diff --git a/benchmarks/README.md b/benchmarks/README.md index ee172642033de..269a4d51ec2ef 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -1,874 +1,20 @@ -# Benchmarking vLLM +# Benchmarks -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. +This directory used to contain vLLM's benchmark scripts and utilities for performance testing and evaluation. -## Dataset Overview +## Contents - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
DatasetOnlineOfflineData Path
ShareGPT✅✅wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
ShareGPT4V (Image)✅✅ - wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json -
-
Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:
- wget http://images.cocodataset.org/zips/train2017.zip -
ShareGPT4Video (Video)✅✅ - git clone https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video -
BurstGPT✅✅wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
Sonnet (deprecated)✅✅Local file: benchmarks/sonnet.txt
Random✅✅synthetic
RandomMultiModal (Image/Video)🟡🚧synthetic
Prefix Repetition✅✅synthetic
HuggingFace-VisionArena✅✅lmarena-ai/VisionArena-Chat
HuggingFace-InstructCoder✅✅likaixin/InstructCoder
HuggingFace-AIMO✅✅AI-MO/aimo-validation-aime , AI-MO/NuminaMath-1.5, AI-MO/NuminaMath-CoT
HuggingFace-Other✅✅lmms-lab/LLaVA-OneVision-Data, Aeala/ShareGPT_Vicuna_unfiltered
HuggingFace-MTBench✅✅philschmid/mt-bench
HuggingFace-Blazedit✅✅vdaita/edit_5k_char, vdaita/edit_10k_char
Spec Bench✅✅wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl
Custom✅✅Local file: data.jsonl
+- **Serving benchmarks**: Scripts for testing online inference performance (latency, throughput) +- **Throughput benchmarks**: Scripts for testing offline batch inference performance +- **Specialized benchmarks**: Tools for testing specific features like structured output, prefix caching, long document QA, request prioritization, and multi-modal inference +- **Dataset utilities**: Framework for loading and sampling from various benchmark datasets (ShareGPT, HuggingFace datasets, synthetic data, etc.) -✅: supported +## Usage -🟡: Partial support +For detailed usage instructions, examples, and dataset information, see the [Benchmark CLI documentation](https://docs.vllm.ai/en/latest/contributing/benchmarks.html#benchmark-cli). -🚧: to be supported +For full CLI reference see: -**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`. -For local `dataset-path`, please set `hf-name` to its Hugging Face ID like - -```bash ---dataset-path /datasets/VisionArena-Chat/ --hf-name lmarena-ai/VisionArena-Chat -``` - -## 🚀 Example - Online Benchmark - -
-Show more - -
- -First start serving your model - -```bash -vllm serve NousResearch/Hermes-3-Llama-3.1-8B -``` - -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 -vllm bench serve \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --endpoint /v1/completions \ - --dataset-name sharegpt \ - --dataset-path /ShareGPT_V3_unfiltered_cleaned_split.json \ - --num-prompts 10 -``` - -If successful, you will see the following output - -```text -============ 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 -================================================== -``` - -### Custom Dataset - -If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl - -```json -{"prompt": "What is the capital of India?"} -{"prompt": "What is the capital of Iran?"} -{"prompt": "What is the capital of China?"} -``` - -```bash -# start server -VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct -``` - -```bash -# run benchmarking script -vllm bench serve --port 9001 --save-result --save-detailed \ - --backend vllm \ - --model meta-llama/Llama-3.1-8B-Instruct \ - --endpoint /v1/completions \ - --dataset-name custom \ - --dataset-path \ - --custom-skip-chat-template \ - --num-prompts 80 \ - --max-concurrency 1 \ - --temperature=0.3 \ - --top-p=0.75 \ - --result-dir "./log/" -``` - -You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`. - -### VisionArena Benchmark for Vision Language Models - -```bash -# need a model with vision capability here -vllm serve Qwen/Qwen2-VL-7B-Instruct -``` - -```bash -vllm bench serve \ - --backend openai-chat \ - --endpoint-type openai-chat \ - --model Qwen/Qwen2-VL-7B-Instruct \ - --endpoint /v1/chat/completions \ - --dataset-name hf \ - --dataset-path lmarena-ai/VisionArena-Chat \ - --hf-split train \ - --num-prompts 1000 -``` - -### InstructCoder Benchmark with Speculative Decoding - -``` bash -VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \ - --speculative-config $'{"method": "ngram", - "num_speculative_tokens": 5, "prompt_lookup_max": 5, - "prompt_lookup_min": 2}' -``` - -``` bash -vllm bench serve \ - --model meta-llama/Meta-Llama-3-8B-Instruct \ - --dataset-name hf \ - --dataset-path likaixin/InstructCoder \ - --num-prompts 2048 -``` - -### Spec Bench Benchmark with Speculative Decoding - -``` bash -VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \ - --speculative-config $'{"method": "ngram", - "num_speculative_tokens": 5, "prompt_lookup_max": 5, - "prompt_lookup_min": 2}' -``` - -[SpecBench dataset](https://github.com/hemingkx/Spec-Bench) - -Run all categories: - -``` bash -# Download the dataset using: -# wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl - -vllm bench serve \ - --model meta-llama/Meta-Llama-3-8B-Instruct \ - --dataset-name spec_bench \ - --dataset-path "/data/spec_bench/question.jsonl" \ - --num-prompts -1 -``` - -Available categories include `[writing, roleplay, reasoning, math, coding, extraction, stem, humanities, translation, summarization, qa, math_reasoning, rag]`. - -Run only a specific category like "summarization": - -``` bash -vllm bench serve \ - --model meta-llama/Meta-Llama-3-8B-Instruct \ - --dataset-name spec_bench \ - --dataset-path "/data/spec_bench/question.jsonl" \ - --num-prompts -1 - --spec-bench-category "summarization" -``` - -### Other HuggingFaceDataset Examples - -```bash -vllm serve Qwen/Qwen2-VL-7B-Instruct -``` - -`lmms-lab/LLaVA-OneVision-Data`: - -```bash -vllm bench serve \ - --backend openai-chat \ - --endpoint-type openai-chat \ - --model Qwen/Qwen2-VL-7B-Instruct \ - --endpoint /v1/chat/completions \ - --dataset-name hf \ - --dataset-path lmms-lab/LLaVA-OneVision-Data \ - --hf-split train \ - --hf-subset "chart2text(cauldron)" \ - --num-prompts 10 -``` - -`Aeala/ShareGPT_Vicuna_unfiltered`: - -```bash -vllm bench serve \ - --backend openai-chat \ - --endpoint-type openai-chat \ - --model Qwen/Qwen2-VL-7B-Instruct \ - --endpoint /v1/chat/completions \ - --dataset-name hf \ - --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \ - --hf-split train \ - --num-prompts 10 -``` - -`AI-MO/aimo-validation-aime`: - -``` bash -vllm bench serve \ - --model Qwen/QwQ-32B \ - --dataset-name hf \ - --dataset-path AI-MO/aimo-validation-aime \ - --num-prompts 10 \ - --seed 42 -``` - -`philschmid/mt-bench`: - -``` bash -vllm bench serve \ - --model Qwen/QwQ-32B \ - --dataset-name hf \ - --dataset-path philschmid/mt-bench \ - --num-prompts 80 -``` - -`vdaita/edit_5k_char` or `vdaita/edit_10k_char`: - -``` bash -vllm bench serve \ - --model Qwen/QwQ-32B \ - --dataset-name hf \ - --dataset-path vdaita/edit_5k_char \ - --num-prompts 90 \ - --blazedit-min-distance 0.01 \ - --blazedit-max-distance 0.99 -``` - -### Running With Sampling Parameters - -When using OpenAI-compatible backends such as `vllm`, optional sampling -parameters can be specified. Example client command: - -```bash -vllm bench serve \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --endpoint /v1/completions \ - --dataset-name sharegpt \ - --dataset-path /ShareGPT_V3_unfiltered_cleaned_split.json \ - --top-k 10 \ - --top-p 0.9 \ - --temperature 0.5 \ - --num-prompts 10 -``` - -### Running With Ramp-Up Request Rate - -The benchmark tool also supports ramping up the request rate over the -duration of the benchmark run. This can be useful for stress testing the -server or finding the maximum throughput that it can handle, given some latency budget. - -Two ramp-up strategies are supported: - -- `linear`: Increases the request rate linearly from a start value to an end value. -- `exponential`: Increases the request rate exponentially. - -The following arguments can be used to control the ramp-up: - -- `--ramp-up-strategy`: The ramp-up strategy to use (`linear` or `exponential`). -- `--ramp-up-start-rps`: The request rate at the beginning of the benchmark. -- `--ramp-up-end-rps`: The request rate at the end of the benchmark. - -
- -## 📈 Example - Offline Throughput Benchmark - -
-Show more - -
- -```bash -vllm bench throughput \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --dataset-name sonnet \ - --dataset-path vllm/benchmarks/sonnet.txt \ - --num-prompts 10 -``` - -If successful, you will see the following output - -```text -Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s -Total num prompt tokens: 5014 -Total num output tokens: 1500 -``` - -### VisionArena Benchmark for Vision Language Models - -```bash -vllm bench throughput \ - --model Qwen/Qwen2-VL-7B-Instruct \ - --backend vllm-chat \ - --dataset-name hf \ - --dataset-path lmarena-ai/VisionArena-Chat \ - --num-prompts 1000 \ - --hf-split train -``` - -The `num prompt tokens` now includes image token counts - -```text -Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s -Total num prompt tokens: 14527 -Total num output tokens: 1280 -``` - -### InstructCoder Benchmark with Speculative Decoding - -``` bash -VLLM_WORKER_MULTIPROC_METHOD=spawn \ -VLLM_USE_V1=1 \ -vllm bench throughput \ - --dataset-name=hf \ - --dataset-path=likaixin/InstructCoder \ - --model=meta-llama/Meta-Llama-3-8B-Instruct \ - --input-len=1000 \ - --output-len=100 \ - --num-prompts=2048 \ - --async-engine \ - --speculative-config $'{"method": "ngram", - "num_speculative_tokens": 5, "prompt_lookup_max": 5, - "prompt_lookup_min": 2}' -``` - -```text -Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s -Total num prompt tokens: 261136 -Total num output tokens: 204800 -``` - -### Other HuggingFaceDataset Examples - -`lmms-lab/LLaVA-OneVision-Data`: - -```bash -vllm bench throughput \ - --model Qwen/Qwen2-VL-7B-Instruct \ - --backend vllm-chat \ - --dataset-name hf \ - --dataset-path lmms-lab/LLaVA-OneVision-Data \ - --hf-split train \ - --hf-subset "chart2text(cauldron)" \ - --num-prompts 10 -``` - -`Aeala/ShareGPT_Vicuna_unfiltered`: - -```bash -vllm bench throughput \ - --model Qwen/Qwen2-VL-7B-Instruct \ - --backend vllm-chat \ - --dataset-name hf \ - --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \ - --hf-split train \ - --num-prompts 10 -``` - -`AI-MO/aimo-validation-aime`: - -```bash -vllm bench throughput \ - --model Qwen/QwQ-32B \ - --backend vllm \ - --dataset-name hf \ - --dataset-path AI-MO/aimo-validation-aime \ - --hf-split train \ - --num-prompts 10 -``` - -Benchmark with LoRA adapters: - -``` bash -# download dataset -# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json -vllm bench throughput \ - --model meta-llama/Llama-2-7b-hf \ - --backend vllm \ - --dataset_path /ShareGPT_V3_unfiltered_cleaned_split.json \ - --dataset_name sharegpt \ - --num-prompts 10 \ - --max-loras 2 \ - --max-lora-rank 8 \ - --enable-lora \ - --lora-path yard1/llama-2-7b-sql-lora-test - ``` - -
