disagg_same_gpu.sh

#!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Disaggregated prefill/decode on a SINGLE GPU.
# Per-worker VRAM is estimated from model parameters below. Override individual
# knobs (CONTEXT_LENGTH, MAX_RUNNING_REQUESTS) via env vars, or set
# DYN_GPU_MEMORY_FRACTION_OVERRIDE to bypass the calculation entirely.
#
# Measured reference (Qwen/Qwen3-0.6B, --context-length 4096, RTX 6000 Ada 48 GiB):
#   estimate (from gpu_utils.sh) : ~5.7 GiB per worker (w=1.1 + kv=0.9 + oh=3.7)
#   actual (nvidia-smi)          : ~5.3 GiB per worker (~10.9 GiB total)
#   fraction per worker (48 GiB)  : 0.12
#   KV cache                      : 25,536-29,712 tokens per worker
#   Handles full 4096-token context with --max-running-requests 2.

SCRIPT_DIR="$(dirname "$(readlink -f "$0")")"
source "$SCRIPT_DIR/../../../common/gpu_utils.sh"

MODEL="Qwen/Qwen3-0.6B"

# ---- Tunable (override via env vars) ----
CONTEXT_LENGTH="${CONTEXT_LENGTH:-4096}"
MAX_RUNNING_REQUESTS="${MAX_RUNNING_REQUESTS:-2}"

# ---- Estimate per-worker VRAM (see examples/common/gpu_utils.md) ----
# Sets _EW_WEIGHTS_GIB, _EW_KV_GIB, _EW_OVERHEAD_GIB, _EW_TOTAL_GIB
estimate_worker_vram "$MODEL" "$CONTEXT_LENGTH" "$MAX_RUNNING_REQUESTS" sglang

# DYN_GPU_MEMORY_FRACTION_OVERRIDE takes precedence (profiler binary search).
# In single-GPU mode, split the override evenly between the two workers.
if [[ -n "${DYN_GPU_MEMORY_FRACTION_OVERRIDE:-}" ]]; then
    GPU_MEM_FRACTION=$(awk -v f="$DYN_GPU_MEMORY_FRACTION_OVERRIDE" 'BEGIN { printf "%.2f", f / 2 }')
else
    GPU_MEM_FRACTION=$(gpu_worker_fraction sglang)
fi

# Setup cleanup trap
cleanup() {
    echo "Cleaning up background processes..."
    kill $DYNAMO_PID $PREFILL_PID 2>/dev/null || true
    wait $DYNAMO_PID $PREFILL_PID 2>/dev/null || true
    echo "Cleanup complete."
}
trap cleanup EXIT INT TERM


HTTP_PORT="${DYN_HTTP_PORT:-8000}"
echo "=========================================="
echo "Launching Disaggregated (same GPU)"
echo "=========================================="
echo "Model:       $MODEL"
echo "Frontend:    http://localhost:$HTTP_PORT"
echo "Context len: $CONTEXT_LENGTH"
echo "GPU Mem:     ${GPU_MEM_FRACTION} per worker (~${_EW_TOTAL_GIB} GiB each)"
echo "  estimate:  weights=${_EW_WEIGHTS_GIB} + kv=${_EW_KV_GIB} + overhead=${_EW_OVERHEAD_GIB} GiB"
echo "=========================================="
echo ""
echo "Example test command:"
echo ""
echo "  curl http://localhost:${HTTP_PORT}/v1/chat/completions \\"
echo "    -H 'Content-Type: application/json' \\"
echo "    -d '{"
echo "      \"model\": \"${MODEL}\","
echo "      \"messages\": [{\"role\": \"user\", \"content\": \"Explain why Roger Federer is considered one of the greatest tennis players of all time\"}],"
echo "      \"max_tokens\": 32"
echo "    }'"
echo ""
echo "=========================================="

# run ingress with KV router mode for disaggregated setup
# dynamo.frontend accepts either --http-port flag or DYN_HTTP_PORT env var (defaults to 8000)
python3 -m dynamo.frontend --router-mode kv &
DYNAMO_PID=$!

# run prefill worker with metrics on port 8081
DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT1:-8081} \
python3 -m dynamo.sglang \
  --model-path "$MODEL" \
  --served-model-name "$MODEL" \
  --page-size 16 \
  --tp 1 \
  --trust-remote-code \
  --disaggregation-mode prefill \
  --disaggregation-bootstrap-port 12345 \
  --host 0.0.0.0 \
  --disaggregation-transfer-backend nixl \
  --mem-fraction-static "${GPU_MEM_FRACTION}" \
  --context-length "$CONTEXT_LENGTH" \
  --chunked-prefill-size "$CONTEXT_LENGTH" \
  --max-prefill-tokens "$CONTEXT_LENGTH" \
  --enable-memory-saver \
  --delete-ckpt-after-loading \
  --max-running-requests "$MAX_RUNNING_REQUESTS" \
  --enable-metrics &
PREFILL_PID=$!

# Wait for prefill worker to initialize before starting decode worker
# This prevents both workers from competing for GPU memory simultaneously, which can cause OOM.
# The prefill worker needs time to:
# 1. Load model weights and allocate its memory fraction
# 2. Initialize KV cache with --delete-ckpt-after-loading to free checkpoint memory
# 3. Register with NATS service discovery so decode worker can find it
echo "Waiting for prefill worker to initialize..."
sleep 5

# run decode worker with metrics on port 8082 (foreground)
DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT2:-8082} \
python3 -m dynamo.sglang \
  --model-path "$MODEL" \
  --served-model-name "$MODEL" \
  --page-size 16 \
  --tp 1 \
  --trust-remote-code \
  --disaggregation-mode decode \
  --disaggregation-bootstrap-port 12345 \
  --host 0.0.0.0 \
  --disaggregation-transfer-backend nixl \
  --mem-fraction-static "${GPU_MEM_FRACTION}" \
  --context-length "$CONTEXT_LENGTH" \
  --chunked-prefill-size "$CONTEXT_LENGTH" \
  --max-prefill-tokens "$CONTEXT_LENGTH" \
  --enable-memory-saver \
  --delete-ckpt-after-loading \
  --max-running-requests "$MAX_RUNNING_REQUESTS" \
  --enable-metrics