import torch
import torch.nn as nn
import utils
from models.layers import Attention, Mlp
from models.conditions import TimestepEmbedder, CategoricalEmbedder, ClusterContinuousEmbedder
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class Denoiser(nn.Module):
def __init__(
self,
max_n_nodes,
hidden_size=384,
depth=12,
num_heads=16,
mlp_ratio=4.0,
drop_condition=0.1,
Xdim=118,
Edim=5,
ydim=3,
task_type='regression',
):
super().__init__()
self.num_heads = num_heads
self.ydim = ydim
self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
self.t_embedder = TimestepEmbedder(hidden_size)
self.y_embedding_list = torch.nn.ModuleList()
self.y_embedding_list.append(ClusterContinuousEmbedder(2, hidden_size, drop_condition))
for i in range(ydim - 2):
if task_type == 'regression':
self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
else:
self.y_embedding_list.append(CategoricalEmbedder(2, hidden_size, drop_condition))
self.encoders = nn.ModuleList(
[
SELayer(hidden_size, num_heads, mlp_ratio=mlp_ratio)
for _ in range(depth)
]
)
self.out_layer = OutLayer(
max_n_nodes=max_n_nodes,
hidden_size=hidden_size,
atom_type=Xdim,
bond_type=Edim,
mlp_ratio=mlp_ratio,
num_heads=num_heads,
)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
def _constant_init(module, i):
if isinstance(module, nn.Linear):
nn.init.constant_(module.weight, i)
if module.bias is not None:
nn.init.constant_(module.bias, i)
self.apply(_basic_init)
for block in self.encoders :
_constant_init(block.adaLN_modulation[0], 0)
_constant_init(self.out_layer.adaLN_modulation[0], 0)
"""
Input Parameters:
x: Node features.
e: Edge features.
node_mask: Mask indicating valid nodes.
y: Condition features.
t: Current timestep in the diffusion process.
unconditioned: Boolean flag indicating whether to ignore conditions.
"""
def forward(self, x, e, node_mask, y, t, unconditioned):
print("Denoiser Forward")
print(x.shape, e.shape, y.shape, t.shape, unconditioned)
force_drop_id = torch.zeros_like(y.sum(-1))
# drop the nan values
force_drop_id[torch.isnan(y.sum(-1))] = 1
if unconditioned:
force_drop_id = torch.ones_like(y[:, 0])
x_in, e_in, y_in = x, e, y
# bs = batch size, n = number of nodes
bs, n, _ = x.size()
x = torch.cat([x, e.reshape(bs, n, -1)], dim=-1)
print("X after concat with E")
print(x.shape)
# self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
x = self.x_embedder(x)
print("X after x_embedder")
print(x.shape)
# self.t_embedder = TimestepEmbedder(hidden_size)
c1 = self.t_embedder(t)
print("C1 after t_embedder")
print(c1.shape)
for i in range(1, self.ydim):
if i == 1:
c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
else:
c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
print("C2 after y_embedding_list")
print(c2.shape)
print("C1 + C2")
c = c1 + c2
print(c.shape)
for i, block in enumerate(self.encoders):
x = block(x, c, node_mask)
print("X after block")
print(x.shape)
# X: B * N * dx, E: B * N * N * de
X, E, y = self.out_layer(x, x_in, e_in, c, t, node_mask)
return utils.PlaceHolder(X=X, E=E, y=y).mask(node_mask)
class SELayer(nn.Module):
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.dropout = 0.
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False)
self.attn = Attention(
hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=True, **block_kwargs
)
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
drop=self.dropout,
)
self.adaLN_modulation = nn.Sequential(
nn.Linear(hidden_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c, node_mask):
(
shift_msa,
scale_msa,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
) = self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * modulate(self.norm1(self.attn(x, node_mask=node_mask)), shift_msa, scale_msa)
x = x + gate_mlp.unsqueeze(1) * modulate(self.norm2(self.mlp(x)), shift_mlp, scale_mlp)
return x
class OutLayer(nn.Module):
# Structure Output Layer
def __init__(self, max_n_nodes, hidden_size, atom_type, bond_type, mlp_ratio, num_heads=None):
super().__init__()
self.atom_type = atom_type
self.bond_type = bond_type
final_size = atom_type + max_n_nodes * bond_type
self.xedecoder = Mlp(in_features=hidden_size,
out_features=final_size, drop=0)
self.norm_final = nn.LayerNorm(final_size, elementwise_affine=False)
self.adaLN_modulation = nn.Sequential(
nn.Linear(hidden_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, 2 * final_size, bias=True)
)
def forward(self, x, x_in, e_in, c, t, node_mask):
x_all = self.xedecoder(x)
B, N, D = x_all.size()
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x_all = modulate(self.norm_final(x_all), shift, scale)
atom_out = x_all[:, :, :self.atom_type]
atom_out = x_in + atom_out
bond_out = x_all[:, :, self.atom_type:].reshape(B, N, N, self.bond_type)
bond_out = e_in + bond_out
##### standardize adj_out
edge_mask = (~node_mask)[:, :, None] & (~node_mask)[:, None, :]
diag_mask = (
torch.eye(N, dtype=torch.bool)
.unsqueeze(0)
.expand(B, -1, -1)
.type_as(edge_mask)
)
bond_out.masked_fill_(edge_mask[:, :, :, None], 0)
bond_out.masked_fill_(diag_mask[:, :, :, None], 0)
bond_out = 1 / 2 * (bond_out + torch.transpose(bond_out, 1, 2))
return atom_out, bond_out, None