48 lines
1.8 KiB
ReStructuredText
48 lines
1.8 KiB
ReStructuredText
==============
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Profiling vLLM
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==============
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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/``
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The OpenAI server also needs to be started with the ``VLLM_TORCH_PROFILER_DIR`` environment variable set.
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When using ``benchmarks/benchmark_serving.py``, you can enable profiling by passing the ``--profile`` flag.
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.. warning::
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Only enable profiling in a development environment.
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Traces can be visualized using https://ui.perfetto.dev/.
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.. tip::
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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.
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.. tip::
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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.
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Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes.
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``export VLLM_RPC_TIMEOUT=1800000``
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Example commands and usage:
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===========================
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Offline Inference:
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------------------
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Refer to `examples/offline_inference_with_profiler.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_with_profiler.py>`_ for an example.
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OpenAI Server:
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--------------
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.. code-block:: bash
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VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
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benchmark_serving.py:
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.. code-block:: bash
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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 |