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torchmd_et.py
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torchmd_et.py
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from typing import Optional, Tuple
import torch
from torch import Tensor, nn
from torch_geometric.nn import MessagePassing
from torch_scatter import scatter
from torchmdnet.models.utils import (
NeighborEmbedding,
CosineCutoff,
Distance,
rbf_class_mapping,
act_class_mapping,
)
class TorchMD_ET(nn.Module):
r"""The TorchMD equivariant Transformer architecture.
Args:
hidden_channels (int, optional): Hidden embedding size.
(default: :obj:`128`)
num_layers (int, optional): The number of attention layers.
(default: :obj:`6`)
num_rbf (int, optional): The number of radial basis functions :math:`\mu`.
(default: :obj:`50`)
rbf_type (string, optional): The type of radial basis function to use.
(default: :obj:`"expnorm"`)
trainable_rbf (bool, optional): Whether to train RBF parameters with
backpropagation. (default: :obj:`True`)
activation (string, optional): The type of activation function to use.
(default: :obj:`"silu"`)
attn_activation (string, optional): The type of activation function to use
inside the attention mechanism. (default: :obj:`"silu"`)
neighbor_embedding (bool, optional): Whether to perform an initial neighbor
embedding step. (default: :obj:`True`)
num_heads (int, optional): Number of attention heads.
(default: :obj:`8`)
distance_influence (string, optional): Where distance information is used inside
the attention mechanism. (default: :obj:`"both"`)
cutoff_lower (float, optional): Lower cutoff distance for interatomic interactions.
(default: :obj:`0.0`)
cutoff_upper (float, optional): Upper cutoff distance for interatomic interactions.
(default: :obj:`5.0`)
max_z (int, optional): Maximum atomic number. Used for initializing embeddings.
(default: :obj:`100`)
max_num_neighbors (int, optional): Maximum number of neighbors to return for a
given node/atom when constructing the molecular graph during forward passes.
This attribute is passed to the torch_cluster radius_graph routine keyword
max_num_neighbors, which normally defaults to 32. Users should set this to
higher values if they are using higher upper distance cutoffs and expect more
than 32 neighbors per node/atom.
(default: :obj:`32`)
"""
def __init__(
self,
hidden_channels=128,
num_layers=6,
num_rbf=50,
rbf_type="expnorm",
trainable_rbf=True,
activation="silu",
attn_activation="silu",
neighbor_embedding=True,
num_heads=8,
distance_influence="both",
cutoff_lower=0.0,
cutoff_upper=5.0,
max_z=100,
max_num_neighbors=32,
):
super(TorchMD_ET, self).__init__()
assert distance_influence in ["keys", "values", "both", "none"]
assert rbf_type in rbf_class_mapping, (
f'Unknown RBF type "{rbf_type}". '
f'Choose from {", ".join(rbf_class_mapping.keys())}.'
)
assert activation in act_class_mapping, (
f'Unknown activation function "{activation}". '
f'Choose from {", ".join(act_class_mapping.keys())}.'
)
assert attn_activation in act_class_mapping, (
f'Unknown attention activation function "{attn_activation}". '
f'Choose from {", ".join(act_class_mapping.keys())}.'
)
self.hidden_channels = hidden_channels
self.num_layers = num_layers
self.num_rbf = num_rbf
self.rbf_type = rbf_type
self.trainable_rbf = trainable_rbf
self.activation = activation
self.attn_activation = attn_activation
self.neighbor_embedding = neighbor_embedding
self.num_heads = num_heads
self.distance_influence = distance_influence
self.cutoff_lower = cutoff_lower
self.cutoff_upper = cutoff_upper
self.max_z = max_z
act_class = act_class_mapping[activation]
self.embedding = nn.Embedding(self.max_z, hidden_channels)
self.distance = Distance(
cutoff_lower,
cutoff_upper,
max_num_neighbors=max_num_neighbors,
return_vecs=True,
loop=True,
)
self.distance_expansion = rbf_class_mapping[rbf_type](
cutoff_lower, cutoff_upper, num_rbf, trainable_rbf
)
self.neighbor_embedding = (
NeighborEmbedding(
hidden_channels, num_rbf, cutoff_lower, cutoff_upper, self.max_z
).jittable()
if neighbor_embedding
else None
)
self.attention_layers = nn.ModuleList()
for _ in range(num_layers):
layer = EquivariantMultiHeadAttention(
hidden_channels,
num_rbf,
distance_influence,
num_heads,
act_class,
attn_activation,
cutoff_lower,
cutoff_upper,
).jittable()
self.attention_layers.append(layer)
self.out_norm = nn.LayerNorm(hidden_channels)
self.reset_parameters()
def reset_parameters(self):
self.embedding.reset_parameters()
self.distance_expansion.reset_parameters()
if self.neighbor_embedding is not None:
self.neighbor_embedding.reset_parameters()
for attn in self.attention_layers:
attn.reset_parameters()
self.out_norm.reset_parameters()
def forward(self,
z: Tensor,
pos: Tensor,
batch: Tensor,
q: Optional[Tensor] = None,
s: Optional[Tensor] = None
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
x = self.embedding(z)
edge_index, edge_weight, edge_vec = self.distance(pos, batch)
assert (
edge_vec is not None
), "Distance module did not return directional information"
edge_attr = self.distance_expansion(edge_weight)
mask = edge_index[0] != edge_index[1]
edge_vec[mask] = edge_vec[mask] / torch.norm(edge_vec[mask], dim=1).unsqueeze(1)
if self.neighbor_embedding is not None:
x = self.neighbor_embedding(z, x, edge_index, edge_weight, edge_attr)
vec = torch.zeros(x.size(0), 3, x.size(1), device=x.device)
for attn in self.attention_layers:
dx, dvec = attn(x, vec, edge_index, edge_weight, edge_attr, edge_vec)
x = x + dx
vec = vec + dvec
x = self.out_norm(x)
return x, vec, z, pos, batch
def __repr__(self):
return (
f"{self.__class__.__name__}("
f"hidden_channels={self.hidden_channels}, "
f"num_layers={self.num_layers}, "
f"num_rbf={self.num_rbf}, "
f"rbf_type={self.rbf_type}, "
f"trainable_rbf={self.trainable_rbf}, "
f"activation={self.activation}, "
f"attn_activation={self.attn_activation}, "
f"neighbor_embedding={self.neighbor_embedding}, "
f"num_heads={self.num_heads}, "
f"distance_influence={self.distance_influence}, "
f"cutoff_lower={self.cutoff_lower}, "
f"cutoff_upper={self.cutoff_upper})"
)
class EquivariantMultiHeadAttention(MessagePassing):
def __init__(
self,
hidden_channels,
num_rbf,
distance_influence,
num_heads,
activation,
attn_activation,
cutoff_lower,
cutoff_upper,
):
super(EquivariantMultiHeadAttention, self).__init__(aggr="add", node_dim=0)
assert hidden_channels % num_heads == 0, (
f"The number of hidden channels ({hidden_channels}) "
f"must be evenly divisible by the number of "
f"attention heads ({num_heads})"
)
self.distance_influence = distance_influence
self.num_heads = num_heads
self.hidden_channels = hidden_channels
self.head_dim = hidden_channels // num_heads
