init-commit

examples/verl_usage/run_bootcamp.sh

@@ -0,0 +1,97 @@
+#!/bin/bash
+
+# 定义变量
+project_name="bootcamp_example_project"
+experiment_name="default_experiment_name"
+internbootcamp_path="/path/to/bootcamp"
+actor_model="/path/to/actor_model"
+verl_path="/path/to/verl"
+export WANDB_API_KEY="your_wandb_api_key"
+
+# 安装依赖
+pip install -e "$internbootcamp_path"
+
+# 设置环境变量
+export VERL_PPO_LOGGING_LEVEL=DEBUG
+# Do not use these in new version of Verl
+# export HYDRA_FULL_ERROR=1
+# export VLLM_ATTENTION_BACKEND=XFORMERS
+
+# 定义文件路径数组
+train_files=(
+    "examples/bootcamp_generator_outputs/<time_stamp>_for_verl_merged/train/bootcamps.parquet"
+)
+test_files=(
+    "examples/bootcamp_generator_outputs/<time_stamp>_for_verl_merged/test/bootcamps.parquet"
+    "examples/bootcamp_generator_outputs/<time_stamp>_for_verl/test/aime.parquet"
+    "examples/bootcamp_generator_outputs/<time_stamp>_for_verl/test/math.parquet"
+)
+
+# 构建 train_files 和 test_files 字符串
+build_json_array() {
+  local array=("$@")
+  if [[ ${#array[@]} -eq 0 ]]; then
+    echo "[]"
+  else
+    local result="["
+    for item in "${array[@]}"; do
+      result+="\"$item\","
+    done
+    result="${result%,}]"
+    echo "$result"
+  fi
+}
+
+train_files_str=$(build_json_array "${train_files[@]}")
+test_files_str=$(build_json_array "${test_files[@]}")
+
+
+# 调试模式
+set -x
+
+# 运行训练脚本
+python -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files="$train_files_str" \
+    data.val_files="$test_files_str" \
+    +data.no_chat_template=False \
+    data.train_batch_size=64 \
+    data.val_batch_size=64 \
+    data.truncation=right \
+    data.max_prompt_length=4096 \
+    data.max_response_length=8192 \
+    actor_rollout_ref.model.path=$actor_model \
+    trainer.default_hdfs_dir=null \
+    trainer.default_local_dir=$verl_path/ckpts/$experiment_name \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=32 \
+    actor_rollout_ref.actor.use_dynamic_bsz=True \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=20000 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=4 \
+    actor_rollout_ref.rollout.disable_log_stats=True \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.max_num_batched_tokens=32768 \
+    actor_rollout_ref.rollout.max_num_seqs=32 \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.65 \
+    actor_rollout_ref.rollout.n=4 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    actor_rollout_ref.rollout.enforce_eager=False \
+    actor_rollout_ref.rollout.free_cache_engine=False \
+    algorithm.kl_ctrl.kl_coef=0.001 \
+    trainer.critic_warmup=0 \
+    trainer.logger=['console','wandb'] \
+    trainer.project_name=$project_name \
+    trainer.experiment_name=$experiment_name \
+    trainer.val_before_train=False \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.save_freq=20 \
+    trainer.test_freq=20 \
+    trainer.total_epochs=1 $@