content"])}]
        return [{"role": "user", "content": json.dumps(prompt_obj, ensure_ascii=False)}]

    if isinstance(prompt_obj, list):
        messages = []
        for item in prompt_obj:
            if isinstance(item, dict) and "role" in item and "content" in item:
                messages.append({"role": str(item["role"]), "content": str(item["content"])})
            else:
                messages.append({"role": "user", "content": str(item)})
        if messages:
            return messages

    return [{"role": "user", "content": str(prompt_obj)}]


def build_prompt_text(tokenizer: AutoTokenizer, prompt_obj: Any) -> str:
    messages = _normalize_messages(prompt_obj)
    if tokenizer.chat_template:
        return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    return "\n".join(m["content"] for m in messages) + "\n\nAssistant:"


def sanitize_model_tag(model_path: str, max_len: int = 80) -> str:
    tag = re.sub(r"[^A-Za-z0-9]+", "-", model_path).strip("-").lower()
    if not tag:
        return "unknown-model"
    if len(tag) > max_len:
        return tag[:max_len].rstrip("-")
    return tag


def check_server(base_url: str, headers: Dict[str, str], timeout_sec: int) -> Optional[List[Dict[str, Any]]]:
    models_url = f"{base_url.rstrip('/')}/models"
    resp = requests.get(models_url, headers=headers, timeout=timeout_sec)
    resp.raise_for_status()
    payload = resp.json()
    return payload.get("data", [])


def batched_completion_request(
    base_url: str,
    headers: Dict[str, str],
    model_name: str,
    prompts: List[str],
    max_tokens: int,
    temperature: float,
    top_p: float,
    timeout_sec: int,
) -> List[str]:
    payload = {
        "model": model_name,
        "prompt": prompts,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
    }
    url = f"{base_url.rstrip('/')}/completions"
    resp = requests.post(url, headers=headers, json=payload, timeout=timeout_sec)
    resp.raise_for_status()
    data = resp.json()
    choices = data.get("choices", [])

    preds = [""] * len(prompts)
    for choice in choices:
        idx = choice.get("index", None)
        text = str(choice.get("text", "")).strip()
        if isinstance(idx, int) and 0 <= idx < len(preds) and not preds[idx]:
            preds[idx] = text

    if any(not p for p in preds):
        fallback_texts = [str(c.get("text", "")).strip() for c in choices]
        for i in range(len(preds)):
            if not preds[i]:
                preds[i] = fallback_texts[i] if i < len(fallback_texts) else ""

    return preds


def main() -> None:
    args = parse_args()
    os.makedirs(args.output_dir, exist_ok=True)

    run_ts = datetime.now().strftime("%Y%m%d_%H%M%S")
    model_tag = sanitize_model_tag(args.model_path)
    output_jsonl = os.path.join(args.output_dir, f"vllm_inference_{model_tag}_{run_ts}.jsonl")
    output_parquet = os.path.join(args.output_dir, f"vllm_inference_{model_tag}_{run_ts}.parquet")
    meta_path = os.path.join(args.output_dir, f"vllm_inference_{model_tag}_{run_ts}_meta.json")

    headers = {
        "Authorization": f"Bearer {args.api_key}",
        "Content-Type": "application/json",
    }

    print(f"[INFO] Checking vLLM server: {args.base_url}")
    models = check_server(args.base_url, headers=headers, timeout_sec=args.timeout_sec)
    available_model_ids = [m.get("id", "") for m in models or []]
    print(f"[INFO] Server models: {available_model_ids}")
    if args.served_model_name not in available_model_ids:
        print(
            f"[WARN] Served model '{args.served_model_name}' not found in /models. "
            "Will still try requests with provided name."
        )

    print(f"[INFO] Loading tokenizer from: {args.model_path}")
    tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)

    print(f"[INFO] Reading dataset: {args.dataset_path}")
    templates = load_prompt_templates(args)
    rows = load_verified_rows(args.dataset_path)
    parsed_items: List[Dict[str, Any]] = []
    for idx, row in enumerate(rows):
        gold_label = str(row.get("label", "")).strip()
        fulltext = str(row.get("fulltext", "")).strip()
        summary = str(row.get("summary", "")).strip()
        if gold_label not in VALID_LABELS:
            continue
        if not fulltext or not summary:
            continue
        source_lang = infer_source_lang(fulltext)
        prompt = build_prompt(
            template=templates[gold_label],
            fulltext=fulltext,
            summary=summary,
            source_lang=source_lang,
        )
        parsed_items.append(
            {
                "row_index": idx,
                "doc_id": row.get("doc_id"),
                "gold_label": gold_label,
                "source_lang": source_lang,
                "prompt": prompt,
            }
        )

    df = pd.DataFrame(parsed_items)
    if args.max_samples > 0:
        df = df.head(args.max_samples)
    print(f"[INFO] Rows to process: {len(df)}")
    if df.empty:
        raise RuntimeError("No valid rows found in input file.")

    outputs: List[Dict[str, Any]] = []
    with open(output_jsonl, "w", encoding="utf-8") as f_out:
        for start in tqdm(range(0, len(df), args.batch_size), desc="Batches"):
            batch_df = df.iloc[start : start + args.batch_size]
            prompts = [build_prompt_text(tokenizer, row.get("prompt", "")) for _, row in batch_df.iterrows()]

            preds = batched_completion_request(
                base_url=args.base_url,
                headers=headers,
                model_name=args.served_model_name,
                prompts=prompts,
                max_tokens=args.max_tokens,
                temperature=args.temperature,
                top_p=args.top_p,
                timeout_sec=args.timeout_sec,
            )

            for (row_idx, row), pred in zip(batch_df.iterrows(), preds):
                gold_label = str(row.get("gold_label", ""))
                record = {
                    "row_index": int(row.get("row_index", row_idx)),
                    "doc_id": row.get("doc_id"),
                    "gold_label": gold_label,
                    "source_lang": row.get("source_lang"),
                    "prediction": pred,
                    "generated_text": extract_generated_text(pred, gold_label) if gold_label else pred.strip(),
                }
                outputs.append(record)
                f_out.write(json.dumps(record, ensure_ascii=False) + "\n")

    pd.DataFrame(outputs).to_parquet(output_parquet, index=False)

    with open(meta_path, "w", encoding="utf-8") as f_meta:
        json.dump(
            {
                "model_path_for_tokenizer": args.model_path,
                "dataset_path": args.dataset_path,
                "base_url": args.base_url,
                "served_model_name": args.served_model_name,
                "batch_size": args.batch_size,
                "num_samples": len(outputs),
                "output_jsonl": output_jsonl,
                "output_parquet": output_parquet,
            },
            f_meta,
            ensure_ascii=False,
            indent=2,
        )

    print("[DONE] vLLM batch inference complete.")
    print(f"[DONE] JSONL: {output_jsonl}")
    print(f"[DONE] Parquet: {output_parquet}")
    print(f"[DONE] Meta: {meta_path}")


if __name__ == "__main__":
    main()