Guide on Hyperparameter Tuning

Guide on 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.

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.

Try Advanced Options#

  • To enable the experimental overlapped scheduler, add --enable-overlap-schedule. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currently.

  • To enable torch.compile acceleration, add --enable-torch-compile. It accelerates small models on small batch sizes. This does not work for FP8 currently.

Tune --schedule-policy#

If the workload has many shared prefixes, use the default --schedule-policy lpm. 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. fcfs stands for first come first serve. fcfs has a lower scheduling overhead.