Hyperparameter Tuning

Hyperparameter Tuning#

Achieving Peak Throughput#

Achieving a large batch size is the most important thing for attaining high throughput.

When the server is running at full load, look for the following in the log:

Decode batch. #running-req: 233, #token: 370959, token usage: 0.82, gen throughput (token/s): 4594.01, #queue-req: 317

Tune Your Request Submission Speed#

#queue-req indicates the number of requests in the queue. If you frequently see #queue-req == 0, it suggests you are bottlenecked by the request submission speed.

A healthy range for #queue-req is 50 - 500.

On the other hand, do not make #queue-req too large because it will also increase the scheduling overhead on the server, especially when using the default longest-prefix-match schedule policy (--schedule-policy lpm).

Tune --schedule-conservativeness#

token usage indicates the KV cache memory utilization of the server. token usage > 0.9 means good utilization. If you frequently see token usage < 0.9 and #queue-req > 0, it means the server is too conservative about taking in new requests. You can decrease --schedule-conservativeness to a value like 0.3. The case of server being too conservative can happen when users send many requests with a large max_new_tokens but the requests stop very early due to EOS or stop strings.

On the other hand, if you see token usage very high and you frequently see warnings like decode out of memory happened, #retracted_reqs: 1, #new_token_ratio: 0.9998 -> 1.0000, you can increase --schedule-conservativeness to a value like 1.3. If you see decode out of memory happened occasionally but not frequently, it is okay.

Tune --dp-size and --tp-size#

Data parallelism is better for throughput. When there is enough GPU memory, always favor data parallelism for throughput. Refer to sglang router for a better data parallelism rather than using dp_size parameter.

Avoid out-of-memory by Tuning --chunked-prefill-size, --mem-fraction-static, --max-running-requests#

If you see out of memory (OOM) errors, you can try to tune the following parameters.

  • If OOM happens during prefill, try to decrease --chunked-prefill-size to 4096 or 2048.

  • If OOM happens during decoding, try to decrease --max-running-requests.

  • You can also try to decrease --mem-fraction-static, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding.

Enabling cache for torch.compile#

To enable torch.compile acceleration, add --enable-torch-compile. It accelerates small models on small batch sizes. This does not work for FP8 currently. By default, torch.compile will automatically cache the FX graph and Triton in /tmp/torchinductor_root, which might be cleared according to the system policy. You can export the environment variable TORCHINDUCTOR_CACHE_DIR to save compilation cache in your desired directory to avoid unwanted deletion. You can also share the cache with other machines to reduce the compilation time.

SGLang uses max-autotune-no-cudagraphs mode of torch.compile. The auto-tuning can be slow. If you want to deploy a model on many different machines, you can ship the torch.compile cache to these machines and skip the compilation steps. This is based on PyTorch official documentation.

Examples：

  1. Generate the cache by setting TORCHINDUCTOR_CACHE_DIR and running the model once.

    TORCHINDUCTOR_CACHE_DIR=/root/inductor_root_cache python3 -m sglang.launch_server --model meta-llama/Llama-3.1-8B-Instruct --enable-torch-compile

  2. Copy the cache folder to other machines and launch the server with TORCHINDUCTOR_CACHE_DIR.

Tune --schedule-policy#

If the workload has many shared prefixes, use the default --schedule-policy lpm. Where lpm stands for longest prefix match.

When you have no shared prefixes at all or you always send the requests with the shared prefixes together, you can try --schedule-policy fcfs. Where fcfs stands for first come first serve. This policy has a lower scheduling overhead.