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config.py
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config.py
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import ml_collections as mlc
import copy
from typing import Any
N_RES = "number of residues"
N_MSA = "number of MSA sequences"
N_EXTRA_MSA = "number of extra MSA sequences"
N_TPL = "number of templates"
d_pair = mlc.FieldReference(128, field_type=int)
d_msa = mlc.FieldReference(256, field_type=int)
d_template = mlc.FieldReference(64, field_type=int)
d_extra_msa = mlc.FieldReference(64, field_type=int)
d_single = mlc.FieldReference(384, field_type=int)
max_recycling_iters = mlc.FieldReference(3, field_type=int)
chunk_size = mlc.FieldReference(4, field_type=int)
aux_distogram_bins = mlc.FieldReference(64, field_type=int)
eps = mlc.FieldReference(1e-8, field_type=float)
inf = mlc.FieldReference(3e4, field_type=float)
use_templates = mlc.FieldReference(True, field_type=bool)
is_multimer = mlc.FieldReference(False, field_type=bool)
def base_config():
return mlc.ConfigDict(
{
"data": {
"common": {
"features": {
"aatype": [N_RES],
"all_atom_mask": [N_RES, None],
"all_atom_positions": [N_RES, None, None],
"alt_chi_angles": [N_RES, None],
"atom14_alt_gt_exists": [N_RES, None],
"atom14_alt_gt_positions": [N_RES, None, None],
"atom14_atom_exists": [N_RES, None],
"atom14_atom_is_ambiguous": [N_RES, None],
"atom14_gt_exists": [N_RES, None],
"atom14_gt_positions": [N_RES, None, None],
"atom37_atom_exists": [N_RES, None],
"frame_mask": [N_RES],
"true_frame_tensor": [N_RES, None, None],
"bert_mask": [N_MSA, N_RES],
"chi_angles_sin_cos": [N_RES, None, None],
"chi_mask": [N_RES, None],
"extra_msa_deletion_value": [N_EXTRA_MSA, N_RES],
"extra_msa_has_deletion": [N_EXTRA_MSA, N_RES],
"extra_msa": [N_EXTRA_MSA, N_RES],
"extra_msa_mask": [N_EXTRA_MSA, N_RES],
"extra_msa_row_mask": [N_EXTRA_MSA],
"is_distillation": [],
"msa_feat": [N_MSA, N_RES, None],
"msa_mask": [N_MSA, N_RES],
"msa_chains": [N_MSA, None],
"msa_row_mask": [N_MSA],
"num_recycling_iters": [],
"pseudo_beta": [N_RES, None],
"pseudo_beta_mask": [N_RES],
"residue_index": [N_RES],
"residx_atom14_to_atom37": [N_RES, None],
"residx_atom37_to_atom14": [N_RES, None],
"resolution": [],
"rigidgroups_alt_gt_frames": [N_RES, None, None, None],
"rigidgroups_group_exists": [N_RES, None],
"rigidgroups_group_is_ambiguous": [N_RES, None],
"rigidgroups_gt_exists": [N_RES, None],
"rigidgroups_gt_frames": [N_RES, None, None, None],
"seq_length": [],
"seq_mask": [N_RES],
"target_feat": [N_RES, None],
"template_aatype": [N_TPL, N_RES],
"template_all_atom_mask": [N_TPL, N_RES, None],
"template_all_atom_positions": [N_TPL, N_RES, None, None],
"template_alt_torsion_angles_sin_cos": [
N_TPL,
N_RES,
None,
None,
],
"template_frame_mask": [N_TPL, N_RES],
"template_frame_tensor": [N_TPL, N_RES, None, None],
"template_mask": [N_TPL],
"template_pseudo_beta": [N_TPL, N_RES, None],
"template_pseudo_beta_mask": [N_TPL, N_RES],
"template_sum_probs": [N_TPL, None],
"template_torsion_angles_mask": [N_TPL, N_RES, None],
"template_torsion_angles_sin_cos": [N_TPL, N_RES, None, None],
"true_msa": [N_MSA, N_RES],
"use_clamped_fape": [],
"assembly_num_chains": [1],
"asym_id": [N_RES],
"sym_id": [N_RES],
"entity_id": [N_RES],
"num_sym": [N_RES],
"asym_len": [None],
"cluster_bias_mask": [N_MSA],
},
"masked_msa": {
"profile_prob": 0.1,
"same_prob": 0.1,
"uniform_prob": 0.1,
},
"block_delete_msa": {
"msa_fraction_per_block": 0.3,
"randomize_num_blocks": False,
"num_blocks": 5,
"min_num_msa": 16,
},
"random_delete_msa": {
"max_msa_entry": 1 << 25, # := 33554432
},
"v2_feature": False,
"gumbel_sample": False,
"max_extra_msa": 1024,
"msa_cluster_features": True,
"reduce_msa_clusters_by_max_templates": True,
