#!/usr/bin/env bash

source "$(dirname "$0")/../../benchmark_lib.sh"

check_env_vars \
    MODEL \
    TP \
    CONC \
    ISL \
    OSL \
    MAX_MODEL_LEN \
    RANDOM_RANGE_RATIO \
    RESULT_FILENAME

if [[ -n "$SLURM_JOB_ID" ]]; then
  echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
fi

if [[ "$MODEL" != /* ]]; then hf download "$MODEL"; fi

nvidia-smi

export PYTHONNOUSERSITE=1
export VLLM_USE_FLASHINFER_MOE_INT4=1

SERVER_LOG=/workspace/server.log

if [ "${EVAL_ONLY}" = "true" ]; then
    setup_eval_context
    MAX_MODEL_LEN="$EVAL_MAX_MODEL_LEN"
fi
# Start GPU monitoring (power, temperature, clocks every second)
start_gpu_monitor

set -x
vllm serve $MODEL --host 0.0.0.0 --port $PORT \
--gpu-memory-utilization 0.95 \
--tensor-parallel-size $TP \
--max-model-len $MAX_MODEL_LEN \
--max-num-seqs $CONC \
--reasoning-parser kimi_k2 \
--tool-call-parser kimi_k2 \
--compilation_config.pass_config.fuse_allreduce_rms true \
--trust-remote-code \
--no-enable-prefix-caching > $SERVER_LOG 2>&1 &

SERVER_PID=$!

# Wait for server to be ready
wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"

pip install -q datasets pandas

run_benchmark_serving \
    --model "$MODEL" \
    --port "$PORT" \
    --backend vllm \
    --input-len "$ISL" \
    --output-len "$OSL" \
    --random-range-ratio "$RANDOM_RANGE_RATIO" \
    --num-prompts $(( CONC * 10 )) \
    --max-concurrency "$CONC" \
    --result-filename "$RESULT_FILENAME" \
    --result-dir /workspace/ \
    --trust-remote-code

# After throughput, run evaluation only if RUN_EVAL is true
if [ "${RUN_EVAL}" = "true" ]; then
    run_eval --framework lm-eval --port "$PORT"
    append_lm_eval_summary
fi

# Stop GPU monitoring
stop_gpu_monitor
set +x
