AISBench Benchmark Tool

AISBench Benchmark is a model evaluation tool built based on OpenCompass. It supports evaluation scenarios for both accuracy and performance testing of AI models on Ascend NPU.

Overview

• Accuracy Evaluation: Accuracy verification of service-deployed models and local models on various QA and reasoning benchmark datasets, covering text, multimodal, and other scenarios.
• Performance Evaluation: Latency and throughput evaluation of service-deployed models, extreme performance testing under stress test scenarios, steady-state performance evaluation, and real business traffic simulation.

Supported Scenarios

Supported Benchmarks

• Text: GSM8K, MMLU, Ceval, FewCLUE series, dapo_math, leval
• Multimodal: docvqa, infovqa, ocrbench_v2, omnidocbench, mmmu, mmmu_pro, mmstar, videomme, textvqa, videobench, vocalsound
• Multi-turn Dialogue: sharegpt, mtbench
• Function Call: BFCL (Berkeley Function Calling Leaderboard)

Installation

Environment Requirements

Python Version: Only Python 3.10, 3.11, or 3.12 is supported.

# Create conda environment
conda create --name ais_bench python=3.10 -y
conda activate ais_bench

Install from Source

git clone https://github.com/AISBench/benchmark.git
cd benchmark/
pip3 install -e ./ --use-pep517

Verify installation:

ais_bench -h

Optional Dependencies

# For service-deployed model evaluation (vLLM, Triton, etc.)
pip3 install -r requirements/api.txt
pip3 install -r requirements/extra.txt

# For Huggingface multimodal / vLLM offline inference
pip3 install -r requirements/hf_vl_dependency.txt

# For BFCL Function Calling evaluation
pip3 install -r requirements/datasets/bfcl_dependencies.txt --no-deps

Quick Start

Basic Command Structure

ais_bench --models <model_task> --datasets <dataset_task> [--summarizer example]

• --models: Specifies the model task configuration
• --datasets: Specifies the dataset task configuration
• --summarizer: Result presentation task (default: example)

Find Configuration Files

# List all available task configurations
ais_bench --models vllm_api_general_chat --datasets demo_gsm8k_gen_4_shot_cot_chat_prompt --search

Example: Service Model Accuracy Evaluation

1. Start vLLM inference service (follow vLLM documentation)

2. Prepare dataset:

   • Download GSM8K from opencompass
   • Extract to ais_bench/datasets/gsm8k/

3. Modify model configuration (vllm_api_general_chat.py):

   from ais_bench.benchmark.models import VLLMCustomAPIChat
   
   models = [
       dict(
           attr="service",
           type=VLLMCustomAPIChat,
           abbr='vllm-api-general-chat',
           path="",
           model="",
           stream=False,
           request_rate=0,
           retry=2,
           api_key="",
           host_ip="localhost",
           host_port=8080,
           url="",
           max_out_len=512,
           batch_size=1,
           trust_remote_code=False,
           generation_kwargs=dict(
               temperature=0.01,
               ignore_eos=False,
           )
       )
   ]
   

4. Run evaluation:

   ais_bench --models vllm_api_general_chat --datasets demo_gsm8k_gen_4_shot_cot_chat_prompt
   

Output Results

dataset                 version  metric   mode  vllm_api_general_chat
----------------------- -------- -------- ----- ----------------------
demo_gsm8k              401e4c   accuracy gen                   62.50

Model Task Types

Service-Deployed Models

Local Models

Performance Evaluation

Key Metrics

Performance Test Example

ais_bench --models vllm_api_general_chat --datasets custom_performance \
    --mode performance --concurrency 100

Steady-State Performance

For obtaining true optimal system performance:

ais_bench --models vllm_api_general_chat --datasets sharegpt \
    --stable-stage --duration 300

Real Traffic Simulation

ais_bench --models vllm_api_general_chat --datasets custom \
    --rps-distribution rps_config.json

Multi-task Evaluation

Multiple Models

ais_bench --models model1 model2 model3 --datasets dataset1

Multiple Datasets

ais_bench --models model1 --datasets dataset1 dataset2 dataset3

Parallel Execution

ais_bench --models model1 model2 --datasets dataset1 dataset2 --parallel 4

Custom Datasets

Performance Custom Dataset

Create a JSONL file with custom requests:

{"input": "Your prompt here", "max_output_length": 512}

Accuracy Custom Dataset

Refer to Custom Dataset Guide

Output Structure

outputs/default/20250628_151326/
├── configs/           # Combined configuration
├── logs/              # Execution logs
│   ├── eval/          # Evaluation logs
│   └── infer/         # Inference logs
├── predictions/       # Raw inference results
├── results/           # Calculated scores
└── summary/           # Final summaries
    ├── summary_*.csv
    ├── summary_*.md
    └── summary_*.txt

Task Management Interface

During execution, a real-time task management interface displays:

• Task name and progress
• Time cost and status
• Log path
• Extended parameters

Controls:

• P key: Pause/Resume screen refresh
• Ctrl+C: Exit

Common CLI Options

Advanced Features

Breakpoint Resume

ais_bench --models model1 --datasets dataset1 --resume outputs/default/20250628_151326

Failed Case Re-run

ais_bench --models model1 --datasets dataset1 --failed-only --resume outputs/default/20250628_151326

Multi-file Dataset Merge

For datasets like MMLU with multiple files:

ais_bench --models model1 --datasets mmlu_merged

Repeated Inference for pass@k

ais_bench --models model1 --datasets dataset1 --repeat-n 5

Troubleshooting

Installation Issues

1. Python version mismatch: Use Python 3.10/3.11/3.12
2. Dependency conflicts: Use conda environment
3. bfcl_eval pathlib issue: Use --no-deps flag

Runtime Issues

1. Model connection failed: Check host_ip, host_port, and service status
2. Dataset not found: Download dataset to ais_bench/datasets/
3. Memory issues: Reduce batch_size or use smaller dataset

Helper Scripts

Quick utility scripts for common operations:

# Check environment
bash scripts/check_env.sh

# Quick accuracy test
bash scripts/run_accuracy_test.sh vllm_api_general_chat demo_gsm8k --host-port 8080

# Quick performance test
bash scripts/run_performance_test.sh vllm_api_general_chat sharegpt --concurrency 100

# Parse results
python scripts/parse_results.py outputs/default/20250628_151326

References

Detailed documentation for specific use cases:

• Model Configuration Reference: All model types (vLLM, MindIE, Triton, TGI, offline) with parameter explanations
• CLI Reference: Complete CLI options for accuracy and performance evaluation

Templates

Ready-to-use templates for custom evaluation: