topobench.nn.backbones.graph.nsd module#

This module implements a Discrete Neural Sheaf Diffusion-based model[1] that can be used with the training framework.

Neural Sheaf Diffusion is a method for learning representations of graphs using sheaf structure: node and edge stalks communicating via transport maps / restriction maps. Adapted and simplified from Bodnar et al. [1]

[1] Bodnar et al. “Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs” https://arxiv.org/abs/2202.04579

class InductiveDiscreteBundleSheafDiffusion(config)#

Bases: SheafDiffusion

Inductive sheaf diffusion with orthogonal bundle restriction maps.

This model learns orthogonal d x d restriction maps for each edge, ensuring isometric transport between stalks. Uses normalized Laplacian and Cayley/matrix exponential parameterization for orthogonality.

Parameters:

configdict: Configuration dictionary containing: - d (int): Dimension of stalk space (must be > 1). - layers (int): Number of diffusion layers. - hidden_channels (int): Hidden channels per stalk dimension. - input_dim (int): Input feature dimension. - output_dim (int): Output feature dimension. - device (str): Device to run on. - input_dropout (float): Input layer dropout rate. - dropout (float): Hidden layer dropout rate. - sheaf_act (str): Activation for sheaf learning. - orth (str): Orthogonalization method (‘cayley’ or ‘matrix_exp’).

Raises:

AssertionError: If d is not greater than 1 or hidden_dim is not divisible by d.

__init__(config)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x, edge_index)#

Forward pass of bundle sheaf diffusion.

Parameters:

xtorch.Tensor: Node feature matrix of shape [num_nodes, input_dim].
edge_indextorch.Tensor: Edge indices of shape [2, num_edges].

Returns:

torch.Tensor: Output node features of shape [num_nodes, output_dim].

get_param_size()#

Get the number of parameters needed for orthogonal maps.

Returns:

int: Number of parameters (d*(d+1)/2 for lower triangular parameterization).

left_right_linear(x, left, right, actual_num_nodes)#

Apply left and right linear transformations to stalk vectors.

Parameters:

xtorch.Tensor: Input tensor of shape [num_nodes * d, hidden_channels].
leftnn.Linear: Left linear transformation (acts on stalk dimension).
rightnn.Linear: Right linear transformation (acts on hidden channels).
actual_num_nodesint: Number of nodes in the current graph.

Returns:

torch.Tensor: Transformed tensor of shape [num_nodes * d, hidden_channels].

class InductiveDiscreteDiagSheafDiffusion(config)#

Bases: SheafDiffusion

Inductive sheaf diffusion with diagonal restriction maps.

This model learns diagonal d x d restriction maps for each edge, parameterized by d scalar values. Suitable for problems where feature channels can be processed independently.

Parameters:

configdict: Configuration dictionary containing: - d (int): Dimension of stalk space (must be > 0). - layers (int): Number of diffusion layers. - hidden_channels (int): Hidden channels per stalk dimension. - input_dim (int): Input feature dimension. - output_dim (int): Output feature dimension. - device (str): Device to run on. - input_dropout (float): Input layer dropout rate. - dropout (float): Hidden layer dropout rate. - sheaf_act (str): Activation for sheaf learning.

__init__(config)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x, edge_index)#

Forward pass of diagonal sheaf diffusion.

Parameters:

xtorch.Tensor: Node feature matrix of shape [num_nodes, input_dim].
edge_indextorch.Tensor: Edge indices of shape [2, num_edges].

Returns:

torch.Tensor: Output node features of shape [num_nodes, output_dim].

class InductiveDiscreteGeneralSheafDiffusion(config)#

Bases: SheafDiffusion

Inductive sheaf diffusion with general (unrestricted) restriction maps.

This model learns arbitrary d x d restriction maps for each edge, providing maximum expressiveness but requiring more parameters. Each restriction map is a full d x d matrix.

