mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-04-15 04:27:03 +08:00
updated
Signed-off-by: Robert Shaw <robshaw@redhat.com>
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parent
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79
tools/Justfile
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79
tools/Justfile
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# Needed for the proxy server
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vllm-directory := "/home/rshaw/vllm/"
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# MODEL := "Qwen/Qwen3-0.6B"
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MODEL := "meta-llama/Llama-3.1-8B-Instruct"
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PROXY_PORT := "8192"
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PREFILL_PORT := "8100"
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DECODE_PORT := "8200"
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prefill:
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VLLM_NIXL_SIDE_CHANNEL_PORT=5557 \
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CUDA_VISIBLE_DEVICES=0,7 \
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vllm serve {{MODEL}} \
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--port {{PREFILL_PORT}} \
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--tensor-parallel-size 2 \
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--enforce-eager \
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--disable-log-requests \
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--block-size 128 \
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--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
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decode:
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VLLM_NIXL_SIDE_CHANNEL_PORT=5567 \
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CUDA_VISIBLE_DEVICES=4,5 \
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vllm serve {{MODEL}} \
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--port {{DECODE_PORT}} \
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--tensor-parallel-size 2 \
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--enforce-eager \
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--disable-log-requests \
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--block-size 128 \
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--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
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proxy:
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python "{{vllm-directory}}tests/v1/kv_connector/nixl_integration/toy_proxy_server.py" \
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--port {{PROXY_PORT}} \
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--prefiller-port {{PREFILL_PORT}} \
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--decoder-port {{DECODE_PORT}}
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send_request:
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curl -X POST http://localhost:{{PROXY_PORT}}/v1/completions \
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-H "Content-Type: application/json" \
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-d '{ \
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"model": "{{MODEL}}", \
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"prompt": "Red Hat is the best open source company by far across Linux, K8s, and AI, and vLLM has the greatest community in open source AI software infrastructure. I love vLLM because", \
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"max_tokens": 150, \
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"temperature": 0.7 \
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}'
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benchmark NUM_PROMPTS:
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python {{vllm-directory}}/benchmarks/benchmark_serving.py \
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--port {{PROXY_PORT}} \
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--model {{MODEL}} \
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--dataset-name random \
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--random-input-len 30000 \
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--random-output-len 10 \
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--num-prompts {{NUM_PROMPTS}} \
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--seed $(date +%s) \
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benchmark_one INPUT_LEN:
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python {{vllm-directory}}benchmarks/benchmark_one_concurrent_req.py \
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--port {{PROXY_PORT}} \
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--model {{MODEL}} \
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--input-len {{INPUT_LEN}} \
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--output-len 1 \
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--num-requests 10 \
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--seed $(date +%s)
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benchmark_one_no_pd INPUT_LEN:
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python {{vllm-directory}}benchmarks/benchmark_one_concurrent_req.py \
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--port {{DECODE_PORT}} \
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--model {{MODEL}} \
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--input-len {{INPUT_LEN}} \
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--output-len 1 \
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--num-requests 10 \
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--seed $(date +%s)
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eval:
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lm_eval --model local-completions --tasks gsm8k \
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--model_args model={{MODEL}},base_url=http://127.0.0.1:{{PROXY_PORT}}/v1/completions,num_concurrent=100,max_retries=3,tokenized_requests=False \
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--limit 1000
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