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Copy pathTorchNet_impl.py
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347 lines (282 loc) · 14 KB
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from typing import Tuple
import torch
import torch.nn as nn
from torch_geometric.nn.conv.message_passing import MessagePassing
from torch import Tensor, exp
def split_tensor(x: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
x: Tensor of shape (..., 3, 3)
Returns:
I: Tensor of shape (..., 3, 3)
A: Tensor of shape (..., 3, 3)
S: Tensor of shape (..., 3, 3)
Splits the input tensor into its scalar, vector, and tensor parts.
"""
assert x.shape[-2] == 3 and x.shape[-1] == 3, "Input tensor must have last two dimensions of size 3"
I = torch.diagonal(x, dim1=-2, dim2=-1).mean(dim=-1, keepdim=True).unsqueeze(-1) * torch.eye(3, device=x.device)
A = (x - x.transpose(-2, -1)) / 2
S = (x + x.transpose(-2, -1)) / 2 - I
return I, A, S
def phi(r: Tensor, rc: float = 5) -> Tensor:
"""
Args:
r: Tensor of shape (N,) distance between two atoms
rc: float (default: 5) cutoff distance
Returns:
Tensor of shape (N,)
Determines how strongly atoms influence each other, based on their distance.
"""
full_phi = .5 * torch.cos(r * (torch.pi / rc)) + .5
return full_phi * (r < rc).float()
def vector_to_skewtensor(vector):
"""
Args:
vector: Tensor of shape (...,3)
Returns:
Tensor of shape (...,3,3)
Creates a skew-symmetric tensor from a vector.
"""
zero = torch.zeros((*vector.size()[:-1],), device=vector.device, dtype=vector.dtype)
x, y, z = vector.unbind(dim=-1)
return torch.stack([zero, z, -y, -z, zero, x, y, -x, zero], dim=-1).reshape(*vector.size()[:-1], 3, 3)
def skewtensor_to_vector(skewtensor):
"""
Args:
skewtensor: Tensor of shape (...,3,3)
Returns:
Tensor of shape (...,3)
Extracts the vector from a skew-symmetric tensor.
"""
mask = torch.tensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).to(skewtensor.device)
up_shift = torch.tensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).to(skewtensor.device)
masked_values = skewtensor * mask
ordered_values = up_shift @ masked_values
return ordered_values.sum(dim=-1)
def e_rbf(r: Tensor, beta: Tensor|float = 1, mu: Tensor = torch.tensor(0.)) -> Tensor:
"""
Args:
r: Tensor of shape (N,) distance between two atoms
beta: float (default: 1) width of the basis function
mu: Tensor of shape (D,) center of the basis function
Returns:
Tensor of shape (N,D) with the basis function applied to each distance
Exponent
Determines how strongly atoms influence each other, based on their distance.
"""
return exp(-beta * (exp(r).unsqueeze(-1) - mu.unsqueeze(0)) ** 2)
class EquivariantMPLayer(MessagePassing):
def __init__(self, in_channels, out_channels, equivariance_class='SO(3)', rbf_dim=64):
super(EquivariantMPLayer, self).__init__(aggr='add') # "Add" aggregation.