Parameters:

configdict: Configuration dictionary containing: - d (int): Dimension of stalk space (must be > 1). - layers (int): Number of diffusion layers. - hidden_channels (int): Hidden channels per stalk dimension. - input_dim (int): Input feature dimension. - output_dim (int): Output feature dimension. - device (str): Device to run on. - input_dropout (float): Input layer dropout rate. - dropout (float): Hidden layer dropout rate. - sheaf_act (str): Activation for sheaf learning.

Raises:

AssertionError: If d is not greater than 1.

__init__(config)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x, edge_index)#

Forward pass of general sheaf diffusion.

Parameters:

xtorch.Tensor: Node feature matrix of shape [num_nodes, input_dim].
edge_indextorch.Tensor: Edge indices of shape [2, num_edges].

Returns:

torch.Tensor: Output node features of shape [num_nodes, output_dim].

left_right_linear(x, left, right, actual_num_nodes)#

Apply left and right linear transformations to stalk vectors.

Parameters:

xtorch.Tensor: Input tensor of shape [num_nodes * d, hidden_channels].
leftnn.Linear: Left linear transformation (acts on stalk dimension).
rightnn.Linear: Right linear transformation (acts on hidden channels).
actual_num_nodesint: Number of nodes in the current graph.

Returns:

torch.Tensor: Transformed tensor of shape [num_nodes * d, hidden_channels].

class Module(*args, **kwargs)#

Bases: object

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

__init__(*args, **kwargs)#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

add_module(name, module)#

Add a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:

name (str) – name of the child module. The child module can be accessed from this module using the given name
module (Module) – child module to be added to the module.

apply(fn)#

Apply fn recursively to every submodule (as returned by .children()) as well as self.

Typical use includes initializing the parameters of a model (see also nn-init-doc).

Parameters:: fn (Module -> None) – function to be applied to each submodule
Returns:: self
Return type:: Module

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
        [1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
        [1., 1.]], requires_grad=True)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)

bfloat16()#

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:: self
Return type:: Module

buffers(recurse=True)#

Return an iterator over module buffers.

Parameters:: recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:: torch.Tensor – module buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

children()#

Return an iterator over immediate children modules.

Yields:: Module – a child module

compile(*args, **kwargs)#

Compile this Module’s forward using torch.compile().

This Module’s __call__ method is compiled and all arguments are passed as-is to torch.compile().

See torch.compile() for details on the arguments for this function.

cpu()#

Move all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns:: self
Return type:: Module

cuda(device=None)#

Move all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Note

This method modifies the module in-place.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: self
Return type:: Module

double()#

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns:: self
Return type:: Module

eval()#

Set the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

See locally-disable-grad-doc for a comparison between .eval() and several similar mechanisms that may be confused with it.

Returns:: self
Return type:: Module

extra_repr()#

Set the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

float()#

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns:: self
Return type:: Module

forward(*input)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_buffer(target)#

Return the buffer given by target if it exists, otherwise throw an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Parameters:: target (str) – The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify a fully-qualified string.)
Returns:: The buffer referenced by target
Return type:: torch.Tensor
Raises:: AttributeError – If the target string references an invalid path or resolves to something that is not a buffer

get_extra_state()#

Return any extra state to include in the module’s state_dict.

Implement this and a corresponding set_extra_state() for your module if you need to store extra state. This function is called when building the module’s state_dict().

Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.

Returns:: Any extra state to store in the module’s state_dict
Return type:: object

get_parameter(target)#

Return the parameter given by target if it exists, otherwise throw an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Parameters:: target (str) – The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify a fully-qualified string.)
Returns:: The Parameter referenced by target
Return type:: torch.nn.Parameter
Raises:: AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Parameter

get_submodule(target)#

Return the submodule given by target if it exists, otherwise throw an error.

For example, let’s say you have an nn.Module A that looks like this:

A(
    (net_b): Module(
        (net_c): Module(
            (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
        )
        (linear): Linear(in_features=100, out_features=200, bias=True)
    )
)

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used.

Parameters:: target (str) – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
Returns:: The submodule referenced by target
Return type:: torch.nn.Module
Raises:: AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Module

half()#

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns:: self
Return type:: Module

ipu(device=None)#

Move all model parameters and buffers to the IPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.

