#!/usr/bin/env bash
set -xeuo pipefail
wandb login 4bb06b87c63e7580c48a1e9df6ee8192cdeacabd
bash ../verl/start_ray_cluster_default.sh

project_name='ForRelease'
exp_name='DAPO-Qwen2.5-7b-MATH-0529_real_offcial-3k_0704rerun'


adv_estimator=grpo

use_kl_in_reward=False
kl_coef=0.0
use_kl_loss=False
kl_loss_coef=0.0

clip_ratio_low=0.2
clip_ratio_high=0.28

max_prompt_length=$((1024 * 1))
max_response_length=$((1024 * 3))
enable_overlong_buffer=True
overlong_buffer_len=$((512))
overlong_penalty_factor=1.0

loss_agg_mode="token-mean"

enable_filter_groups=True
filter_groups_metric=acc
max_num_gen_batches=10
train_prompt_bsz=512
gen_prompt_bsz=$((train_prompt_bsz * 3))
train_prompt_mini_bsz=32
n_resp_per_prompt=16

# # Ray
# RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
# WORKING_DIR=${WORKING_DIR:-"${PWD}"}
# RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
# NNODES=${NNODES:-4}
# # Paths
# RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
# MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"}
# CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
# TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
# TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}


# Ray
WORKING_DIR=${WORKING_DIR:-"${PWD}"}
# RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
NNODES=${NNODES:-8}
# Paths
# RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
MODEL_PATH="/mmu_nlp_hdd/fukai07/opensource/models/Qwen1.5/Qwen2.5-Math-7B"

MODEL_PATH=/nlp_group/zhanghongzhi/rlexpert/ckpts/Qwen2.5-Math-7B

CKPTS_DIR="/nlp_group/zhanghongzhi/verl/save/${project_name}/${exp_name}"

TRAIN_FILE="/nlp_group/zhanghongzhi/verl_new_version/recipe/dapo-math-17k.parquet"
# TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
# TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
# TEST_FILE="/nlp_group/zhanghongzhi/verl_new_version/recipe/aime-2024.parquet"

TEST_FILE=/nlp_group/zhanghongzhi/verl_v0531/recipe/math_test_set/dapo_format_aime2024_aime2025_amc2023.parquet

# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout

# Mathematically equivalent
use_dynamic_bsz=True
infer_micro_batch_size=null
train_micro_batch_size=null
offload=False


with_negative_replay=False
with_negative_replay_pos=True
with_negative_replay_neg=True


python -m recipe.dapo.main_dapo \
    data.train_files="${TRAIN_FILE}" \
    data.val_files="${TEST_FILE}" \
    data.prompt_key=prompt \
    data.truncation='left' \
    data.max_prompt_length=${max_prompt_length} \
    data.max_response_length=${max_response_length} \
    data.gen_batch_size=${gen_prompt_bsz} \
    data.train_batch_size=${train_prompt_bsz} \
    +data.with_negative_replay=${with_negative_replay} \
    +data.with_negative_replay_pos=${with_negative_replay_pos} \
    +data.with_negative_replay_neg=${with_negative_replay_neg} \
    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
    actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
    actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
    actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
    actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
    actor_rollout_ref.actor.clip_ratio_c=10.0 \
    algorithm.adv_estimator=${adv_estimator} \
    algorithm.use_kl_in_reward=${use_kl_in_reward} \
    algorithm.kl_ctrl.kl_coef=${kl_coef} \
    algorithm.filter_groups.enable=${enable_filter_groups} \
    algorithm.filter_groups.metric=${filter_groups_metric} \
    algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$((max_prompt_length + max_response_length)) \
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=$((max_prompt_length + max_response_length)) \
    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=$((max_prompt_length + max_response_length)) \
    actor_rollout_ref.model.path="${MODEL_PATH}" \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
    actor_rollout_ref.actor.optim.weight_decay=0.1 \
    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
    actor_rollout_ref.actor.ppo_micro_batch_size=${train_micro_batch_size} \
    actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.actor.grad_clip=1.0 \
    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
    actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \
    actor_rollout_ref.rollout.log_prob_micro_batch_size=${infer_micro_batch_size} \
    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
    actor_rollout_ref.rollout.enable_chunked_prefill=True \
    actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
    actor_rollout_ref.rollout.temperature=${temperature} \
    actor_rollout_ref.rollout.top_p=${top_p} \
    actor_rollout_ref.rollout.top_k="${top_k}" \
    actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
    actor_rollout_ref.rollout.val_kwargs.top_p=${top_p} \
    actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
    actor_rollout_ref.rollout.val_kwargs.n=1 \
    actor_rollout_ref.ref.log_prob_micro_batch_size=${infer_micro_batch_size} \
    actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \
    actor_rollout_ref.ref.ulysses_sequence_parallel_size=1 \
    actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
    reward_model.reward_manager=dapo \
    reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
    reward_model.overlong_buffer.len=${overlong_buffer_len} \
    reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
    trainer.logger=['console','wandb'] \
    trainer.project_name="${project_name}" \
    trainer.experiment_name="${exp_name}" \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes="${NNODES}" \
    trainer.val_before_train=False \
    trainer.test_freq=5 \
    trainer.save_freq=5 \
    +trainer.dynamic_replay_exp=False \
    trainer.total_epochs=1 \
    trainer.default_local_dir="${CKPTS_DIR}" \
    trainer.resume_mode=disable