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#!/bin/bash
set -x
# Warning: Export VLLM_ATTENTION_BACKEND on every machine before starting Ray cluster.
# vLLM without XFORMERS will results in CUDA errors.
export VLLM_ATTENTION_BACKEND=XFORMERS
# Parse command line arguments
while [[ $# -gt 0 ]]; do
case $1 in
--model)
MODEL_PATH="$2"
shift 2
;;
*)
break
;;
esac
done
# Check if model path is provided
if [ -z "$MODEL_PATH" ]; then
MODEL_PATH="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
fi
# Train over 4 nodes, 8 A100-80GB GPUs per node.
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=$HOME/deepscaler/data/train.parquet \
data.val_files=$HOME/deepscaler/data/aime.parquet \
data.train_batch_size=64 \
data.val_batch_size=128 \
data.max_prompt_length=1024 \
data.max_response_length=24576 \
actor_rollout_ref.model.path=$MODEL_PATH \
actor_rollout_ref.actor.optim.lr=1e-6 \
actor_rollout_ref.model.use_remove_padding=True \
actor_rollout_ref.actor.ppo_mini_batch_size=64 \
actor_rollout_ref.actor.ppo_epochs=1 \
actor_rollout_ref.actor.use_dynamic_bsz=True \
actor_rollout_ref.actor.ppo_max_token_len_per_gpu=32768 \
actor_rollout_ref.actor.use_kl_loss=True \
actor_rollout_ref.actor.kl_loss_coef=0.001 \
actor_rollout_ref.actor.kl_loss_type=low_var_kl \
actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
actor_rollout_ref.model.enable_gradient_checkpointing=True \
actor_rollout_ref.actor.fsdp_config.param_offload=False \
actor_rollout_ref.actor.fsdp_config.grad_offload=False \
actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
actor_rollout_ref.rollout.name=vllm \
actor_rollout_ref.rollout.temperature=0.6 \
actor_rollout_ref.rollout.val_temperature=0.6 \
actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
actor_rollout_ref.rollout.n=16 \
actor_rollout_ref.rollout.n_val=16 \
actor_rollout_ref.ref.fsdp_config.param_offload=True \
algorithm.kl_ctrl.kl_coef=0.001 \
trainer.critic_warmup=0 \
trainer.logger=['wandb'] \
trainer.project_name='deepscaler' \
trainer.experiment_name='deepscaler-1.5b-24k' \
+trainer.val_before_train=False \
trainer.n_gpus_per_node=8 \
trainer.nnodes=1 \
trainer.save_freq=10 \
trainer.test_freq=10 \
trainer.default_hdfs_dir=null \
trainer.total_epochs=30 "${@:1}" |