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misc: update doc (#715)

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Yineng Zhang
2024-07-25 06:05:46 +10:00
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@@ -14,9 +14,10 @@ pip install -e "python[all]"
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/
```
### Set up HF_TOKEN
### Set up ulimit and HF_TOKEN
```bash
ulimit -n 65535
# Change the token to a real and usable one, with access permissions for the Llama 3 models.
export HF_TOKEN=hf_token
```
@@ -36,6 +37,13 @@ python -m sglang.launch_server --model-path neuralmagic/Meta-Llama-3-70B-Instruc
## Benchmark
### Hardware Requirements
- 8B models: Single NVIDIA A100 80GB GPU
- 70B models: 8 x NVIDIA A100 80GB GPUs with Tensor Parallelism (TP) 8
- 70B FP8 models: 8 x NVIDIA H100 GPUs with Tensor Parallelism (TP) 8
Please ensure you have the appropriate hardware before running the benchmarks.
#### Offline benchmark
@@ -86,3 +94,153 @@ cat sglang_online_benchmark.jsonl | cut -d':' -f9 | cut -d',' -f1
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.
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.
```bash
# vLLM
pip install vllm==0.5.2
# Meta-Llama-3-8B-Instruct
python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-8B-Instruct --disable-log-requests
# meta-llama/Meta-Llama-3-70B-Instruct
python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B-Instruct --disable-log-requests --tensor 8
# neuralmagic/Meta-Llama-3-70B-Instruct-FP8
python -m vllm.entrypoints.openai.api_server --model neuralmagic/Meta-Llama-3-70B-Instruct-FP8 --disable-log-requests --tensor 8
```
```bash
wget https://raw.githubusercontent.com/sgl-project/sglang/main/python/sglang/bench_serving.py
```
```bash
# vLLM Offline
# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k
python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k
python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
# ShareGPT dataset, num prompts 3k
python3 bench_serving.py --backend vllm --num-prompts 3000 --output-file vllm_offline_benchmark.jsonl
# get output token throughput
cat vllm_offline_benchmark.jsonl | cut -d':' -f12 | cut -d',' -f1
```
```bash
# vLLM Online
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file vllm_online_benchmark.jsonl
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file vllm_online_benchmark.jsonl
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file vllm_online_benchmark.jsonl
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file vllm_online_benchmark.jsonl
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file vllm_online_benchmark.jsonl
# get median e2e latency
cat vllm_online_benchmark.jsonl | cut -d':' -f9 | cut -d',' -f1
```
```bash
# TensorRT LLM Offline 8B
# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# ShareGPT dataset, num prompts 3k
python3 bench_serving.py --backend trt --num-prompts 3000 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# get output token throughput
cat trt_offline_benchmark_8b.jsonl | cut -d':' -f12 | cut -d',' -f1
```
```bash
# TensorRT LLM Online 8B
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
# get median e2e latency
cat trt_online_benchmark_8b.jsonl | cut -d':' -f9 | cut -d',' -f1
```
```bash
# TensorRT LLM Offline 70B
# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# ShareGPT dataset, num prompts 3k
python3 bench_serving.py --backend trt --num-prompts 3000 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# get output token throughput
cat trt_offline_benchmark_70b.jsonl | cut -d':' -f12 | cut -d',' -f1
```
```bash
# TensorRT LLM Online 70B
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
# get median e2e latency
cat trt_online_benchmark_70b.jsonl | cut -d':' -f9 | cut -d',' -f1
```