run_qwen3_vl_8b_fsdp.sh

#!/usr/bin/env bash
# GRPO | vision | vLLM rollout | FSDP training | GPU/NPU
# Canonical Qwen3-VL baseline on Geo3K.

set -xeuo pipefail

########################### user-adjustable ###########################
# DEVICE is auto-detected by probing torch_npu; override only for special cases.
DEVICE=${DEVICE:-$(python3 -c 'import torch_npu' 2>/dev/null && echo npu || echo gpu)}
MODEL_PATH=${MODEL_PATH:-Qwen/Qwen3-VL-8B-Instruct}
NNODES=${NNODES:-1}
NDEVICES_PER_NODE=${NDEVICES_PER_NODE:-}

TRAIN_BATCH_SIZE=${TRAIN_BATCH_SIZE:-512}
PPO_MINI_BATCH_SIZE=${PPO_MINI_BATCH_SIZE:-128}
MAX_PROMPT_LENGTH=${MAX_PROMPT_LENGTH:-1024}
MAX_RESPONSE_LENGTH=${MAX_RESPONSE_LENGTH:-2048}
PPO_MAX_TOKEN_LEN_PER_GPU=${PPO_MAX_TOKEN_LEN_PER_GPU:-24576}

ACTOR_LR=${ACTOR_LR:-1e-6}
KL_LOSS_COEF=${KL_LOSS_COEF:-0.01}
ENTROPY_COEFF=${ENTROPY_COEFF:-0}

ROLLOUT_TP=${ROLLOUT_TP:-2}
ROLLOUT_GPU_MEM_UTIL=${ROLLOUT_GPU_MEM_UTIL:-}
ROLLOUT_N=${ROLLOUT_N:-5}
SP_SIZE=${SP_SIZE:-1}

TOTAL_EPOCHS=${TOTAL_EPOCHS:-15}
SAVE_FREQ=${SAVE_FREQ:-20}
TEST_FREQ=${TEST_FREQ:-5}

PROJECT_NAME=${PROJECT_NAME:-verl_grpo_geo3k}
EXPERIMENT_NAME=${EXPERIMENT_NAME:-qwen3_vl_8b_grpo_vllm_fsdp_$(date +%Y%m%d_%H%M)}

TRAIN_FILE=${TRAIN_FILE:-$HOME/data/geo3k/train.parquet}
TEST_FILE=${TEST_FILE:-$HOME/data/geo3k/test.parquet}
########################### end user-adjustable ###########################

########################### derived defaults ###########################
n_devices_per_node=${NDEVICES_PER_NODE:-8}

case "${DEVICE}" in
    gpu)
        rollout_gpu_mem_util=${ROLLOUT_GPU_MEM_UTIL:-0.6}
        ;;
    npu)
        export HCCL_CONNECT_TIMEOUT=1500
        export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050
        export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050
        export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1

        rollout_gpu_mem_util=${ROLLOUT_GPU_MEM_UTIL:-0.5}
        ;;
    *)
        echo "Unsupported DEVICE=${DEVICE}. Expected 'gpu' or 'npu'." >&2
        exit 1
        ;;
esac

########################### parameter arrays ###########################

DATA=(
    algorithm.adv_estimator=grpo
    algorithm.use_kl_in_reward=False
    data.train_files=${TRAIN_FILE}
    data.val_files=${TEST_FILE}
    data.image_key=images
    data.train_batch_size=${TRAIN_BATCH_SIZE}
    data.max_prompt_length=${MAX_PROMPT_LENGTH}
    data.max_response_length=${MAX_RESPONSE_LENGTH}
    data.filter_overlong_prompts=True
    data.truncation='error'
)

MODEL=(
    actor_rollout_ref.model.path="$MODEL_PATH"
    actor_rollout_ref.model.use_remove_padding=True
    actor_rollout_ref.model.enable_gradient_checkpointing=True
)

ACTOR=(
    actor_rollout_ref.actor.strategy=fsdp2
    actor_rollout_ref.actor.optim.lr=${ACTOR_LR}
    actor_rollout_ref.actor.ppo_mini_batch_size=${PPO_MINI_BATCH_SIZE}
    actor_rollout_ref.actor.use_dynamic_bsz=True
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${PPO_MAX_TOKEN_LEN_PER_GPU}
    actor_rollout_ref.actor.use_kl_loss=True
    actor_rollout_ref.actor.kl_loss_coef=${KL_LOSS_COEF}
    actor_rollout_ref.actor.kl_loss_type=low_var_kl
    actor_rollout_ref.actor.entropy_coeff=${ENTROPY_COEFF}
)

ROLLOUT=(
    actor_rollout_ref.rollout.name=vllm
    actor_rollout_ref.rollout.tensor_model_parallel_size=${ROLLOUT_TP}
    actor_rollout_ref.rollout.gpu_memory_utilization=${rollout_gpu_mem_util}
    actor_rollout_ref.rollout.enable_chunked_prefill=False
    actor_rollout_ref.rollout.n=${ROLLOUT_N}
    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True
    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${PPO_MAX_TOKEN_LEN_PER_GPU}
)

REF=(
    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${PPO_MAX_TOKEN_LEN_PER_GPU}
    actor_rollout_ref.ref.fsdp_config.param_offload=True
)

TRAINER=(
    trainer.balance_batch=True
    trainer.logger='["console","wandb"]'
    trainer.project_name=${PROJECT_NAME}
    trainer.experiment_name=${EXPERIMENT_NAME}
    trainer.n_gpus_per_node=${n_devices_per_node}
    trainer.nnodes=${NNODES}
    trainer.save_freq=${SAVE_FREQ}
    trainer.test_freq=${TEST_FREQ}
    trainer.total_epochs=${TOTAL_EPOCHS}
)

# Per-device extras (single trailing array, never empty).
if [ "${DEVICE}" = npu ]; then
    EXTRA=(
        actor_rollout_ref.actor.use_torch_compile=False
        actor_rollout_ref.actor.fsdp_config.param_offload=True
        actor_rollout_ref.actor.fsdp_config.optimizer_offload=True
        actor_rollout_ref.actor.fsdp_config.ulysses_sequence_parallel_size=${SP_SIZE}
        actor_rollout_ref.ref.fsdp_config.ulysses_sequence_parallel_size=${SP_SIZE}
        +actor_rollout_ref.rollout.engine_kwargs.vllm.mm_processor_cache_gb=0
        actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20
    )
else
    EXTRA=(
        actor_rollout_ref.model.use_fused_kernels=True
        actor_rollout_ref.actor.fsdp_config.param_offload=False
        actor_rollout_ref.actor.fsdp_config.optimizer_offload=False
        actor_rollout_ref.rollout.enforce_eager=False
        actor_rollout_ref.rollout.free_cache_engine=True
    )
fi

########################### launch ###########################
python3 -m verl.trainer.main_ppo \
    "${DATA[@]}" \
    "${MODEL[@]}" \
    "${ACTOR[@]}" \
    "${ROLLOUT[@]}" \
    "${REF[@]}" \
    "${TRAINER[@]}" \
    "${EXTRA[@]}" \
    "$@"