data:
  tokenizer: null
  train_files: ~/data/rlhf/gsm8k/train.parquet
  val_files: ~/data/rlhf/gsm8k/test.parquet
  prompt_key: prompt
  max_prompt_length: 512
  max_response_length: 512
  train_batch_size: 1024
  val_batch_size: null # DEPRECATED: Validation datasets are sent to inference engines as a whole batch, which will schedule the memory themselves
  return_raw_input_ids: False  # This should be set to true when the tokenizer between policy and rm differs
  return_raw_chat: False
  shuffle: True
  filter_overlong_prompts: False # for large-scale dataset, filtering overlong prompts could be timeconsuming. You should disable this and set `truncation='left'
  truncation: error

actor_rollout_ref:
  hybrid_engine: True
  model:
    path: ~/models/deepseek-llm-7b-chat
    external_lib: null
    override_config: {}
    enable_gradient_checkpointing: False
  actor:
    strategy: megatron  # This is for backward-compatibility
    ppo_mini_batch_size: 256
    ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
    ppo_micro_batch_size_per_gpu: null
    use_dynamic_bsz: False
    clip_ratio: 0.2
    entropy_coeff: 0.001
    ppo_epochs: 1
    shuffle: True
    optim:
      lr: 1e-6
      clip_grad: 1.0
      lr_warmup_steps_ratio: 0.  # the total steps will be injected during runtime
      min_lr_ratio: null   # only useful for warmup with cosine
      warmup_style: constant  # select from constant/cosine
      total_training_steps: -1  # must be override by program
    megatron:
      tensor_model_parallel_size: 4
      pipeline_model_parallel_size: 1
      virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests
      sequence_parallel: True
      seed: 1
    load_weight: True
  ref:
    megatron:
      tensor_model_parallel_size: 4
      pipeline_model_parallel_size: 1
      virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests
      sequence_parallel: True
      seed: 1
    load_weight: True
    param_offload: False
    log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
    log_prob_micro_batch_size_per_gpu: null
  rollout:
    name: vllm
    temperature: 1.0
    top_k: -1 # 0 for hf rollout, -1 for vllm rollout
    top_p: 1
    prompt_length: ${data.max_prompt_length}  # for xperf_gpt
    response_length: ${data.max_response_length}
    # for vllm rollout
    dtype: bfloat16 # should align with FSDP
    gpu_memory_utilization: 0.5
    ignore_eos: False
    enforce_eager: True
    free_cache_engine: True
    load_format: dummy_megatron
    tensor_model_parallel_size: 2
    max_num_batched_tokens: 8192
    max_model_len: null
    max_num_seqs: 1024
    log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
    log_prob_micro_batch_size_per_gpu: null
    disable_log_stats: True
    enable_chunked_prefill: False # could get higher throughput
    # for hf rollout
    do_sample: True
    layer_name_map:
      qkv_layer_name: qkv
      gate_proj_layer_name: gate_up
    # number of responses (i.e. num sample times)
    n: 1

critic:
  strategy: megatron
  optim:
    lr: 1e-5
    clip_grad: 1.0
    lr_warmup_steps_ratio: 0.  # the total steps will be injected during runtime
    min_lr_ratio: null   # only useful for warmup with cosine
    warmup_style: constant  # select from constant/cosine
    total_training_steps: -1  # must be override by program
  model:
    path: ~/models/deepseek-llm-7b-chat
    tokenizer_path: ${actor_rollout_ref.model.path}
    override_config: {}
    external_lib: ${actor_rollout_ref.model.external_lib}
    enable_gradient_checkpointing: False
  megatron:
    tensor_model_parallel_size: 4
    pipeline_model_parallel_size: 1
    virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests
    sequence_parallel: True
    seed: 1
  load_weight: True
  ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size}
  ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
  ppo_micro_batch_size_per_gpu: null
  use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
  ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs}
  shuffle: ${actor_rollout_ref.actor.shuffle}
  cliprange_value: 0.5
  kl_ctrl:
    type: fixed
    kl_coef: 0.001

reward_model:
  enable: False
  strategy: megatron
  megatron:
    tensor_model_parallel_size: 4
    pipeline_model_parallel_size: 1
    virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests
    sequence_parallel: True
    seed: 1
  model:
    input_tokenizer: ${actor_rollout_ref.model.path}  # set this to null if the chat template is identical
    path: ~/models/FsfairX-LLaMA3-RM-v0.1
    external_lib: ${actor_rollout_ref.model.external_lib}
  load_weight: True
  param_offload: False
  micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu
  micro_batch_size_per_gpu: null
  use_dynamic_bsz: ${critic.use_dynamic_bsz}
  max_length: null

custom_reward_function:
  path: null
  name: compute_score

algorithm:
  gamma: 1.0
  lam: 1.0
  adv_estimator: gae
  kl_penalty: kl  # how to estimate kl divergence
  kl_ctrl:
    type: fixed
    kl_coef: 0.001

trainer:
  total_epochs: 30
  total_training_steps: null
  project_name: verl_examples
  experiment_name: gsm8k
  logger: ['console', 'wandb']
  val_generations_to_log_to_wandb: 0
  nnodes: 1
  n_gpus_per_node: 8
  save_freq: -1
  # auto: find the last ckpt to resume. If can't find, start from scratch
  resume_mode: disable # or auto or resume_path if 
  resume_from_path: False
  test_freq: 2
  critic_warmup: 0
  default_hdfs_dir: null
  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
