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https://github.com/volcengine/verl.git
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58c847b17f
### What does this PR do? Set use_dist_checkpointing to False for ref model in qwen3moe-30b script, because there is not dist_megatron_ckpt model path for ref model.
196 lines
8.9 KiB
Bash
196 lines
8.9 KiB
Bash
set -x
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# tested in NNODES=1~4 * 96G H20 GPU
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NNODES=${NNODES:-1}
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NGPUS_PER_NODES=${NGPUS_PER_NODES:-8}
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project_name='DAPO-Qwen3-30b-MATH'
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exp_name='DAPO-Qwen3-30b-MATH-megatron'
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adv_estimator=grpo
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use_kl_in_reward=False
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kl_coef=0.0
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use_kl_loss=False
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kl_loss_coef=0.0
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clip_ratio_low=0.2
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clip_ratio_high=0.28
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max_prompt_length=$((1024 * 2))
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max_response_length=$((1024 * 8))
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enable_overlong_buffer=True
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overlong_buffer_len=$((1024 * 4))
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overlong_penalty_factor=1.0
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loss_agg_mode="token-mean"
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train_prompt_bsz=512
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n_resp_per_prompt=16
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train_prompt_mini_bsz=128
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train_ppo_micro_batch_size_per_gpu=2
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infer_ppo_micro_batch_size_per_gpu=2
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# Paths
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MODEL_PATH=Qwen/Qwen3-30B-A3B
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RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
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TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet
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TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet
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TEST_FILE="['$aime24_test_path']"
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# Algorithm
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temperature=1.0
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top_p=1.0
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top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
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val_top_p=0.7
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# Performance Related Parameter
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use_dynamic_bsz=True
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actor_ppo_max_token_len=$(((max_prompt_length + max_response_length)))
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infer_ppo_max_token_len=$(((max_prompt_length + max_response_length)))
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offload=True
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optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.}
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COMMON_PP=${COMMON_PP:-1}
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COMMON_VPP=${COMMON_VPP:-null}
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COMMON_CP=${COMMON_CP:-1}
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COMMON_TP=${COMMON_TP:-1}
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COMMON_EP=${COMMON_EP:-8}
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COMMON_ETP=${COMMON_ETP:-1}
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TRAIN_TP=${TRAIN_TP:-$COMMON_TP}
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INFER_TP=${INFER_TP:-4}
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ACTOR_PP=${ACTOR_PP:-$COMMON_PP}
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ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP}
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ACTOR_CP=${ACTOR_CP:-$COMMON_CP}
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ACTOR_TP=${ACTOR_TP:-$TRAIN_TP}
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ACTOR_EP=${ACTOR_EP:-$COMMON_EP}
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ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP}
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ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP}
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REF_PP=${REF_PP:-$COMMON_PP}
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REF_VPP=${REF_VPP:-$COMMON_VPP}
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REF_CP=${REF_CP:-$COMMON_CP}
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REF_TP=${REF_TP:-$TRAIN_TP}
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REF_EP=${REF_EP:-$COMMON_EP}
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REF_ETP=${REF_ETP:-$COMMON_ETP}
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CRITIC_PP=${CRITIC_PP:-$COMMON_PP}
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CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP}
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CRITIC_CP=${CRITIC_CP:-$COMMON_CP}
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CRITIC_TP=${CRITIC_TP:-$TRAIN_TP}
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CRITIC_EP=${CRITIC_EP:-$COMMON_EP}
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CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP}
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RM_PP=${RM_PP:-$COMMON_PP}
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RM_VPP=${RM_VPP:-$COMMON_VPP}
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RM_CP=${RM_CP:-$COMMON_CP}
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RM_TP=${RM_TP:-$TRAIN_TP}
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RM_EP=${RM_EP:-$COMMON_EP}
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RM_ETP=${RM_ETP:-$COMMON_ETP}
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# install mbridge
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# pip3 install git+https://github.com/ISEEKYAN/mbridge
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USE_MBRIDGE=True
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USE_DIST_CKPT=False
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python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\
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data.train_files="${TRAIN_FILE}" \
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data.val_files="${TEST_FILE}" \
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data.prompt_key=prompt \
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data.truncation='left' \
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data.max_prompt_length=${max_prompt_length} \
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data.max_response_length=${max_response_length} \
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data.train_batch_size=${train_prompt_bsz} \
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actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
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algorithm.adv_estimator=${adv_estimator} \
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algorithm.use_kl_in_reward=${use_kl_in_reward} \
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algorithm.kl_ctrl.kl_coef=${kl_coef} \
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actor_rollout_ref.model.path="${MODEL_PATH}" \
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actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
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actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
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actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
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actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
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actor_rollout_ref.actor.clip_ratio_c=10.0 \
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+actor_rollout_ref.model.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \
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actor_rollout_ref.model.use_fused_kernels=False \
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actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
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actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \
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actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
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actor_rollout_ref.actor.optim.lr=1e-6 \
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actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
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actor_rollout_ref.actor.optim.lr_decay_style='constant' \
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actor_rollout_ref.actor.optim.weight_decay=0.1 \
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+actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \
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+actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \
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+actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \
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+actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \
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actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \
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actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \
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actor_rollout_ref.actor.megatron.param_offload=${offload} \
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actor_rollout_ref.actor.megatron.grad_offload=${offload} \
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actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \
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actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \
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actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \
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actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \
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actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \
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actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \
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actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \
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+actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \
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+actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \
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+actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \
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+actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \
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actor_rollout_ref.actor.entropy_coeff=0 \
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actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
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actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
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actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
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actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \
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actor_rollout_ref.rollout.enable_chunked_prefill=True \
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actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
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actor_rollout_ref.rollout.temperature=${temperature} \
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actor_rollout_ref.rollout.top_p=${top_p} \
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actor_rollout_ref.rollout.top_k=${top_k} \
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actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
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actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
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actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
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actor_rollout_ref.rollout.val_kwargs.do_sample=True \
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actor_rollout_ref.rollout.val_kwargs.n=1 \
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actor_rollout_ref.rollout.name=vllm \
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actor_rollout_ref.rollout.enforce_eager=True \
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actor_rollout_ref.rollout.free_cache_engine=True \
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actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \
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actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
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actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \
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actor_rollout_ref.ref.megatron.param_offload=${offload} \
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actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \
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actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \
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actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \
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actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \
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actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \
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actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \
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reward_model.reward_manager=dapo \
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+reward_model.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \
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+reward_model.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \
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+reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \
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+reward_model.reward_kwargs.overlong_buffer_cfg.log=False \
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+reward_model.reward_kwargs.max_resp_len=${max_response_length} \
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trainer.logger=['console','wandb'] \
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trainer.project_name="${project_name}" \
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trainer.experiment_name="${exp_name}" \
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trainer.n_gpus_per_node="${NGPUS_PER_NODES}" \
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trainer.nnodes="${NNODES}" \
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trainer.val_before_train=False \
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trainer.test_freq=10 \
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trainer.save_freq=100 \
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trainer.total_epochs=10 \
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trainer.resume_mode=auto \
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trainer.log_val_generations=10
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