Note

This method modifies the module in-place.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: self
Return type:: Module

load_state_dict(state_dict, strict=True, assign=False)#

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Warning

If assign is True the optimizer must be created after the call to load_state_dict unless get_swap_module_params_on_conversion() is True.

Parameters:

state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True
assign (bool, optional) – When False, the properties of the tensors in the current module are preserved while when True, the properties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of Default: ``False`

Returns:

missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

modules()#

Return an iterator over all modules in the network.

Yields:: Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
...     print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)

named_buffers(prefix='', recurse=True, remove_duplicate=True)#

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

prefix (str) – prefix to prepend to all buffer names.
recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.
remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_children()#

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields:: (str, Module) – Tuple containing a name and child module

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)

named_modules(memo=None, prefix='', remove_duplicate=True)#

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Parameters:

memo (Set[Module] | None) – a memo to store the set of modules already added to the result
prefix (str) – a prefix that will be added to the name of the module
remove_duplicate (bool) – whether to remove the duplicated module instances in the result or not

Yields:

(str, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
...     print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

named_parameters(prefix='', recurse=True, remove_duplicate=True)#

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

prefix (str) – prefix to prepend to all parameter names.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

parameters(recurse=True)#

Return an iterator over module parameters.

This is typically passed to an optimizer.

Parameters:: recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields:: Parameter – module parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for param in model.parameters():
>>>     print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

register_backward_hook(hook)#

This function is deprecated in favor of register_full_backward_hook() and the behavior of this function will change in future versions.

Returns:: a handle that can be used to remove the added hook by calling handle.remove()
Return type:: torch.utils.hooks.RemovableHandle

register_buffer(name, tensor, persistent=True)#

Add a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Parameters:

name (str) – name of the buffer. The buffer can be accessed from this module using the given name
tensor (Tensor or None) – buffer to be registered. If None, then operations that run on buffers, such as cuda, are ignored. If None, the buffer is not included in the module’s state_dict.
persistent (bool) – whether the buffer is part of this module’s state_dict.

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> self.register_buffer('running_mean', torch.zeros(num_features))

register_forward_hook(hook, *, prepend=False, with_kwargs=False, always_call=False)#

The hook will be called every time after forward() has computed an output.

If with_kwargs is False or not specified, the input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called. The hook should have the following signature:

hook(module, args, output) -> None or modified output

If with_kwargs is True, the forward hook will be passed the kwargs given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:

hook(module, args, kwargs, output) -> None or modified output

Parameters:

hook (Callable) – The user defined hook to be registered.
prepend (bool) – If True, the provided hook will be fired before all existing forward hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this torch.nn.modules.Module. Note that global forward hooks registered with register_module_forward_hook() will fire before all hooks registered by this method. Default: False
with_kwargs (bool) – If True, the hook will be passed the kwargs given to the forward function. Default: False
always_call (bool) – If True the hook will be run regardless of whether an exception is raised while calling the Module. Default: False

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_forward_pre_hook(hook, *, prepend=False, with_kwargs=False)#

The hook will be called every time before forward() is invoked.

If with_kwargs is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:

hook(module, args) -> None or modified input

If with_kwargs is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:

hook(module, args, kwargs) -> None or a tuple of modified input and kwargs

Parameters:

hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing forward_pre hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this torch.nn.modules.Module. Note that global forward_pre hooks registered with register_module_forward_pre_hook() will fire before all hooks registered by this method. Default: False
with_kwargs (bool) – If true, the hook will be passed the kwargs given to the forward function. Default: False

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_full_backward_hook(hook, prepend=False)#

The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and grad_output will be None for all non-Tensor arguments.

For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.

Warning

Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.

Parameters:

hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing backward hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this torch.nn.modules.Module. Note that global backward hooks registered with register_module_full_backward_hook() will fire before all hooks registered by this method.

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_full_backward_pre_hook(hook, prepend=False)#

The hook will be called every time the gradients for the module are computed. The hook should have the following signature:

hook(module, grad_output) -> tuple[Tensor] or None

The grad_output is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of grad_output in subsequent computations. Entries in grad_output will be None for all non-Tensor arguments.

