verl/examples/ppo_trainer/run_qwen1.5_moe_a2.7b-gsm8k_megatron.sh at main · sutro-planet/verl · GitHub

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set -x

# If you are using vllm<=0.6.3, you might need to set the following environment variable to avoid bugs:
# export VLLM_ATTENTION_BACKEND=XFORMERS
export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping

# 0. download the model
huggingface-cli download Qwen/Qwen1.5-MoE-A2.7B-Chat

# 1. convert the model to mcore format
# change the HF_MODEL_PATH and DIST_CKPT_PATH to your own path
HF_MODEL_PATH=/data/models/Qwen/Qwen1.5-MoE-A2.7B-Chat
DIST_CKPT_PATH=/data/mcore_ckpt/Qwen1.5-MoE-A2.7B-Chat
python scripts/converter_hf_to_mcore.py --hf_model_path $HF_MODEL_PATH --output_path $DIST_CKPT_PATH

# 2. run the script
gsm8k_train_path=$HOME/data/gsm8k/train.parquet
gsm8k_test_path=$HOME/data/gsm8k/test.parquet
train_files=$gsm8k_train_path
test_files=$gsm8k_test_path

NODES=4
PP=2
TP=4
CP=1
VLLM_TP=4

# RAY_ADDRESS='auto' ray job submit --working-dir . --
python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\
    algorithm.adv_estimator=gae \
    data.train_files="$train_files" \
    data.val_files="$test_files" \
    data.train_batch_size=1024 \
    data.max_prompt_length=1024 \
    data.max_response_length=512 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    actor_rollout_ref.model.path=$HF_MODEL_PATH \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
    actor_rollout_ref.actor.use_kl_loss=False \
    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \
    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \
    actor_rollout_ref.actor.megatron.context_parallel_size=$CP \
    actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \
    actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
    actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \
    actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \
    actor_rollout_ref.ref.megatron.context_parallel_size=$CP \
    actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \
    actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=$VLLM_TP \
    critic.optim.lr=1e-5 \
    critic.model.path=$HF_MODEL_PATH \
    critic.model.enable_gradient_checkpointing=False \
    critic.ppo_micro_batch_size_per_gpu=4 \
    critic.megatron.tensor_model_parallel_size=$TP \
    critic.megatron.pipeline_model_parallel_size=$PP \
    critic.megatron.context_parallel_size=$CP \
    critic.megatron.use_dist_checkpointing=True \
    critic.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name='verl_megatron_gsm8k_examples' \
    trainer.experiment_name='qwen1.5_moe_nochat' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=$NODES \
    trainer.save_freq=20 \
    trainer.test_freq=5 \
    trainer.total_epochs=100 $@