ROLL/examples/qwen2.5-7B-agentic_megatron/agentic_val_webshop_async.yaml at 277c2e3bfcaabaf0a246394b3ae5d3c99d798ca9 · alibaba/ROLL · GitHub

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defaults:
  - ../config/envs@_here_
  - ../config/deepspeed_zero@_here_
  - ../config/deepspeed_zero2@_here_
  - ../config/deepspeed_zero3@_here_
  - ../config/deepspeed_zero3_cpuoffload@_here_

hydra:
  run:
    dir: .
  output_subdir: null

exp_name: "agentic_pipeline_webshop_async"
seed: 42
logging_dir: ./output/logs
output_dir: ./output
render_save_dir: ./output/render
system_envs:
  USE_MODELSCOPE: '1'

#track_with: wandb
#tracker_kwargs:
#  api_key:
#  project: roll-agentic
#  name: ${exp_name}_webshop
#  notes: "agentic_pipeline"
#  tags:
#    - agentic
#    - roll
#    - baseline

#track_with: swanlab
#tracker_kwargs:
#  login_kwargs:
#    api_key: your_api_key
#  project: roll-agentic
#  logdir: debug
#  experiment_name: ${exp_name}
#  tags:
#    - roll
#    - agentic
#    - debug

track_with: tensorboard
tracker_kwargs:
  log_dir: /data/oss_bucket_0/yali/llm/tensorboard/roll_exp/agentic_webshop

num_gpus_per_node: 8

max_steps: 1024
save_steps: 10000
logging_steps: 1
eval_steps: 10
resume_from_checkpoint: false

async_generation_ratio: 1

rollout_batch_size: 64
val_batch_size: 64
sequence_length: 8192

reward_clip: 20
advantage_clip: 0.2 # 0.1-0.3
ppo_epochs: 1
adv_estimator: "grpo"
#pg_clip: 0.1
max_grad_norm: 1.0
#dual_clip_loss: True
init_kl_coef: 0.0
whiten_advantages: true
entropy_loss_coef: 0

pretrain: Qwen/Qwen2.5-7B-Instruct
reward_pretrain: Qwen/Qwen2.5-7B-Instruct

actor_train:
  model_args:
    attn_implementation: fa2
    disable_gradient_checkpointing: false
    dtype: bf16
    model_type: ~
  training_args:
    learning_rate: 1.0e-6
    weight_decay: 0
    per_device_train_batch_size: 1
    gradient_accumulation_steps: 16
    warmup_steps: 10
  data_args:
    template: qwen2_5
  strategy_args:
    strategy_name: megatron_train
    strategy_config:
      tensor_model_parallel_size: 1
      context_parallel_size: 1
      pipeline_model_parallel_size: 1
      expert_model_parallel_size: 1
      use_distributed_optimizer: true
      recompute_granularity: full
      max_grad_norm: ${max_grad_norm}
  device_mapping: list(range(0,4))
  infer_batch_size: 1

actor_infer:
  model_args:
    disable_gradient_checkpointing: true
    dtype: bf16
  generating_args:
    max_new_tokens: 1024 # single-turn response length
    top_p: 0.99
    top_k: 100
    num_beams: 1
    temperature: 0.99
    num_return_sequences: 1
  data_args:
    template: qwen2_5
  strategy_args:
    strategy_name: vllm
    strategy_config:
      gpu_memory_utilization: 0.8
      block_size: 16
      load_format: auto
  device_mapping: list(range(4,8))
  infer_batch_size: 1

reference:
  model_args:
    attn_implementation: fa2
    disable_gradient_checkpointing: true
    dtype: bf16
    model_type: ~
  data_args:
    template: qwen2_5
  strategy_args:
    strategy_name: hf_infer
    strategy_config: ~
  device_mapping: list(range(0,4))
  infer_batch_size: 1

reward_normalization:
  grouping: traj_group_id # 可以tags(env_type)/traj_group_id(group)/batch(rollout_batch)... group_by计算reward/adv
  method: mean_std # asym_clip / identity / mean_std

train_env_manager:
  format_penalty: -0.05
  num_env_groups: 8
  group_size: 8
  max_env_num_per_worker: 4
  tags: [WebShopEnv]
  num_groups_partition: [8] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation

val_env_manager:
  num_env_groups: 64
  group_size: 1 # should be set to 1 because val temperature is set to 0 and same prompt leads to same output
  max_env_num_per_worker: 4
  tags: [WebShopEnv]
  num_groups_partition: [64] # TODO: If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation

max_tokens_per_step: 128
max_actions_per_traj: 20
action_pattern: <answer>(.*?)</answer>
think_action_pattern: <think>(.*?)</think>\s*<answer>(.*?)</answer>
user_prompt_no_think_format: <answer> [your answer] </answer>
user_prompt_think_format: <think> [Your thoughts] </think> <answer> [your answer] </answer>
added_text_no_think: <answer>
added_text_think: <think>

custom_envs:
  WebShopEnv:
    env_type: webshop
    max_tokens_per_step: ${max_tokens_per_step}
    user_prompt_format: ${user_prompt_no_think_format}
    added_text: ${added_text_no_think}
    env_manager_cls: roll.pipeline.agentic.env_manager.traj_env_manager.TrajEnvManager
    use_thread_lock: true
    env_config:
      observation_mode: text
      max_steps: ${max_actions_per_traj}