| set -x
|
|
|
| export VLLM_ATTENTION_BACKEND=FLASH_ATTN
|
| export PYTORCH_CUDA_ALLOC_CONF="expandable_segments:False"
|
| export VLLM_USE_V1=1
|
| export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
|
| export VLLM_ENGINE_ITERATION_TIMEOUT_S=100000000000
|
|
|
|
|
| RLLM_DIR=$(python3 -c "import rllm; import os; print(os.path.dirname(os.path.dirname(rllm.__file__)))")
|
|
|
|
|
| python3 -m rllm.trainer.verl.train_agent_ppo \
|
| algorithm.adv_estimator=grpo \
|
| data.train_files=${RLLM_DIR}/data/math_train.parquet \
|
| data.val_files=${RLLM_DIR}/data/math.parquet \
|
| data.train_batch_size=64 \
|
| data.val_batch_size=512 \
|
| data.max_prompt_length=2048 \
|
| data.max_response_length=2048 \
|
| actor_rollout_ref.model.path=deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
|
| actor_rollout_ref.hybrid_engine=True \
|
| actor_rollout_ref.actor.optim.lr=1e-6 \
|
| actor_rollout_ref.model.use_remove_padding=True \
|
| actor_rollout_ref.actor.ppo_mini_batch_size=16 \
|
| actor_rollout_ref.actor.use_dynamic_bsz=True \
|
| actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \
|
| actor_rollout_ref.actor.use_kl_loss=False \
|
| 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.optimizer_offload=False \
|
| actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
|
| actor_rollout_ref.rollout.name=vllm \
|
| actor_rollout_ref.rollout.mode="async" \
|
| actor_rollout_ref.rollout.chat_scheduler=verl.schedulers.completions_scheduler.CompletionsScheduler \
|
| actor_rollout_ref.rollout.temperature=0.6 \
|
| actor_rollout_ref.rollout.val_kwargs.temperature=0.6 \
|
| actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \
|
| actor_rollout_ref.rollout.n=4 \
|
| actor_rollout_ref.rollout.val_kwargs.n=1 \
|
| actor_rollout_ref.rollout.val_kwargs.top_p=0.95 \
|
| actor_rollout_ref.rollout.enforce_eager=False \
|
| actor_rollout_ref.ref.fsdp_config.param_offload=True \
|
| algorithm.kl_ctrl.kl_coef=0.001 \
|
| trainer.critic_warmup=0 \
|
| trainer.logger=['console','wandb'] \
|
| trainer.project_name='deepscaler-agent' \
|
| trainer.experiment_name='deepscaler-math-debug-sync' \
|
| 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 \
|
| env.name=math \
|
| agent.name=math_agent \
|
| agent.max_steps=1 \
|
| agent.async_engine=True \
|
| trainer.total_epochs=30 "${@:1}" \ |
