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 (MAX_SEQ_LEN, MAX_CONCURRENT_SEQS) via env vars, or set
# DYN_GPU_MEMORY_FRACTION_OVERRIDE to bypass the calculation entirely.
#
# NOTE — trtllm fraction semantics differ from vllm/sglang:
#   vllm/sglang:  fraction of TOTAL VRAM  (weights + KV + activations all inside)
#   trtllm:       fraction of FREE  VRAM  (KV cache only, after model load)
# gpu_worker_fraction("trtllm") handles this — see gpu_utils.sh / gpu_utils.md.
#
# Measured reference (Qwen/Qwen3-0.6B, --max-seq-len 4096, RTX 6000 Ada 48 GiB):
#   estimate (from gpu_utils.sh) : ~8.0 GiB per worker (~16.0 GiB total)
#   actual (nvidia-smi)          : ~7.4 GiB per worker (~14.8 GiB total)
#   fraction per worker (free)   : 0.05
#   Overestimating is intentional -- better to pad than OOM.

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

MODEL="Qwen/Qwen3-0.6B"

# ---- Tunable (override via env vars) ----
MAX_SEQ_LEN="${MAX_SEQ_LEN:-4096}"
MAX_CONCURRENT_SEQS="${MAX_CONCURRENT_SEQS:-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 "$MODEL" "$MAX_SEQ_LEN" "$MAX_CONCURRENT_SEQS" trtllm

# 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 trtllm)
fi

# Environment variables with defaults
export DYNAMO_HOME=${DYNAMO_HOME:-"/workspace"}
export PREFILL_ENGINE_ARGS=${PREFILL_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/qwen3/prefill.yaml"}
export DECODE_ENGINE_ARGS=${DECODE_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/qwen3/decode.yaml"}
export CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-"0"}
export MODALITY=${MODALITY:-"text"}

# 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

ENABLE_OTEL=false
while [[ $# -gt 0 ]]; do
    case $1 in
        --enable-otel)
            ENABLE_OTEL=true
            shift
            ;;
        -h|--help)
            echo "Usage: $0 [OPTIONS]"
            echo "Options:"
            echo "  --enable-otel        Enable OpenTelemetry tracing"
            echo "  -h, --help           Show this help message"
            echo ""
            exit 0
            ;;
        *)
            echo "Unknown option: $1"
            echo "Use --help for usage information"
            exit 1
            ;;
    esac
done

# Build --override-engine-args JSON.
# Always override free_gpu_memory_fraction so the script controls KV cache size,
# matching how vllm (--gpu-memory-utilization) and sglang (--mem-fraction-static)
# pass memory parameters from the launch script.
OVERRIDE_PAIRS="\"kv_cache_config\": {\"free_gpu_memory_fraction\": ${GPU_MEM_FRACTION}}"
if [ "$ENABLE_OTEL" = true ]; then
    export DYN_LOGGING_JSONL=true
    export OTEL_EXPORT_ENABLED=1
    export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=${OTEL_EXPORTER_OTLP_TRACES_ENDPOINT:-http://localhost:4317}
    OVERRIDE_PAIRS="${OVERRIDE_PAIRS}, \"return_perf_metrics\": true, \"otlp_traces_endpoint\": \"${OTEL_EXPORTER_OTLP_TRACES_ENDPOINT}\""
fi
OVERRIDE_ARGS=(--override-engine-args "{${OVERRIDE_PAIRS}}")

echo "=========================================="
echo "Launching Disaggregated on Same GPU (1 GPU)"
echo "=========================================="
echo "Model:       $MODEL"
echo "Max seq len: $MAX_SEQ_LEN"
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 "=========================================="

# run frontend
# dynamo.frontend accepts either --http-port flag or DYN_HTTP_PORT env var (defaults to 8000)
OTEL_SERVICE_NAME=dynamo-frontend \
python3 -m dynamo.frontend &
DYNAMO_PID=$!

# run prefill worker (shares GPU with decode)
OTEL_SERVICE_NAME=dynamo-worker-prefill \
CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES \
DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT1:-8081} \
python3 -m dynamo.trtllm \
  --model-path "$MODEL" \
  --served-model-name "$MODEL" \
  --extra-engine-args  "$PREFILL_ENGINE_ARGS" \
  --modality "$MODALITY" \
  --publish-events-and-metrics \
  --disaggregation-mode prefill \
  "${OVERRIDE_ARGS[@]}" &
PREFILL_PID=$!

# run decode worker (shares GPU with prefill) - foreground
OTEL_SERVICE_NAME=dynamo-worker-decode \
CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES \
DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT2:-8082} \
python3 -m dynamo.trtllm \
  --model-path "$MODEL" \
  --served-model-name "$MODEL" \
  --extra-engine-args  "$DECODE_ENGINE_ARGS" \
  --modality "$MODALITY" \
  --publish-events-and-metrics \
  --disaggregation-mode decode \
  "${OVERRIDE_ARGS[@]}"