#!/bin/bash

#### run qwen2.5 3B model with slime
#### copy from slime: https://github.com/THUDM/slime/blob/main/examples/search-r1/run_qwen2.5_3B.sh



# for rerun the task
pkill -9 sglang
sleep 3
ray stop --force
pkill -9 ray
pkill -9 python
sleep 3
pkill -9 ray
pkill -9 python

set -ex

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

SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
source "${SCRIPT_DIR}/../../scripts/models/qwen2.5-3B.sh"

CKPT_ARGS=(
   --hf-checkpoint /root/Qwen2.5-3B/
   --ref-load /root/Qwen2.5-3B_torch_dist/
   --load /root/Qwen2.5-3B_slime/
   --save /root/Qwen2.5-3B_slime/
   --save-interval 20
)

ROLLOUT_ARGS=(
   --prompt-data /root/nq_search/train.parquet
   --input-key prompt
   --label-key reward_model
   --apply-chat-template
   --rollout-shuffle
   --num-rollout 3000
   --rollout-batch-size 32
   --n-samples-per-prompt 8
   --rollout-max-response-len 512
   --rollout-temperature 0.8

   --global-batch-size 256
   --balance-data
)

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

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

   # --micro-batch-size 1
   --use-dynamic-batch-size
   --max-tokens-per-gpu 9216
)

GRPO_ARGS=(
   --advantage-estimator grpo
   --use-kl-loss
   --kl-loss-coef 0.00
   --kl-loss-type low_var_kl
   --entropy-coef 0.00
   --eps-clip 0.2
   --eps-clip-high 0.28
)

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

WANDB_ARGS=(
   # --use-wandb
   # --wandb-project slime-dev
   # --wandb-group search-r1_qwen2.5-3B-test
   # --wandb-key ${WANDB_KEY}
)

SGLANG_ARGS=(
   --rollout-num-gpus-per-engine 2
   --sglang-mem-fraction-static 0.7
)

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
)

# Change Here !!!!
CUSTOM_ARGS=(
   --custom-generate-function-path multituren_llm_reward.generate
   --custom-rm-path multituren_llm_reward.compute_score
)
# Change Here !!!!

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

RUNTIME_ENV_JSON="{
  \"env_vars\": {
    \"PYTHONPATH\": \"/root/Megatron-LM/:${SCRIPT_DIR}\",
    \"CUDA_DEVICE_MAX_CONNECTIONS\": \"1\"
  }
}"

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 4 \
   --rollout-num-gpus 4 \
   --colocate \
   ${MODEL_ARGS[@]} \
   ${CKPT_ARGS[@]} \
   ${ROLLOUT_ARGS[@]} \
   ${OPTIMIZER_ARGS[@]} \
   ${GRPO_ARGS[@]} \
   ${DISTRIBUTED_ARGS[@]} \
   ${WANDB_ARGS[@]} \
   ${PERF_ARGS[@]} \
   ${SGLANG_ARGS[@]} \
   ${MISC_ARGS[@]} \
   ${CUSTOM_ARGS[@]}
