#!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
set -e
trap 'echo Cleaning up...; kill 0' EXIT

# Set deterministic hash for KV event IDs
export PYTHONHASHSEED=0

# Common configuration
MODEL="Qwen/Qwen3-0.6B"
BLOCK_SIZE=64

# Start frontend with KV routing
# The frontend will automatically detect prefill workers and activate an internal prefill router
# dynamo.frontend accepts either --http-port flag or DYN_HTTP_PORT env var (defaults to 8000)
python -m dynamo.frontend \
    --router-mode kv \
    --router-reset-states &

# two decode workers
# --enforce-eager is added for quick deployment. for production use, need to remove this flag
CUDA_VISIBLE_DEVICES=0 python3 -m dynamo.vllm \
    --model $MODEL \
    --block-size $BLOCK_SIZE \
    --enforce-eager \
    --is-decode-worker &

VLLM_NIXL_SIDE_CHANNEL_PORT=20097 \
CUDA_VISIBLE_DEVICES=1 python3 -m dynamo.vllm \
    --model $MODEL \
    --block-size $BLOCK_SIZE \
    --enforce-eager \
    --is-decode-worker &

# two prefill workers
# When registered with --is-prefill-worker, these workers are automatically detected
# by the frontend, which activates an internal prefill router for KV-aware prefill routing
VLLM_NIXL_SIDE_CHANNEL_PORT=20098 \
CUDA_VISIBLE_DEVICES=2 python3 -m dynamo.vllm \
    --model $MODEL \
    --block-size $BLOCK_SIZE \
    --enforce-eager \
    --is-prefill-worker \
    --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20082","enable_kv_cache_events":true}'&

VLLM_NIXL_SIDE_CHANNEL_PORT=20099 \
CUDA_VISIBLE_DEVICES=3 python3 -m dynamo.vllm \
    --model $MODEL \
    --block-size $BLOCK_SIZE \
    --enforce-eager \
    --is-prefill-worker \
    --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20083","enable_kv_cache_events":true}'
