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NonlinearParameter< T > Class Template Reference
The base class for nonlinear parameters. More...
Detailed Description
template<typename T>
class neml2::NonlinearParameter< T >
class neml2::NonlinearParameter< T >
The base class for nonlinear parameters.
The word "nonlinear" refers to the fact that the parameter can change as a function of state or forces. In other words, in the context of updating an implicit model, the value of the parameter can change fron nonlinear iteration to nonlinear iteration, as the guess of the solution keeps updating.
The output of a nonlinear parameter is not a "parameter" in our usual definition as one does not calibrate or optimize the nonlinear parameter in a training loop. However, the definition of the nonlinear parameter itself is oftentimes parameterized on a set of parameters (in the canonical definition). Those parameters can be calibrated or optimized.
- Template Parameters
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T The class is templated on the output tensor type which can be any NEML2 primitive tensor type.
#include <NonlinearParameter.h>
Inheritance diagram for NonlinearParameter< T >:
Public Member Functions | |
| NonlinearParameter (const OptionSet &options) | |
| const Variable< T > & | param () const |
| Get the nonlinear parameter. | |
 Public Member Functions inherited from Model | |
| Model (const OptionSet &options) | |
| Construct a new Model object. | |
| virtual std::vector< Diagnosis > | preflight () const |
| Check for common problems. | |
| virtual bool | is_nonlinear_system () const |
| Whether this model defines one or more nonlinear equations to be solved. | |
| virtual void | reinit (TorchShapeRef batch_shape, int deriv_order=0, const torch::Device &device=torch::kCPU, const torch::Dtype &dtype=NEML2_DTYPE) |
| Allocate storage and setup views for all the variables of this model and recursively all of the sub-models. | |
| virtual void | reinit (const BatchTensor &tensor, int deriv_order) |
| Allocate storage and setup views for all the variables of this model and recursively all of the sub-models. See the other overload for detailed description. | |
| bool | requires_grad () const |
| Whether derivative has been requested for this model. | |
| bool | requires_2nd_grad () const |
| Whether 2nd derivative has been requested for this model. | |
| TorchSize | batch_dim () const |
| This model's batch dim. | |
| TorchShapeRef | batch_sizes () const |
| This model's batch shape. | |
| const torch::TensorOptions & | options () const |
| This model's tensor options. | |
| const std::vector< Model * > & | registered_models () const |
| The models that may be used during the evaluation of this model. | |
| Model * | registered_model (const std::string &name) const |
| Get a registered model by its name. | |
| virtual const std::set< VariableName > | consumed_items () const override |
| The variables that this model depends on. | |
| virtual const std::set< VariableName > | provided_items () const override |
| The variables that this model defines as part of its output. | |
| virtual void | check_AD_limitation () const |
| void | input_requires_grad_ (bool req=true) |
| Set requires_grad for the input variables. | |
| bool | using_AD_1st_derivative () const |
| Whether this model is using AD to get 1st derivatives. | |
| bool | using_AD_2nd_derivative () const |
| Whether this model is using AD to get 2nd derivatives. | |
| void | use_AD_derivatives (bool first=true, bool second=true) |
| Tell this model to use AD to get derivatives. | |
| virtual void | set_input (const LabeledVector &in) |
Set in to be the input of this model. | |
| virtual LabeledVector | get_output () |
| virtual LabeledMatrix | get_doutput_dinput () |
| virtual LabeledTensor3D | get_d2output_dinput2 () |
| virtual LabeledVector | value (const LabeledVector &in) |
| Convenient shortcut to construct and return the model value. | |
| virtual std::tuple< LabeledVector, LabeledMatrix > | value_and_dvalue (const LabeledVector &in) |
| Convenient shortcut to construct and return the model value and its derivative. | |
| virtual std::tuple< LabeledVector, LabeledMatrix, LabeledTensor3D > | value_and_dvalue_and_d2value (const LabeledVector &in) |
| Convenient shortcut to construct and return the model's value, first and second derivative. | |
| virtual void | value () |
| virtual void | value_and_dvalue () |
| virtual void | value_and_dvalue_and_d2value () |
| Public Member Functions inherited from Data | |
| Data (const OptionSet &options) | |
| Construct a new Data object. | |
| const std::vector< Data * > & | registered_data () const |
| All the registered data objects. | |
| Public Member Functions inherited from NEML2Object | |
| NEML2Object (const OptionSet &options) | |
| Construct a new NEML2Object object. | |
| virtual | ~NEML2Object ()=default |
| const OptionSet & | input_options () const |
| const std::string & | name () const |
| A readonly reference to the object's name. | |
| const std::string & | type () const |
| A readonly reference to the object's type. | |
| const std::string & | path () const |
| A readonly reference to the object's path. | |
| const std::string & | doc () const |
| A readonly reference to the object's docstring. | |
| template<typename T = NEML2Object> | |
| const T * | host () const |
| Get a readonly pointer to the host. | |
| template<typename T = NEML2Object> | |
| T * | host () |
| Get a writable pointer to the host. | |
| Public Member Functions inherited from BufferStore | |
| BufferStore (const OptionSet &options, NEML2Object *object) | |
| const Storage< std::string, TensorValueBase > & | named_buffers () const |
| Storage< std::string, TensorValueBase > & | named_buffers () |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<BatchTensorBase<T>, T>>> | |
| T & | get_buffer (const std::string &name) |
| }@ | |
| Public Member Functions inherited from ParameterStore | |
| ParameterStore (const OptionSet &options, NEML2Object *object) | |
| const Storage< std::string, TensorValueBase > & | named_parameters () const |
| Storage< std::string, TensorValueBase > & | named_parameters () |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<BatchTensorBase<T>, T>>> | |
| T & | get_parameter (const std::string &name) |
| }@ | |
| bool | has_nl_param () const |
| Whether this parameter store has any nonlinear parameter. | |
| const std::map< std::string, const VariableBase * > & | nl_params () const |
| Get all nonlinear parameters. | |
| const VariableBase * | nl_param (const std::string &) const |
| Query the existence of a nonlinear parameter. | |
| Public Member Functions inherited from VariableStore | |
| VariableStore (const OptionSet &options, NEML2Object *object) | |
| LabeledAxis & | declare_axis (const std::string &name) |
| virtual void | setup_layout () |
| Setup the layouts of all the registered axes. | |
| VariableBase * | input_view (const VariableName &) |
| