configs/inference_config.yaml

# ============================================
# Protenix 推理完整配置
# 基于 configs_base.py + configs_data.py + configs_inference.py
# 保持原有层级结构
# ============================================

inference:
  # ============================================
  # configs_inference.py: 推理基础配置
  # ============================================
  verbose: false
  seeds: [101]
  dump_dir: "./output_unified"
  need_atom_confidence: false
  sorted_by_ranking_score: true
  input_json_path: "./examples/7r6r.json"
  load_checkpoint_path: "./weight/model_v0.5.0.pt"
  num_workers: 16
  use_msa: true

  # ============================================
  # configs_base.py: basic_configs - 基础配置
  # ============================================
  project: "protenix"
  run_name: "run_001"
  base_dir: "./outputs"
  # 训练控制
  eval_interval: 1000
  log_interval: 100
  checkpoint_interval: -1
  eval_first: false
  iters_to_accumulate: 1
  eval_only: false
  load_ema_checkpoint_path: ""
  load_strict: true
  load_params_only: true
  skip_load_step: false
  skip_load_optimizer: false
  skip_load_scheduler: false
  train_confidence_only: false
  # 日志与跟踪
  use_wandb: true
  wandb_id: ""
  # 随机种子与确定性
  seed: 42
  deterministic: false
  deterministic_seed: false
  # EMA 设置
  ema_decay: -1.0
  eval_ema_only: false
  ema_mutable_param_keywords: [""]

  # ============================================
  # configs_base.py: data_configs - 数据配置
  # ============================================
  train_crop_size: 256
  test_max_n_token: -1
  train_lig_atom_rename: false
  train_shuffle_mols: false
  train_shuffle_sym_ids: false
  test_lig_atom_rename: false
  test_shuffle_mols: false
  test_shuffle_sym_ids: false

  # ============================================
  # configs_base.py: optim_configs - 优化器配置
  # ============================================
  lr: 0.0018
  lr_scheduler: "af3"
  warmup_steps: 10
  max_steps: 100000
  min_lr_ratio: 0.1
  decay_every_n_steps: 50000
  grad_clip_norm: 10
  # Adam 优化器
  adam:
    beta1: 0.9
    beta2: 0.95
    weight_decay: 1.0e-8
    lr: 0.0018
    use_adamw: false
  # AF3 学习率调度器
  af3_lr_scheduler:
    warmup_steps: 10
    decay_every_n_steps: 50000
    decay_factor: 0.95
    lr: 0.0018

  # ============================================
  # configs_base.py: model_configs - 模型基础配置
  # ============================================
  c_s: 384
  c_z: 128
  c_s_inputs: 449
  c_atom: 128
  c_atompair: 16
  c_token: 384
  n_blocks: 48
  max_atoms_per_token: 24
  no_bins: 64
  sigma_data: 16.0
  diffusion_batch_size: 48
  diffusion_chunk_size: 4
  blocks_per_ckpt: 1
  # 内核开关
  use_memory_efficient_kernel: false
  use_deepspeed_evo_attention: false
  use_flash: false
  use_lma: false
  use_xformer: false
  find_unused_parameters: false
  # 数据类型与损失
  dtype: "bf16"
  loss_metrics_sparse_enable: true
  # AMP 跳过配置
  skip_amp:
    sample_diffusion: true
    confidence_head: true
    sample_diffusion_training: true
    loss: true
  # 推理设置
  infer_setting:
    chunk_size: 64
    sample_diffusion_chunk_size: 1
    lddt_metrics_sparse_enable: true
    lddt_metrics_chunk_size: 1
  # 训练噪声采样器
  train_noise_sampler:
    p_mean: -1.2
    p_std: 1.5
    sigma_data: 16.0
  # 推理噪声调度器
  inference_noise_scheduler:
    s_max: 160.0
    s_min: 0.0004
    rho: 7
    sigma_data: 16.0
  # 扩散采样配置
  sample_diffusion:
    gamma0: 0.8
    gamma_min: 1.0
    noise_scale_lambda: 1.003
    step_scale_eta: 1.5
    N_step: 200
    N_sample: 5
    N_step_mini_rollout: 20
    N_sample_mini_rollout: 1