self.layernorm = nn.LayerNorm(hidden_channels)
self.act = activation()
self.attn_activation = act_class_mapping[attn_activation]()
self.cutoff = CosineCutoff(cutoff_lower, cutoff_upper)
self.q_proj = nn.Linear(hidden_channels, hidden_channels)
self.k_proj = nn.Linear(hidden_channels, hidden_channels)
self.v_proj = nn.Linear(hidden_channels, hidden_channels * 3)
self.o_proj = nn.Linear(hidden_channels, hidden_channels * 3)
self.vec_proj = nn.Linear(hidden_channels, hidden_channels * 3, bias=False)
self.dk_proj = None
if distance_influence in ["keys", "both"]:
self.dk_proj = nn.Linear(num_rbf, hidden_channels)
self.dv_proj = None
if distance_influence in ["values", "both"]:
self.dv_proj = nn.Linear(num_rbf, hidden_channels * 3)
self.reset_parameters()
def reset_parameters(self):
self.layernorm.reset_parameters()
nn.init.xavier_uniform_(self.q_proj.weight)
self.q_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.k_proj.weight)
self.k_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.v_proj.weight)
self.v_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.o_proj.weight)
self.o_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.vec_proj.weight)
if self.dk_proj:
nn.init.xavier_uniform_(self.dk_proj.weight)
self.dk_proj.bias.data.fill_(0)
if self.dv_proj:
nn.init.xavier_uniform_(self.dv_proj.weight)
self.dv_proj.bias.data.fill_(0)
def forward(self, x, vec, edge_index, r_ij, f_ij, d_ij):
x = self.layernorm(x)
q = self.q_proj(x).reshape(-1, self.num_heads, self.head_dim)
k = self.k_proj(x).reshape(-1, self.num_heads, self.head_dim)
v = self.v_proj(x).reshape(-1, self.num_heads, self.head_dim * 3)
vec1, vec2, vec3 = torch.split(self.vec_proj(vec), self.hidden_channels, dim=-1)
vec = vec.reshape(-1, 3, self.num_heads, self.head_dim)
vec_dot = (vec1 * vec2).sum(dim=1)
dk = (
self.act(self.dk_proj(f_ij)).reshape(-1, self.num_heads, self.head_dim)
if self.dk_proj is not None
else None
)
dv = (
self.act(self.dv_proj(f_ij)).reshape(-1, self.num_heads, self.head_dim * 3)
if self.dv_proj is not None
else None
)
# propagate_type: (q: Tensor, k: Tensor, v: Tensor, vec: Tensor, dk: Tensor, dv: Tensor, r_ij: Tensor, d_ij: Tensor)
x, vec = self.propagate(
edge_index,
q=q,
k=k,
v=v,
vec=vec,
dk=dk,
dv=dv,
r_ij=r_ij,
d_ij=d_ij,
size=None,
)
x = x.reshape(-1, self.hidden_channels)
vec = vec.reshape(-1, 3, self.hidden_channels)
o1, o2, o3 = torch.split(self.o_proj(x), self.hidden_channels, dim=1)
dx = vec_dot * o2 + o3
dvec = vec3 * o1.unsqueeze(1) + vec
return dx, dvec
def message(self, q_i, k_j, v_j, vec_j, dk, dv, r_ij, d_ij):
# attention mechanism
if dk is None:
attn = (q_i * k_j).sum(dim=-1)
else:
attn = (q_i * k_j * dk).sum(dim=-1)
# attention activation function
attn = self.attn_activation(attn) * self.cutoff(r_ij).unsqueeze(1)
# value pathway
if dv is not None:
v_j = v_j * dv
x, vec1, vec2 = torch.split(v_j, self.head_dim, dim=2)
# update scalar features
x = x * attn.unsqueeze(2)
# update vector features
vec = vec_j * vec1.unsqueeze(1) + vec2.unsqueeze(1) * d_ij.unsqueeze(
2
).unsqueeze(3)
return x, vec
def aggregate(
self,
features: Tuple[torch.Tensor, torch.Tensor],
index: torch.Tensor,
ptr: Optional[torch.Tensor],
dim_size: Optional[int],
) -> Tuple[torch.Tensor, torch.Tensor]:
x, vec = features
x = scatter(x, index, dim=self.node_dim, dim_size=dim_size)
vec = scatter(vec, index, dim=self.node_dim, dim_size=dim_size)
return x, vec
def update(
self, inputs: Tuple[torch.Tensor, torch.Tensor]
) -> Tuple[torch.Tensor, torch.Tensor]:
return inputs