tfep.nn.flows.continuous.ContinuousFlow

class tfep.nn.flows.continuous.ContinuousFlow(dynamics, trace_estimator='hutchinson', solver='dopri5', solver_options=None, n_hutchinson_samples=1, adjoint=True, regularization=True, vmap=False, requires_backward=True)

Bases: Module

Continuous normalizing flow.

This implements continuous normalizing flows as proposed in [1]. The trace can be estimated using Hutchinson’s stochastic estimator [2] at the cost of one backpropagation or exactly using D backpropagations, where D is the dimension of each sample.

Optionally, the flow can return also a regularization term as proposed in [3] that can be incorporated into the loss to keep the ODE dynamics used for the flow smoother.

Parameters:

• dynamics (torch.nn.Module) – The neural network taking a time tensor (shape (1)) and the current positions (shape (batch_size, n_particles*3)) and returning the velocity of the dynamics (shape (batch_size, n_particles*3)).

• trace_estimator (‘exact’ or ‘hutchinson’, optional) – Whether the trace (and the Frobenious norm if regularization is True) of the Jacobian is computed exactly with n_particles*3 backpropagation passes or using the hutchinson estimates described in [3] using n_hutchinson_samples backpropagation passes. The random variable is sampled from a normal distribution.

• solver (str, optional) – One of the solvers supported by the torchdiffeq package.

• solver_options (dict, optional) – A dictionary of solver options to pass to torchdiffeq.odeint.

• n_hutchinson_samples (int, optional) – The number of normally-distributed sampled to be drawn for the Hutchinson estimate of the trace. If trace_estimator == 'exact' this is ignored.

• adjoint (bool, optional) – If True the backpropagation is performed using the adjoint method as described in [1]. Otherwise, automatic differentiation is used.

• regularization (bool, optional) – If True, forward() returns also a regularization term, which is the sum of the velocity norm and the Frobenious norm of the Jacobian as described in [3].

• vmap (bool, optional) – If True, the estimato of the trace and Frobenious norm are performed using the experimental vectorization features of torch.autograd.grad (which are currently only in the unreleased development version).

• requires_backward (bool, optional) – If False, the autograd calls used to compute the trace and regularization terms will not create a graph for differentiation. This means that backpropagation (even with the adjoint method) will not take into account the contribution from these terms.

References

[1] Chen RT, Rubanova Y, Bettencourt J, Duvenaud D. Neural ordinary differential

equations. arXiv preprint arXiv:1806.07366. 2018 Jun 19.

[2] Grathwohl W, Chen RT, Bettencourt J, Sutskever I, Duvenaud D. Ffjord:

Free-form continuous dynamics for scalable reversible generative models. arXiv preprint arXiv:1810.01367. 2018 Oct 2.

[3] Finlay C, Jacobsen JH, Nurbekyan L, Oberman A. How to train your neural

ODE: the world of Jacobian and kinetic regularization. In International Conference on Machine Learning 2020 Nov 21 (pp. 3154-3164). PMLR.

__init__(dynamics, trace_estimator='hutchinson', solver='dopri5', solver_options=None, n_hutchinson_samples=1, adjoint=True, regularization=True, vmap=False, requires_backward=True)

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

Methods

__init__(dynamics[, trace_estimator, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
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)	Map the input data.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
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.
inverse(y)
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
training

forward(x)

Map the input data.

Parameters:

x (torch.Tensor) – An input batch of data of shape (batch_size, dimension_in).

Returns:

• y (torch.Tensor) – The mapped data of shape (batch_size, dimension_in).

• trace (torch.Tensor) – The instantaneous log absolute value of the Jacobian of the flow (equal to the trace of the jacobian) as a tensor of shape (batch_size,).

• reg (torch.Tensor, optional) – A regularization term of shape (batch_size,) that can be included in the loss for regularization. This is returned only if self.regularization is True.