/
config.py
956 lines (924 loc) · 35.4 KB
/
config.py
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import re
import copy
import importlib
import ml_collections as mlc
def set_inf(c, inf):
for k, v in c.items():
if isinstance(v, mlc.ConfigDict):
set_inf(v, inf)
elif k == "inf":
c[k] = inf
def enforce_config_constraints(config):
def string_to_setting(s):
path = s.split('.')
setting = config
for p in path:
setting = setting.get(p)
return setting
mutually_exclusive_bools = [
(
"model.template.average_templates",
"model.template.offload_templates"
),
(
"globals.use_lma",
"globals.use_flash",
"globals.use_deepspeed_evo_attention"
),
]
for options in mutually_exclusive_bools:
option_settings = [string_to_setting(o) for o in options]
if sum(option_settings) > 1:
raise ValueError(f"Only one of {', '.join(options)} may be set at a time")
fa_is_installed = importlib.util.find_spec("flash_attn") is not None
if config.globals.use_flash and not fa_is_installed:
raise ValueError("use_flash requires that FlashAttention is installed")
deepspeed_is_installed = importlib.util.find_spec("deepspeed") is not None
ds4s_is_installed = deepspeed_is_installed and importlib.util.find_spec(
"deepspeed.ops.deepspeed4science") is not None
if config.globals.use_deepspeed_evo_attention and not ds4s_is_installed:
raise ValueError(
"use_deepspeed_evo_attention requires that DeepSpeed be installed "
"and that the deepspeed.ops.deepspeed4science package exists"
)
if(
config.globals.offload_inference and
not config.model.template.average_templates
):
config.model.template.offload_templates = True
def model_config(
name,
train=False,
low_prec=False,
long_sequence_inference=False,
use_deepspeed_evoformer_attention=False,
):
c = copy.deepcopy(config)
# TRAINING PRESETS
if name == "initial_training":
# AF2 Suppl. Table 4, "initial training" setting
pass
elif name == "finetuning":
# AF2 Suppl. Table 4, "finetuning" setting
c.data.train.crop_size = 384
c.data.train.max_extra_msa = 5120
c.data.train.max_msa_clusters = 512
c.loss.violation.weight = 1.
c.loss.experimentally_resolved.weight = 0.01
elif name == "finetuning_ptm":
c.data.train.max_extra_msa = 5120
c.data.train.crop_size = 384
c.data.train.max_msa_clusters = 512
c.loss.violation.weight = 1.
c.loss.experimentally_resolved.weight = 0.01
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif name == "finetuning_no_templ":
# AF2 Suppl. Table 4, "finetuning" setting
c.data.train.crop_size = 384
c.data.train.max_extra_msa = 5120
c.data.train.max_msa_clusters = 512
c.model.template.enabled = False
c.loss.violation.weight = 1.
c.loss.experimentally_resolved.weight = 0.01
elif name == "finetuning_no_templ_ptm":
# AF2 Suppl. Table 4, "finetuning" setting
c.data.train.crop_size = 384
c.data.train.max_extra_msa = 5120
c.data.train.max_msa_clusters = 512
c.model.template.enabled = False
c.loss.violation.weight = 1.
