mirror of
https://github.com/InternLM/InternBootcamp.git
synced 2026-04-19 12:58:04 +00:00
97 lines
No EOL
3.3 KiB
Bash
Executable file
97 lines
No EOL
3.3 KiB
Bash
Executable file
#!/bin/bash
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# 定义变量
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project_name="bootcamp_example_project"
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experiment_name="default_experiment_name"
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internbootcamp_path="/path/to/bootcamp"
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actor_model="/path/to/actor_model"
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verl_path="/path/to/verl"
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export WANDB_API_KEY="your_wandb_api_key"
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# 安装依赖
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pip install -e "$internbootcamp_path"
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# 设置环境变量
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export VERL_PPO_LOGGING_LEVEL=DEBUG
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# Do not use these in new version of Verl
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# export HYDRA_FULL_ERROR=1
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# export VLLM_ATTENTION_BACKEND=XFORMERS
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# 定义文件路径数组
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train_files=(
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"examples/bootcamp_generator_outputs/<time_stamp>_for_verl_merged/train/bootcamps.parquet"
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)
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test_files=(
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"examples/bootcamp_generator_outputs/<time_stamp>_for_verl_merged/test/bootcamps.parquet"
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"examples/bootcamp_generator_outputs/<time_stamp>_for_verl/test/aime.parquet"
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"examples/bootcamp_generator_outputs/<time_stamp>_for_verl/test/math.parquet"
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)
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# 构建 train_files 和 test_files 字符串
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build_json_array() {
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local array=("$@")
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if [[ ${#array[@]} -eq 0 ]]; then
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echo "[]"
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else
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local result="["
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for item in "${array[@]}"; do
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result+="\"$item\","
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done
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result="${result%,}]"
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echo "$result"
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fi
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}
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train_files_str=$(build_json_array "${train_files[@]}")
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test_files_str=$(build_json_array "${test_files[@]}")
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# 调试模式
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set -x
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# 运行训练脚本
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python -m verl.trainer.main_ppo \
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algorithm.adv_estimator=grpo \
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data.train_files="$train_files_str" \
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data.val_files="$test_files_str" \
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+data.no_chat_template=False \
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data.train_batch_size=64 \
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data.val_batch_size=64 \
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data.truncation=right \
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data.max_prompt_length=4096 \
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data.max_response_length=8192 \
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actor_rollout_ref.model.path=$actor_model \
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trainer.default_hdfs_dir=null \
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trainer.default_local_dir=$verl_path/ckpts/$experiment_name \
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actor_rollout_ref.actor.optim.lr=1e-6 \
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actor_rollout_ref.model.use_remove_padding=True \
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actor_rollout_ref.actor.ppo_mini_batch_size=32 \
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actor_rollout_ref.actor.use_dynamic_bsz=True \
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actor_rollout_ref.actor.ppo_max_token_len_per_gpu=20000 \
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actor_rollout_ref.actor.use_kl_loss=True \
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actor_rollout_ref.actor.kl_loss_coef=0.001 \
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actor_rollout_ref.actor.kl_loss_type=low_var_kl \
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actor_rollout_ref.model.enable_gradient_checkpointing=True \
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actor_rollout_ref.actor.fsdp_config.param_offload=False \
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actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
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actor_rollout_ref.rollout.tensor_model_parallel_size=4 \
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actor_rollout_ref.rollout.disable_log_stats=True \
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actor_rollout_ref.rollout.name=vllm \
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actor_rollout_ref.rollout.max_num_batched_tokens=32768 \
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actor_rollout_ref.rollout.max_num_seqs=32 \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.65 \
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actor_rollout_ref.rollout.n=4 \
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actor_rollout_ref.ref.fsdp_config.param_offload=True \
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actor_rollout_ref.rollout.enforce_eager=False \
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actor_rollout_ref.rollout.free_cache_engine=False \
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algorithm.kl_ctrl.kl_coef=0.001 \
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trainer.critic_warmup=0 \
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trainer.logger=['console','wandb'] \
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trainer.project_name=$project_name \
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trainer.experiment_name=$experiment_name \
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trainer.val_before_train=False \
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trainer.n_gpus_per_node=8 \
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trainer.nnodes=1 \
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trainer.save_freq=20 \
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trainer.test_freq=20 \
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trainer.total_epochs=1 $@ |