- -## 🛠️ Example - Structured Output Benchmark - -
-Show more - -
- -Benchmark the performance of structured output generation (JSON, grammar, regex). - -### Server Setup - -```bash -vllm serve NousResearch/Hermes-3-Llama-3.1-8B -``` - -### JSON Schema Benchmark - -```bash -python3 benchmarks/benchmark_serving_structured_output.py \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --dataset json \ - --structured-output-ratio 1.0 \ - --request-rate 10 \ - --num-prompts 1000 -``` - -### Grammar-based Generation Benchmark - -```bash -python3 benchmarks/benchmark_serving_structured_output.py \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --dataset grammar \ - --structure-type grammar \ - --request-rate 10 \ - --num-prompts 1000 -``` - -### Regex-based Generation Benchmark - -```bash -python3 benchmarks/benchmark_serving_structured_output.py \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --dataset regex \ - --request-rate 10 \ - --num-prompts 1000 -``` - -### Choice-based Generation Benchmark - -```bash -python3 benchmarks/benchmark_serving_structured_output.py \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --dataset choice \ - --request-rate 10 \ - --num-prompts 1000 -``` - -### XGrammar Benchmark Dataset - -```bash -python3 benchmarks/benchmark_serving_structured_output.py \ - --backend vllm \ - --model NousResearch/Hermes-3-Llama-3.1-8B \ - --dataset xgrammar_bench \ - --request-rate 10 \ - --num-prompts 1000 -``` - -
- -## 📚 Example - Long Document QA Benchmark - -
-Show more - -
- -Benchmark the performance of long document question-answering with prefix caching. - -### Basic Long Document QA Test - -```bash -python3 benchmarks/benchmark_long_document_qa_throughput.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --enable-prefix-caching \ - --num-documents 16 \ - --document-length 2000 \ - --output-len 50 \ - --repeat-count 5 -``` - -### Different Repeat Modes - -```bash -# Random mode (default) - shuffle prompts randomly -python3 benchmarks/benchmark_long_document_qa_throughput.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --enable-prefix-caching \ - --num-documents 8 \ - --document-length 3000 \ - --repeat-count 3 \ - --repeat-mode random - -# Tile mode - repeat entire prompt list in sequence -python3 benchmarks/benchmark_long_document_qa_throughput.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --enable-prefix-caching \ - --num-documents 8 \ - --document-length 3000 \ - --repeat-count 3 \ - --repeat-mode tile - -# Interleave mode - repeat each prompt consecutively -python3 benchmarks/benchmark_long_document_qa_throughput.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --enable-prefix-caching \ - --num-documents 8 \ - --document-length 3000 \ - --repeat-count 3 \ - --repeat-mode interleave -``` - -
- -## 🗂️ Example - Prefix Caching Benchmark - -
-Show more - -
- -Benchmark the efficiency of automatic prefix caching. - -### Fixed Prompt with Prefix Caching - -```bash -python3 benchmarks/benchmark_prefix_caching.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --enable-prefix-caching \ - --num-prompts 1 \ - --repeat-count 100 \ - --input-length-range 128:256 -``` - -### ShareGPT Dataset with Prefix Caching - -```bash -# download dataset -# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json - -python3 benchmarks/benchmark_prefix_caching.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \ - --enable-prefix-caching \ - --num-prompts 20 \ - --repeat-count 5 \ - --input-length-range 128:256 -``` - -### Prefix Repetition Dataset - -```bash -vllm bench serve \ - --backend openai \ - --model meta-llama/Llama-2-7b-chat-hf \ - --dataset-name prefix_repetition \ - --num-prompts 100 \ - --prefix-repetition-prefix-len 512 \ - --prefix-repetition-suffix-len 128 \ - --prefix-repetition-num-prefixes 5 \ - --prefix-repetition-output-len 128 -``` - -
- -## ⚡ Example - Request Prioritization Benchmark - -
-Show more - -
- -Benchmark the performance of request prioritization in vLLM. - -### Basic Prioritization Test - -```bash -python3 benchmarks/benchmark_prioritization.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --input-len 128 \ - --output-len 64 \ - --num-prompts 100 \ - --scheduling-policy priority -``` - -### Multiple Sequences per Prompt - -```bash -python3 benchmarks/benchmark_prioritization.py \ - --model meta-llama/Llama-2-7b-chat-hf \ - --input-len 128 \ - --output-len 64 \ - --num-prompts 100 \ - --scheduling-policy priority \ - --n 2 -``` - -
- -## 👁️ Example - Multi-Modal Benchmark - -
-Show more - -