c.loss.experimentally_resolved.weight = 0.01
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
# INFERENCE PRESETS
elif name == "model_1":
# AF2 Suppl. Table 5, Model 1.1.1
c.data.train.max_extra_msa = 5120
c.data.predict.max_extra_msa = 5120
c.data.common.reduce_max_clusters_by_max_templates = True
c.data.common.use_templates = True
c.data.common.use_template_torsion_angles = True
c.model.template.enabled = True
elif name == "model_2":
# AF2 Suppl. Table 5, Model 1.1.2
c.data.common.reduce_max_clusters_by_max_templates = True
c.data.common.use_templates = True
c.data.common.use_template_torsion_angles = True
c.model.template.enabled = True
elif name == "model_3":
# AF2 Suppl. Table 5, Model 1.2.1
c.data.train.max_extra_msa = 5120
c.data.predict.max_extra_msa = 5120
c.model.template.enabled = False
elif name == "model_4":
# AF2 Suppl. Table 5, Model 1.2.2
c.data.train.max_extra_msa = 5120
c.data.predict.max_extra_msa = 5120
c.model.template.enabled = False
elif name == "model_5":
# AF2 Suppl. Table 5, Model 1.2.3
c.model.template.enabled = False
elif name == "model_1_ptm":
c.data.train.max_extra_msa = 5120
c.data.predict.max_extra_msa = 5120
c.data.common.reduce_max_clusters_by_max_templates = True
c.data.common.use_templates = True
c.data.common.use_template_torsion_angles = True
c.model.template.enabled = True
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif name == "model_2_ptm":
c.data.common.reduce_max_clusters_by_max_templates = True
c.data.common.use_templates = True
c.data.common.use_template_torsion_angles = True
c.model.template.enabled = True
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif name == "model_3_ptm":
c.data.train.max_extra_msa = 5120
c.data.predict.max_extra_msa = 5120
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif name == "model_4_ptm":
c.data.train.max_extra_msa = 5120
c.data.predict.max_extra_msa = 5120
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif name == "model_5_ptm":
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif name.startswith("seq"): # SINGLE SEQUENCE EMBEDDING PRESETS
c.update(seq_mode_config.copy_and_resolve_references())
if name == "seqemb_initial_training":
c.data.train.max_msa_clusters = 1
c.data.eval.max_msa_clusters = 1
c.data.train.block_delete_msa = False
c.data.train.max_distillation_msa_clusters = 1
elif name == "seqemb_finetuning":
c.data.train.max_msa_clusters = 1
c.data.eval.max_msa_clusters = 1
c.data.train.block_delete_msa = False
c.data.train.max_distillation_msa_clusters = 1
c.data.train.crop_size = 384
c.loss.violation.weight = 1.
c.loss.experimentally_resolved.weight = 0.01
elif name == "seq_model_esm1b":
c.data.common.use_templates = True
c.data.common.use_template_torsion_angles = True
c.model.template.enabled = True
c.data.predict.max_msa_clusters = 1
elif name == "seq_model_esm1b_ptm":
c.data.common.use_templates = True
c.data.common.use_template_torsion_angles = True
c.model.template.enabled = True
c.data.predict.max_msa_clusters = 1
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif "multimer" in name: # MULTIMER PRESETS
c.update(multimer_config_update.copy_and_resolve_references())
# Not used in multimer
del c.model.template.template_pointwise_attention
del c.loss.fape.backbone
# TODO: Change max_msa_clusters and max_extra_msa to multimer feats within model
if re.fullmatch("^model_[1-5]_multimer(_v2)?$", name):
#c.model.input_embedder.num_msa = 252
#c.model.extra_msa.extra_msa_embedder.num_extra_msa = 1152
c.data.train.crop_size = 384
c.data.train.max_msa_clusters = 252
c.data.eval.max_msa_clusters = 252
c.data.predict.max_msa_clusters = 252
c.data.train.max_extra_msa = 1152
c.data.eval.max_extra_msa = 1152
c.data.predict.max_extra_msa = 1152
c.model.evoformer_stack.fuse_projection_weights = False
c.model.extra_msa.extra_msa_stack.fuse_projection_weights = False
c.model.template.template_pair_stack.fuse_projection_weights = False
elif name == 'model_4_multimer_v3':
#c.model.extra_msa.extra_msa_embedder.num_extra_msa = 1152
c.data.train.max_extra_msa = 1152
c.data.eval.max_extra_msa = 1152
c.data.predict.max_extra_msa = 1152
elif name == 'model_5_multimer_v3':
#c.model.extra_msa.extra_msa_embedder.num_extra_msa = 1152
c.data.train.max_extra_msa = 1152
c.data.eval.max_extra_msa = 1152
c.data.predict.max_extra_msa = 1152
else:
raise ValueError("Invalid model name")
if long_sequence_inference:
assert(not train)
c.globals.offload_inference = True
# Default to DeepSpeed memory-efficient attention kernel unless use_lma is explicitly set
c.globals.use_deepspeed_evo_attention = True if not c.globals.use_lma else False
c.globals.use_flash = False
c.model.template.offload_inference = True
c.model.template.template_pair_stack.tune_chunk_size = False
c.model.extra_msa.extra_msa_stack.tune_chunk_size = False
c.model.evoformer_stack.tune_chunk_size = False
if use_deepspeed_evoformer_attention:
c.globals.use_deepspeed_evo_attention = True
if train:
c.globals.blocks_per_ckpt = 1
c.globals.chunk_size = None
c.globals.use_lma = False
c.globals.offload_inference = False
c.model.template.average_templates = False
c.model.template.offload_templates = False
if low_prec:
c.globals.eps = 1e-4
# If we want exact numerical parity with the original, inf can't be
# a global constant
set_inf(c, 1e4)
enforce_config_constraints(c)
return c
c_z = mlc.FieldReference(128, field_type=int)
c_m = mlc.FieldReference(256, field_type=int)
c_t = mlc.FieldReference(64, field_type=int)
c_e = mlc.FieldReference(64, field_type=int)
c_s = mlc.FieldReference(384, field_type=int)
