verl
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Published
4 months ago
•
volcengine
volcengine
Qwen3-30B-A3B OOM with GRPO on 4x8H200 141G
With the max_prompt_length = 4096, max_response_length=8192, tp=4,pp=2,ep=2, gpu_utilization=0.65, the script confronted OOM issue. I can train it when cpt, why grpo failed
Can you test the recommanded script? https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh
Can you test the recommanded script? https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh
I tried the script but raised OOM too. 8 nodes * 8 gpus, h20-96g, why failed?
set -x
# tested in NNODES=1~4 * 96G H20 GPU
# NNODES=${NNODES:-1}
# NGPUS_PER_NODES=${NGPUS_PER_NODES:-8}
NNODES=$HOST_NUM
NGPUS_PER_NODES=$HOST_GPU_NUM
project_name='DAPO-Qwen3-30b-MATH'
exp_name='DAPO-Qwen3-30b-MATH-megatron'
adv_estimator=grpo
use_kl_in_reward=False
kl_coef=0.0
use_kl_loss=False
kl_loss_coef=0.0
clip_ratio_low=0.2
clip_ratio_high=0.28
max_prompt_length=$((1024 * 2))
max_response_length=$((1024 * 8))
enable_overlong_buffer=True
overlong_buffer_len=$((1024 * 4))
overlong_penalty_factor=1.0
loss_agg_mode="token-mean"
train_prompt_bsz=512
n_resp_per_prompt=16
train_prompt_mini_bsz=128
train_ppo_micro_batch_size_per_gpu=2
infer_ppo_micro_batch_size_per_gpu=2
# Paths
MODEL_PATH=/common/Qwen3-30B-A3B
TRAIN_FILE=/data/retool/DAPO-Math-17k/data/dapo-math-17k.parquet
TEST_FILE=/data/aime_2024/aime-2024-dapo.parquet
# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
val_top_p=0.7
# Performance Related Parameter
use_dynamic_bsz=True
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length)))
infer_ppo_max_token_len=$(((max_prompt_length + max_response_length)))
offload=True
optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.}
COMMON_PP=${COMMON_PP:-1}
COMMON_VPP=${COMMON_VPP:-null}
COMMON_CP=${COMMON_CP:-1}
COMMON_TP=${COMMON_TP:-1}
COMMON_EP=${COMMON_EP:-8}
COMMON_ETP=${COMMON_ETP:-1}
TRAIN_TP=${TRAIN_TP:-$COMMON_TP}
INFER_TP=${INFER_TP:-4}
ACTOR_PP=${ACTOR_PP:-$COMMON_PP}
ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP}
ACTOR_CP=${ACTOR_CP:-$COMMON_CP}
ACTOR_TP=${ACTOR_TP:-$TRAIN_TP}
ACTOR_EP=${ACTOR_EP:-$COMMON_EP}
ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP}
ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP}
REF_PP=${REF_PP:-$COMMON_PP}
REF_VPP=${REF_VPP:-$COMMON_VPP}
REF_CP=${REF_CP:-$COMMON_CP}
REF_TP=${REF_TP:-$TRAIN_TP}
REF_EP=${REF_EP:-$COMMON_EP}
REF_ETP=${REF_ETP:-$COMMON_ETP}
CRITIC_PP=${CRITIC_PP:-$COMMON_PP}
CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP}
CRITIC_CP=${CRITIC_CP:-$COMMON_CP}
CRITIC_TP=${CRITIC_TP:-$TRAIN_TP}
CRITIC_EP=${CRITIC_EP:-$COMMON_EP}
CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP}
RM_PP=${RM_PP:-$COMMON_PP}
RM_VPP=${RM_VPP:-$COMMON_VPP}
RM_CP=${RM_CP:-$COMMON_CP}
RM_TP=${RM_TP:-$TRAIN_TP}
RM_EP=${RM_EP:-$COMMON_EP}
RM_ETP=${RM_ETP:-$COMMON_ETP}
# install mbridge
# pip3 install git+https://github.com/ISEEKYAN/mbridge
USE_MBRIDGE=True
USE_DIST_CKPT=False
current_time=$(date "+%Y.%m.%d-%H.%M.%S")
EXPERIMENT_NAME=$exp_name-${current_time}
SOURCE_CODE_DIR=/test
MODEL_DATA_DIR=/temp
SAVE_PATH=${MODEL_DATA_DIR}/outputs/$PROJECT_NAME/$EXPERIMENT_NAME
ROLLOUT_SAVE_PATH=${SOURCE_CODE_DIR}/outputs/$PROJECT_NAME/$EXPERIMENT_NAME
export TENSORBOARD_DIR="$SOURCE_CODE_DIR/outputs/tensorboard/$PROJECT_NAME/$EXPERIMENT_NAME"
if [ ! -d "$SAVE_PATH" ]; then
mkdir -p $SAVE_PATH
fi
cp "$0" "$SAVE_PATH/$(basename "$0")"
envsubst < search_agent/environment/runtime_env.yaml > /tmp/temp_runtime_env.yaml
ray job submit \
--no-wait --runtime-env=/tmp/temp_runtime_env.yaml \
--working-dir=`pwd` \
-- \
python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\
data.train_files="${TRAIN_FILE}" \
data.val_files="${TEST_FILE}" \
data.prompt_key=prompt \
data.truncation='left' \
data.max_prompt_length=${max_prompt_length} \
