[Doc] Update benchmarks README (vllm-project#14646)

Signed-off-by: Jennifer Zhao <[email protected]>
Co-authored-by: Jennifer Zhao <[email protected]>
Co-authored-by: Roger Wang <[email protected]>
Signed-off-by: Louis Ulmer <[email protected]>

Author: 2 people

benchmarks/README.md

@@ -1,29 +1,181 @@
 # Benchmarking vLLM
 
-## Downloading the ShareGPT dataset
+This README guides you through running benchmark tests with the extensive
+datasets supported on vLLM. It’s a living document, updated as new features and datasets
+become available.
 
-You can download the dataset by running:
+## Dataset Overview
+
+<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
+Then run the benchmarking script
+
+```bash
+# download dataset
+# 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"
+NUM_PROMPTS=10
+BACKEND="openai-chat"
+DATASET_NAME="sharegpt"
+DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
+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}
+```
+
+If successful, you will see the following output
+
+```
+============ Serving Benchmark Result ============
+Successful requests:                     10        
+Benchmark duration (s):                  5.78      
+Total input tokens:                      1369      
+Total generated tokens:                  2212      
+Request throughput (req/s):              1.73      
+Output token throughput (tok/s):         382.89    
+Total Token throughput (tok/s):          619.85    
+---------------Time to First Token----------------
+Mean TTFT (ms):                          71.54     
+Median TTFT (ms):                        73.88     
+P99 TTFT (ms):                           79.49     
+-----Time per Output Token (excl. 1st token)------
+Mean TPOT (ms):                          7.91      
+Median TPOT (ms):                        7.96      
+P99 TPOT (ms):                           8.03      
+---------------Inter-token Latency----------------
+Mean ITL (ms):                           7.74      
+Median ITL (ms):                         7.70      
+P99 ITL (ms):                            8.39      
+==================================================
+```
 
-The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
-will ignore a datapoint if the referred image is missing.
+### VisionArena Benchmark for Vision Language Models
 
 ```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/
+# need a model with vision capability here
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
-# Downloading the BurstGPT dataset
+```bash
+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}"
+```
 
-You can download the BurstGPT v1.1 dataset by running:
+---
+## Example - Offline Throughput Benchmark
 
 ```bash
-wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
+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}"
+  ```
+
+If successful, you will see the following output
+
+```
+Throughput: 7.35 requests/s, 4789.20 total tokens/s, 1102.83 output tokens/s
 ```
+
+### Benchmark with LoRA Adapters
+
+``` 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}"
+  ```