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# For licensing see accompanying LICENSE file.
# Copyright (c) 2025 Apple Inc. Licensed under MIT License.
#
import math
import mlx.nn as nn
import mlx.core as mx
from models.simplefold.mlx.layers import FinalLayer, ConditionEmbedder
from onescience.utils.simplefold.esm_utils import esm_model_dict
# MLX does not have a native one_hot implementation
def one_hot(indices, num_classes, dtype=None):
"""
MLX version of torch.one_hot.
Args:
indices: integer MLX array of any shape, containing class indices in [0, num_classes).
num_classes: number of classes for the one-hot dimension.
dtype: output data type (defaults to float32).
Returns:
MLX array of shape indices.shape + (num_classes,) and given dtype.
"""
# Default to float32 if no dtype is given
if dtype is None:
dtype = mx.float32
classes = mx.arange(num_classes, dtype=indices.dtype)
# Broadcast-compare: result has shape indices.shape + (num_classes,)
# For each position, only the matched class index gives True
mask = indices[..., mx.newaxis] == classes
# Cast boolean mask to desired dtype
return mask.astype(dtype)
class FoldingDiT(nn.Module):
def __init__(
self,
trunk,
time_embedder,
aminoacid_pos_embedder,
pos_embedder,
atom_encoder_transformer,
atom_decoder_transformer,
hidden_size=1152,
num_heads=16,
atom_num_heads=4,
output_channels=3,
atom_hidden_size_enc=256,
atom_hidden_size_dec=256,
atom_n_queries_enc=32,
atom_n_keys_enc=128,
atom_n_queries_dec=32,
atom_n_keys_dec=128,
esm_model="esm2_3B",
esm_dropout_prob=0.0,
use_atom_mask=False,
use_length_condition=True,
):
super().__init__()
self.pos_embedder = pos_embedder
pos_embed_channels = pos_embedder.embed_dim
self.aminoacid_pos_embedder = aminoacid_pos_embedder
aminoacid_pos_embed_channels = aminoacid_pos_embedder.embed_dim
self.time_embedder = time_embedder
self.atom_encoder_transformer = atom_encoder_transformer
self.atom_decoder_transformer = atom_decoder_transformer
self.trunk = trunk
self.hidden_size = hidden_size
self.output_channels = output_channels
self.num_heads = num_heads
self.atom_num_heads = atom_num_heads
self.use_atom_mask = use_atom_mask
self.esm_dropout_prob = esm_dropout_prob
self.use_length_condition = use_length_condition
esm_s_dim = esm_model_dict[esm_model]["esm_s_dim"]
esm_num_layers = esm_model_dict[esm_model]["esm_num_layers"]
self.atom_hidden_size_enc = atom_hidden_size_enc
self.atom_hidden_size_dec = atom_hidden_size_dec
self.atom_n_queries_enc = atom_n_queries_enc
self.atom_n_keys_enc = atom_n_keys_enc
self.atom_n_queries_dec = atom_n_queries_dec
self.atom_n_keys_dec = atom_n_keys_dec
atom_feat_dim = pos_embed_channels + aminoacid_pos_embed_channels + 427
self.atom_feat_proj = nn.Sequential(
nn.Linear(atom_feat_dim, hidden_size),
nn.LayerNorm(hidden_size),
nn.SiLU(),
)
self.atom_pos_proj = nn.Linear(pos_embed_channels, hidden_size, bias=False)
if self.use_length_condition:
self.length_embedder = nn.Sequential(
nn.Linear(1, hidden_size, bias=False),
nn.LayerNorm(hidden_size),
)
self.atom_in_proj = nn.Linear(hidden_size * 2, hidden_size, bias=False)