  # ============================================
  # configs_base.py: model_configs.model - 模型结构配置
  # ============================================
  model:
    N_model_seed: 1
    N_cycle: 4
    condition_embedding_drop_rate: 0.0
    confidence_embedding_drop_rate: 0.0
    input_embedder:
      c_atom: 128
      c_atompair: 16
      c_token: 384
    relative_position_encoding:
      r_max: 32
      s_max: 2
      c_z: 128
    template_embedder:
      c: 64
      c_z: 128
      n_blocks: 0
      dropout: 0.25
      blocks_per_ckpt: 1
    msa_module:
      c_m: 64
      c_z: 128
      c_s_inputs: 449
      n_blocks: 4
      msa_dropout: 0.15
      pair_dropout: 0.25
      blocks_per_ckpt: 1
      msa_chunk_size: 2048
    pairformer:
      n_blocks: 48
      c_z: 128
      c_s: 384
      n_heads: 16
      dropout: 0.25
      blocks_per_ckpt: 1
    diffusion_module:
      use_fine_grained_checkpoint: true
      sigma_data: 16.0
      c_token: 768
      c_atom: 128
      c_atompair: 16
      c_z: 128
      c_s: 384
      c_s_inputs: 449
      blocks_per_ckpt: 1
      atom_encoder:
        n_blocks: 3
        n_heads: 4
      transformer:
        n_blocks: 24
        n_heads: 16
      atom_decoder:
        n_blocks: 3
        n_heads: 4
    confidence_head:
      c_z: 128
      c_s: 384
      c_s_inputs: 449
      n_blocks: 4
      max_atoms_per_token: 24
      pairformer_dropout: 0.0
      blocks_per_ckpt: 1
      distance_bin_start: 3.25
      distance_bin_end: 52.0
      distance_bin_step: 1.25
      stop_gradient: true
    distogram_head:
      c_z: 128
      no_bins: 64

  # ============================================
  # configs_base.py: perm_configs - 置换配置
  # ============================================
  chain_permutation:
    train:
      mini_rollout: true
      diffusion_sample: false
    test:
      diffusion_sample: true
    permute_by_pocket: true
    configs:
      use_center_rmsd: false
      find_gt_anchor_first: false
      accept_it_as_it_is: false
      enumerate_all_anchor_pairs: false
      selection_metric: "aligned_rmsd"

  atom_permutation:
    train:
      mini_rollout: true
      diffusion_sample: false
    test:
      diffusion_sample: true
    permute_by_pocket: true
    global_align_wo_symmetric_atom: false

  # ============================================
  # configs_base.py: loss_configs - 损失函数配置
  # ============================================
  loss:
    diffusion_lddt_chunk_size: 1
    diffusion_bond_chunk_size: 1
    diffusion_chunk_size_outer: -1
    diffusion_sparse_loss_enable: true
    diffusion_lddt_loss_dense: true
    resolution:
      min: 0.1
      max: 4.0
    weight:
      alpha_confidence: 1.0e-4
      alpha_pae: 0.0
      alpha_except_pae: 1.0
      alpha_diffusion: 4.0
      alpha_distogram: 3.0e-2
      alpha_bond: 0.0
      smooth_lddt: 1.0
    plddt:
      min_bin: 0
      max_bin: 1.0
      no_bins: 50
      normalize: true
      eps: 1.0e-6
    pde:
      min_bin: 0
      max_bin: 32
      no_bins: 64
      eps: 1.0e-6
    resolved:
      eps: 1.0e-6
    pae:
      min_bin: 0
      max_bin: 32
      no_bins: 64
      eps: 1.0e-6
    diffusion:
      mse:
        weight_mse: 0.333333
        weight_dna: 5.0
        weight_rna: 5.0
        weight_ligand: 10.0
        eps: 1.0e-6
      bond:
        eps: 1.0e-6
      smooth_lddt:
        eps: 1.0e-6
    distogram:
      min_bin: 2.3125
      max_bin: 21.6875
      no_bins: 64
      eps: 1.0e-6

  # ============================================
  # configs_base.py: loss_configs.metrics - 评估指标
  # ============================================
  metrics:
    lddt:
      eps: 1.0e-6
    complex_ranker_keys: ["plddt", "gpde", "ranking_score"]
    chain_ranker_keys: ["chain_ptm", "chain_plddt"]
    interface_ranker_keys: ["chain_pair_iptm", "chain_pair_iptm_global", "chain_pair_plddt"]
    clash:
      af3_clash_threshold: 1.1
      vdw_clash_threshold: 0.75

  # ============================================
  # configs_data.py: data_configs - 数据加载配置
  # ============================================
  # 注意：msa 和 template 必须放在 data 下供模型读取
  # ============================================
  data:
    # CCD 组件文件
    ccd_components_file: "${DATA_ROOT_DIR}/components.v20240608.cif"
    ccd_components_rdkit_mol_file: "${DATA_ROOT_DIR}/components.v20240608.cif.rdkit_mol.pkl"