- -Benchmark the performance of multi-modal requests in vLLM. - -### Images (ShareGPT4V) - -Start vLLM: - -```bash -python -m vllm.entrypoints.openai.api_server \ - --model Qwen/Qwen2.5-VL-7B-Instruct \ - --dtype bfloat16 \ - --limit-mm-per-prompt '{"image": 1}' \ - --allowed-local-media-path /path/to/sharegpt4v/images -``` - -Send requests with images: - -```bash -vllm bench serve \ - --backend openai-chat \ - --model Qwen/Qwen2.5-VL-7B-Instruct \ - --dataset-name sharegpt \ - --dataset-path /path/to/ShareGPT4V/sharegpt4v_instruct_gpt4-vision_cap100k.json \ - --num-prompts 100 \ - --save-result \ - --result-dir ~/vllm_benchmark_results \ - --save-detailed \ - --endpoint /v1/chat/completion -``` - -### Videos (ShareGPT4Video) - -Start vLLM: - -```bash -python -m vllm.entrypoints.openai.api_server \ - --model Qwen/Qwen2.5-VL-7B-Instruct \ - --dtype bfloat16 \ - --limit-mm-per-prompt '{"video": 1}' \ - --allowed-local-media-path /path/to/sharegpt4video/videos -``` - -Send requests with videos: - -```bash -vllm bench serve \ - --backend openai-chat \ - --model Qwen/Qwen2.5-VL-7B-Instruct \ - --dataset-name sharegpt \ - --dataset-path /path/to/ShareGPT4Video/llava_v1_5_mix665k_with_video_chatgpt72k_share4video28k.json \ - --num-prompts 100 \ - --save-result \ - --result-dir ~/vllm_benchmark_results \ - --save-detailed \ - --endpoint /v1/chat/completion -``` - -### Synthetic Random Images (random-mm) - -Generate synthetic image inputs alongside random text prompts to stress-test vision models without external datasets. - -Notes: - -- Works only with online benchmark via the OpenAI backend (`--backend openai-chat`) and endpoint `/v1/chat/completions`. -- Video sampling is not yet implemented. - -Start the server (example): - -```bash -vllm serve Qwen/Qwen2.5-VL-3B-Instruct \ - --dtype bfloat16 \ - --max-model-len 16384 \ - --limit-mm-per-prompt '{"image": 3, "video": 0}' \ - --mm-processor-kwargs max_pixels=1003520 -``` - -Benchmark. It is recommended to use the flag `--ignore-eos` to simulate real responses. You can set the size of the output via the arg `random-output-len`. - -Ex.1: Fixed number of items and a single image resolution, enforcing generation of approx 40 tokens: - -```bash -vllm bench serve \ - --backend openai-chat \ - --model Qwen/Qwen2.5-VL-3B-Instruct \ - --endpoint /v1/chat/completions \ - --dataset-name random-mm \ - --num-prompts 100 \ - --max-concurrency 10 \ - --random-prefix-len 25 \ - --random-input-len 300 \ - --random-output-len 40 \ - --random-range-ratio 0.2 \ - --random-mm-base-items-per-request 2 \ - --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \ - --random-mm-bucket-config '{(224, 224, 1): 1.0}' \ - --request-rate inf \ - --ignore-eos \ - --seed 42 -``` - -The number of items per request can be controlled by passing multiple image buckets: - -```bash - --random-mm-base-items-per-request 2 \ - --random-mm-num-mm-items-range-ratio 0.5 \ - --random-mm-limit-mm-per-prompt '{"image": 4, "video": 0}' \ - --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}' \ -``` - -Flags specific to `random-mm`: - -- `--random-mm-base-items-per-request`: base number of multimodal items per request. -- `--random-mm-num-mm-items-range-ratio`: vary item count uniformly in the closed integer range [floor(n·(1−r)), ceil(n·(1+r))]. Set r=0 to keep it fixed; r=1 allows 0 items. -- `--random-mm-limit-mm-per-prompt`: per-modality hard caps, e.g. '{"image": 3, "video": 0}'. -- `--random-mm-bucket-config`: dict mapping (H, W, T) → probability. Entries with probability 0 are removed; remaining probabilities are renormalized to sum to 1. Use T=1 for images. Set any T>1 for videos (video sampling not yet supported). - -Behavioral notes: - -- If the requested base item count cannot be satisfied under the provided per-prompt limits, the tool raises an error rather than silently clamping. - -How sampling works: - -- Determine per-request item count k by sampling uniformly from the integer range defined by `--random-mm-base-items-per-request` and `--random-mm-num-mm-items-range-ratio`, then clamp k to at most the sum of per-modality limits. -- For each of the k items, sample a bucket (H, W, T) according to the normalized probabilities in `--random-mm-bucket-config`, while tracking how many items of each modality have been added. -- If a modality (e.g., image) reaches its limit from `--random-mm-limit-mm-per-prompt`, all buckets of that modality are excluded and the remaining bucket probabilities are renormalized before continuing. -This should be seen as an edge case, and if this behavior can be avoided by setting `--random-mm-limit-mm-per-prompt` to a large number. Note that this might result in errors due to engine config `--limit-mm-per-prompt`. -- The resulting request contains synthetic image data in `multi_modal_data` (OpenAI Chat format). When `random-mm` is used with the OpenAI Chat backend, prompts remain text and MM content is attached via `multi_modal_data`. - -
+- +- +- diff --git a/docs/contributing/benchmarks.md b/docs/contributing/benchmarks.md index 25c2d2955ff2f..13582dadb46e0 100644 --- a/docs/contributing/benchmarks.md +++ b/docs/contributing/benchmarks.md @@ -1,9 +1,789 @@ +--- +toc_depth: 4 +--- + # Benchmark Suites -vLLM contains two sets of benchmarks: +vLLM provides comprehensive benchmarking tools for performance testing and evaluation: -- [Performance benchmarks][performance-benchmarks] -- [Nightly benchmarks][nightly-benchmarks] +- **[Benchmark CLI]**: `vllm bench` CLI tools and specialized benchmark scripts for interactive performance testing +- **[Performance benchmarks][performance-benchmarks]**: Automated CI benchmarks for development +- **[Nightly benchmarks][nightly-benchmarks]**: Comparative benchmarks against alternatives + +[Benchmark CLI]: #benchmark-cli + +## Benchmark CLI + +This section 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. + +### Dataset Overview + + + +| Dataset | Online | Offline | Data Path | +|---------|--------|---------|-----------| +| ShareGPT | ✅ | ✅ | `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json` | +| ShareGPT4V (Image) | ✅ | ✅ | `wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json`
Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:
`wget http://images.cocodataset.org/zips/train2017.zip` | +| ShareGPT4Video (Video) | ✅ | ✅ | `git clone https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video` | +| BurstGPT | ✅ | ✅ | `wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv` | +| Sonnet (deprecated) | ✅ | ✅ | Local file: `benchmarks/sonnet.txt` | +| Random | ✅ | ✅ | `synthetic` | +| RandomMultiModal (Image/Video) | 🟡 | 🚧 | `synthetic` | +| Prefix Repetition | ✅ | ✅ | `synthetic` | +| HuggingFace-VisionArena | ✅ | ✅ | `lmarena-ai/VisionArena-Chat` | +| HuggingFace-InstructCoder | ✅ | ✅ | `likaixin/InstructCoder` | +| HuggingFace-AIMO | ✅ | ✅ | `AI-MO/aimo-validation-aime`, `AI-MO/NuminaMath-1.5`, `AI-MO/NuminaMath-CoT` | +| HuggingFace-Other | ✅ | ✅ | `lmms-lab/LLaVA-OneVision-Data`, `Aeala/ShareGPT_Vicuna_unfiltered` | +| HuggingFace-MTBench | ✅ | ✅ | `philschmid/mt-bench` | +| HuggingFace-Blazedit | ✅ | ✅ | `vdaita/edit_5k_char`, `vdaita/edit_10k_char` | +| Spec Bench | ✅ | ✅ | `wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl` | +| Custom | ✅ | ✅ | Local file: `data.jsonl` | + +Legend: + +- ✅ - supported +- 🟡 - Partial support +- 🚧 - to be supported + +!!! note + HuggingFace dataset's `dataset-name` should be set to `hf`. + For local `dataset-path`, please set `hf-name` to its Hugging Face ID like + + ```bash + --dataset-path /datasets/VisionArena-Chat/ --hf-name lmarena-ai/VisionArena-Chat + ``` + +### Examples + +#### 🚀 Online Benchmark + +
+Show more + +First start serving your model + +```bash +vllm serve NousResearch/Hermes-3-Llama-3.1-8B +``` + +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 +vllm bench serve \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --endpoint /v1/completions \ + --dataset-name sharegpt \ + --dataset-path /ShareGPT_V3_unfiltered_cleaned_split.json \ + --num-prompts 10 +``` + +If successful, you will see the following output + +```text +============ 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 +================================================== +``` + +##### Custom Dataset + +If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl + +```json +{"prompt": "What is the capital of India?"} +{"prompt": "What is the capital of Iran?"} +{"prompt": "What is the capital of China?"} +``` + +```bash +# start server +VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct +``` + +```bash +# run benchmarking script +vllm bench serve --port 9001 --save-result --save-detailed \ + --backend vllm \ + --model meta-llama/Llama-3.1-8B-Instruct \ + --endpoint /v1/completions \ + --dataset-name custom \ + --dataset-path \ + --custom-skip-chat-template \ + --num-prompts 80 \ + --max-concurrency 1 \ + --temperature=0.3 \ + --top-p=0.75 \ + --result-dir "./log/" +``` + +You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`. + +##### VisionArena Benchmark for Vision Language Models + +```bash +# need a model with vision capability here +vllm serve Qwen/Qwen2-VL-7B-Instruct +``` + +```bash +vllm bench serve \ + --backend openai-chat \ + --endpoint-type openai-chat \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --endpoint /v1/chat/completions \ + --dataset-name hf \ + --dataset-path lmarena-ai/VisionArena-Chat \ + --hf-split train \ + --num-prompts 1000 +``` + +##### InstructCoder Benchmark with Speculative Decoding + +``` bash +VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \ + --speculative-config $'{"method": "ngram", + "num_speculative_tokens": 5, "prompt_lookup_max": 5, + "prompt_lookup_min": 2}' +``` + +``` bash +vllm bench serve \ + --model meta-llama/Meta-Llama-3-8B-Instruct \ + --dataset-name hf \ + --dataset-path likaixin/InstructCoder \ + --num-prompts 2048 +``` + +##### Spec Bench Benchmark with Speculative Decoding + +``` bash +VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \ + --speculative-config $'{"method": "ngram", + "num_speculative_tokens": 5, "prompt_lookup_max": 5, + "prompt_lookup_min": 2}' +``` + +[SpecBench dataset](https://github.com/hemingkx/Spec-Bench) + +Run all categories: + +``` bash +# Download the dataset using: +# wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl + +vllm bench serve \ + --model meta-llama/Meta-Llama-3-8B-Instruct \ + --dataset-name spec_bench \ + --dataset-path "/data/spec_bench/question.jsonl" \ + --num-prompts -1 +``` + +Available categories include `[writing, roleplay, reasoning, math, coding, extraction, stem, humanities, translation, summarization, qa, math_reasoning, rag]`. + +Run only a specific category like "summarization": + +``` bash +vllm bench serve \ + --model meta-llama/Meta-Llama-3-8B-Instruct \ + --dataset-name spec_bench \ + --dataset-path "/data/spec_bench/question.jsonl" \ + --num-prompts -1 + --spec-bench-category "summarization" +``` + +##### Other HuggingFaceDataset Examples + +```bash +vllm serve Qwen/Qwen2-VL-7B-Instruct +``` + +`lmms-lab/LLaVA-OneVision-Data`: + +```bash +vllm bench serve \ + --backend openai-chat \ + --endpoint-type openai-chat \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --endpoint /v1/chat/completions \ + --dataset-name hf \ + --dataset-path lmms-lab/LLaVA-OneVision-Data \ + --hf-split train \ + --hf-subset "chart2text(cauldron)" \ + --num-prompts 10 +``` + +`Aeala/ShareGPT_Vicuna_unfiltered`: + +```bash +vllm bench serve \ + --backend openai-chat \ + --endpoint-type openai-chat \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --endpoint /v1/chat/completions \ + --dataset-name hf \ + --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \ + --hf-split train \ + --num-prompts 10 +``` + +`AI-MO/aimo-validation-aime`: + +``` bash +vllm bench serve \ + --model Qwen/QwQ-32B \ + --dataset-name hf \ + --dataset-path AI-MO/aimo-validation-aime \ + --num-prompts 10 \ + --seed 42 +``` + +`philschmid/mt-bench`: + +``` bash +vllm bench serve \ + --model Qwen/QwQ-32B \ + --dataset-name hf \ + --dataset-path philschmid/mt-bench \ + --num-prompts 80 +``` + +`vdaita/edit_5k_char` or `vdaita/edit_10k_char`: + +``` bash +vllm bench serve \ + --model Qwen/QwQ-32B \ + --dataset-name hf \ + --dataset-path vdaita/edit_5k_char \ + --num-prompts 90 \ + --blazedit-min-distance 0.01 \ + --blazedit-max-distance 0.99 +``` + +##### Running With Sampling Parameters + +When using OpenAI-compatible backends such as `vllm`, optional sampling +parameters can be specified. Example client command: + +```bash +vllm bench serve \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --endpoint /v1/completions \ + --dataset-name sharegpt \ + --dataset-path /ShareGPT_V3_unfiltered_cleaned_split.json \ + --top-k 10 \ + --top-p 0.9 \ + --temperature 0.5 \ + --num-prompts 10 +``` + +##### Running With Ramp-Up Request Rate + +The benchmark tool also supports ramping up the request rate over the +duration of the benchmark run. This can be useful for stress testing the +server or finding the maximum throughput that it can handle, given some latency budget. + +Two ramp-up strategies are supported: + +- `linear`: Increases the request rate linearly from a start value to an end value. +- `exponential`: Increases the request rate exponentially. + +The following arguments can be used to control the ramp-up: + +- `--ramp-up-strategy`: The ramp-up strategy to use (`linear` or `exponential`). +- `--ramp-up-start-rps`: The request rate at the beginning of the benchmark. +- `--ramp-up-end-rps`: The request rate at the end of the benchmark. + +
+ +#### 📈 Offline Throughput Benchmark + +
+Show more + +```bash +vllm bench throughput \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --dataset-name sonnet \ + --dataset-path vllm/benchmarks/sonnet.txt \ + --num-prompts 10 +``` + +If successful, you will see the following output + +```text +Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s +Total num prompt tokens: 5014 +Total num output tokens: 1500 +``` + +##### VisionArena Benchmark for Vision Language Models + +```bash +vllm bench throughput \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --backend vllm-chat \ + --dataset-name hf \ + --dataset-path lmarena-ai/VisionArena-Chat \ + --num-prompts 1000 \ + --hf-split train +``` + +The `num prompt tokens` now includes image token counts + +```text +Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s +Total num prompt tokens: 14527 +Total num output tokens: 1280 +``` + +##### InstructCoder Benchmark with Speculative Decoding + +``` bash +VLLM_WORKER_MULTIPROC_METHOD=spawn \ +VLLM_USE_V1=1 \ +vllm bench throughput \ + --dataset-name=hf \ + --dataset-path=likaixin/InstructCoder \ + --model=meta-llama/Meta-Llama-3-8B-Instruct \ + --input-len=1000 \ + --output-len=100 \ + --num-prompts=2048 \ + --async-engine \ + --speculative-config $'{"method": "ngram", + "num_speculative_tokens": 5, "prompt_lookup_max": 5, + "prompt_lookup_min": 2}' +``` + +```text +Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s +Total num prompt tokens: 261136 +Total num output tokens: 204800 +``` + +##### Other HuggingFaceDataset Examples + +`lmms-lab/LLaVA-OneVision-Data`: + +```bash +vllm bench throughput \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --backend vllm-chat \ + --dataset-name hf \ + --dataset-path lmms-lab/LLaVA-OneVision-Data \ + --hf-split train \ + --hf-subset "chart2text(cauldron)" \ + --num-prompts 10 +``` + +`Aeala/ShareGPT_Vicuna_unfiltered`: + +```bash +vllm bench throughput \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --backend vllm-chat \ + --dataset-name hf \ + --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \ + --hf-split train \ + --num-prompts 10 +``` + +`AI-MO/aimo-validation-aime`: + +```bash +vllm bench throughput \ + --model Qwen/QwQ-32B \ + --backend vllm \ + --dataset-name hf \ + --dataset-path AI-MO/aimo-validation-aime \ + --hf-split train \ + --num-prompts 10 +``` + +Benchmark with LoRA adapters: + +``` bash +# download dataset +# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json +vllm bench throughput \ + --model meta-llama/Llama-2-7b-hf \ + --backend vllm \ + --dataset_path /ShareGPT_V3_unfiltered_cleaned_split.json \ + --dataset_name sharegpt \ + --num-prompts 10 \ + --max-loras 2 \ + --max-lora-rank 8 \ + --enable-lora \ + --lora-path yard1/llama-2-7b-sql-lora-test +``` + +
+ +#### 🛠️ Structured Output Benchmark + +
