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docs: update doc (#716)

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Ying Sheng
2024-07-24 13:38:06 -07:00
parent 321a963b01
commit 08a3bd19cc
5 changed files with 83 additions and 3 deletions
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2
README.md
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@@ -37,7 +37,7 @@ The core features include:
### Method 1: With pip
```
pip install --upgrade pip
pip install --upgrade pip setuptools wheel
pip install "sglang[all]"
# Install FlashInfer CUDA kernels

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benchmark/blog_v0_2/405b_sglang.sh Normal file
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# create ~/llama-3.1-405b-fp8-dummy and create config.json and tokenizer:
# config.json from https://gist.github.com/zhyncs/748597c44d47b45fa15866a4ae2c2b29?permalink_comment_id=5128893
# wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer.json?download=true
# wget wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer_config.json?download=true
# Launch sglang
# python -m sglang.launch_server --model ~/llama-3.1-405b-fp8-dummy/ --load-format dummy --tp 8 --quant fp8 --disable-radix --mem-frac 0.88
# offline
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 > sglang/log11
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 > sglang/log12
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 512 --random-range-ratio 0.5 > sglang/log13
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 512 --random-range-ratio 0.5 > sglang/log14
python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 2500 > sglang/log21
# online
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 300 --request-rate 1 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log31
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 600 --request-rate 2 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log32
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log33
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log34
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log35
# python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 1000 --request-rate 32 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log36
# python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 1000 --request-rate 1 > sglang/log41
# python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 1000 --request-rate 2 > sglang/log42
# python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 1000 --request-rate 4 > sglang/log43
# python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 1000 --request-rate 8 > sglang/log44
# python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 1000 --request-rate 16 > sglang/log45
# python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 1000 --request-rate 32 > sglang/log46

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benchmark/blog_v0_2/405b_trt.sh Normal file
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# Launch trtllm
# https://gist.github.com/zhyncs/748597c44d47b45fa15866a4ae2c2b29?permalink_comment_id=5129302
# offline
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log11
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log12
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log13
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log14
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 2500 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log21
# online
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 300 --request-rate 1 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log31
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 600 --request-rate 2 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log32
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log33
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log34
python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log35
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 1000 --request-rate 32 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log36
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 1000 --request-rate 1 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log41
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 1000 --request-rate 2 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log42
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 1000 --request-rate 4 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log43
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 1000 --request-rate 8 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log44
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 1000 --request-rate 16 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log45
# python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 1000 --request-rate 32 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log46

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benchmark/blog_v0_2/405b_vllm.sh Normal file
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@@ -0,0 +1,29 @@
# create ~/llama-3.1-405b-fp8-dummy and create config.json and tokenizer:
# config.json from https://gist.github.com/zhyncs/748597c44d47b45fa15866a4ae2c2b29?permalink_comment_id=5128893
# (remove the new llama3 rope_scaling entry to run with vLLM 0.5.2)
# wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer.json?download=true
# wget wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer_config.json?download=true
# Launch vllm
# python3 -m vllm.entrypoints.openai.api_server --model ~/llama-3.1-405b-fp8-dummy/ --load-format dummy --disable-log-requests --tensor-parallel-size 8 --max-model-len 10000
# offline
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 > vllm/log11
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 > vllm/log12
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 512 --random-range-ratio 0.5 > vllm/log13
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 512 --random-range-ratio 0.5 > vllm/log14
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 2500 > vllm/log21
# online
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 300 --request-rate 1 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log31
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 600 --request-rate 2 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log32
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log33
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log34
python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log35
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 1000 --request-rate 32 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log36
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 1000 --request-rate 1 > vllm/log41
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 1000 --request-rate 2 > vllm/log42
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 1000 --request-rate 4 > vllm/log43
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 1000 --request-rate 8 > vllm/log44
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 1000 --request-rate 16 > vllm/log45
# python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 1000 --request-rate 32 > vllm/log46

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benchmark/blog_v0_2/README.md
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@@ -8,7 +8,7 @@
git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip
pip install --upgrade pip setuptools wheel
pip install -e "python[all]"
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/
@@ -91,7 +91,7 @@ cat sglang_online_benchmark.jsonl | cut -d':' -f9 | cut -d',' -f1
## Other
We tried using vLLM 0.5.3.post1, but it often crashes under high loads, so we are using the older version, vLLM 0.5.2.
We tried using vLLM 0.5.3.post1, but it often crashes under high loads, and it seems to have similar or worse performance compared to vLLM 0.5.2 from our partial benchmarking, so we are using the older version, vLLM 0.5.2.
Preparation for TensorRT LLM can refer to https://github.com/sgl-project/tensorrt-demo. Specifically, we used a batch size of 512, a max input length of 8192, and a max number of tokens of 8192. The instance count for preprocessing and postprocessing in Triton Server is 16.