biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[doc] improve readability for long commands (#19920)

Browse Source 
Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>
This commit is contained in:
Reid
2025-06-23 22:27:07 +08:00
committed by GitHub
parent a6e6604d32
commit 53243e5c42
3 changed files with 48 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
docs/contributing/profiling.md
View File

@@ -30,13 +30,21 @@ Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example
#### OpenAI Server
```bash
VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
VLLM_TORCH_PROFILER_DIR=./vllm_profile \
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Meta-Llama-3-70B
```
benchmark_serving.py:
```bash
python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2
python benchmarks/benchmark_serving.py \
--backend vllm \
--model meta-llama/Meta-Llama-3-70B \
--dataset-name sharegpt \
--dataset-path sharegpt.json \
--profile \
--num-prompts 2
```
## Profile with NVIDIA Nsight Systems
@@ -64,7 +72,16 @@ For basic usage, you can just append `nsys profile -o report.nsys-rep --trace-fo
The following is an example using the `benchmarks/benchmark_latency.py` script:
```bash
nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node python benchmarks/benchmark_latency.py --model meta-llama/Llama-3.1-8B-Instruct --num-iters-warmup 5 --num-iters 1 --batch-size 16 --input-len 512 --output-len 8
nsys profile -o report.nsys-rep \
--trace-fork-before-exec=true \
--cuda-graph-trace=node \
python benchmarks/benchmark_latency.py \
--model meta-llama/Llama-3.1-8B-Instruct \
--num-iters-warmup 5 \
--num-iters 1 \
--batch-size 16 \
--input-len 512 \
--output-len 8
```
#### OpenAI Server
@@ -73,10 +90,21 @@ To profile the server, you will want to prepend your `vllm serve` command with `
```bash
# server
nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node --delay 30 --duration 60 vllm serve meta-llama/Llama-3.1-8B-Instruct
nsys profile -o report.nsys-rep \
--trace-fork-before-exec=true \
--cuda-graph-trace=node \
--delay 30 \
--duration 60 \
vllm serve meta-llama/Llama-3.1-8B-Instruct
# client
python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 1 --dataset-name random --random-input 1024 --random-output 512
python benchmarks/benchmark_serving.py \
--backend vllm \
--model meta-llama/Llama-3.1-8B-Instruct \
--num-prompts 1 \
--dataset-name random \
--random-input 1024 \
--random-output 512
```
In practice, you should set the `--duration` argument to a large value. Whenever you want the server to stop profiling, run: