[tab] scripts/run-kimi-k2-Instruct.sh (shellscript)
#!/bin/bash

# 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

NVLINK_COUNT=$(nvidia-smi topo -m 2>/dev/null | grep -o 'NV[0-9][0-9]*' | wc -l)
if [ "$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}/models/kimi-k2.sh"

CKPT_ARGS=(
   --hf-checkpoint $BASE_DIR/Kimi-K2-Instruct/
   # --hf-checkpoint $BASE_DIR/Kimi-K2-bf16/
   --ref-load $BASE_DIR/Kimi-K2_torch_dist/
   --load $BASE_DIR/Kimi-K2_miles/
   --save $BASE_DIR/Kimi-K2_miles/
   --save-interval 20
)

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

   --rm-type math

   --num-rollout 100
   --rollout-batch-size 128
   --n-samples-per-prompt 8
   --rollout-max-response-len 32768
   --rollout-temperature 0.8

   # --global-batch-size 1024

   --over-sampling-batch-size 256
   --dynamic-sampling-filter-path miles.rollout.filter_hub.dynamic_sampling_filters.check_reward_nonzero_std

   --num-steps-per-rollout 4
   --balance-data
)

EVAL_ARGS=(
   --eval-interval 20
   --eval-prompt-data aime $BASE_DIR/rl_data/aime-2024.jsonl
   --n-samples-per-eval-prompt 8
   --eval-max-response-len 32768
   --eval-top-p 0.7
)

PERF_ARGS=(
   --tensor-model-parallel-size 8
   --sequence-parallel
   --pipeline-model-parallel-size 8
   --context-parallel-size 4
   --expert-model-parallel-size 32
   --expert-tensor-parallel-size 1
   --decoder-last-pipeline-num-layers 5

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

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

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

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

WANDB_ARGS=(
   # --use-wandb
   # --wandb-project miles-dev
   # --wandb-group kimi-k2-test
   # --wandb-key ${WANDB_KEY}
)

SGLANG_ARGS=(
   --rollout-num-gpus-per-engine 16
   --sglang-mem-fraction-static 0.7

   # dp attention
   --sglang-enable-dp-attention
   --sglang-dp-size 8
   --sglang-moe-dense-tp-size 1
   --sglang-enable-dp-lm-head

   --sglang-ep-size 16

   # enable deepep for sglang
#    --sglang-enable-deepep-moe
#    --sglang-deepep-mode auto

   # make every dp rank has 128 concurrency
   --sglang-server-concurrency 1024
)


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

   # use deepep for megatron
   --moe-enable-deepep
   --moe-token-dispatcher-type flex
)

# 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}\",
    \"MASTER_ADDR\": \"${MASTER_ADDR}\"
  }
}"

ray job submit --address="http://127.0.0.1:8265" \
   --runtime-env-json="${RUNTIME_ENV_JSON}" \
   -- python3 train.py \
   --actor-num-nodes 32 \
   --actor-num-gpus-per-node 8 \
   --colocate \
   --update-weight-buffer-size $(( 4 * 512 * 1024 * 1024))
   ${MODEL_ARGS[@]} \
   ${CKPT_ARGS[@]} \
   ${ROLLOUT_ARGS[@]} \
   ${OPTIMIZER_ARGS[@]} \
   ${GRPO_ARGS[@]} \
   ${WANDB_ARGS[@]} \
   ${PERF_ARGS[@]} \
   ${EVAL_ARGS[@]} \
   ${SGLANG_ARGS[@]} \
   ${MISC_ARGS[@]}