+Show more + +Benchmark the performance of structured output generation (JSON, grammar, regex). + +##### Server Setup + +```bash +vllm serve NousResearch/Hermes-3-Llama-3.1-8B +``` + +##### JSON Schema Benchmark + +```bash +python3 benchmarks/benchmark_serving_structured_output.py \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --dataset json \ + --structured-output-ratio 1.0 \ + --request-rate 10 \ + --num-prompts 1000 +``` + +##### Grammar-based Generation Benchmark + +```bash +python3 benchmarks/benchmark_serving_structured_output.py \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --dataset grammar \ + --structure-type grammar \ + --request-rate 10 \ + --num-prompts 1000 +``` + +##### Regex-based Generation Benchmark + +```bash +python3 benchmarks/benchmark_serving_structured_output.py \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --dataset regex \ + --request-rate 10 \ + --num-prompts 1000 +``` + +##### Choice-based Generation Benchmark + +```bash +python3 benchmarks/benchmark_serving_structured_output.py \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --dataset choice \ + --request-rate 10 \ + --num-prompts 1000 +``` + +##### XGrammar Benchmark Dataset + +```bash +python3 benchmarks/benchmark_serving_structured_output.py \ + --backend vllm \ + --model NousResearch/Hermes-3-Llama-3.1-8B \ + --dataset xgrammar_bench \ + --request-rate 10 \ + --num-prompts 1000 +``` + +
+ +#### 📚 Long Document QA Benchmark + +
+Show more + +Benchmark the performance of long document question-answering with prefix caching. + +##### Basic Long Document QA Test + +```bash +python3 benchmarks/benchmark_long_document_qa_throughput.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --enable-prefix-caching \ + --num-documents 16 \ + --document-length 2000 \ + --output-len 50 \ + --repeat-count 5 +``` + +##### Different Repeat Modes + +```bash +# Random mode (default) - shuffle prompts randomly +python3 benchmarks/benchmark_long_document_qa_throughput.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --enable-prefix-caching \ + --num-documents 8 \ + --document-length 3000 \ + --repeat-count 3 \ + --repeat-mode random + +# Tile mode - repeat entire prompt list in sequence +python3 benchmarks/benchmark_long_document_qa_throughput.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --enable-prefix-caching \ + --num-documents 8 \ + --document-length 3000 \ + --repeat-count 3 \ + --repeat-mode tile + +# Interleave mode - repeat each prompt consecutively +python3 benchmarks/benchmark_long_document_qa_throughput.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --enable-prefix-caching \ + --num-documents 8 \ + --document-length 3000 \ + --repeat-count 3 \ + --repeat-mode interleave +``` + +
+ +#### 🗂️ Prefix Caching Benchmark + +
+Show more + +Benchmark the efficiency of automatic prefix caching. + +##### Fixed Prompt with Prefix Caching + +```bash +python3 benchmarks/benchmark_prefix_caching.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --enable-prefix-caching \ + --num-prompts 1 \ + --repeat-count 100 \ + --input-length-range 128:256 +``` + +##### ShareGPT Dataset with Prefix Caching + +```bash +# download dataset +# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json + +python3 benchmarks/benchmark_prefix_caching.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \ + --enable-prefix-caching \ + --num-prompts 20 \ + --repeat-count 5 \ + --input-length-range 128:256 +``` + +##### Prefix Repetition Dataset + +```bash +vllm bench serve \ + --backend openai \ + --model meta-llama/Llama-2-7b-chat-hf \ + --dataset-name prefix_repetition \ + --num-prompts 100 \ + --prefix-repetition-prefix-len 512 \ + --prefix-repetition-suffix-len 128 \ + --prefix-repetition-num-prefixes 5 \ + --prefix-repetition-output-len 128 +``` + +
+ +#### ⚡ Request Prioritization Benchmark + +
+Show more + +Benchmark the performance of request prioritization in vLLM. + +##### Basic Prioritization Test + +```bash +python3 benchmarks/benchmark_prioritization.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --input-len 128 \ + --output-len 64 \ + --num-prompts 100 \ + --scheduling-policy priority +``` + +##### Multiple Sequences per Prompt + +```bash +python3 benchmarks/benchmark_prioritization.py \ + --model meta-llama/Llama-2-7b-chat-hf \ + --input-len 128 \ + --output-len 64 \ + --num-prompts 100 \ + --scheduling-policy priority \ + --n 2 +``` + +
+ +#### 👁️ Multi-Modal Benchmark + +
+Show more + +Benchmark the performance of multi-modal requests in vLLM. + +##### Images (ShareGPT4V) + +Start vLLM: + +```bash +python -m vllm.entrypoints.openai.api_server \ + --model Qwen/Qwen2.5-VL-7B-Instruct \ + --dtype bfloat16 \ + --limit-mm-per-prompt '{"image": 1}' \ + --allowed-local-media-path /path/to/sharegpt4v/images +``` + +Send requests with images: + +```bash +vllm bench serve \ + --backend openai-chat \ + --model Qwen/Qwen2.5-VL-7B-Instruct \ + --dataset-name sharegpt \ + --dataset-path /path/to/ShareGPT4V/sharegpt4v_instruct_gpt4-vision_cap100k.json \ + --num-prompts 100 \ + --save-result \ + --result-dir ~/vllm_benchmark_results \ + --save-detailed \ + --endpoint /v1/chat/completion +``` + +##### Videos (ShareGPT4Video) + +Start vLLM: + +```bash +python -m vllm.entrypoints.openai.api_server \ + --model Qwen/Qwen2.5-VL-7B-Instruct \ + --dtype bfloat16 \ + --limit-mm-per-prompt '{"video": 1}' \ + --allowed-local-media-path /path/to/sharegpt4video/videos +``` + +Send requests with videos: + +```bash +vllm bench serve \ + --backend openai-chat \ + --model Qwen/Qwen2.5-VL-7B-Instruct \ + --dataset-name sharegpt \ + --dataset-path /path/to/ShareGPT4Video/llava_v1_5_mix665k_with_video_chatgpt72k_share4video28k.json \ + --num-prompts 100 \ + --save-result \ + --result-dir ~/vllm_benchmark_results \ + --save-detailed \ + --endpoint /v1/chat/completion +``` + +##### Synthetic Random Images (random-mm) + +Generate synthetic image inputs alongside random text prompts to stress-test vision models without external datasets. + +Notes: + +- Works only with online benchmark via the OpenAI backend (`--backend openai-chat`) and endpoint `/v1/chat/completions`. +- Video sampling is not yet implemented. + +Start the server (example): + +```bash +vllm serve Qwen/Qwen2.5-VL-3B-Instruct \ + --dtype bfloat16 \ + --max-model-len 16384 \ + --limit-mm-per-prompt '{"image": 3, "video": 0}' \ + --mm-processor-kwargs max_pixels=1003520 +``` + +Benchmark. It is recommended to use the flag `--ignore-eos` to simulate real responses. You can set the size of the output via the arg `random-output-len`. + +Ex.1: Fixed number of items and a single image resolution, enforcing generation of approx 40 tokens: + +```bash +vllm bench serve \ + --backend openai-chat \ + --model Qwen/Qwen2.5-VL-3B-Instruct \ + --endpoint /v1/chat/completions \ + --dataset-name random-mm \ + --num-prompts 100 \ + --max-concurrency 10 \ + --random-prefix-len 25 \ + --random-input-len 300 \ + --random-output-len 40 \ + --random-range-ratio 0.2 \ + --random-mm-base-items-per-request 2 \ + --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \ + --random-mm-bucket-config '{(224, 224, 1): 1.0}' \ + --request-rate inf \ + --ignore-eos \ + --seed 42 +``` + +The number of items per request can be controlled by passing multiple image buckets: + +```bash + --random-mm-base-items-per-request 2 \ + --random-mm-num-mm-items-range-ratio 0.5 \ + --random-mm-limit-mm-per-prompt '{"image": 4, "video": 0}' \ + --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}' \ +``` + +Flags specific to `random-mm`: + +- `--random-mm-base-items-per-request`: base number of multimodal items per request. +- `--random-mm-num-mm-items-range-ratio`: vary item count uniformly in the closed integer range [floor(n·(1−r)), ceil(n·(1+r))]. Set r=0 to keep it fixed; r=1 allows 0 items. +- `--random-mm-limit-mm-per-prompt`: per-modality hard caps, e.g. '{"image": 3, "video": 0}'. +- `--random-mm-bucket-config`: dict mapping (H, W, T) → probability. Entries with probability 0 are removed; remaining probabilities are renormalized to sum to 1. Use T=1 for images. Set any T>1 for videos (video sampling not yet supported). + +Behavioral notes: + +- If the requested base item count cannot be satisfied under the provided per-prompt limits, the tool raises an error rather than silently clamping. + +How sampling works: + +- Determine per-request item count k by sampling uniformly from the integer range defined by `--random-mm-base-items-per-request` and `--random-mm-num-mm-items-range-ratio`, then clamp k to at most the sum of per-modality limits. +- For each of the k items, sample a bucket (H, W, T) according to the normalized probabilities in `--random-mm-bucket-config`, while tracking how many items of each modality have been added. +- If a modality (e.g., image) reaches its limit from `--random-mm-limit-mm-per-prompt`, all buckets of that modality are excluded and the remaining bucket probabilities are renormalized before continuing. +This should be seen as an edge case, and if this behavior can be avoided by setting `--random-mm-limit-mm-per-prompt` to a large number. Note that this might result in errors due to engine config `--limit-mm-per-prompt`. +- The resulting request contains synthetic image data in `multi_modal_data` (OpenAI Chat format). When `random-mm` is used with the OpenAI Chat backend, prompts remain text and MM content is attached via `multi_modal_data`. + +
[](){ #performance-benchmarks } @@ -13,22 +793,22 @@ The performance benchmarks are used for development to confirm whether new chang ### Manually Trigger the benchmark -Use [vllm-ci-test-repo images](https://gallery.ecr.aws/q9t5s3a7/vllm-ci-test-repo) with vLLM benchmark suite. +Use [vllm-ci-test-repo images](https://gallery.ecr.aws/q9t5s3a7/vllm-ci-test-repo) with vLLM benchmark suite. For CPU environment, please use the image with "-cpu" postfix. -Here is an example for docker run command for CPU. +Here is an example for docker run command for CPU. ```bash docker run -it --entrypoint /bin/bash -v /data/huggingface:/root/.cache/huggingface -e HF_TOKEN='' --shm-size=16g --name vllm-cpu-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:1da94e673c257373280026f75ceb4effac80e892-cpu ``` -Then, run below command inside the docker instance. +Then, run below command inside the docker instance. ```bash bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh ``` -When run, benchmark script generates results under **benchmark/results** folder, along with the benchmark_results.md and benchmark_results.json. +When run, benchmark script generates results under **benchmark/results** folder, along with the benchmark_results.md and benchmark_results.json. #### Runtime environment variables