# For seqemb mode, dimension size of the per-residue sequence embedding passed to the model
# In current model, the dimension size is the ESM-1b dimension size i.e. 1280.
preemb_dim_size = mlc.FieldReference(1280, field_type=int)
blocks_per_ckpt = mlc.FieldReference(None, field_type=int)
chunk_size = mlc.FieldReference(4, field_type=int)
aux_distogram_bins = mlc.FieldReference(64, field_type=int)
tm_enabled = mlc.FieldReference(False, field_type=bool)
eps = mlc.FieldReference(1e-8, field_type=float)
templates_enabled = mlc.FieldReference(True, field_type=bool)
embed_template_torsion_angles = mlc.FieldReference(True, field_type=bool)
tune_chunk_size = mlc.FieldReference(True, field_type=bool)
NUM_RES = "num residues placeholder"
NUM_MSA_SEQ = "msa placeholder"
NUM_EXTRA_SEQ = "extra msa placeholder"
NUM_TEMPLATES = "num templates placeholder"
config = mlc.ConfigDict(
{
"data": {
"common": {
"feat": {
"aatype": [NUM_RES],
"all_atom_mask": [NUM_RES, None],
"all_atom_positions": [NUM_RES, None, None],
"alt_chi_angles": [NUM_RES, None],
"atom14_alt_gt_exists": [NUM_RES, None],
"atom14_alt_gt_positions": [NUM_RES, None, None],
"atom14_atom_exists": [NUM_RES, None],
"atom14_atom_is_ambiguous": [NUM_RES, None],
"atom14_gt_exists": [NUM_RES, None],
"atom14_gt_positions": [NUM_RES, None, None],
"atom37_atom_exists": [NUM_RES, None],
"backbone_rigid_mask": [NUM_RES],
"backbone_rigid_tensor": [NUM_RES, None, None],
"bert_mask": [NUM_MSA_SEQ, NUM_RES],
"chi_angles_sin_cos": [NUM_RES, None, None],
"chi_mask": [NUM_RES, None],
"extra_deletion_value": [NUM_EXTRA_SEQ, NUM_RES],
"extra_has_deletion": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa_mask": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa_row_mask": [NUM_EXTRA_SEQ],
"is_distillation": [],
"msa_feat": [NUM_MSA_SEQ, NUM_RES, None],
"msa_mask": [NUM_MSA_SEQ, NUM_RES],
"msa_row_mask": [NUM_MSA_SEQ],
"no_recycling_iters": [],
"pseudo_beta": [NUM_RES, None],
"pseudo_beta_mask": [NUM_RES],
"residue_index": [NUM_RES],
"residx_atom14_to_atom37": [NUM_RES, None],
"residx_atom37_to_atom14": [NUM_RES, None],
"resolution": [],
"rigidgroups_alt_gt_frames": [NUM_RES, None, None, None],
"rigidgroups_group_exists": [NUM_RES, None],
"rigidgroups_group_is_ambiguous": [NUM_RES, None],
"rigidgroups_gt_exists": [NUM_RES, None],
"rigidgroups_gt_frames": [NUM_RES, None, None, None],
"seq_length": [],
"seq_mask": [NUM_RES],
"target_feat": [NUM_RES, None],
"template_aatype": [NUM_TEMPLATES, NUM_RES],
"template_all_atom_mask": [NUM_TEMPLATES, NUM_RES, None],
"template_all_atom_positions": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"template_alt_torsion_angles_sin_cos": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"template_backbone_rigid_mask": [NUM_TEMPLATES, NUM_RES],
"template_backbone_rigid_tensor": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"template_mask": [NUM_TEMPLATES],
"template_pseudo_beta": [NUM_TEMPLATES, NUM_RES, None],
"template_pseudo_beta_mask": [NUM_TEMPLATES, NUM_RES],
"template_sum_probs": [NUM_TEMPLATES, None],
"template_torsion_angles_mask": [
NUM_TEMPLATES, NUM_RES, None,
],
"template_torsion_angles_sin_cos": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"true_msa": [NUM_MSA_SEQ, NUM_RES],
"use_clamped_fape": [],
},
"block_delete_msa": {
"msa_fraction_per_block": 0.3,
"randomize_num_blocks": False,
"num_blocks": 5,
},
"masked_msa": {
"profile_prob": 0.1,
"same_prob": 0.1,
"uniform_prob": 0.1,
},
"max_recycling_iters": 3,
"msa_cluster_features": True,
"reduce_msa_clusters_by_max_templates": False,
"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",
"num_alignments",
"seq_length",
"between_segment_residues",
"deletion_matrix",
"no_recycling_iters",
],
"use_templates": templates_enabled,
"use_template_torsion_angles": embed_template_torsion_angles,
},
"seqemb_mode": { # Configuration for sequence embedding mode
"enabled": False, # If True, use seq emb instead of MSA
},
"supervised": {
"clamp_prob": 0.9,
"supervised_features": [
"all_atom_mask",
"all_atom_positions",
"resolution",
"use_clamped_fape",
"is_distillation",
],
},
"predict": {
"fixed_size": True,
"subsample_templates": False, # We want top templates.
"block_delete_msa": False,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 512,
"max_extra_msa": 1024,
"max_template_hits": 4,
"max_templates": 4,
"crop": False,
"crop_size": None,
"spatial_crop_prob": None,
"interface_threshold": None,
"supervised": False,
"uniform_recycling": False,
},
"eval": {
"fixed_size": True,
"subsample_templates": False, # We want top templates.
"block_delete_msa": False,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 128,
"max_extra_msa": 1024,
"max_template_hits": 4,
"max_templates": 4,
"crop": False,
"crop_size": None,
"spatial_crop_prob": None,
"interface_threshold": None,
"supervised": True,
"uniform_recycling": False,
},
"train": {
"fixed_size": True,
"subsample_templates": True,
"block_delete_msa": True,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 128,
