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2e1a1a6603
### What does this PR do?
As title
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127 lines
5.1 KiB
Bash
127 lines
5.1 KiB
Bash
#!/usr/bin/env bash
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set -euxo pipefail
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# DAPO (w/o Dynamic Sampling)
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project_name='DAPO-verl'
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exp_name='DAPO-wo-DS-Qwen2.5-32B'
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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 * 20))
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enable_overlong_buffer=True
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overlong_buffer_len=$((1024 * 4))
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overlong_penalty_factor=1.0
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loss_agg_mode="token-mean"
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enable_filter_groups=False
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train_prompt_bsz=512
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n_resp_per_prompt=16
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train_prompt_mini_bsz=32
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# Ray
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RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
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WORKING_DIR=${WORKING_DIR:-"${PWD}"}
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RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
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NNODES=${NNODES:-16}
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# Paths
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RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
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MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-32B"}
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CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
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TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
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TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
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# Algorithm
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temperature=1.0
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top_p=1.0
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top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
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val_top_p=0.7
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# Performance Related Parameter
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sp_size=8
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use_dynamic_bsz=True
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actor_ppo_max_token_len=$((max_prompt_length + max_response_length))
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infer_ppo_max_token_len=$((max_prompt_length + max_response_length))
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offload=True
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gen_tp=4
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ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \
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--working-dir "${WORKING_DIR}" \
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-- python3 -m recipe.dapo.main_dapo \
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data.train_files="${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.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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algorithm.filter_groups.enable=${enable_filter_groups} \
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actor_rollout_ref.model.use_remove_padding=True \
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actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
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actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
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actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
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actor_rollout_ref.model.path="${MODEL_PATH}" \
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actor_rollout_ref.model.enable_gradient_checkpointing=True \
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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.weight_decay=0.1 \
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actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
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actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \
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actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \
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actor_rollout_ref.actor.entropy_coeff=0 \
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actor_rollout_ref.actor.grad_clip=1.0 \
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actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
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actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
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actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_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=${top_k} \
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actor_rollout_ref.rollout.val_kwargs.do_sample=True \
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actor_rollout_ref.rollout.val_kwargs.n=1 \
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actor_rollout_ref.rollout.name=vllm \
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actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \
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actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \
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actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
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reward_model.reward_manager=dapo \
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reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
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reward_model.overlong_buffer.len=${overlong_buffer_len} \
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reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
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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=8 \
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trainer.nnodes="${NNODES}" \
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trainer.val_before_train=True \
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trainer.test_freq=5 \
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trainer.save_freq=5 \
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trainer.total_epochs=1 \
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trainer.default_local_dir="${CKPTS_DIR}" \
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trainer.resume_mode=auto
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