File size: 3,069 Bytes
030876e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # 1. Force cleanup
pkill -9 python3
sleep 2
# 2. Set dynamic port to avoid collisions
export MASTER_PORT=$(shuf -i 20000-65000 -n 1)
export MASTER_ADDR=127.0.0.1
# 3. Enable P2P for performance (A100s love NVLink)
unset NCCL_P2P_DISABLE
unset NCCL_IB_DISABLE
set -x
# Enable P2P for A100s to leverage NVLink speed
export PYTORCH_CUDA_ALLOC_CONF=""
export EXPERIMENT_NAME=qwen3-4b-instruct-optimized-multiclinsum-gs
export WAND_PROJECT='readctrl-verl'
export CUDA_DEVICE_ORDER="PCI_BUS_ID"
export CUDA_VISIBLE_DEVICES=2,3
export VLLM_ATTENTION_BACKEND=FLASH_ATTN
export NCCL_P2P_DISABLE=1
export NCCL_IB_DISABLE=1
# export NCCL_NET_GDR_LEVEL=2 # Enable GPUDirect RDMA
# High-performance settings for A100
PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=/home/mshahidul/readctrl/code/RL_model/verl/verl_train/dataset/train.parquet \
data.val_files=/home/mshahidul/readctrl/code/RL_model/verl/verl_train/dataset/test.parquet \
custom_reward_function.path=/home/mshahidul/readctrl/code/RL_model/verl/verl_train/reward_func/reward.py \
data.train_batch_size=512 \
data.max_prompt_length=1024 \
data.max_response_length=2048 \
data.filter_overlong_prompts=True \
data.truncation='error' \
actor_rollout_ref.model.path=Qwen/Qwen3-4B-Instruct-2507 \
actor_rollout_ref.actor.optim.lr=1e-6 \
actor_rollout_ref.model.use_remove_padding=True \
actor_rollout_ref.actor.ppo_mini_batch_size=256 \
actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \
actor_rollout_ref.actor.use_kl_loss=True \
actor_rollout_ref.actor.kl_loss_coef=0.001 \
actor_rollout_ref.actor.kl_loss_type=low_var_kl \
actor_rollout_ref.actor.entropy_coeff=0 \
actor_rollout_ref.model.enable_gradient_checkpointing=True \
actor_rollout_ref.actor.fsdp_config.param_offload=False \
actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \
actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
actor_rollout_ref.rollout.name=vllm \
actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
actor_rollout_ref.rollout.max_model_len=8192 \
actor_rollout_ref.rollout.n=3 \
actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \
actor_rollout_ref.ref.fsdp_config.param_offload=False \
algorithm.use_kl_in_reward=False \
trainer.critic_warmup=0 \
trainer.logger='["console","wandb"]' \
trainer.project_name=$WAND_PROJECT \
trainer.experiment_name=$EXPERIMENT_NAME \
trainer.n_gpus_per_node=2 \
trainer.nnodes=1 \
trainer.save_freq=20 \
trainer.test_freq=5 \
+trainer.remove_previous_ckpt_in_save=true \
trainer.max_actor_ckpt_to_keep=1 \
trainer.max_critic_ckpt_to_keep=1 \
trainer.resume_mode=auto \
trainer.default_local_dir=/home/mshahidul/readctrl/code/RL_model/train_v2 \
trainer.total_epochs=15 $@ \
2>&1 | tee $EXPERIMENT_NAME.log
# python "/home/mshahidul/readctrl/code/readability_control.py" |