Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

more mantainable code for optimized angle update #39

Merged
merged 1 commit into from
Jan 31, 2025
Merged

more mantainable code for optimized angle update #39

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
65 changes: 29 additions & 36 deletions deepmd/pt/model/descriptor/repflow_layer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -623,48 +623,47 @@ def optim_angle_update(
angle_ebd: torch.Tensor,
node_ebd: torch.Tensor,
edge_ebd: torch.Tensor,
angle_dim: int,
node_dim: int,
edge_dim: int,
feat: str = "edge",
) -> torch.Tensor:
angle_dim = angle_ebd.shape[-1]
node_dim = node_ebd.shape[-1]
edge_dim = edge_ebd.shape[-1]
sub_angle_idx = (0, angle_dim)
sub_node_idx = (angle_dim, angle_dim + node_dim)
sub_edge_idx_ij = (angle_dim + node_dim, angle_dim + node_dim + edge_dim)
sub_edge_idx_ik = (
angle_dim + node_dim + edge_dim,
angle_dim + node_dim + 2 * edge_dim,
)

if feat == "edge":
# fot jit
sub_angle_matrix = self.edge_angle_linear1.matrix[:angle_dim]
sub_node_matrix = self.edge_angle_linear1.matrix[
angle_dim : angle_dim + node_dim
]
sub_edge_matrix_ij = self.edge_angle_linear1.matrix[
angle_dim + node_dim : angle_dim + node_dim + edge_dim
]
sub_edge_matrix_ik = self.edge_angle_linear1.matrix[
angle_dim + node_dim + edge_dim : angle_dim + node_dim + 2 * edge_dim
]
bias = self.edge_angle_linear1.bias
matrix, bias = self.edge_angle_linear1.matrix, self.edge_angle_linear1.bias
elif feat == "angle":
sub_angle_matrix = self.angle_self_linear.matrix[:angle_dim]
sub_node_matrix = self.angle_self_linear.matrix[
angle_dim : angle_dim + node_dim
]
sub_edge_matrix_ij = self.angle_self_linear.matrix[
angle_dim + node_dim : angle_dim + node_dim + edge_dim
]
sub_edge_matrix_ik = self.angle_self_linear.matrix[
angle_dim + node_dim + edge_dim : angle_dim + node_dim + 2 * edge_dim
]
bias = self.angle_self_linear.bias
matrix, bias = self.angle_self_linear.matrix, self.angle_self_linear.bias
else:
matrix, bias = None, None
raise NotImplementedError
assert matrix is not None
assert bias is not None
assert angle_dim + node_dim + 2 * edge_dim == matrix.size()[0]

# nf * nloc * a_sel * a_sel * angle_dim
sub_angle_update = torch.matmul(angle_ebd, sub_angle_matrix)
sub_angle_update = torch.matmul(
angle_ebd, matrix[sub_angle_idx[0] : sub_angle_idx[1]]
)

# nf * nloc * angle_dim
sub_node_update = torch.matmul(node_ebd, sub_node_matrix)
sub_node_update = torch.matmul(
node_ebd, matrix[sub_node_idx[0] : sub_node_idx[1]]
)

# nf * nloc * a_nnei * angle_dim
sub_edge_update_ij = torch.matmul(edge_ebd, sub_edge_matrix_ij)
sub_edge_update_ik = torch.matmul(edge_ebd, sub_edge_matrix_ik)
sub_edge_update_ij = torch.matmul(
edge_ebd, matrix[sub_edge_idx_ij[0] : sub_edge_idx_ij[1]]
)
sub_edge_update_ik = torch.matmul(
edge_ebd, matrix[sub_edge_idx_ik[0] : sub_edge_idx_ik[1]]
)

result_update = (
sub_angle_update
Expand Down Expand Up @@ -1038,9 +1037,6 @@ def forward(
angle_ebd,
node_ebd_for_angle,
edge_for_angle,
self.a_dim,
self.a_dim,
self.a_dim,
"edge",
)
)
Expand Down Expand Up @@ -1105,9 +1101,6 @@ def forward(
angle_ebd,
node_ebd_for_angle,
edge_for_angle,
self.a_dim,
self.a_dim,
self.a_dim,
"angle",
)
)
Expand Down