[Docs] Clean up benchmark_and_profiling.md (#4297)

Signed-off-by: windsonsea <haifeng.yao@daocloud.io>

docs/references/benchmark_and_profiling.md

@@ -1,23 +1,32 @@
 # Benchmark and Profiling
 
 ## Benchmark
+
 - Benchmark the latency of running a single static batch without a server. The arguments are the same as for `launch_server.py`.
   Note that this is a simplified test script without 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 simplified script does not.
-  ```
+
+  ```bash
   python -m sglang.bench_one_batch --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --batch 32 --input-len 256 --output-len 32
   ```
+
 - Benchmark offline processing. This script will start an offline engine and run the benchmark.
-  ```
+
+  ```bash
   python3 -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --num-prompts 10
   ```
+
 - Benchmark online serving. Please use `sglang.launch_server` to launch a server first and run the following command.
-  ```
+
+  ```bash
   python3 -m sglang.bench_serving --backend sglang --num-prompt 10
   ```
 
 ## Profile with PyTorch Profiler
-Pytorch Profiler is a convenient basic tool to inspect kernel execution time, call stack, and kernel overlap and occupancy.
+
+[Pytorch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) is a convenient basic tool to inspect kernel execution time, call stack, and kernel overlap and occupancy.
+
 - To profile a server
+
   ```bash
   # set trace path
   export SGLANG_TORCH_PROFILER_DIR=/root/sglang/profile_log
@@ -28,9 +37,11 @@ python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct
   # send profiling request from client
   python -m sglang.bench_serving --backend sglang --model-path meta-llama/Llama-3.1-8B-Instruct --num-prompts 10 --sharegpt-output-len 100 --profile
   ```
+
   Please make sure that the `SGLANG_TORCH_PROFILER_DIR` should be set at both server and client side, otherwise the trace file cannot be generated correctly . A secure way will be setting `SGLANG_TORCH_PROFILER_DIR` in the `.*rc` file of shell (e.g. `~/.bashrc` for bash shells).
 
 - To profile offline
+
   ```bash
   export SGLANG_TORCH_PROFILER_DIR=/root/sglang/profile_log
   python -m sglang.bench_offline_throughput --model-path meta-llama/Llama-3.1-8B-Instruct --dataset-name random --num-prompts 10 --profile --mem-frac=0.8
@@ -39,21 +50,27 @@ python -m sglang.bench_offline_throughput --model-path meta-llama/Llama-3.1-8B-I
 - View Traces
 
   Trace files can be loaded and visualized from:
+
   1. https://ui.perfetto.dev/ (any browser)
   2. chrome://tracing (Chrome browser only)
 
   If browser cannot open trace file due to its large size,
   client can generate a small trace file (<100MB) by controlling number of prompts and lengths of prompt outputs.
   For example, when profiling a server,
+
   ```bash
   python -m sglang.bench_serving --backend sglang --model-path meta-llama/Llama-3.1-8B-Instruct --num-prompts 2 --sharegpt-output-len 100 --profile
   ```
-sets the number of prompts to 2 with `--num-prompts` argument and limits the length of output sequences to 100 with `--sharegpt-output-len` argument, which can generate a small trace file for browser to open smoothly.
+
+  This command sets the number of prompts to 2 with `--num-prompts` argument and limits the length of output sequences to 100 with `--sharegpt-output-len` argument, which can generate a small trace file for browser to open smoothly.
 
 ## Profile with Nsight
-Nsight systems is an advanced tool that exposes more profiling details, such as register and shared memory usage, annotated code regions and low-level CUDA APIs and events.
 
-0. Prerequisite: install using apt, or run inside a [NVIDIA Docker container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags) or [SGLang Docker container](https://github.com/sgl-project/sglang/tree/main/docker).
+[Nsight systems](https://docs.nvidia.com/nsight-systems/) is an advanced tool that exposes more profiling details, such as register and shared memory usage, annotated code regions and low-level CUDA APIs and events.
+
+1. Prerequisite:
+
+   Install using apt, or run inside a [NVIDIA Docker container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags) or [SGLang Docker container](https://github.com/sgl-project/sglang/tree/main/docker).
 
    ```bash
    # install nsys
@@ -66,9 +83,13 @@ 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_one_batch --model meta-llama/Meta-Llama-3-8B --batch-size 64 --input-len 512`
+2. To profile a single batch, use
 
-2. To profile a server, e.g.
+   ```bash
+   nsys profile --trace-fork-before-exec=true --cuda-graph-trace=node python3 -m sglang.bench_one_batch --model meta-llama/Meta-Llama-3-8B --batch-size 64 --input-len 512
+   ```
+
+3. To profile a server, e.g.
 
    ```bash
    # launch the server, set the delay and duration times according to needs
@@ -94,12 +115,13 @@ nsys stop --session=profile-XXXXX
 
    to manually kill the profiler and generate `nsys-rep` files instantly.
 
-3. Use NVTX to annotate code regions, e.g. to see their execution time.
+4. Use NVTX to annotate code regions, e.g. to see their execution time.
 
    ```bash
    # install nvtx
    pip install nvtx
    ```
+
    ```python
    # code snippets
    import nvtx
@@ -108,7 +130,13 @@ with nvtx.annotate("description", color="color"):
    ```
 
 ## Other tips
+
 1. You can benchmark a model using dummy weights by only providing the config.json file. This allows for quick testing of model variants without training. To do so, add `--load-format dummy` to the above commands and then you only need a correct `config.json` under the checkpoint folder.
-2. You can benchmark a model with modified configs (e.g., less layers) by using `--json-model-override-args`. For example, you can benchmark a model with only 2 layers and 2 kv heads using `python -m sglang.bench_one_batch --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --batch 32 --input-len 256 --output-len 32 --load-format dummy --json-model-override-args '{"num_hidden_layers": 1, "num_key_value_heads": 1}'`
+2. You can benchmark a model with modified configs (e.g., less layers) by using `--json-model-override-args`. For example, you can benchmark a model with only 2 layers and 2 kv heads using:
+
+   ```bash
+   python -m sglang.bench_one_batch --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --batch 32 --input-len 256 --output-len 32 --load-format dummy --json-model-override-args '{"num_hidden_layers": 1, "num_key_value_heads": 1}'
+   ```
+
 3. You can use `--python-backtrace=cuda` to see python call stack for all CUDA kernels, as in PyTorch Profiler. (Caveat: this can cause inaccurately long kernel runtimes for CUDA event based timing)
-4. For more args please see https://docs.nvidia.com/nsight-systems/UserGuide/index.html
+4. For more arguments see [Nsight Systems User Guide](https://docs.nvidia.com/nsight-systems/UserGuide/index.html).