self.esm_s_combine = mx.zeros(esm_num_layers)
self.esm_s_proj = ConditionEmbedder(
input_dim=esm_s_dim,
hidden_size=hidden_size,
dropout_prob=0,
)
latent_cat_dim = hidden_size * 2
self.esm_cat_proj = nn.Linear(latent_cat_dim, hidden_size)
self.context2atom_proj = nn.Sequential(
nn.Linear(hidden_size, self.atom_hidden_size_enc),
nn.LayerNorm(self.atom_hidden_size_enc),
)
self.atom_enc_cond_proj = nn.Sequential(
nn.Linear(hidden_size, self.atom_hidden_size_enc),
nn.LayerNorm(self.atom_hidden_size_enc),
)
self.atom2latent_proj = nn.Sequential(
nn.Linear(self.atom_hidden_size_enc, hidden_size),
nn.LayerNorm(hidden_size),
)
self.atom_dec_cond_proj = nn.Sequential(
nn.Linear(hidden_size, self.atom_hidden_size_dec),
nn.LayerNorm(self.atom_hidden_size_dec),
)
self.latent2atom_proj = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.SiLU(),
nn.LayerNorm(hidden_size),
nn.Linear(hidden_size, self.atom_hidden_size_dec),
)
self.final_layer = FinalLayer(
self.atom_hidden_size_dec, output_channels, c_dim=hidden_size
)
def create_local_attn_bias(
self,
n: int,
n_queries: int,
n_keys: int,
inf: float = 1e10,
):
"""Create local attention bias based on query window n_queries and kv window n_keys.
Args:
n (int): the length of quiries
n_queries (int): window size of quiries
n_keys (int): window size of keys/values
inf (float, optional): the inf to mask attention. Defaults to 1e10.
device (torch.device, optional): cuda|cpu|None. Defaults to None.
Returns:
torch.Tensor: the diagonal-like global attention bias
"""
n_trunks = int(math.ceil(n / n_queries))
padded_n = n_trunks * n_queries
attn_mask = mx.zeros((padded_n, padded_n))
for block_index in range(0, n_trunks):
i = block_index * n_queries
j1 = max(0, n_queries * block_index - (n_keys - n_queries) // 2)
j2 = n_queries * block_index + (n_queries + n_keys) // 2
attn_mask[i : i + n_queries, j1:j2] = 1.0
attn_bias = (1 - attn_mask) * -inf
return attn_bias[:n, :n]
def create_atom_attn_mask(
self, feats, natoms, atom_n_queries=None, atom_n_keys=None, inf: float = 1e10
):
if atom_n_queries is not None and atom_n_keys is not None:
atom_attn_mask = self.create_local_attn_bias(
n=natoms, n_queries=atom_n_queries, n_keys=atom_n_keys, inf=inf
)
else:
atom_attn_mask = None
return atom_attn_mask
def __call__(self, noised_pos, t, feats, self_cond=None):
B, N, _ = feats["ref_pos"].shape
M = feats["mol_type"].shape[1]
atom_to_token = feats["atom_to_token"].astype(mx.float32)
atom_to_token_idx = feats["atom_to_token_idx"]
ref_space_uid = feats["ref_space_uid"]
# create atom attention masks
atom_attn_mask_enc = self.create_atom_attn_mask(
feats,
natoms=N,
atom_n_queries=self.atom_n_queries_enc,
atom_n_keys=self.atom_n_keys_enc,
)
atom_attn_mask_dec = self.create_atom_attn_mask(
feats,
natoms=N,
atom_n_queries=self.atom_n_queries_dec,
atom_n_keys=self.atom_n_keys_dec,
)
# create condition embeddings for AdaLN
c_emb = self.time_embedder(t) # (B, D)
if self.use_length_condition:
length = feats["max_num_tokens"].astype(mx.float32)[..., None]
c_emb = c_emb + self.length_embedder(mx.log(length))
mol_type = feats["mol_type"]
