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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.api.model_interface import PairFormerInput
from src.model.modules.primitives import LinearNoBias
from src.model.modules.transformer import AtomAttentionEncoder
from src.model.modules.feature_dims import (
INPUTFEATS_DIMS,
RELPOSENC_DIMS,
)
class InputFeatureEmbedder(nn.Module):
"""
Implements Algorithm 2 in AF3
"""
def __init__(
self,
c_atom: int = 128,
c_atompair: int = 16,
c_token: int = 384,
) -> None:
"""
Args:
c_atom (int, optional): atom embedding dim. Defaults to 128.
c_atompair (int, optional): atom pair embedding dim. Defaults to 16.
c_token (int, optional): token embedding dim. Defaults to 384.
"""
super(InputFeatureEmbedder, self).__init__()
self.c_atom = c_atom
self.c_atompair = c_atompair
self.c_token = c_token
self.atom_attention_encoder = AtomAttentionEncoder(
c_atom=c_atom,
c_atompair=c_atompair,
c_token=c_token,
has_coords=False,
)
self.input_feature_dims = INPUTFEATS_DIMS
def _check_input(self, input_data: PairFormerInput):
for name in self.input_feature_dims:
if name not in input_data:
raise ValueError(f"Input data must contain {name}.")
elif input_data[name] is None:
raise ValueError(f"Input data must contain {name} and it cannot be None.")
def forward(
self,
input_data: PairFormerInput,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> torch.Tensor:
"""
Args:
input_data (Dict[str, Any]): dict of input features
inplace_safe (bool): Whether it is safe to use inplace operations. Defaults to False.
chunk_size (Optional[int]): Chunk size for memory-efficient operations. Defaults to None.
Returns:
torch.Tensor: token embedding
[..., N_token, 384 (c_token) + 32 + 32 + 1 :=449]
"""
self._check_input(input_data)
# Embed per-atom features.
a, _, _, _ = self.atom_attention_encoder(
input_data=input_data,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
) # [..., N_token, c_token]
# Concatenate the per-token features.
batch_shape = input_data.restype.shape[:-1]
s_inputs = torch.cat(
[a]
+ [
input_data[name].reshape(*batch_shape, d)
for name, d in self.input_feature_dims.items()
],
dim=-1,
)
return s_inputs
class RelativePositionEncoding(nn.Module):
"""
Implements Algorithm 3 in AF3
"""
def __init__(self, r_max: int = 32, s_max: int = 2, c_z: int = 128) -> None:
"""
Args:
r_max (int, optional): Relative position indices clip value. Defaults to 32.
s_max (int, optional): Relative chain indices clip value. Defaults to 2.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
"""
super(RelativePositionEncoding, self).__init__()
self.r_max = r_max
self.s_max = s_max
self.c_z = c_z
self.linear_no_bias = LinearNoBias(
in_features=(4 * self.r_max + 2 * self.s_max + 7), out_features=self.c_z
)
self.input_feature_dims = RELPOSENC_DIMS
def _check_input(self, input_data: PairFormerInput):
for name in self.input_feature_dims:
if name not in input_data:
raise ValueError(f"Input data must contain {name}.")
elif input_data[name] is None:
raise ValueError(f"Input data must contain {name} and it cannot be None.")
def forward(self, input_data: PairFormerInput) -> torch.Tensor:
"""
Args:
input_data (Dict[str, Any]): input meta feature dict.
asym_id / residue_index / entity_id / sym_id / token_index
[..., N_tokens]
Returns:
torch.Tensor: relative position encoding
[..., N_token, N_token, c_z]
"""
self._check_input(input_data)
b_same_chain = (
input_data.asym_id[..., :, None]
== input_data.asym_id[..., None, :]
).long() # [..., N_token, N_token]
b_same_residue = (
input_data.residue_index[..., :, None]
== input_data.residue_index[..., None, :]