Warning

Modifying inputs inplace is not allowed when using backward hooks and will raise an error.

Parameters:

hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing backward_pre hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this torch.nn.modules.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook() will fire before all hooks registered by this method.

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_load_state_dict_post_hook(hook)#

It should have the following signature::: hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling load_state_dict() with strict=True are affected by modifications the hook makes to missing_keys or unexpected_keys, as expected. Additions to either set of keys will result in an error being thrown when strict=True, and clearing out both missing and unexpected keys will avoid an error.

Returns:: a handle that can be used to remove the added hook by calling handle.remove()
Return type:: torch.utils.hooks.RemovableHandle

register_module(name, module)#

Alias for add_module().

register_parameter(name, param)#

Add a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:

name (str) – name of the parameter. The parameter can be accessed from this module using the given name
param (Parameter or None) – parameter to be added to the module. If None, then operations that run on parameters, such as cuda, are ignored. If None, the parameter is not included in the module’s state_dict.

register_state_dict_pre_hook(hook)#

These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self. The registered hooks can be used to perform pre-processing before the state_dict call is made.

requires_grad_(requires_grad=True)#

Change if autograd should record operations on parameters in this module.

This method sets the parameters’ requires_grad attributes in-place.

This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).

See locally-disable-grad-doc for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.

Parameters:: requires_grad (bool) – whether autograd should record operations on parameters in this module. Default: True.
Returns:: self
Return type:: Module

set_extra_state(state)#

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict() to handle any extra state found within the state_dict. Implement this function and a corresponding get_extra_state() for your module if you need to store extra state within its state_dict.

Parameters:: state (dict) – Extra state from the state_dict

share_memory()#

See torch.Tensor.share_memory_().

state_dict(*, destination: T_destination, prefix: str = '', keep_vars: bool = False) → T_destination#

state_dict(*, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Return a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.
prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.
keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

to(device: str | device | int | None = ..., dtype: dtype | None = ..., non_blocking: bool = ...) → Self#

to(dtype: dtype, non_blocking: bool = ...) → Self

to(tensor: Tensor, non_blocking: bool = ...) → Self

Move and/or cast the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)

to(dtype, non_blocking=False)

to(tensor, non_blocking=False)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point or complex dtypes. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters:

device (torch.device) – the desired device of the parameters and buffers in this module
dtype (torch.dtype) – the desired floating point or complex dtype of the parameters and buffers in this module
tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns:

self

Return type:

Module

Examples:

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

to_empty(*, device, recurse=True)#

Move the parameters and buffers to the specified device without copying storage.

Parameters:

device (torch.device) – The desired device of the parameters and buffers in this module.
recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.

Returns:

self

Return type:

Module

train(mode=True)#

Set the module in training mode.

Parameters:: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns:: self
Return type:: Module

type(dst_type)#

Casts all parameters and buffers to dst_type.

Note

This method modifies the module in-place.

Parameters:: dst_type (type or string) – the desired type
Returns:: self
Return type:: Module

xpu(device=None)#

Move all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.

Note

This method modifies the module in-place.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: self
Return type:: Module

zero_grad(set_to_none=True)#

Reset gradients of all model parameters.

See similar function under torch.optim.Optimizer for more context.

Parameters:: set_to_none (bool) – instead of setting to zero, set the grads to None. See torch.optim.Optimizer.zero_grad() for details.

T_destination = ~T_destination#

call_super_init: bool = False#

dump_patches: bool = False#

training: bool#

class NSDEncoder(input_dim, hidden_dim, num_layers=2, sheaf_type='diag', d=2, dropout=0.1, input_dropout=0.1, device='cpu', sheaf_act='tanh', orth='cayley', **kwargs)#

Bases: Module

Neural Sheaf Diffusion Encoder that can be used with the training framework.

This encoder learns representations using sheaf structure with node and edge stalks communicating via transport maps / restriction maps. Supports three types of sheaf structures: diagonal, bundle, and general.

