fix: drop cuda graph bs (batch size) on dsr1 h100 sgl

components/backends/sglang/docs/dsr1-wideep-h100.md

@@ -9,22 +9,14 @@ Dynamo supports SGLang's implementation of wide expert parallelism and large sca
 
 ## Instructions
 
-1. Pull the SGLang container.
-
-```bash
-docker pull lmsysorg/sglang:latest
-```
-
-You can also pull a specific tag from the [lmsys dockerhub](https://hub.docker.com/r/lmsysorg/sglang/tags)
-
-2. Build the Dynamo container
+1. Build the Dynamo container
 
 ```bash
 cd $DYNAMO_ROOT
 docker build -f container/Dockerfile.sglang-wideep . -t dynamo-wideep --no-cache
 ```
 
-3. You can run this container on each 8xH100 node using the following command.
+2. You can run this container on each 8xH100 node using the following command.
 
 > [!IMPORTANT]
 > We recommend downloading DeepSeek-R1 and then mounting it to the container. You can find the model [here](https://huggingface.co/deepseek-ai/DeepSeek-R1)
@@ -47,13 +39,13 @@ docker run \
 
 In each container, you should be in the `/sgl-workspace/dynamo/components/backends/sglang` directory.
 
-4. On the head prefill node, run the helper script provided to generate commands to start the `nats-server`, `etcd`. This script will also tell you which environment variables to export on each node to make deployment easier.
+3. On the head prefill node, run the helper script provided to generate commands to start the `nats-server`, `etcd`. This script will also tell you which environment variables to export on each node to make deployment easier.
 
 ```bash
 ./utils/gen_env_vars.sh
 ```
 
-5. Run the ingress and prefill worker
+4. Run the ingress and prefill worker
 
 ```bash
 # run ingress
@@ -93,7 +85,7 @@ python3 -m dynamo.sglang.worker \
 
 On the other prefill node (since this example has 4 total prefill nodes), run the same command but change `--node-rank` to 1,2, and 3
 
-7. Run the decode worker on the head decode node
+5. Run the decode worker on the head decode node
 
 ```bash
 python3 -m dynamo.sglang.decode_worker \
@@ -121,7 +113,7 @@ python3 -m dynamo.sglang.decode_worker \
   --deepep-mode low_latency \
   --mem-fraction-static 0.835 \
   --ep-num-redundant-experts 32 \
-  --cuda-graph-bs 256
+  --cuda-graph-bs 128
 ```
 
 On the other decode nodes (this example has 9 total decode nodes), run the same command but change `--node-rank` to 1, 2, 3, 4, 5, 6, 7, and 8
@@ -131,6 +123,7 @@ On the other decode nodes (this example has 9 total decode nodes), run the same
 In the official [blog post repro instructions](https://github.com/sgl-project/sglang/issues/6017), SGL uses batch inference to benchmark their prefill and decode workers. They do this by pretokenizing the ShareGPT dataset and then creating a batch of 8192 requests with ISL 4096 and OSL 5 (for prefill stress test) and a batch of 40000 with ISL 2000 and OSL 100 (for decode stress test). If you want to repro these benchmarks, you will need to add the following flags to the prefill and decode commands:
 
 prefill:
+
 ```bash
 ...
 --max-running-requests 8192 \
@@ -142,6 +135,7 @@ prefill:
 ```
 
 decode:
+
 ```bash
 ...
 --max-running-requests 18432 \
@@ -152,9 +146,10 @@ decode:
 We currently provide 2 different ways to perform an end to end benchmark which includes using our OpenAI frontend and tokenization. We will continue to add better support for these sorts of large single batch workloads in the future.
 
 1. **GenAI Perf to benchmark end to end performance with 8k ISL 256 OSL**
-We've found that 8k ISL 256 OSL provides a good baseline for measuring end to end disaggregated serving performance for DSR1. As WideEP allows for a higher throughput, we provide a script that runs this workload at high concurrencies. DeepGEMM kernels can sometimes take a while to warm up. We provide a short ramping warmup script that can be used.
+   We've found that 8k ISL 256 OSL provides a good baseline for measuring end to end disaggregated serving performance for DSR1. As WideEP allows for a higher throughput, we provide a script that runs this workload at high concurrencies. DeepGEMM kernels can sometimes take a while to warm up. We provide a short ramping warmup script that can be used.
 
 Example usage:
+
 ```bash
 # warmup
 ./utils/bench.sh HEAD_PREFILL_NODE_IP --type warmup
@@ -165,9 +160,10 @@ curl -X POST http://${HEAD_PREFILL_NODE_IP}:9001/flush_cache
 ```
 
 2. **GenAI Perf to benchmark completions with custom dataset**
-We provide a script that generates a JSONL file of the ShareGPT dataset and then use GenAI Perf to benchmark the prefill and decode workers. We use ShareGPT in order to leverage the pre-existing EPLB distributions provided by the SGLang team. If you don't want to use ShareGPT - you can also use GenAIPerf's synthetic dataset setup But note you will have to use dynamic EPLB configurations or record your own as the `init-expert-location` provided by SGLang is tuned specifically for the ShareGPT dataset at a 4096 ISL and 5 OSL.
+   We provide a script that generates a JSONL file of the ShareGPT dataset and then use GenAI Perf to benchmark the prefill and decode workers. We use ShareGPT in order to leverage the pre-existing EPLB distributions provided by the SGLang team. If you don't want to use ShareGPT - you can also use GenAIPerf's synthetic dataset setup But note you will have to use dynamic EPLB configurations or record your own as the `init-expert-location` provided by SGLang is tuned specifically for the ShareGPT dataset at a 4096 ISL and 5 OSL.
 
 Example usage:
+
 ```bash
 # generate data
 python3 src/dynamo/sglang/utils/generate_bench_data.py --output data.jsonl --num-prompts 8192 --input-len 4096 --output-len 5 --model deepseek-ai/DeepSeek-R1