#!/bin/bash

set -e

# will prevent ray from buffering stdout/stderr
export PYTHONBUFFERED=16

NVLINK_COUNT=$(nvidia-smi | grep -o "NVLink" | wc -l)
if [ "$NVLINK_COUNT" -gt 0 ]; then
    HAS_NVLINK=1
else
    HAS_NVLINK=0
fi
echo "HAS_NVLINK: $HAS_NVLINK (detected $NVLINK_COUNT NVLink references)"

SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
source "${SCRIPT_DIR}/../scripts/models/qwen3-30B-A3B.sh"

CKPT_ARGS=(
   --hf-checkpoint /root/models/Qwen3-30B-A3B-FP8
   --ref-load /root/Qwen3-30B-A3B_torch_dist
)

ROLLOUT_ARGS=(
   --prompt-data /root/datasets/dapo-math-17k/dapo-math-17k.jsonl
   --input-key prompt
   --label-key label
   --apply-chat-template
   --rollout-shuffle

   --rm-type deepscaler

   --num-rollout 3
   --rollout-batch-size 8
   --n-samples-per-prompt 8
   --rollout-max-response-len 8192
   --rollout-temperature 0.8

   --global-batch-size 32
   --balance-data
)

EVAL_ARGS=(
   --eval-interval 20
   --eval-prompt-data aime24 /root/datasets/aime-2024/aime-2024.jsonl
   --n-samples-per-eval-prompt 1
   --eval-max-response-len 16384
   --eval-top-k 1
)

PERF_ARGS=(
   --tensor-model-parallel-size 4
   --sequence-parallel
   --pipeline-model-parallel-size 1
   --context-parallel-size 2
   --expert-model-parallel-size 8
   --expert-tensor-parallel-size 1

   --recompute-granularity full
   --recompute-method uniform
   --recompute-num-layers 1

   --use-dynamic-batch-size
   --max-tokens-per-gpu 16384
)

GRPO_ARGS=(
   --advantage-estimator gspo
   --use-kl-loss
   --kl-loss-coef 0.00
   --kl-loss-type low_var_kl
   --kl-coef 0.00
   --entropy-coef 0.00
   --eps-clip 4e-4

   --use-tis
)

OPTIMIZER_ARGS=(
   --optimizer adam
   --lr 1e-6
   --lr-decay-style constant
   --weight-decay 0.1
   --adam-beta1 0.9
   --adam-beta2 0.98

   --optimizer-cpu-offload
   --overlap-cpu-optimizer-d2h-h2d
   --use-precision-aware-optimizer
)

SGLANG_ARGS=(
   --rollout-num-gpus-per-engine 8
   --sglang-mem-fraction-static 0.8

   --sglang-moe-a2a-backend deepep
   --sglang-deepep-mode auto

   --sglang-max-running-requests 512
   --sglang-disable-radix-cache
)

MISC_ARGS=(
   # default dropout in megatron is 0.1
   --attention-dropout 0.0
   --hidden-dropout 0.0
   # should be good for model performance
   --accumulate-allreduce-grads-in-fp32
   --attention-softmax-in-fp32
   # need to comment this when using model with MLA
   --attention-backend flash

   --moe-token-dispatcher-type flex
   --moe-enable-deepep
)

# launch the master node of ray in container
export MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
export no_proxy="127.0.0.1,${MASTER_ADDR}"
ray start --head --node-ip-address ${MASTER_ADDR} --num-gpus 8 --disable-usage-stats

# Build the runtime environment JSON with proper variable substitution
RUNTIME_ENV_JSON="{
  \"env_vars\": {
    \"PYTHONPATH\": \"/root/Megatron-LM/\",
    \"CUDA_DEVICE_MAX_CONNECTIONS\": \"1\",
    \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\",
    \"no_proxy\": \"${no_proxy}\"
  }
}"

ray job submit --address="http://127.0.0.1:8265" \
   --runtime-env-json="${RUNTIME_ENV_JSON}" \
   -- python3 train.py \
   --actor-num-nodes 1 \
   --actor-num-gpus-per-node 8 \
   --colocate \
   ${MODEL_ARGS[@]} \
   ${CKPT_ARGS[@]} \
   ${ROLLOUT_ARGS[@]} \
   ${OPTIMIZER_ARGS[@]} \
   ${GRPO_ARGS[@]} \
   ${DISTRIBUTED_ARGS[@]} \
   ${WANDB_ARGS[@]} \
   ${PERF_ARGS[@]} \
   ${EVAL_ARGS[@]} \
   ${SGLANG_ARGS[@]} \
   ${MISC_ARGS[@]} \
   --ci-test