Parameters:

input_dimint: Dimension of input node features.
hidden_dimint: Dimension of hidden layers. Must be divisible by d.
num_layersint, optional: Number of sheaf diffusion layers. Default is 2.
sheaf_typestr, optional: Type of sheaf structure. Options are ‘diag’, ‘bundle’, or ‘general’. Default is ‘diag’.
dint, optional: Dimension of the stalk space. For ‘diag’, d >= 1. For ‘bundle’ and ‘general’, d > 1. Default is 2.
dropoutfloat, optional: Dropout rate for hidden layers. Default is 0.1.
input_dropoutfloat, optional: Dropout rate for input layer. Default is 0.1.
devicestr, optional: Device to run the model on (‘cpu’ or ‘cuda’). Default is ‘cpu’.
sheaf_actstr, optional: Activation function for sheaf learning. Options are ‘tanh’, ‘elu’, ‘id’. Default is ‘tanh’.
orthstr, optional: Orthogonalization method for bundle sheaf type. Options are ‘cayley’ or ‘matrix_exp’. Default is ‘cayley’.
**kwargsdict: Additional keyword arguments (not used).

__init__(input_dim, hidden_dim, num_layers=2, sheaf_type='diag', d=2, dropout=0.1, input_dropout=0.1, device='cpu', sheaf_act='tanh', orth='cayley', **kwargs)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x, edge_index, edge_attr=None, edge_weight=None, batch=None, **kwargs)#

Forward pass of Neural Sheaf Diffusion encoder.

Parameters:

xtorch.Tensor: Node feature matrix of shape [num_nodes, input_dim].
edge_indextorch.Tensor: Edge indices of shape [2, num_edges]. Will be automatically converted to undirected.
edge_attrtorch.Tensor, optional: Edge feature matrix (not used). Default is None.
edge_weighttorch.Tensor, optional: Edge weights (not used). Default is None.
batchtorch.Tensor, optional: Batch vector assigning each node to a specific graph (not used). Default is None.
**kwargsdict: Additional arguments (not used).

Returns:

torch.Tensor: Output node feature matrix of shape [num_nodes, hidden_dim].

get_sheaf_model()#

Get the underlying sheaf model.

Returns:

SheafDiffusion: The sheaf diffusion model instance.

to_undirected(edge_index, edge_attr='???', num_nodes=None, reduce='add')#

Converts the graph given by edge_index to an undirected graph such that \((j,i) \in \mathcal{E}\) for every edge \((i,j) \in \mathcal{E}\).

Parameters:

edge_index (LongTensor) – The edge indices.
edge_attr (Tensor or List[Tensor], optional) – Edge weights or multi- dimensional edge features. If given as a list, will remove duplicates for all its entries. (default: None)
num_nodes (int, optional) – The number of nodes, i.e. max(edge_index) + 1. (default: None)
reduce (str, optional) – The reduce operation to use for merging edge features ("add", "mean", "min", "max", "mul"). (default: "add")

Return type:

LongTensor if edge_attr is not passed, else (LongTensor, Optional[Tensor] or List[Tensor]])

Warning

From :pyg:`PyG >= 2.3.0` onwards, this function will always return a tuple whenever edge_attr is passed as an argument (even in case it is set to None).

Examples

>>> edge_index = torch.tensor([[0, 1, 1],
...                            [1, 0, 2]])
>>> to_undirected(edge_index)
tensor([[0, 1, 1, 2],
        [1, 0, 2, 1]])

>>> edge_index = torch.tensor([[0, 1, 1],
...                            [1, 0, 2]])
>>> edge_weight = torch.tensor([1., 1., 1.])
>>> to_undirected(edge_index, edge_weight)
(tensor([[0, 1, 1, 2],
        [1, 0, 2, 1]]),
tensor([2., 2., 1., 1.]))

>>> # Use 'mean' operation to merge edge features
>>>  to_undirected(edge_index, edge_weight, reduce='mean')
(tensor([[0, 1, 1, 2],
        [1, 0, 2, 1]]),
tensor([1., 1., 1., 1.]))