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add some examples for other benchmark scripts (#19893)
Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
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@ -387,3 +387,178 @@ python3 vllm/benchmarks/benchmark_throughput.py \
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--enable-lora \
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--lora-path yard1/llama-2-7b-sql-lora-test
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```
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---
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## Example - Structured Output Benchmark
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Benchmark the performance of structured output generation (JSON, grammar, regex).
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### Server Setup
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```bash
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vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
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```
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### JSON Schema Benchmark
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```bash
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python3 benchmarks/benchmark_serving_structured_output.py \
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--backend vllm \
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--model NousResearch/Hermes-3-Llama-3.1-8B \
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--dataset json \
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--structured-output-ratio 1.0 \
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--request-rate 10 \
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--num-prompts 1000
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```
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### Grammar-based Generation Benchmark
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```bash
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python3 benchmarks/benchmark_serving_structured_output.py \
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--backend vllm \
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--model NousResearch/Hermes-3-Llama-3.1-8B \
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--dataset grammar \
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--structure-type grammar \
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--request-rate 10 \
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--num-prompts 1000
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```
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### Regex-based Generation Benchmark
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```bash
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python3 benchmarks/benchmark_serving_structured_output.py \
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--backend vllm \
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--model NousResearch/Hermes-3-Llama-3.1-8B \
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--dataset regex \
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--request-rate 10 \
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--num-prompts 1000
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```
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### Choice-based Generation Benchmark
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```bash
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python3 benchmarks/benchmark_serving_structured_output.py \
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--backend vllm \
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--model NousResearch/Hermes-3-Llama-3.1-8B \
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--dataset choice \
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--request-rate 10 \
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--num-prompts 1000
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```
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### XGrammar Benchmark Dataset
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```bash
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python3 benchmarks/benchmark_serving_structured_output.py \
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--backend vllm \
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--model NousResearch/Hermes-3-Llama-3.1-8B \
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--dataset xgrammar_bench \
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--request-rate 10 \
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--num-prompts 1000
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```
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---
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## Example - Long Document QA Throughput Benchmark
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Benchmark the performance of long document question-answering with prefix caching.
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### Basic Long Document QA Test
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```bash
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python3 benchmarks/benchmark_long_document_qa_throughput.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--enable-prefix-caching \
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--num-documents 16 \
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--document-length 2000 \
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--output-len 50 \
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--repeat-count 5
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```
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### Different Repeat Modes
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```bash
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# Random mode (default) - shuffle prompts randomly
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python3 benchmarks/benchmark_long_document_qa_throughput.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--enable-prefix-caching \
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--num-documents 8 \
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--document-length 3000 \
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--repeat-count 3 \
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--repeat-mode random
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# Tile mode - repeat entire prompt list in sequence
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python3 benchmarks/benchmark_long_document_qa_throughput.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--enable-prefix-caching \
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--num-documents 8 \
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--document-length 3000 \
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--repeat-count 3 \
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--repeat-mode tile
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# Interleave mode - repeat each prompt consecutively
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python3 benchmarks/benchmark_long_document_qa_throughput.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--enable-prefix-caching \
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--num-documents 8 \
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--document-length 3000 \
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--repeat-count 3 \
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--repeat-mode interleave
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```
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---
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## Example - Prefix Caching Benchmark
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Benchmark the efficiency of automatic prefix caching.
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### Fixed Prompt with Prefix Caching
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```bash
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python3 benchmarks/benchmark_prefix_caching.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--enable-prefix-caching \
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--num-prompts 1 \
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--repeat-count 100 \
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--input-length-range 128:256
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```
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### ShareGPT Dataset with Prefix Caching
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```bash
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# download dataset
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# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
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python3 benchmarks/benchmark_prefix_caching.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \
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--enable-prefix-caching \
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--num-prompts 20 \
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--repeat-count 5 \
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--input-length-range 128:256
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```
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---
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## Example - Request Prioritization Benchmark
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Benchmark the performance of request prioritization in vLLM.
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### Basic Prioritization Test
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```bash
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python3 benchmarks/benchmark_prioritization.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--input-len 128 \
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--output-len 64 \
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--num-prompts 100 \
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--scheduling-policy priority
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```
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### Multiple Sequences per Prompt
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```bash
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python3 benchmarks/benchmark_prioritization.py \
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--model meta-llama/Llama-2-7b-chat-hf \
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--input-len 128 \
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--output-len 64 \
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--num-prompts 100 \
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--scheduling-policy priority \
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--n 2
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```
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