#!/usr/bin/env bash

# GLM-5 FP8 on H200 (Hopper) with EAGLE / MTP speculative decoding.
# Mirrors glm5_fp8_h200.sh but adds the speculative-* flags. We keep the
# server-arg shape from the non-MTP H200 recipe (sglang defaults — no
# nsa/trtllm-mha) since those backends are Blackwell-specific and not
# applicable to Hopper.

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

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

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

nvidia-smi

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

SERVER_LOG=/workspace/server.log

EVAL_CONTEXT_ARGS=""
if [ "${EVAL_ONLY}" = "true" ]; then
    setup_eval_context
    EVAL_CONTEXT_ARGS="--context-length $EVAL_MAX_MODEL_LEN"
fi

start_gpu_monitor

set -x
python3 -m sglang.launch_server \
  --model-path "$MODEL" \
  --host 0.0.0.0 \
  --port "$PORT" \
  --tp-size "$TP" \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --mem-fraction-static 0.85 \
  --served-model-name glm-5-fp8 \
  --trust-remote-code \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  $EVAL_CONTEXT_ARGS > "$SERVER_LOG" 2>&1 &

SERVER_PID=$!

wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"

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 \
    --use-chat-template

if [ "${RUN_EVAL}" = "true" ]; then
    export MODEL_NAME=glm-5-fp8
    run_eval --framework lm-eval --port "$PORT"
    append_lm_eval_summary
fi

stop_gpu_monitor
set +x