"resample_msa_in_recycling": True,
"template_features": [
"template_all_atom_positions",
"template_sum_probs",
"template_aatype",
"template_all_atom_mask",
],
"unsupervised_features": [
"aatype",
"residue_index",
"msa",
"msa_chains",
"num_alignments",
"seq_length",
"between_segment_residues",
"deletion_matrix",
"num_recycling_iters",
"crop_and_fix_size_seed",
],
"recycling_features": [
"msa_chains",
"msa_mask",
"msa_row_mask",
"bert_mask",
"true_msa",
"msa_feat",
"extra_msa_deletion_value",
"extra_msa_has_deletion",
"extra_msa",
"extra_msa_mask",
"extra_msa_row_mask",
"is_distillation",
],
"multimer_features": [
"assembly_num_chains",
"asym_id",
"sym_id",
"num_sym",
"entity_id",
"asym_len",
"cluster_bias_mask",
],
"use_templates": use_templates,
"is_multimer": is_multimer,
"use_template_torsion_angles": use_templates,
"max_recycling_iters": max_recycling_iters,
},
"supervised": {
"use_clamped_fape_prob": 1.0,
"supervised_features": [
"all_atom_mask",
"all_atom_positions",
"resolution",
"use_clamped_fape",
"is_distillation",
],
},
"predict": {
"fixed_size": True,
"subsample_templates": False,
"block_delete_msa": False,
"random_delete_msa": True,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 128,
"max_templates": 4,
"num_ensembles": 2,
"crop": False,
"crop_size": None,
"supervised": False,
"biased_msa_by_chain": False,
"share_mask": False,
},
"eval": {
"fixed_size": True,
"subsample_templates": False,
"block_delete_msa": False,
"random_delete_msa": True,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 128,
"max_templates": 4,
"num_ensembles": 1,
"crop": False,
"crop_size": None,
"spatial_crop_prob": 0.5,
"ca_ca_threshold": 10.0,
"supervised": True,
"biased_msa_by_chain": False,
"share_mask": False,
},
"train": {
"fixed_size": True,
"subsample_templates": True,
"block_delete_msa": True,
"random_delete_msa": True,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 128,
"max_templates": 4,
"num_ensembles": 1,
"crop": True,
"crop_size": 256,
"spatial_crop_prob": 0.5,
"ca_ca_threshold": 10.0,
"supervised": True,
"use_clamped_fape_prob": 1.0,
"max_distillation_msa_clusters": 1000,
"biased_msa_by_chain": True,
"share_mask": True,
},
},
"globals": {
"chunk_size": chunk_size,
"d_pair": d_pair,
"d_msa": d_msa,
"d_template": d_template,
"d_extra_msa": d_extra_msa,
"d_single": d_single,
"eps": eps,
"inf": inf,
"max_recycling_iters": max_recycling_iters,
"alphafold_original_mode": False,
},
"model": {
"is_multimer": is_multimer,
"input_embedder": {
"tf_dim": 22,
"msa_dim": 49,
"d_pair": d_pair,
"d_msa": d_msa,
"relpos_k": 32,
"max_relative_chain": 2,
},
"recycling_embedder": {
"d_pair": d_pair,
"d_msa": d_msa,
"min_bin": 3.25,
"max_bin": 20.75,
"num_bins": 15,
"inf": 1e8,
},
"template": {
"distogram": {
"min_bin": 3.25,
"max_bin": 50.75,
"num_bins": 39,
},
"template_angle_embedder": {
"d_in": 57,
"d_out": d_msa,
},
"template_pair_embedder": {
"d_in": 88,
"v2_d_in": [39, 1, 22, 22, 1, 1, 1, 1],
"d_pair": d_pair,
"d_out": d_template,
"v2_feature": False,
},
"template_pair_stack": {
"d_template": d_template,
"d_hid_tri_att": 16,
"d_hid_tri_mul": 64,
"num_blocks": 2,
"num_heads": 4,
"pair_transition_n": 2,
"dropout_rate": 0.25,
"inf": 1e9,
"tri_attn_first": True,
},
"template_pointwise_attention": {
"enabled": True,
"d_template": d_template,
"d_pair": d_pair,
"d_hid": 16,
"num_heads": 4,
"inf": 1e5,
},
"inf": 1e5,
"eps": 1e-6,
"enabled": use_templates,
"embed_angles": use_templates,
},
"extra_msa": {
"extra_msa_embedder": {
"d_in": 25,
"d_out": d_extra_msa,
},
"extra_msa_stack": {
"d_msa": d_extra_msa,
"d_pair": d_pair,
"d_hid_msa_att": 8,
"d_hid_opm": 32,
"d_hid_mul": 128,
"d_hid_pair_att": 32,
"num_heads_msa": 8,
"num_heads_pair": 4,