self.eq_class = equivariance_class
self.node_dim = 0 # usually -2, for N, D, but we have N, D, 3, 3
self.rbf_dim = rbf_dim
self.rc = torch.tensor(5)
self.in_dim = in_channels
self.out_dim = out_channels
self.msg_mlp = nn.Sequential(
nn.Linear(rbf_dim, out_channels), nn.SiLU(),
nn.Linear(out_channels, out_channels * 2), nn.SiLU(),
nn.Linear(out_channels * 2, out_channels * 3), nn.SiLU(),
)
# first weighted transform
self.upd_scaler = nn.Linear(in_channels, out_channels, bias=False)
self.upd_vector = nn.Linear(in_channels, out_channels, bias=False)
self.upd_tensor = nn.Linear(in_channels, out_channels, bias=False)
# second weighted transform
self.upd_final_scaler = nn.Linear(in_channels, out_channels, bias=False)
self.upd_final_vector = nn.Linear(in_channels, out_channels, bias=False)
self.upd_final_tensor = nn.Linear(in_channels, out_channels, bias=False)
def forward(self, x, pos, edge_index):
# x has shape [N, in_channels, 3, 3]
# pos has shape [N, 3]
# edge_index has shape [2, E]
return self.propagate(edge_index, x=x, p=pos)
def message(self, x_j, p_i, p_j):
# x_j has shape [N, in_channels, 3, 3]
# p_j has shape [N, 3]
# normalize x, the paper defines the norm as Tr(X.T @X), which is the square of the Frobenius norm
x_j = x_j / (torch.norm(x_j, dim=(-2, -1), p='fro', keepdim=True) ** 2 + 1)
# decompose x into scalar, vector, and tensor parts
I, A, S = split_tensor(x_j)
# compute r_ij
r = torch.norm(p_i - p_j, dim=-1)
# get the weights
beta = 2 / self.rbf_dim * (1 - exp(-self.rc)) # number
beta = 1 / beta**2
mu = torch.linspace(1, exp(-self.rc), self.rbf_dim).to(r.device)
w = phi(r).unsqueeze(-1) * self.msg_mlp(e_rbf(r, beta=beta, mu=mu))
f_i, f_a, f_s = w.chunk(3, dim=-1)
# w is f shape (N, 3D)
# f_x are of shape (N, D)
# compute the message
x_j = f_i.view(*f_i.size(), 1, 1) * I +\
f_a.view(*f_a.size(), 1, 1) * A +\
f_s.view(*f_s.size(), 1, 1) * S
return x_j
def update(self, aggr_out, x):
# aggr_out has shape [N, out_channels, 3, 3]
# x has shape [N, in_channels, 3, 3]
# transform previous node embeddings to have same size as aggr_out
I, A, S = split_tensor(x)
# move channels to last dimension
I = self.upd_scaler(I.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
A = self.upd_vector(A.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
S = self.upd_tensor(S.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
Y = I + A + S
# compute the new node embeddings
if self.eq_class == 'SO(3)':
reps = 2*torch.matmul(Y, aggr_out)
elif self.eq_class == 'O(3)':
reps = torch.matmul(Y, aggr_out) + torch.matmul(aggr_out, Y)
else:
raise AttributeError(f"Unknown equivariance class {self.eq_class}")
Iu, Au, Su = split_tensor(reps)
x_norm = torch.norm(reps, dim=(-2, -1), p='fro', keepdim=True) ** 2 + 1
Iu, Au, Su = Iu / x_norm, Au / x_norm, Su / x_norm
I = self.upd_final_scaler(Iu.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
A = self.upd_final_vector(Au.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
S = self.upd_final_tensor(Su.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
dX = I + A + S
dX = dX + torch.matmul(dX, dX)
return x + dX if self.in_dim == self.out_dim else Y + dX
class TensorNetEmbedding(nn.Module):
def __init__(self, hidden_dim=64, max_z=128, rbf_dim=64):
