Improve benchmark (#1140)

docs/en/benchmark_and_profiling.md

@@ -0,0 +1,49 @@
+# Benchmark and Profiling
+
+## Benchmark
+- Benchmark a single static batch by running the following command without launching a server. The arguments are the same as for `launch_server.py`. Note that this is not a dynamic batching server, so it may run out of memory for a batch size that a real server can handle. A real server truncates the prefill into several batches, while this unit test does not. For accurate large batch testing, consider using `sglang.bench_serving`.
+  ```
+  python -m sglang.bench_latency --model-path meta-llama/Meta-Llama-3-8B-Instruct --batch 32 --input-len 256 --output-len 32
+  ```
+- Benchmark online serving. Launch a server first and run the following command.
+  ```
+  python3 -m sglang.bench_serving --backend sglang --num-prompt 10
+  ```
+
+## Profile with Nsight
+0. Prerequisite
+```bash
+# install nsys
+# https://docs.nvidia.com/nsight-systems/InstallationGuide/index.html
+apt update
+apt install -y --no-install-recommends gnupg
+echo "deb http://developer.download.nvidia.com/devtools/repos/ubuntu$(source /etc/lsb-release; echo "$DISTRIB_RELEASE" | tr -d .)/$(dpkg --print-architecture) /" | tee /etc/apt/sources.list.d/nvidia-devtools.list
+apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
+apt update
+apt install nsight-systems-cli
+```
+
+1. To profile a single batch, use `nsys profile --trace-fork-before-exec=true --cuda-graph-trace=node python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 64 --input-len 512`
+
+2. To profile a server, e.g.
+
+```bash
+# server
+# set the delay and duration times according to needs
+nsys profile --trace-fork-before-exec=true --cuda-graph-trace=node -o sglang.out --delay 60 --duration 70 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --disable-radix-cache
+
+# client
+python3 -m sglang.bench_serving --backend sglang --num-prompts 6000 --dataset-name random --random-input 4096 --random-output 2048
+```
+
+3. Use NVTX, e.g.
+
+```bash
+# install nvtx
+pip install nvtx
+
+# code snippets
+import nvtx
+with nvtx.annotate("description", color="color"):
+    # some critical code
+```