common:
  # The number of historical images
  img_history_size: 1
  # The number of historical point clouds
  pcd_history_size: 1
  # The number of future actions to predict
  action_chunk_size: 16
  # The number of cameras to be used in the model
  num_cameras: 3
  # Dimension for state
  state_dim: 14
  # Dimension for action
  action_dim: 14
  # The number of patches in the image
  num_patches: 196


dataset:
  # We will extract the data from raw dataset
  # and store them in the disk buffer by producer
  # When training, we will read the data
  # randomly from the buffer by consumer
  # The producer will replace the data which has been
  # read by the consumer with new data

  # The path to the buffer (at least 400GB)
  buf_path: /ssd/lingxuan/data/buffer
  # The number of chunks in the buffer
  buf_num_chunks: 512
  # The number of samples (step rather than episode) in each chunk
  buf_chunk_size: 512

  # We will filter the episodes with length less than `epsd_len_thresh_low`
  epsd_len_thresh_low: 32
  # For those more than `epsd_len_thresh_high`,
  # we will randomly sample `epsd_len_thresh_high` steps each time we load the episode
  # to better balance the training datasets
  epsd_len_thresh_high: 2048
  # How to fit the image size
  image_aspect_ratio: pad
  # Maximum number of language tokens
  tokenizer_max_length: 1024

model:
  # Config for condition adpators
  act_adaptor: mlp3x_silu  # Will be reinitialized in finetune mode
  st_adaptor: mlp3x_silu   # Will be reinitialized in finetune mode
  img_adapter: mlp2x_silu  # Shared between pretrain and finetune
  lang_adapter: mlp2x_silu # Shared between pretrain and finetune
  
  # Config for H-RDT structure (backbone - shared between pretrain and finetune)
  hrdt:
    hidden_size: 2176
    depth: 16
    num_heads: 16
    norm_eps: 0.00001
    # make SwiGLU hidden layer size multiple of large power of 2
    multiple_of: 256
    ffn_dim_multiplier: null
    # Grouped Query Attention
    num_kv_heads: 8
    # output_size: ${...common.action_dim} # i.e., action dimension (TODO)
    output_size: 14
    use_flash_attn: true
  # For noise scheduler (flow matching)
  noise_scheduler:
    num_inference_timesteps: 5
    timestep_max: 0.999
    sampler_type: uniform
  time_noise:
    a: 5
    beta_m: 100
  # For EMA (params averaging)
  # We do not use EMA currently
  ema:
    update_after_step: 0
    inv_gamma: 1.0
    power: 0.75
    min_value: 0.0
    max_value: 0.9999

  # Encoder configurations
  vision:
    feature_dim: 2176

  text:
    feature_dim: 4096