super(TensorNetEmbedding, self).__init__()
self.rc = torch.tensor(5)
self.hid_dim = hidden_dim
self.rbf_dim = rbf_dim
self.z_emb = nn.Embedding(max_z + 1, hidden_dim)
self.z_map = nn.Linear(2 * hidden_dim, hidden_dim)
self.scalar_map = nn.Linear(rbf_dim, hidden_dim)
self.vector_map = nn.Linear(rbf_dim, hidden_dim)
self.tensor_map = nn.Linear(rbf_dim, hidden_dim)
self.node_mlp = nn.Sequential(nn.LayerNorm(hidden_dim),
nn.Linear(hidden_dim, 2 * hidden_dim), nn.SiLU(),
nn.Linear(2 * hidden_dim, 3 * hidden_dim), nn.SiLU(),
)
self.node_scalar = nn.Linear(hidden_dim, hidden_dim, bias=False)
self.node_vector = nn.Linear(hidden_dim, hidden_dim, bias=False)
self.node_tensor = nn.Linear(hidden_dim, hidden_dim, bias=False)
def forward(self, data):
p = data.pos
r_ij = p[data.edge_index[0]] - p[data.edge_index[1]] # shape [E, 3]
mask = torch.norm(r_ij, dim=-1) < self.rc # cutoff distance is 5 Amstrong
r_ij = r_ij[mask] / r_ij[mask].norm(dim=-1).unsqueeze(-1)
# the trace of the outer product of a vector with itself is the square of its norm
# S has dimension (E, 3, 3)
S = r_ij.unsqueeze(-1) @ r_ij.unsqueeze(-2) - torch.norm(r_ij, dim=-1, keepdim=True).unsqueeze(-1) ** 2 / 3
A = vector_to_skewtensor(r_ij)
I = torch.eye(3, device=r_ij.device).unsqueeze(0) # shape [1, 3, 3]
# Z_ij has shape
Z = self.z_emb(data.z) # shape [N, hidden_dim]
z_i = Z[data.edge_index[0]]
z_j = Z[data.edge_index[1]]
Z_ij = torch.cat([z_i, z_j], dim=-1) # shape [E, 2 * hidden_dim]
Z_ij = self.z_map(Z_ij) # shape [E, hidden_dim]
beta = 2 / self.rbf_dim * (1 - exp(-self.rc)) # number
beta = 1 / beta**2
mu = torch.linspace(1, exp(-self.rc), self.rbf_dim).to(r_ij.device)
rbf = e_rbf(r_ij.norm(dim=-1), beta=beta, mu=mu)
f_i = self.scalar_map(rbf) # shape [E, hidden_dim]
f_a = self.vector_map(rbf)
f_s = self.tensor_map(rbf)
# final edge features, shape [E, hidden_dim, 3, 3]
X = f_i.view(-1, self.hid_dim, 1, 1) * I + \
f_a.view(-1, self.hid_dim, 1, 1) * A.view(-1, 1, 3, 3) + \
f_s.view(-1, self.hid_dim, 1, 1) * S.view(-1, 1, 3, 3)
X = Z_ij.view(-1, self.hid_dim, 1, 1) * phi(r_ij.norm(dim=-1)).view(-1, 1, 1, 1) * X
# turn edge features into node features, by adding together incoming edge features
# for molecules, the edge index is usually symmetric tho
X_n = torch.zeros(data.num_nodes, self.hid_dim, 3, 3, device=r_ij.device)
X_n.index_add_(0, data.edge_index[1], X)
f_i, f_a, f_s = self.node_mlp(X_n.norm(p='fro', dim=(-2, -1))**2).chunk(3, dim=-1)
I, A, S = split_tensor(X_n)
I = f_i.view(-1, self.hid_dim, 1, 1) * self.node_scalar(I.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
A = f_a.view(-1, self.hid_dim, 1, 1) * self.node_vector(A.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
S = f_s.view(-1, self.hid_dim, 1, 1) * self.node_tensor(S.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
X_n = I + A + S
return X_n
class ScalarPredictionHead(nn.Module):
def __init__(self, hidden_dim):
super(ScalarPredictionHead, self).__init__()
self.predict = nn.Sequential(
nn.LayerNorm(3 * hidden_dim),
nn.Linear(3 * hidden_dim, hidden_dim), nn.SiLU(),
nn.Linear(hidden_dim, hidden_dim//2), nn.SiLU(),
nn.Linear(hidden_dim//2, 1)
)
def forward(self, I, A, S, batch):
In, An, Sn = I.norm(dim=(-2, -1))**2, A.norm(dim=(-2, -1))**2, S.norm(dim=(-2, -1))**2
