ba-agent-posttrain-scripts / scripts /run_ba_role_ppo.sh
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#!/usr/bin/env bash
set -euo pipefail
script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
exp_root="$(cd "${script_dir}/.." && pwd)"
role_name="${ROLE_NAME:-writer}"
deepspeed_config_name="${DEEPSPEED_CONFIG_NAME:-${exp_root}/config/ds.json}"
dataset_sub_name="${DATASET_SUB_NAME:-}"
dataset_split="${DATASET_SPLIT:-train}"
system_prompt="${SYSTEM_PROMPT:-}"
output_root="${OUTPUT_ROOT:-${exp_root}/output/${role_name}}"
policy_model_path="${POLICY_MODEL_PATH:-${output_root}/dpo_full}"
reward_model_name="${REWARD_MODEL_NAME:-${output_root}/reward_model_lora}"
prompt_dataset_name="${PROMPT_DATASET_NAME:-${exp_root}/data/${role_name}_prompts.jsonl}"
model_ppo_lora_path="${output_root}/ppo_lora"
model_ppo_full_path="${output_root}/ppo_full"
mkdir -p "${output_root}"
if [ ! -f "${prompt_dataset_name}" ] && [ ! -d "${prompt_dataset_name}" ]; then
echo "Missing PPO prompt dataset: ${prompt_dataset_name}"
echo "See ${exp_root}/README.md and the sample JSONL files."
exit 1
fi
deepspeed "${exp_root}/ppo_multi_adapter.py" \
--dataset_name="${prompt_dataset_name}" \
--dataset_sub_name="${dataset_sub_name}" \
--dataset_split="${dataset_split}" \
--system_prompt="${system_prompt}" \
--model_name="${policy_model_path}" \
--reward_model_name="${reward_model_name}" \
--output_name="${model_ppo_lora_path}" \
--use_QLora="${USE_QLORA:-True}" \
--use_flash_attention_2="${USE_FLASH_ATTENTION_2:-True}" \
--deepspeed_config_name="${deepspeed_config_name}" \
--batch_size="${BATCH_SIZE:-8}" \
--mini_batch_size="${MINI_BATCH_SIZE:-1}" \
--ppo_epochs="${PPO_EPOCHS:-1}" \
--output_max_length="${OUTPUT_MAX_LENGTH:-512}" \
--seq_length="${SEQ_LENGTH:-512}" \
--gradient_accumulation_steps="${GRADIENT_ACCUMULATION_STEPS:-2}"
python "${exp_root}/merge_adapter.py" \
--base_model_name="${policy_model_path}" \
--model_name="${model_ppo_lora_path}" \
--merged_model_name="${model_ppo_full_path}"