"num_blocks": 4,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"inf": 1e9,
"eps": 1e-10,
"outer_product_mean_first": False,
},
"enabled": True,
},
"evoformer_stack": {
"d_msa": d_msa,
"d_pair": d_pair,
"d_hid_msa_att": 32,
"d_hid_opm": 32,
"d_hid_mul": 128,
"d_hid_pair_att": 32,
"d_single": d_single,
"num_heads_msa": 8,
"num_heads_pair": 4,
"num_blocks": 48,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"inf": 1e9,
"eps": 1e-10,
"outer_product_mean_first": False,
},
"structure_module": {
"d_single": d_single,
"d_pair": d_pair,
"d_ipa": 16,
"d_angle": 128,
"num_heads_ipa": 12,
"num_qk_points": 4,
"num_v_points": 8,
"dropout_rate": 0.1,
"num_blocks": 8,
"no_transition_layers": 1,
"num_resnet_blocks": 2,
"num_angles": 7,
"trans_scale_factor": 10,
"epsilon": 1e-12,
"inf": 1e5,
"separate_kv": False,
"ipa_bias": True,
},
"heads": {
"plddt": {
"num_bins": 50,
"d_in": d_single,
"d_hid": 128,
},
"distogram": {
"d_pair": d_pair,
"num_bins": aux_distogram_bins,
"disable_enhance_head": False,
},
"pae": {
"d_pair": d_pair,
"num_bins": aux_distogram_bins,
"enabled": False,
"iptm_weight": 0.8,
"disable_enhance_head": False,
},
"masked_msa": {
"d_msa": d_msa,
"d_out": 23,
"disable_enhance_head": False,
},
"experimentally_resolved": {
"d_single": d_single,
"d_out": 37,
"enabled": False,
"disable_enhance_head": False,
},
},
},
"loss": {
"distogram": {
"min_bin": 2.3125,
"max_bin": 21.6875,
"num_bins": 64,
"eps": 1e-6,
"weight": 0.3,
},
"experimentally_resolved": {
"eps": 1e-8,
"min_resolution": 0.1,
"max_resolution": 3.0,
"weight": 0.0,
},
"fape": {
"backbone": {
"clamp_distance": 10.0,
"clamp_distance_between_chains": 30.0,
"loss_unit_distance": 10.0,
"loss_unit_distance_between_chains": 20.0,
"weight": 0.5,
"eps": 1e-4,
},
"sidechain": {
"clamp_distance": 10.0,
"length_scale": 10.0,
"weight": 0.5,
"eps": 1e-4,
},
"weight": 1.0,
},
"plddt": {
"min_resolution": 0.1,
"max_resolution": 3.0,
"cutoff": 15.0,
"num_bins": 50,
"eps": 1e-10,
"weight": 0.01,
},
"masked_msa": {
"eps": 1e-8,
"weight": 2.0,
},
"supervised_chi": {
"chi_weight": 0.5,
"angle_norm_weight": 0.01,
"eps": 1e-6,
"weight": 1.0,
},
"violation": {
"violation_tolerance_factor": 12.0,
"clash_overlap_tolerance": 1.5,
"bond_angle_loss_weight": 0.3,
"eps": 1e-6,
"weight": 0.0,
},
"pae": {
"max_bin": 31,
"num_bins": 64,
"min_resolution": 0.1,
"max_resolution": 3.0,
"eps": 1e-8,
"weight": 0.0,
},
"repr_norm": {
"weight": 0.01,
"tolerance": 1.0,
},
"chain_centre_mass": {
"weight": 0.0,
"eps": 1e-8,
},
},
}
)
def recursive_set(c: mlc.ConfigDict, key: str, value: Any, ignore: str = None):
with c.unlocked():
for k, v in c.items():
if ignore is not None and k == ignore:
continue
if isinstance(v, mlc.ConfigDict):
recursive_set(v, key, value)
elif k == key:
c[k] = value
def model_config(name, train=False):
c = copy.deepcopy(base_config())
def model_2_v2(c):
recursive_set(c, "v2_feature", True)
recursive_set(c, "gumbel_sample", True)
c.model.heads.masked_msa.d_out = 22
c.model.structure_module.separate_kv = True
c.model.structure_module.ipa_bias = False
c.model.template.template_angle_embedder.d_in = 34
return c
def multimer(c):
recursive_set(c, "is_multimer", True)
recursive_set(c, "max_extra_msa", 1152)
recursive_set(c, "max_msa_clusters", 128)
recursive_set(c, "v2_feature", True)
recursive_set(c, "gumbel_sample", True)
c.model.template.template_angle_embedder.d_in = 34
c.model.template.template_pair_stack.tri_attn_first = False
c.model.template.template_pointwise_attention.enabled = False
c.model.heads.pae.enabled = True
# we forget to enable it in our training, so disable it here
c.model.heads.pae.disable_enhance_head = True
c.model.heads.masked_msa.d_out = 22
c.model.structure_module.separate_kv = True
c.model.structure_module.ipa_bias = False
