verl/examples/ppo_trainer/run_moonlight16b_a3b_gsm8k_megatron.sh at main · sutro-planet/verl · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
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 moonshotai/Moonlight-16B-A3B-Instruct

# 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/moonshotai/Moonlight-16B-A3B-Instruct
DIST_CKPT_PATH=/data/mcore_ckpt/Moonlight-16B-A3B-Instruct
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

ALL_OFFLOAD=${ALL_OFFLOAD:-False}
COMMON_PARAM_OFFLOAD=${COMMON_PARAM_OFFLOAD:-$ALL_OFFLOAD}
COMMON_GRAD_OFFLOAD=${COMMON_GRAD_OFFLOAD:-$ALL_OFFLOAD}
COMMON_OPTIMIZER_OFFLOAD=${COMMON_OPTIMIZER_OFFLOAD:-$ALL_OFFLOAD}

ACTOR_PARAM_OFFLOAD=${ACTOR_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD}
ACTOR_GRAD_OFFLOAD=${ACTOR_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD}
ACTOR_OPTIMIZER_OFFLOAD=${ACTOR_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD}
REF_PARAM_OFFLOAD=${REF_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD}
CRITIC_PARAM_OFFLOAD=${CRITIC_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD}
CRITIC_GRAD_OFFLOAD=${CRITIC_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD}
CRITIC_OPTIMIZER_OFFLOAD=${CRITIC_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD}
RM_PARAM_OFFLOAD=${RM_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD}


NODES=4
PP=2
TP=8
EP=8
ETP=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' \
    data.trust_remote_code=True \
    actor_rollout_ref.model.path=$LLM \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
    actor_rollout_ref.actor.use_kl_loss=False \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
    critic.optim.lr=1e-5 \
    critic.model.path=$LLM \
    critic.model.enable_gradient_checkpointing=False \
    critic.ppo_micro_batch_size_per_gpu=4 \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name='verl_megatron_gsm8k_examples' \
    trainer.experiment_name='moonlight_16b_a3b_instruct_1node' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=$NODES \
    trainer.save_freq=-1 \
    trainer.test_freq=5 \
    actor_rollout_ref.model.trust_remote_code=True \
    critic.model.trust_remote_code=True \
    +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=13 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=$VLLM_TP \
    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \
    actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \
    critic.megatron.pipeline_model_parallel_size=$PP \
    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \
    actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \
    critic.megatron.tensor_model_parallel_size=$TP \
    actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \
    actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \
    critic.megatron.expert_model_parallel_size=$EP \
    actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \
    actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \
    critic.megatron.expert_tensor_parallel_size=$ETP \
    actor_rollout_ref.actor.megatron.param_offload=${ACTOR_PARAM_OFFLOAD} \
    actor_rollout_ref.actor.megatron.optimizer_offload=${ACTOR_OPTIMIZER_OFFLOAD} \
    actor_rollout_ref.actor.megatron.grad_offload=${ACTOR_GRAD_OFFLOAD} \
    actor_rollout_ref.ref.megatron.param_offload=${REF_PARAM_OFFLOAD} \
    critic.megatron.param_offload=${CRITIC_PARAM_OFFLOAD} \
    critic.megatron.optimizer_offload=${CRITIC_OPTIMIZER_OFFLOAD} \
    critic.megatron.grad_offload=${CRITIC_GRAD_OFFLOAD} \
    actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \
    actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \
    critic.megatron.use_dist_checkpointing=True \
    actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
    actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
    critic.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
    trainer.val_before_train=False \
    trainer.total_epochs=100 $@