"max_extra_msa": 1024,
"max_template_hits": 4,
"max_templates": 4,
"shuffle_top_k_prefiltered": 20,
"crop": True,
"crop_size": 256,
"spatial_crop_prob": 0.,
"interface_threshold": None,
"supervised": True,
"clamp_prob": 0.9,
"max_distillation_msa_clusters": 1000,
"uniform_recycling": True,
"distillation_prob": 0.75,
},
"data_module": {
"use_small_bfd": False,
"data_loaders": {
"batch_size": 1,
"num_workers": 16,
"pin_memory": True,
},
},
},
# Recurring FieldReferences that can be changed globally here
"globals": {
"blocks_per_ckpt": blocks_per_ckpt,
"chunk_size": chunk_size,
# Use DeepSpeed memory-efficient attention kernel. Mutually
# exclusive with use_lma and use_flash.
"use_deepspeed_evo_attention": False,
# Use Staats & Rabe's low-memory attention algorithm. Mutually
# exclusive with use_deepspeed_evo_attention and use_flash.
"use_lma": False,
# Use FlashAttention in selected modules. Mutually exclusive with
# use_deepspeed_evo_attention and use_lma. Doesn't work that well
# on long sequences (>1000 residues).
"use_flash": False,
"offload_inference": False,
"c_z": c_z,
"c_m": c_m,
"c_t": c_t,
"c_e": c_e,
"c_s": c_s,
"eps": eps,
"is_multimer": False,
"seqemb_mode_enabled": False, # Global flag for enabling seq emb mode
},
"model": {
"_mask_trans": False,
"input_embedder": {
"tf_dim": 22,
"msa_dim": 49,
"c_z": c_z,
"c_m": c_m,
"relpos_k": 32,
},
"recycling_embedder": {
"c_z": c_z,
"c_m": c_m,
"min_bin": 3.25,
"max_bin": 20.75,
"no_bins": 15,
"inf": 1e8,
},
"template": {
"distogram": {
"min_bin": 3.25,
"max_bin": 50.75,
"no_bins": 39,
},
"template_single_embedder": {
# DISCREPANCY: c_in is supposed to be 51.
"c_in": 57,
"c_out": c_m,
},
"template_pair_embedder": {
"c_in": 88,
"c_out": c_t,
},
"template_pair_stack": {
"c_t": c_t,
# DISCREPANCY: c_hidden_tri_att here is given in the supplement
# as 64. In the code, it's 16.
"c_hidden_tri_att": 16,
"c_hidden_tri_mul": 64,
"no_blocks": 2,
"no_heads": 4,
"pair_transition_n": 2,
"dropout_rate": 0.25,
"tri_mul_first": False,
"fuse_projection_weights": False,
"blocks_per_ckpt": blocks_per_ckpt,
"tune_chunk_size": tune_chunk_size,
"inf": 1e9,
},
"template_pointwise_attention": {
"c_t": c_t,
"c_z": c_z,
# DISCREPANCY: c_hidden here is given in the supplement as 64.
# It's actually 16.
"c_hidden": 16,
"no_heads": 4,
"inf": 1e5, # 1e9,
},
"inf": 1e5, # 1e9,
"eps": eps, # 1e-6,
"enabled": templates_enabled,
"embed_angles": embed_template_torsion_angles,
"use_unit_vector": False,
