1.8 KiB
Profiling vLLM
:::{warning} Profiling is only intended for vLLM developers and maintainers to understand the proportion of time spent in different parts of the codebase. vLLM end-users should never turn on profiling as it will significantly slow down the inference. :::
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.
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
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Example commands and usage
Offline Inference
Refer to gh-file:examples/offline_inference/simple_profiling.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