mol_type = one_hot(mol_type, num_classes=4).astype(mx.float32) # [B, M, 4]
res_type = feats["res_type"].astype(mx.float32) # [B, M, 33]
pocket_feature = feats["pocket_feature"].astype(mx.float32) # [B, M, 4]
res_feat = mx.concatenate(
[mol_type, res_type, pocket_feature], axis=-1
) # [B, M, 41]
atom_feat_from_res = mx.matmul(atom_to_token, res_feat) # [B, N, 41]
atom_res_pos = self.aminoacid_pos_embedder(
pos=atom_to_token_idx[..., None].astype(mx.float32)
)
ref_pos_emb = self.pos_embedder(pos=feats["ref_pos"])
atom_feat = mx.concatenate(
[
ref_pos_emb, # (B, N, PD1)
atom_feat_from_res, # (B, N, 41)
atom_res_pos, # (B, N, PD2)
feats["ref_charge"][..., None], # (B, N, 1)
feats["atom_pad_mask"][..., None], # (B, N, 1)
feats["ref_element"], # (B, N, 128)
feats["ref_atom_name_chars"].reshape(B, N, 4 * 64), # (B, N, 256)
],
axis=-1,
) # (B, N, PD1+PD2+427)
atom_feat = self.atom_feat_proj(atom_feat) # (B, N, D)
atom_coord = self.pos_embedder(pos=noised_pos) # (B, N, PD1)
atom_coord = self.atom_pos_proj(atom_coord) # (B, N, D)
atom_in = mx.concatenate([atom_feat, atom_coord], axis=-1)
atom_in = self.atom_in_proj(atom_in) # (B, N, D)
# position embeddings for Axial RoPE
atom_pe_pos = mx.concatenate(
[
ref_space_uid[..., None].astype(mx.float32), # (B, N, 1)
feats["ref_pos"], # (B, N, 3)
],
axis=-1,
) # (B, N, 4)
token_pe_pos = mx.concatenate(
[
feats["residue_index"][..., None].astype(mx.float32), # (B, M, 1)
feats["entity_id"][..., None].astype(mx.float32), # (B, M, 1)
feats["asym_id"][..., None].astype(mx.float32), # (B, M, 1)
feats["sym_id"][..., None].astype(mx.float32), # (B, M, 1)
],
axis=-1,
) # (B, M, 4)
atom_c_emb_enc = self.atom_enc_cond_proj(c_emb)
atom_latent = self.context2atom_proj(atom_in)
atom_latent = self.atom_encoder_transformer(
latents=atom_latent,
c=atom_c_emb_enc,
attention_mask=atom_attn_mask_enc,
pos=atom_pe_pos,
)
atom_latent = self.atom2latent_proj(atom_latent)
# grouping: aggregate atom tokens to residue tokens
atom_to_token_mean = atom_to_token / (
atom_to_token.sum(axis=1, keepdims=True) + 1e-6
)
latent = mx.matmul(atom_to_token_mean.swapaxes(axis1=1, axis2=2), atom_latent)
assert latent.shape[1] == M
esm_s = (
mx.softmax(self.esm_s_combine, axis=0)[None, ...] @ feats["esm_s"]
).squeeze(axis=2)
# MLX is only interended for inference, we do not drop any ids
esm_emb = self.esm_s_proj(esm_s, train=False)
assert esm_emb.shape[1] == latent.shape[1]
latent = self.esm_cat_proj(mx.concatenate([latent, esm_emb], axis=-1))
# residue trunk
latent = self.trunk(
latents=latent,
c=c_emb,
attention_mask=None,
pos=token_pe_pos,
)
# ungrouping: broadcast residue tokens to atom tokens
output = mx.matmul(atom_to_token, latent)
assert output.shape[1] == N
# add skip connection
output = output + atom_latent
output = self.latent2atom_proj(output)
# atom decoder
atom_c_emb_dec = self.atom_dec_cond_proj(c_emb)
output = self.atom_decoder_transformer(
latents=output,
c=atom_c_emb_dec,
attention_mask=atom_attn_mask_dec,
pos=atom_pe_pos,
)
output = self.final_layer(output, c=c_emb)
return {
"predict_velocity": output,
"latent": latent,
}
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