#!/usr/bin/env bash
set -xeuo pipefail


export CUDA_VISIBLE_DEVICES=0,1,2,3
# export CUDA_VISIBLE_DEVICES=4,5,6,7
export WANDB_API_KEY='d43e20c18646e9db5cfd716b8debc8a7ecd56a4c'
project_name='qqt_gsm8k_new'
exp_name='grip_gsm8k_weight'

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=$((1024 * 3))
# max_prompt_length=$((1024 * 2))
max_response_length=$((1024 * 3))
# max_response_length=$((1024 * 6))
enable_overlong_buffer=False
overlong_buffer_len=$((1024 * 4))
overlong_penalty_factor=1.0

loss_agg_mode="token-mean"

enable_filter_groups=False
# enable_filter_groups=False
# filter_groups_metric=acc
filter_groups_metric=seq_reward
max_num_gen_batches=10
train_prompt_bsz=64
gen_prompt_bsz=$((train_prompt_bsz * 3))
n_resp_per_prompt=8
# n_resp_per_prompt=4
train_prompt_mini_bsz=32

# Ray
RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8266"}
WORKING_DIR=${WORKING_DIR:-"${PWD}"}
RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
NNODES=1

# Paths
MODEL_PATH='Qwen/Qwen2.5-1.5B-Instruct'
RAY_DATA_HOME=${RAY_DATA_HOME:-"$(pwd)"}
CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"}
TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"}

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

# Performance Related Parameter
sp_size=4
use_dynamic_bsz=True
actor_ppo_max_token_len=$((max_prompt_length + max_response_length))
infer_ppo_max_token_len=$((max_prompt_length + max_response_length))
offload=True
gen_tp=4

# ray start --address 127.0.0.1:6381
python3 -m recipe.grip.src.main_grip \
    debug=False \
    +fliter=1 \
    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} \
    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
    algorithm.adv_estimator=${adv_estimator} \
    algorithm.use_kl_in_reward=${use_kl_in_reward} \
    algorithm.kl_ctrl.kl_coef=${kl_coef} \
    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.filter_groups.enable=${enable_filter_groups} \
    algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \
    algorithm.filter_groups.metric=${filter_groups_metric} \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.use_dynamic_bsz=${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=${actor_ppo_max_token_len} \
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
    actor_rollout_ref.model.path="${MODEL_PATH}" \
    +actor_rollout_ref.model.override_config.attention_dropout=0. \
    +actor_rollout_ref.model.override_config.embd_pdrop=0. \
    +actor_rollout_ref.model.override_config.resid_pdrop=0. \
    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=${train_prompt_mini_bsz} \
    actor_rollout_ref.actor.fsdp_config.param_offload=${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=${sp_size} \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.60 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
    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=${top_k} \
    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
    actor_rollout_ref.rollout.val_kwargs.n=1 \
    actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \
    actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \
    actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
    reward_model.reward_manager=dapo \
    reward_model.overlong_buffer.enable=${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=4 \
    trainer.nnodes="${NNODES}" \
    trainer.val_before_train=True \
    trainer.test_freq=5 \
    trainer.save_freq=5 \
    trainer.total_epochs=3 \
    trainer.default_local_dir="${CKPTS_DIR}" \
    trainer.resume_mode=auto 2>&1 | tee grip_epoch_3_gsm8k_filter.log