data.max_response_length=${max_response_length} \
data.train_batch_size=${train_prompt_bsz} \
actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
algorithm.adv_estimator=${adv_estimator} \
algorithm.use_kl_in_reward=${use_kl_in_reward} \
algorithm.kl_ctrl.kl_coef=${kl_coef} \
actor_rollout_ref.model.path="${MODEL_PATH}" \
actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
actor_rollout_ref.actor.clip_ratio_c=10.0 \
+actor_rollout_ref.model.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \
actor_rollout_ref.model.use_fused_kernels=False \
actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \
actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
actor_rollout_ref.actor.optim.lr=1e-6 \
actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
actor_rollout_ref.actor.optim.lr_decay_style='constant' \
actor_rollout_ref.actor.optim.weight_decay=0.1 \
+actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \
+actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \
+actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \
+actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \
actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \
actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \
actor_rollout_ref.actor.megatron.param_offload=${offload} \
actor_rollout_ref.actor.megatron.grad_offload=${offload} \
actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \
actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \
actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \
actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \
actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \
actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \
actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \
+actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \
+actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \
+actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \
+actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \
actor_rollout_ref.actor.entropy_coeff=0 \
actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \
actor_rollout_ref.rollout.enable_chunked_prefill=True \
actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
actor_rollout_ref.rollout.temperature=${temperature} \
actor_rollout_ref.rollout.top_p=${top_p} \
actor_rollout_ref.rollout.top_k=${top_k} \
actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
actor_rollout_ref.rollout.val_kwargs.do_sample=True \
actor_rollout_ref.rollout.val_kwargs.n=1 \
actor_rollout_ref.rollout.name=vllm \
actor_rollout_ref.rollout.enforce_eager=True \
actor_rollout_ref.rollout.free_cache_engine=True \
actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \
actor_rollout_ref.ref.megatron.param_offload=${offload} \
actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \
actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \
actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \
actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \
actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \
actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \
reward_model.reward_manager=dapo \
+reward_model.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \
+reward_model.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \
+reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \
+reward_model.reward_kwargs.overlong_buffer_cfg.log=False \
+reward_model.reward_kwargs.max_resp_len=${max_response_length} \
trainer.logger='["console","tensorboard"]' \
trainer.project_name="${project_name}" \
trainer.experiment_name="${EXPERIMENT_NAME}" \
trainer.n_gpus_per_node="${NGPUS_PER_NODES}" \
trainer.nnodes="${NNODES}" \
trainer.val_before_train=False \
trainer.test_freq=10 \
trainer.save_freq=100 \
trainer.total_epochs=10 \
trainer.resume_mode=auto \
trainer.log_val_generations=10
