tfep.nn.transformers.affine.VolumePreservingShiftTransformer

class tfep.nn.transformers.affine.VolumePreservingShiftTransformer(periodic_indices: Tensor | None = None, periodic_limits: Tensor | None = None)

Bases: MAFTransformer

Implement a volume-preserving transformer for autoregressive normalizing flows.

This is an implementation of the transformation

\(y_i = x_i + b_i\)

where \(b_i\) is the shift parameter of the transformation that are usually generated by a conditioner.

See also

volume_preserving_shift_transformer()

Functional API for the transformer.

__init__(periodic_indices: Tensor | None = None, periodic_limits: Tensor | None = None)

Constructor.

Parameters:

• periodic_indices (torch.Tensor, optional) – If provided, the features indexed by periodic_indices will be treated as periodic with period periodic_limits.

• periodic_limits (torch.Tensor, optional) – The period of periodic features.

Methods

__init__([periodic_indices, periodic_limits])	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, parameters)	Apply the affine transformation to the input.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_degrees_out(degrees_in)	Returns the degrees associated to the conditioner's output.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_identity_parameters(n_features)	Return the value of the parameters that makes this the identity function.
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.
inverse(y, parameters)	Reverse the affine transformation.
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_parameters_per_feature
training

forward(x: Tensor, parameters: Tensor) → tuple[Tensor]

Apply the affine transformation to the input.

Parameters:

• x (torch.Tensor) – Shape (batch_size, n_features). The input features.

• parameters (torch.Tensor) – Shape (batch_size, n_features). The parameters for the volume- preserving transformation, where parameters[:, i] is the shift parameter \(b_1\) for the i-th feature.

Returns:

• y (torch.Tensor) – Shape (batch_size, n_features). The transformed features.

• log_det_J (torch.Tensor) – Shape (batch_size,). The log absolute value of the Jacobian determinant of the transformation.

get_degrees_out(degrees_in: Tensor) → Tensor

Returns the degrees associated to the conditioner’s output.

Parameters:

degrees_in (torch.Tensor) – Shape (n_transformed_features,). The autoregressive degrees associated to the features provided as input to the transformer.

Returns:

degrees_out – Shape (n_parameters,). The autoregressive degrees associated to each output of the conditioner that will be fed to the transformer as parameters.

Return type:

torch.Tensor

get_identity_parameters(n_features: int) → Tensor

Return the value of the parameters that makes this the identity function.

This can be used to initialize the normalizing flow to perform the identity transformation. The shift must be zero for the transformation to be the identity.

Parameters:

n_features (int) – The dimension of the input vector of the transformer.

Returns:

parameters – Shape (n_features). The parameters for the identity.

Return type:

torch.Tensor

inverse(y: Tensor, parameters: Tensor) → tuple[Tensor]

Reverse the affine transformation.

Parameters:

• y (torch.Tensor) – Shape (batch_size, n_features). The input features.

• parameters (torch.Tensor) – Shape (batch_size, n_features). The parameters for the volume- preserving transformation, where parameters[:, i] is the shift parameter \(b_1\) for the i-th feature.

Returns:

• x (torch.Tensor) – Shape (batch_size, n_features). The transformed features.

• log_det_J (torch.Tensor) – Shape (batch_size,). The log absolute value of the Jacobian determinant of the transformation.