    # 数据加载器配置
    num_dl_workers: 16
    epoch_size: 10000
    train_ref_pos_augment: true
    test_ref_pos_augment: true

    # 训练数据集
    train_sets: ["weightedPDB_before2109_wopb_nometalc_0925"]
    train_sampler:
      train_sample_weights: [1.0]
      sampler_type: "weighted"

    # 测试数据集
    test_sets: ["recentPDB_1536_sample384_0925"]

    # ============================================
    # weightedPDB 训练数据集配置
    # ============================================
    weightedPDB_before2109_wopb_nometalc_0925:
      base_info:
        mmcif_dir: "${DATA_ROOT_DIR}/mmcif"
        bioassembly_dict_dir: "${DATA_ROOT_DIR}/mmcif_bioassembly"
        indices_fpath: "${DATA_ROOT_DIR}/indices/weightedPDB_indices_before_2021-09-30_wo_posebusters_resolution_below_9.csv.gz"
        pdb_list: ""
        random_sample_if_failed: true
        max_n_token: -1
        use_reference_chains_only: false
        exclusion:
          mol_1_type: ["ions"]
          mol_2_type: ["ions"]
      sampler_configs:
        sampler_type: "weighted"
        beta_dict:
          chain: 0.5
          interface: 1
        alpha_dict:
          prot: 3
          nuc: 3
          ligand: 1
        force_recompute_weight: true
      cropping_configs:
        method_weights: [0.2, 0.4, 0.4]
        crop_size: 256
      sample_weight: 0.5
      limits: -1
      lig_atom_rename: false
      shuffle_mols: false
      shuffle_sym_ids: false

    # ============================================
    # recentPDB 测试数据集配置
    # ============================================
    recentPDB_1536_sample384_0925:
      base_info:
        mmcif_dir: "${DATA_ROOT_DIR}/mmcif"
        bioassembly_dict_dir: "${DATA_ROOT_DIR}/recentPDB_bioassembly"
        indices_fpath: "${DATA_ROOT_DIR}/indices/recentPDB_low_homology_maxtoken1536.csv"
        pdb_list: "${DATA_ROOT_DIR}/indices/recentPDB_low_homology_maxtoken1024_sample384_pdb_id.txt"
        max_n_token: -1
        sort_by_n_token: false
        group_by_pdb_id: true
        find_eval_chain_interface: true
      sampler_configs:
        sampler_type: "uniform"
      cropping_configs:
        method_weights: [0.0, 0.0, 1.0]
        crop_size: -1
      lig_atom_rename: false
      shuffle_mols: false
      shuffle_sym_ids: false

    # ============================================
    # posebusters 数据集配置
    # ============================================
    posebusters_0925:
      base_info:
        mmcif_dir: "${DATA_ROOT_DIR}/posebusters_mmcif"
        bioassembly_dict_dir: "${DATA_ROOT_DIR}/posebusters_bioassembly"
        indices_fpath: "${DATA_ROOT_DIR}/indices/posebusters_indices_mainchain_interface.csv"
        pdb_list: ""
        find_pocket: true
        find_all_pockets: false
        max_n_token: -1
      sampler_configs:
        sampler_type: "uniform"
      cropping_configs:
        method_weights: [0.0, 0.0, 1.0]
        crop_size: -1
      lig_atom_rename: false
      shuffle_mols: false
      shuffle_sym_ids: false

    # ============================================
    # MSA 配置 (configs_data.py 中的 data_configs.msa)
    # ============================================
    msa:
      enable: true
      enable_rna_msa: false
      prot:
        pairing_db: "uniref100"
        non_pairing_db: "mmseqs_other"
        pdb_mmseqs_dir: "${DATA_ROOT_DIR}/mmcif_msa"
        seq_to_pdb_idx_path: "${DATA_ROOT_DIR}/seq_to_pdb_index.json"
        indexing_method: "sequence"
      rna:
        seq_to_pdb_idx_path: ""
        rna_msa_dir: ""
        indexing_method: "sequence"
      strategy: "random"
      merge_method: "dense_max"
      min_size:
        train: 1
        test: 1
      max_size:
        train: 16384
        test: 16384
      sample_cutoff:
        train: 16384
        test: 16384

    # ============================================
    # Template 配置 (configs_data.py 中的 data_configs.template)
    # ============================================
    template:
      enable: false
  
    extra:
      use_pipeline: true
      use_msa: true
      use_structure: true
      msa_dirs:
        - path: ${oc.env:PWD}/examples/7r6r/msa/1
          format_hint: a3m
      model_name: protenix_infer_adapter
      use_adapter: true