tfep.nn.embeddings.mafembed.FlipInvariantEmbedding

class tfep.nn.embeddings.mafembed.FlipInvariantEmbedding(n_features_in: int, embedding_dimension: int, embedded_indices: Sequence[int] | None = None, vector_dimension: int = 4, hidden_layer_width: int = 32)

Bases: MAFEmbedding

Embeds vector features into a representation invariant to sign flips.

This implements the embedding proposed in [1] (Equation 46 in the SI).

References

[1] Köhler J, Invernizzi M, De Haan P, Noé F. Rigid body flows for sampling

molecular crystal structures. In International Conference on Machine Learning 2023 Jul 3 (pp. 17301-17326). PMLR.

__init__(n_features_in: int, embedding_dimension: int, embedded_indices: Sequence[int] | None = None, vector_dimension: int = 4, hidden_layer_width: int = 32)

Constructor.

Parameters:

• n_features_in (int) – Number of input features (embedded and not).

• embedding_dimension (int) – The embedding dimension of each vector.

• embedded_indices (Sequence[int] or None, optional) – A sequence of length n_vectors*vector_dimension with the (ordered) indices of the input features corresponding to the vectors to embed. Vectors are assumed to be represented by consecutive elements. If None, all features are embedded.

• vector_dimension (int, optional) – The dimension of the embedded vectors. Default is 4.

• hidden_layer_width (int, optional) – The width of the hidden layer of the fully-connected neural networks used to embed the vectors. Default is 32.

Methods

__init__(n_features_in, embedding_dimension)	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(x)	Forward.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_degrees_out(degrees_in)	Return the degrees of the features after the forward pass.
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
embedding_dimension	The embedding dimension for each vector.
vector_dimension	The input vector dimensionality.
training

property embedding_dimension: int

The embedding dimension for each vector.

Type:

int

forward(x: Tensor) → Tensor

Forward.

Parameters:

x (torch.Tensor) – Shape (batch, n_features). Input tensor.

Returns:

out – Shape (batch, n_features + n_vectors*(embedded_dim - vector_dim)). The transformed features with the n_vectors vectors embedded from the vector_dim to an embedded_dim space.

Return type:

torch.Tensor

get_degrees_out(degrees_in: Tensor) → Tensor

Return the degrees of the features after the forward pass.

This requires that all components of each vector is assigned a single degree.

Parameters:

degrees_in (torch.Tensor) – Shape (n_features_in,). The degrees of the input features.

Returns:

degrees_out – Shape (n_features_out,). The degrees of the features after the forward pass.

Return type:

torch.Tensor

Raises:

ValueError – If there are some embedded vectors whose components have been assigned different degrees.

property vector_dimension: int

The input vector dimensionality.

Type:

int