# Approximate template computation, saving memory.
# In our experiments, results are equivalent to or better than
# the stock implementation. Should be enabled for all new
# training runs.
"average_templates": False,
# Offload template embeddings to CPU memory. Vastly reduced
# memory consumption at the cost of a modest increase in
# runtime. Useful for inference on very long sequences.
# Mutually exclusive with average_templates. Automatically
# enabled if offload_inference is set.
"offload_templates": False,
},
"extra_msa": {
"extra_msa_embedder": {
"c_in": 25,
"c_out": c_e,
},
"extra_msa_stack": {
"c_m": c_e,
"c_z": c_z,
"c_hidden_msa_att": 8,
"c_hidden_opm": 32,
"c_hidden_mul": 128,
"c_hidden_pair_att": 32,
"no_heads_msa": 8,
"no_heads_pair": 4,
"no_blocks": 4,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"opm_first": False,
"fuse_projection_weights": False,
"clear_cache_between_blocks": False,
"tune_chunk_size": tune_chunk_size,
"inf": 1e9,
"eps": eps, # 1e-10,
"ckpt": blocks_per_ckpt is not None,
},
"enabled": True,
},
"evoformer_stack": {
"c_m": c_m,
"c_z": c_z,
"c_hidden_msa_att": 32,
"c_hidden_opm": 32,
"c_hidden_mul": 128,
"c_hidden_pair_att": 32,
"c_s": c_s,
"no_heads_msa": 8,
"no_heads_pair": 4,
"no_blocks": 48,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"no_column_attention": False,
"opm_first": False,
"fuse_projection_weights": False,
"blocks_per_ckpt": blocks_per_ckpt,
"clear_cache_between_blocks": False,
"tune_chunk_size": tune_chunk_size,
"inf": 1e9,
"eps": eps, # 1e-10,
},
"structure_module": {
"c_s": c_s,
"c_z": c_z,
"c_ipa": 16,
"c_resnet": 128,
"no_heads_ipa": 12,
"no_qk_points": 4,
"no_v_points": 8,
"dropout_rate": 0.1,
"no_blocks": 8,
"no_transition_layers": 1,
"no_resnet_blocks": 2,
"no_angles": 7,
"trans_scale_factor": 10,
"epsilon": eps, # 1e-12,
"inf": 1e5,
},
"heads": {
"lddt": {
"no_bins": 50,
"c_in": c_s,
"c_hidden": 128,
},
"distogram": {
"c_z": c_z,
"no_bins": aux_distogram_bins,
},
"tm": {
"c_z": c_z,
"no_bins": aux_distogram_bins,
"enabled": tm_enabled,
},
"masked_msa": {
"c_m": c_m,
"c_out": 23,
},
"experimentally_resolved": {
"c_s": c_s,
"c_out": 37,
},
},
# A negative value indicates that no early stopping will occur, i.e.
# the model will always run `max_recycling_iters` number of recycling
# iterations. A positive value will enable early stopping if the
# difference in pairwise distances is less than the tolerance between
# recycling steps.
"recycle_early_stop_tolerance": -1.
