Gahow Wang
f784e49c07
Two microbenchmarks quantifying the elastic offload decision:
1. Interference (corrected): cold prefill causes 14-214x TPOT p90
degradation on same-worker decode (D∈{1,2,4,8} × P∈{2k,8k,16k,32k}).
Earlier run had a prefix-cache bug (deterministic prompts hit cache
after rep 0); fixed with uuid+time_ns unique prompts.
2. Transfer lifecycle: PD-sep TTFT breakdown via Mooncake proxy,
measuring prefill→RDMA→decode startup overhead.
Key finding: offload wins at all P≥2048 operating points —
transfer cost is 25-50% of interference cost even with bulk Mooncake.
113 lines
3.4 KiB
Bash
113 lines
3.4 KiB
Bash
#!/bin/bash
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# Launch PD-separated pair (TP=1 each) for lifecycle microbenchmark.
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# Uses GPUs 1 (prefill) and 2 (decode) to avoid conflicting with Microbench 1 on GPU 0.
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#
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# Usage: bash launch_pd_pair.sh
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# Requires: ~/agentic-kv/.venv with vLLM 0.18.1 + Mooncake
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set -euo pipefail
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VENV="$HOME/agentic-kv/.venv/bin"
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PYTHON="$VENV/python"
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MODEL_PATH="${MODEL_PATH:-$HOME/models/Qwen/Qwen3-Coder-30B-A3B-Instruct}"
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PREFILL_PORT=8010
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DECODE_PORT=8020
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BOOTSTRAP_PORT=8998
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PREFILL_GPU=1
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DECODE_GPU=2
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LOG_DIR="$HOME/agentic-kv/microbench/lifecycle/logs"
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mkdir -p "$LOG_DIR"
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trap 'echo "Cleaning up..."; kill $(jobs -p) 2>/dev/null; wait 2>/dev/null' EXIT INT TERM
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echo "=== PD Lifecycle Microbench: PD-separated pair ==="
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echo " Model: $MODEL_PATH"
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echo " Prefill: GPU $PREFILL_GPU, port $PREFILL_PORT, bootstrap $BOOTSTRAP_PORT"
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echo " Decode: GPU $DECODE_GPU, port $DECODE_PORT"
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echo ""
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# Start prefill instance (KV producer)
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echo "[1/2] Starting prefill instance on GPU $PREFILL_GPU..."
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VLLM_MOONCAKE_BOOTSTRAP_PORT=$BOOTSTRAP_PORT \
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CUDA_VISIBLE_DEVICES=$PREFILL_GPU \
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$PYTHON -m vllm.entrypoints.openai.api_server \
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--model "$MODEL_PATH" \
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--host 0.0.0.0 \
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--port $PREFILL_PORT \
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--tensor-parallel-size 1 \
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--trust-remote-code \
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--enable-prefix-caching \
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--dtype auto \
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--gpu-memory-utilization 0.9 \
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--max-model-len 200000 \
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--no-enable-log-requests \
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--kv-transfer-config \
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'{"kv_connector":"MooncakeConnector","kv_role":"kv_producer"}' \
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2>&1 | tee "$LOG_DIR/prefill.log" &
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PREFILL_PID=$!
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echo " Prefill PID=$PREFILL_PID"
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# Wait for prefill to be ready
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echo " Waiting for prefill instance..."
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for i in $(seq 1 180); do
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if curl -s "http://127.0.0.1:$PREFILL_PORT/v1/models" > /dev/null 2>&1; then
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echo " Prefill ready after ${i}s"
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break
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fi
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if [ $i -eq 180 ]; then
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echo " ERROR: Prefill did not start within 180s"
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exit 1
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fi
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sleep 1
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done
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# Start decode instance (KV consumer)
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echo "[2/2] Starting decode instance on GPU $DECODE_GPU..."
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CUDA_VISIBLE_DEVICES=$DECODE_GPU \
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$PYTHON -m vllm.entrypoints.openai.api_server \
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--model "$MODEL_PATH" \
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--host 0.0.0.0 \
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--port $DECODE_PORT \
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--tensor-parallel-size 1 \
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--trust-remote-code \
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--enable-prefix-caching \
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--dtype auto \
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--gpu-memory-utilization 0.9 \
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--max-model-len 200000 \
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--no-enable-log-requests \
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--kv-transfer-config \
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"{\"kv_connector\":\"MooncakeConnector\",\"kv_role\":\"kv_consumer\",\"kv_connector_extra_config\":{\"prefill_addr\":\"127.0.0.1:$BOOTSTRAP_PORT\"}}" \
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2>&1 | tee "$LOG_DIR/decode.log" &
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DECODE_PID=$!
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echo " Decode PID=$DECODE_PID"
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# Wait for decode to be ready
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echo " Waiting for decode instance..."
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for i in $(seq 1 180); do
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if curl -s "http://127.0.0.1:$DECODE_PORT/v1/models" > /dev/null 2>&1; then
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echo " Decode ready after ${i}s"
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break
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fi
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if [ $i -eq 180 ]; then
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echo " ERROR: Decode did not start within 180s"
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exit 1
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fi
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sleep 1
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done
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echo ""
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echo "=== Both instances ready ==="
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echo " Prefill: http://127.0.0.1:$PREFILL_PORT (PID $PREFILL_PID)"
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echo " Decode: http://127.0.0.1:$DECODE_PORT (PID $DECODE_PID)"
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echo ""
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echo " Prefill PID: $PREFILL_PID" > "$LOG_DIR/.pids"
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echo " Decode PID: $DECODE_PID" >> "$LOG_DIR/.pids"
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echo "$PREFILL_PID" > "$LOG_DIR/.prefill.pid"
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echo "$DECODE_PID" > "$LOG_DIR/.decode.pid"
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echo "Press Ctrl+C to stop both instances."
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wait
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