c.model.structure_module.trans_scale_factor = 20
c.loss.pae.weight = 0.1
c.model.input_embedder.tf_dim = 21
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
c.loss.chain_centre_mass.weight = 1.0
return c
if name == "model_1":
pass
elif name == "model_1_ft":
recursive_set(c, "max_extra_msa", 5120)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
elif name == "model_1_af2":
recursive_set(c, "max_extra_msa", 5120)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
c.loss.repr_norm.weight = 0
c.model.heads.experimentally_resolved.enabled = True
c.loss.experimentally_resolved.weight = 0.01
c.globals.alphafold_original_mode = True
elif name == "model_2":
pass
elif name == "model_init":
pass
elif name == "model_init_af2":
c.globals.alphafold_original_mode = True
pass
elif name == "model_2_ft":
recursive_set(c, "max_extra_msa", 1024)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
elif name == "model_2_af2":
recursive_set(c, "max_extra_msa", 1024)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
c.loss.repr_norm.weight = 0
c.model.heads.experimentally_resolved.enabled = True
c.loss.experimentally_resolved.weight = 0.01
c.globals.alphafold_original_mode = True
elif name == "model_2_v2":
c = model_2_v2(c)
elif name == "model_2_v2_ft":
c = model_2_v2(c)
recursive_set(c, "max_extra_msa", 1024)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
elif name == "model_3_af2" or name == "model_4_af2":
recursive_set(c, "max_extra_msa", 5120)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
c.loss.repr_norm.weight = 0
c.model.heads.experimentally_resolved.enabled = True
c.loss.experimentally_resolved.weight = 0.01
c.globals.alphafold_original_mode = True
c.model.template.enabled = False
c.model.template.embed_angles = False
recursive_set(c, "use_templates", False)
recursive_set(c, "use_template_torsion_angles", False)
elif name == "model_5_af2":
recursive_set(c, "max_extra_msa", 1024)
recursive_set(c, "max_msa_clusters", 512)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.02
c.loss.repr_norm.weight = 0
c.model.heads.experimentally_resolved.enabled = True
c.loss.experimentally_resolved.weight = 0.01
c.globals.alphafold_original_mode = True
c.model.template.enabled = False
c.model.template.embed_angles = False
recursive_set(c, "use_templates", False)
recursive_set(c, "use_template_torsion_angles", False)
elif name == "multimer":
c = multimer(c)
elif name == "multimer_ft":
c = multimer(c)
recursive_set(c, "max_extra_msa", 1152)
recursive_set(c, "max_msa_clusters", 256)
c.data.train.crop_size = 384
c.loss.violation.weight = 0.5
elif name == "multimer_af2":
recursive_set(c, "max_extra_msa", 1152)
recursive_set(c, "max_msa_clusters", 256)
recursive_set(c, "is_multimer", True)
recursive_set(c, "v2_feature", True)
recursive_set(c, "gumbel_sample", True)
c.model.template.template_angle_embedder.d_in = 34
c.model.template.template_pair_stack.tri_attn_first = False
c.model.template.template_pointwise_attention.enabled = False
c.model.heads.pae.enabled = True
c.model.heads.experimentally_resolved.enabled = True
c.model.heads.masked_msa.d_out = 22
c.model.structure_module.separate_kv = True
c.model.structure_module.ipa_bias = False
c.model.structure_module.trans_scale_factor = 20
c.loss.pae.weight = 0.1
c.loss.violation.weight = 0.5
c.loss.experimentally_resolved.weight = 0.01
c.model.input_embedder.tf_dim = 21
c.globals.alphafold_original_mode = True
c.data.train.crop_size = 384
c.loss.repr_norm.weight = 0
c.loss.chain_centre_mass.weight = 1.0
recursive_set(c, "outer_product_mean_first", True)
else:
raise ValueError(f"invalid --model-name: {name}.")
if train:
c.globals.chunk_size = None
recursive_set(c, "inf", 3e4)
recursive_set(c, "eps", 1e-5, "loss")
return c