},
"relax": {
"max_iterations": 0, # no max
"tolerance": 2.39,
"stiffness": 10.0,
"max_outer_iterations": 20,
"exclude_residues": [],
},
"loss": {
"distogram": {
"min_bin": 2.3125,
"max_bin": 21.6875,
"no_bins": 64,
"eps": eps, # 1e-6,
"weight": 0.3,
},
"experimentally_resolved": {
"eps": eps, # 1e-8,
"min_resolution": 0.1,
"max_resolution": 3.0,
"weight": 0.0,
},
"fape": {
"backbone": {
"clamp_distance": 10.0,
"loss_unit_distance": 10.0,
"weight": 0.5,
},
"sidechain": {
"clamp_distance": 10.0,
"length_scale": 10.0,
"weight": 0.5,
},
"eps": 1e-4,
"weight": 1.0,
},
"plddt_loss": {
"min_resolution": 0.1,
"max_resolution": 3.0,
"cutoff": 15.0,
"no_bins": 50,
"eps": eps, # 1e-10,
"weight": 0.01,
},
"masked_msa": {
"num_classes": 23,
"eps": eps, # 1e-8,
"weight": 2.0,
},
"supervised_chi": {
"chi_weight": 0.5,
"angle_norm_weight": 0.01,
"eps": eps, # 1e-6,
"weight": 1.0,
},
"violation": {
"violation_tolerance_factor": 12.0,
"clash_overlap_tolerance": 1.5,
"average_clashes": False,
"eps": eps, # 1e-6,
"weight": 0.0,
},
"tm": {
"max_bin": 31,
"no_bins": 64,
"min_resolution": 0.1,
"max_resolution": 3.0,
"eps": eps, # 1e-8,
"weight": 0.,
"enabled": tm_enabled,
},
"chain_center_of_mass": {
"clamp_distance": -4.0,
"weight": 0.,
"eps": eps,
"enabled": False,
},
"eps": eps,
},
"ema": {"decay": 0.999},
}
)
multimer_config_update = mlc.ConfigDict({
"globals": {
"is_multimer": True
},
"data": {
"common": {
"feat": {
"aatype": [NUM_RES],
"all_atom_mask": [NUM_RES, None],
"all_atom_positions": [NUM_RES, None, None],
# "all_chains_entity_ids": [], # TODO: Resolve missing features, remove processed msa feats
# "all_crops_all_chains_mask": [],
# "all_crops_all_chains_positions": [],
# "all_crops_all_chains_residue_ids": [],
"assembly_num_chains": [],
"asym_id": [NUM_RES],
"atom14_atom_exists": [NUM_RES, None],
"atom37_atom_exists": [NUM_RES, None],
"bert_mask": [NUM_MSA_SEQ, NUM_RES],
"cluster_bias_mask": [NUM_MSA_SEQ],
"cluster_profile": [NUM_MSA_SEQ, NUM_RES, None],
"cluster_deletion_mean": [NUM_MSA_SEQ, NUM_RES],
"deletion_matrix": [NUM_MSA_SEQ, NUM_RES],
"deletion_mean": [NUM_RES],
"entity_id": [NUM_RES],
"entity_mask": [NUM_RES],
"extra_deletion_matrix": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa_mask": [NUM_EXTRA_SEQ, NUM_RES],
# "mem_peak": [],
"msa": [NUM_MSA_SEQ, NUM_RES],
"msa_feat": [NUM_MSA_SEQ, NUM_RES, None],
"msa_mask": [NUM_MSA_SEQ, NUM_RES],
"msa_profile": [NUM_RES, None],
"num_alignments": [],
"num_templates": [],
# "queue_size": [],
"residue_index": [NUM_RES],
"residx_atom14_to_atom37": [NUM_RES, None],
"residx_atom37_to_atom14": [NUM_RES, None],
"resolution": [],
"seq_length": [],
"seq_mask": [NUM_RES],
"sym_id": [NUM_RES],
"target_feat": [NUM_RES, None],
"template_aatype": [NUM_TEMPLATES, NUM_RES],
"template_all_atom_mask": [NUM_TEMPLATES, NUM_RES, None],
"template_all_atom_positions": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"true_msa": [NUM_MSA_SEQ, NUM_RES]
},
"max_recycling_iters": 20, # For training, value is 3
"unsupervised_features": [
"aatype",
"residue_index",
"msa",
"num_alignments",
"seq_length",
"between_segment_residues",
"deletion_matrix",
"no_recycling_iters",
# Additional multimer features
"msa_mask",
"seq_mask",
"asym_id",
"entity_id",
"sym_id",
]
},
"supervised": {
"clamp_prob": 1.