).long() # [..., N_token, N_token]
b_same_entity = (
input_data.entity_id[..., :, None]
== input_data.entity_id[..., None, :]
).long() # [..., N_token, N_token]
# cycpeptide
offset = (
input_data.residue_index[..., :, None]
- input_data.residue_index[..., None, :]
)
asym_id = input_data.asym_id
unique_asym_ids = torch.unique(asym_id)
is_cyclic_token = input_data.is_cyclic_token
if is_cyclic_token is not None and is_cyclic_token.any():
for asym_id_val in unique_asym_ids:
asym_mask = (asym_id == asym_id_val)
# Check if this asym_id corresponds to a cyclic entity
if asym_mask.any() and is_cyclic_token[asym_mask][0]:
# Apply cyclic logic for this asym_id
len_chain = asym_mask.sum()
if len_chain > 0:
chain_mask = (
(asym_id[..., :, None] == asym_id_val) &
(asym_id[..., None, :] == asym_id_val)
)
offset_plus = offset + len_chain
offset_minus = offset - len_chain
abs_offset = offset.abs()
abs_offset_plus = offset_plus.abs()
abs_offset_minus = offset_minus.abs()
choice_plus_or_minus = torch.where(
abs_offset_plus <= abs_offset_minus,
offset_plus,
offset_minus
)
c_offset = torch.where(
(abs_offset <= abs_offset_plus) & (abs_offset <= abs_offset_minus),
offset,
choice_plus_or_minus
)
offset = torch.where(chain_mask, c_offset, offset)
offset = (offset + self.r_max).clamp(0, 2 * self.r_max)
d_residue = torch.where(
b_same_chain.bool(),
offset,
(2 * self.r_max + 1) * torch.ones_like(offset)
)
a_rel_pos = F.one_hot(d_residue, 2 * (self.r_max + 1))
d_token = torch.clip(
input=input_data.token_index[..., :, None]
- input_data.token_index[..., None, :]
+ self.r_max,
min=0,
max=2 * self.r_max,
) * b_same_chain * b_same_residue + (1 - b_same_chain * b_same_residue) * (
2 * self.r_max + 1
) # [..., N_token, N_token]
a_rel_token = F.one_hot(d_token, 2 * (self.r_max + 1))
d_chain = torch.clip(
input=input_data.sym_id[..., :, None]
- input_data.sym_id[..., None, :]
+ self.s_max,
min=0,
max=2 * self.s_max,
) * b_same_entity + (1 - b_same_entity) * (
2 * self.s_max + 1
) # [..., N_token, N_token]
a_rel_chain = F.one_hot(d_chain, 2 * (self.s_max + 1))
if self.training:
p = self.linear_no_bias(
torch.cat(
[a_rel_pos, a_rel_token, b_same_entity[..., None], a_rel_chain],
dim=-1,
).float()
) # [..., N_token, N_token, 2 * (self.r_max + 1)+ 2 * (self.r_max + 1)+ 1 + 2 * (self.s_max + 1)] -> [..., N_token, N_token, c_z]
return p
else:
del d_chain, d_token, d_residue, b_same_chain, b_same_residue
origin_shape = a_rel_pos.shape[:-1]
Ntoken = a_rel_pos.shape[-2]
a_rel_pos = a_rel_pos.reshape(-1, a_rel_pos.shape[-1])
chunk_num = 1 if Ntoken < 3200 else 8
a_rel_pos_chunks = torch.chunk(
a_rel_pos.reshape(-1, a_rel_pos.shape[-1]), chunk_num, dim=-2
)
a_rel_token_chunks = torch.chunk(
a_rel_token.reshape(-1, a_rel_token.shape[-1]), chunk_num, dim=-2
)
b_same_entity_chunks = torch.chunk(
b_same_entity.reshape(-1, 1), chunk_num, dim=-2
)
a_rel_chain_chunks = torch.chunk(
a_rel_chain.reshape(-1, a_rel_chain.shape[-1]), chunk_num, dim=-2
)
start = 0
p = None
for i in range(len(a_rel_pos_chunks)):
data = torch.cat(
[
a_rel_pos_chunks[i],
a_rel_token_chunks[i],
b_same_entity_chunks[i],
a_rel_chain_chunks[i],
],
dim=-1,
).float()
result = self.linear_no_bias(data)
del data
if p is None:
p = torch.empty(
(a_rel_pos.shape[-2], self.c_z),
device=a_rel_pos.device,
dtype=result.dtype,
)
p[start : start + result.shape[0]] = result
start += result.shape[0]
del result
del a_rel_pos, a_rel_token, b_same_entity, a_rel_chain
p = p.reshape(*origin_shape, -1)
return p
class FourierEmbedding(nn.Module):
"""
Implements Algorithm 22 in AF3
"""
def __init__(self, c: int, seed: int = 42) -> None:
"""
Args:
c (int): embedding dim.
