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142 changes: 128 additions & 14 deletions benchmarks/README.md
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# Benchmarking vLLM

## Downloading the ShareGPT dataset
## Dataset Overview

You can download the dataset by running:
<table style="width:100%; border-collapse: collapse;">
<thead>
<tr>
<th style="width:15%; text-align: left;">Dataset</th>
<th style="width:10%; text-align: center;">Online</th>
<th style="width:10%; text-align: center;">Offline</th>
<th style="width:65%; text-align: left;">Data Path</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>ShareGPT</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json</code></td>
</tr>
<tr>
<td><strong>BurstGPT</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv</code></td>
</tr>
<tr>
<td><strong>Sonnet</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td>Local file: <code>benchmarks/sonnet.txt</code></td>
</tr>
<tr>
<td><strong>Random</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>synthetic</code></td>
</tr>
<tr>
<td><strong>HuggingFace</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">🚧</td>
<td>Specify your dataset path on HuggingFace</td>
</tr>
<tr>
<td><strong>VisionArena</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">🚧</td>
<td><code>lmarena-ai/vision-arena-bench-v0.1</code> (a HuggingFace dataset)</td>
</tr>
</tbody>
</table>
✅: supported
🚧: to be supported

**Note**: VisionArena’s `dataset-name` should be set to `hf`

---
## Example - Online Benchmark

First start serving your model

```bash
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
vllm serve ${MODEL_NAME} --disable-log-requests
```

## Downloading the ShareGPT4V dataset

The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
will ignore a datapoint if the referred image is missing.
Then run the benchmarking script

```bash
wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
mkdir coco -p
wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
unzip coco/train2017.zip -d coco/
MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
NUM_PROMPTS=10
BACKEND="openai-chat"
DATASET_NAME="sonnet"
DATASET_PATH="benchmarks/sonnet.txt"
python3 benchmarks/benchmark_serving.py --backend ${BACKEND} --model ${MODEL_NAME} --endpoint /v1/chat/completions --dataset-name ${DATASET_NAME} --dataset-path ${DATASET_PATH} --num-prompts ${NUM_PROMPTS}
```

# Downloading the BurstGPT dataset
VisionArena example

You can download the BurstGPT v1.1 dataset by running:
```bash
# need a model with vision capability here
vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
```

```bash
wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
NUM_PROMPTS=10
BACKEND="openai-chat"
DATASET_NAME="hf"
DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
DATASET_SPLIT='train'

python3 benchmarks/benchmark_serving.py \
--backend "${BACKEND}" \
--model "${MODEL_NAME}" \
--endpoint "/v1/chat/completions" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--hf-split "${DATASET_SPLIT}" \
--num-prompts "${NUM_PROMPTS}"
```

---
## Example - Offline Throughput Benchmark

```bash
MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
NUM_PROMPTS=10
DATASET_NAME="sonnet"
DATASET_PATH="benchmarks/sonnet.txt"

python3 benchmarks/benchmark_throughput.py \
--model "${MODEL_NAME}" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--num-prompts "${NUM_PROMPTS}"
```

With LoRA

``` bash
MODEL_NAME="meta-llama/Llama-2-7b-hf"
BACKEND="vllm"
DATASET_NAME="sharegpt"
DATASET_PATH="/home/jovyan/data/vllm_benchmark_datasets/ShareGPT_V3_unfiltered_cleaned_split.json"
NUM_PROMPTS=10
MAX_LORAS=2
MAX_LORA_RANK=8
ENABLE_LORA="--enable-lora"
LORA_PATH="yard1/llama-2-7b-sql-lora-test"

python3 benchmarks/benchmark_throughput.py \
--model "${MODEL_NAME}" \
--backend "${BACKEND}" \
--dataset_path "${DATASET_PATH}" \
--dataset_name "${DATASET_NAME}" \
--num-prompts "${NUM_PROMPTS}" \
--max-loras "${MAX_LORAS}" \
--max-lora-rank "${MAX_LORA_RANK}" \
${ENABLE_LORA} \
--lora-path "${LORA_PATH}"
```