infer_vllm.py

# Requires vllm>=0.8.5
import logging
import argparse
from typing import Dict, Optional, List

import json
import logging

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import destroy_model_parallel
import gc
import math
from vllm.inputs.data import TokensPrompt

#  Arguments
parse = argparse.ArgumentParser()
parse.add_argument("--model_name_or_path", type=str, default='Qwen/Qwen3-Reranker-0.6B')
parse.add_argument("--number_of_gpu", type=int, default=1)
args = parse.parse_args()

def format_instruction(instruction, query, doc):
    text = [
        {"role": "system", "content": "Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\"."},
        {"role": "user", "content": f"<Instruct>: {instruction}\n\n<Query>: {query}\n\n<Document>: {doc}"}
    ]
    return text

def process_inputs(pairs, instruction, max_length, suffix_tokens):
    messages = [format_instruction(instruction, query, doc) for query, doc in pairs]
    messages =  tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=False, enable_thinking=False
    )
    messages = [ele[:max_length] + suffix_tokens for ele in messages]
    messages = [TokensPrompt(prompt_token_ids=ele) for ele in messages]
    return messages

def compute_logits(model, messages, sampling_params, true_token, false_token):
    outputs = model.generate(messages, sampling_params, use_tqdm=False)
    scores = []
    for i in range(len(outputs)):
        final_logits = outputs[i].outputs[0].logprobs[-1]
        token_count = len(outputs[i].outputs[0].token_ids)
        if true_token not in final_logits:
            true_logit = -10
        else:
            true_logit = final_logits[true_token].logprob
        if false_token not in final_logits:
            false_logit = -10
        else:
            false_logit = final_logits[false_token].logprob
        true_score = math.exp(true_logit)
        false_score = math.exp(false_logit)
        score = true_score / (true_score + false_score)
        scores.append(score)
    return scores

if __name__ == "__main__":

    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, trust_remote_code=True)
    model = LLM(model=args.model_name_or_path, tensor_parallel_size=args.number_of_gpu, max_model_len=10000, enable_prefix_caching=True, gpu_memory_utilization=0.8)
    tokenizer.padding_side = "left"
    tokenizer.pad_token = tokenizer.eos_token
    suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
    max_length=8192
    suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
    true_token = tokenizer("yes", add_special_tokens=False).input_ids[0]
    false_token = tokenizer("no", add_special_tokens=False).input_ids[0]
    sampling_params = SamplingParams(temperature=0,
        max_tokens=1,
        logprobs=20,
        allowed_token_ids=[true_token, false_token],
    )


    task = 'Given a web search query, retrieve relevant passages that answer the query'
    queries = ["What is the capital of China?",
        "Explain gravity",
    ]
    documents = [
        "The capital of China is Beijing.",
        "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
    ]

    pairs = list(zip(queries, documents))
    inputs = process_inputs(pairs, task, max_length-len(suffix_tokens), suffix_tokens)
    scores = compute_logits(model, inputs, sampling_params, true_token, false_token)
    print('scores', scores)

    destroy_model_parallel()