"""
super(FourierEmbedding, self).__init__()
self.c = c
self.seed = seed
generator = torch.Generator()
generator.manual_seed(seed)
w_value = torch.randn(size=(c,), generator=generator)
self.w = nn.Parameter(w_value, requires_grad=False)
b_value = torch.randn(size=(c,), generator=generator)
self.b = nn.Parameter(b_value, requires_grad=False)
def forward(self, t_hat_noise_level: torch.Tensor) -> torch.Tensor:
"""
Args:
t_hat_noise_level (torch.Tensor): the noise level
[..., N_sample]
Returns:
torch.Tensor: the output fourier embedding
[..., N_sample, c]
"""
return torch.cos(
input=2 * torch.pi * (t_hat_noise_level.unsqueeze(dim=-1) * self.w + self.b)
)
class SubstructureEmbedder(nn.Module):
"""
Implements Substructure Embedder
"""
def __init__(
self,
n_classes: int,
c_pair_dim: int,
architecture: str = "mlp",
hidden_dim: int = 256,
n_layers: int = 3,
dropout: float = 0.1,
) -> None:
"""
Args:
n_classes (int): Number of distance classes in input
c_pair_dim (int): Output pair embedding dimension
architecture (str): Either 'mlp' or 'transformer'
hidden_dim (int): Hidden dimension for both architectures
n_layers (int): Number of layers (MLP or Transformer)
dropout (float): Dropout rate
"""
super().__init__()
self.architecture = architecture.lower()
if self.architecture == "mlp":
layers = []
layers.append(LinearNoBias(n_classes, hidden_dim))
layers.append(nn.ReLU())
layers.append(nn.Dropout(dropout))
for _ in range(n_layers - 2):
layers.append(LinearNoBias(hidden_dim, hidden_dim))
layers.append(nn.ReLU())
layers.append(nn.Dropout(dropout))
# Output layer
layers.append(LinearNoBias(hidden_dim, c_pair_dim))
self.network = nn.Sequential(*layers)
elif self.architecture == "transformer":
self.input_proj = LinearNoBias(n_classes, hidden_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=4,
dim_feedforward=hidden_dim * 4,
dropout=dropout,
batch_first=True,
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)
self.output_proj = LinearNoBias(hidden_dim, c_pair_dim)
else:
raise ValueError(f"Unknown architecture: {architecture}")
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): Input distance map
shape: [..., N_token, N_token, N_classes]
Returns:
torch.Tensor: Output pair embeddings
shape: [..., N_token, N_token, c_pair_dim]
"""
if self.architecture == "mlp":
return self.network(x)
else: # transformer
# Reshape for transformer
orig_shape = x.shape
x = x.view(-1, orig_shape[-3], orig_shape[-2], orig_shape[-1])
batch_size, n_token, _, n_classes = x.shape
# Project and reshape to sequence
x = self.input_proj(x)
x = x.reshape(batch_size, n_token * n_token, -1)
# Apply transformer
x = self.transformer(x)
# Project to output dim and reshape back
x = self.output_proj(x)
x = x.reshape(*orig_shape[:-1], -1)
return x
class ConstraintEmbedder(nn.Module):
"""
Implements Constraint Embedder
"""
def __init__(
self,
pocket_embedder: dict[str:int],
contact_embedder: dict[str:int],
contact_atom_embedder: dict[str:int],
substructure_embedder: dict[str:int],
c_constraint_z: int,
initialize_method: str = "zero",
**kwarg,
) -> None:
"""
Args:
pocket_embedder (dict[str:int]): pocket embedder config
contact_embedder (dict[str:int]): contact embedder config
contact_atom_embedder (dict[str:int]): contact atom embedder config
substructure_embedder (dict[str:int]): substructure embedder config
c_constraint_z (int): constraint z dimension
initialize_method (str): initialize method
"""
super(ConstraintEmbedder, self).__init__()
self.pocket_embedder_config = pocket_embedder
self.contact_embedder_config = contact_embedder
self.contact_atom_embedder_config = contact_atom_embedder
self.substructure_embedder_config = substructure_embedder
# pocket embedder
if self.pocket_embedder_config.get("enable", False):
self.pocket_z_embedder = LinearNoBias(
in_features=self.pocket_embedder_config.get("c_z_input", 1),
out_features=c_constraint_z,
)
# token contact embedder
if self.contact_embedder_config.get("enable", False):
self.contact_z_embedder = LinearNoBias(
in_features=contact_embedder["c_z_input"], out_features=c_constraint_z
)
# atom contact embedder
if self.contact_atom_embedder_config.get("enable", False):
self.contact_atom_z_embedder = LinearNoBias(
in_features=contact_atom_embedder["c_z_input"],
out_features=c_constraint_z,
)
# substructure embedder
if self.substructure_embedder_config.get("enable", False):
self.substructure_z_embedder = SubstructureEmbedder(
n_classes=self.substructure_embedder_config.get("n_classes", 4),
c_pair_dim=c_constraint_z,
architecture=self.substructure_embedder_config.get(
"architecture", "mlp"
),
hidden_dim=self.substructure_embedder_config.get("hidden_dim", 256),
n_layers=self.substructure_embedder_config.get("n_layers", 3),
)
for module in self.modules():
if isinstance(module, nn.Linear):
if initialize_method == "zero":
nn.init.zeros_(module.weight)
def forward(
self,
constraint_feature_dict: dict[str, Union[torch.Tensor, int, float, dict]],
) -> torch.Tensor:
"""
Args:
constraint_feature_dict (dict[str, Union[torch.Tensor, int, float, dict]]): dict of input features
Returns:
torch.Tensor: token embedding
[..., N_token, c_s]
"""
z_constraint = None
if self.pocket_embedder_config.get("enable", False):
z_constraint = self.pocket_z_embedder(constraint_feature_dict["pocket"])
if self.contact_embedder_config.get("enable", False):
z_contact = self.contact_z_embedder(constraint_feature_dict["contact"])
z_constraint = (
z_contact if z_constraint is None else z_constraint + z_contact
)
if self.contact_atom_embedder_config.get("enable", False):
z_contact_atom = self.contact_atom_z_embedder(
constraint_feature_dict["contact_atom"]
)
z_constraint = (
z_contact_atom
if z_constraint is None
else z_constraint + z_contact_atom
)
# substructure embedder
if self.substructure_embedder_config.get("enable", False):
z_substructure = self.substructure_z_embedder(
constraint_feature_dict["substructure"]
)
z_constraint = (
z_substructure
if z_constraint is None
else z_constraint + z_substructure
)
return z_constraint |