node_features = self.predict(torch.cat([In, An, Sn], dim=-1))
zero = torch.zeros(batch.max() + 1, device=node_features.device, dtype=node_features.dtype)
return torch.scatter_add(zero, dim=0, index=batch, src=node_features.squeeze())
class VectorPredictionHead(nn.Module):
def __init__(self, hidden_dim):
super(VectorPredictionHead, self).__init__()
self.predict = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
nn.Linear(hidden_dim, 1)
)
self.mask = torch.tensor([])
def forward(self, I, A, S, batch):
A = self.predict(A.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
node_features = skewtensor_to_vector(A)
zero = torch.zeros(batch.max() + 1, device=node_features.device, dtype=node_features.dtype)
return torch.scatter_add(zero, dim=0, index=batch.repeat(3, 1).T, src=node_features)
class TensorPredictionHead(nn.Module):
def __init__(self, hidden_dim):
super(TensorPredictionHead, self).__init__()
shared = [
nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
]
self.mlp_a1 = nn.Sequential(
*shared, nn.Linear(hidden_dim, 1, bias=False)
)
self.mlp_a2 = nn.Sequential(
*shared, nn.Linear(hidden_dim, 1, bias=False)
)
def forward(self, I, A, S, batch):
A1 = skewtensor_to_vector(self.mlp_a1(A.permute(0, 2, 3, 1)).permute(0, 3, 1, 2))
A2 = skewtensor_to_vector(self.mlp_a2(A.permute(0, 2, 3, 1)).permute(0, 3, 1, 2))
# shape of A is [N, 1, 3]
outer_product = A1.unsqueeze(-1) @ A2.unsqueeze(-2)
# shape of outer_product is [N, 1, 3, 3]
node_features = (outer_product - outer_product.transpose(-2, -1)) / 2
zero = torch.zeros(batch.max() + 1, device=node_features.device, dtype=node_features.dtype)
return torch.scatter_add(zero, dim=0, index=batch.repeat(3, 3, 1).permute(2, 0, 1), src=node_features)
class TensorNet(nn.Module):
def __init__(self, hidden_dims=[64]*3, max_z=128, rbf_dim=64, equivariance_class='SO(3)', predict_type='scalar', mean=None, std=None):
super(TensorNet, self).__init__()
self.embedding = TensorNetEmbedding(hidden_dims[0], max_z, rbf_dim)
lays = []
for i, dim in enumerate(hidden_dims[1:]):
lays.append(EquivariantMPLayer(hidden_dims[i - 1], dim, equivariance_class, rbf_dim=rbf_dim))
self.layers = nn.ModuleList(lays)
if predict_type not in ['scalar', 'vector', 'tensor']:
raise AttributeError(f"Unknown predict_type {predict_type}")
elif predict_type == 'scalar':
self.predict = ScalarPredictionHead(hidden_dims[-1])
shape = (1,)
elif predict_type == 'vector':
self.predict = VectorPredictionHead(hidden_dims[-1])
shape = (3,)
elif predict_type == 'tensor':
self.predict = TensorPredictionHead(hidden_dims[-1])
shape = (3, 3)
self.mean = torch.zeros(shape) if mean is None else mean
self.std = torch.ones(shape) if std is None else std
def forward(self, data):
dat_device = data.pos.device
edge_index = torch.sparse_coo_tensor(
indices=data.edge_index,
values=torch.ones(data.edge_index.shape[1], device=dat_device),
size=(data.num_nodes, data.num_nodes),
device=dat_device).coalesce()
X = self.embedding(data)
for layer in self.layers:
X = layer(X, data.pos, edge_index)
I, A, S = split_tensor(X)
return self.predict(I, A, S, data.batch) * self.std.to(dat_device) + self.mean.to(dat_device)
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