tfep.nn.graph.FixedGraph

class tfep.nn.graph.FixedGraph(node_types: Sequence[int], mask: Tensor | None = None)

Bases: Module

Graph base class with a fixed topology.

The class provides utilities to determine the edges between nodes (optionally based on a mask) and cache them. It is assumed that the edges do not change after constructions.

The edges must be accessed by the parent class through the get_edges() method, which takes care of determining the edges compatible with features in the shape (batch_size*n_nodes, n_feats_per_node).

__init__(node_types: Sequence[int], mask: Tensor | None = None)

Constructor.

Parameters:

• node_types (Sequence[int]) – Shape (n_nodes,). node_types[i] is the ID of the node type for the i-th node. These are usually used to indicate an atom element. These are encoded into a self._node_types_one_hot encoding using torch.nn.functional.one_hot so they should start from 0 and contain only consecutive numbers to limit the size of the encoding (i.e., 0 <= node_types[i] < n_node_types for all i).

• mask (torch.Tensor, optional) – Shape (n_nodes, n_nodes). A (directional) edge from node i to node j is created only if mask[i, j] != 0. If mask is not provided, all nodes are connected to all nodes (excluding self interactions).

Methods

__init__(node_types[, mask])	Constructor.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(*input)	Define the computation performed at every call.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_edges(batch_size)	Return the edges between nodes for the given batch size.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module)	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
n_edges	Number of edges in the graph.
n_nodes	Number of nodes in the graph.
training

get_edges(batch_size)

Return the edges between nodes for the given batch size.

Parameters:

batch_size (int) – The size of the current batch.

Returns:

edges – Shape (2, batch_size*n_edges). The i-th edge is created from node edges[0][i] to edges[1][i], where edges[0][i] is a node index in the range [0, batch_size*n_nodes].

Edges are directional so if a message must be passed in both directions, two entries connecting the nodes with inverse order are present.

Return type:

torch.Tensor

property n_edges

Number of edges in the graph.

Type:

int

property n_nodes

Number of nodes in the graph.

Type:

int