Profiling vLLM

We support tracing vLLM workers using the torch.profiler module. You can enable tracing by setting the VLLM_TORCH_PROFILER_DIR environment variable to the directory where you want to save the traces: VLLM_TORCH_PROFILER_DIR=/mnt/traces/

The OpenAI server also needs to be started with the VLLM_TORCH_PROFILER_DIR environment variable set.

When using benchmarks/benchmark_serving.py, you can enable profiling by passing the --profile flag.

Warning

Only enable profiling in a development environment.

Traces can be visualized using https://ui.perfetto.dev/.

Tip

Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly.

Tip

To stop the profiler - it flushes out all the profile trace files to the directory. This takes time, for example for about 100 requests worth of data for a llama 70b, it takes about 10 minutes to flush out on a H100. Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes. export VLLM_RPC_TIMEOUT=1800000

Example commands and usage:

Offline Inference:

Refer to examples/offline_inference_with_profiler.py for an example.

OpenAI Server:

VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B

benchmark_serving.py:

python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2