},
# TODO: Change max_msa_clusters and max_extra_msa to multimer feats within model:
# c.model.input_embedder.num_msa = 508
# c.model.extra_msa.extra_msa_embedder.num_extra_msa = 2048
"predict": {
"max_msa_clusters": 508,
"max_extra_msa": 2048
},
"eval": {
"max_msa_clusters": 508,
"max_extra_msa": 2048
},
"train": {
"max_msa_clusters": 508,
"max_extra_msa": 2048,
"block_delete_msa" : False,
"crop_size": 640,
"spatial_crop_prob": 0.5,
"interface_threshold": 10.,
"clamp_prob": 1.,
},
},
"model": {
"input_embedder": {
"tf_dim": 21,
#"num_msa": 508,
"max_relative_chain": 2,
"max_relative_idx": 32,
"use_chain_relative": True
},
"template": {
"template_single_embedder": {
"c_in": 34,
"c_out": c_m
},
"template_pair_embedder": {
"c_in": c_z,
"c_out": c_t,
"c_dgram": 39,
"c_aatype": 22
},
"template_pair_stack": {
"tri_mul_first": True,
"fuse_projection_weights": True
},
"c_t": c_t,
"c_z": c_z,
"use_unit_vector": True
},
"extra_msa": {
# "extra_msa_embedder": {
# "num_extra_msa": 2048
# },
"extra_msa_stack": {
"opm_first": True,
"fuse_projection_weights": True
}
},
"evoformer_stack": {
"opm_first": True,
"fuse_projection_weights": True
},
"structure_module": {
"trans_scale_factor": 20
},
"heads": {
"tm": {
"ptm_weight": 0.2,
"iptm_weight": 0.8,
"enabled": True
},
"masked_msa": {
"c_out": 22
},
},
"recycle_early_stop_tolerance": 0.5 # For training, value is -1.
},
"loss": {
"fape": {
"intra_chain_backbone": {
"clamp_distance": 10.0,
"loss_unit_distance": 10.0,
"weight": 0.5
},
"interface_backbone": {
"clamp_distance": 30.0,
"loss_unit_distance": 20.0,
"weight": 0.5
}
},
"masked_msa": {
"num_classes": 22
},
"violation": {
"average_clashes": True,
"weight": 0.03 # Not finetuning
},
"tm": {
"weight": 0.1,
"enabled": True
},
"chain_center_of_mass": {
"weight": 0.05,
"enabled": True
}
}
})
seq_mode_config = mlc.ConfigDict({
"data": {
"common": {
"feat": {
"seq_embedding": [NUM_RES, None],
},
"seqemb_features": [ # List of features to be generated in seqemb mode
"seq_embedding"
],
},
"seqemb_mode": { # Configuration for sequence embedding mode
"enabled": True, # If True, use seq emb instead of MSA
},
},
"globals": {
"seqemb_mode_enabled": True,
},
"model": {
"preembedding_embedder": { # Used in sequence embedding mode
"tf_dim": 22,
"preembedding_dim": preemb_dim_size,
"c_z": c_z,
"c_m": c_m,
"relpos_k": 32,
},
"extra_msa": {
"enabled": False # Disable Extra MSA Stack
},
"evoformer_stack": {
"no_column_attention": True # Turn off Evoformer's column attention
},
}
})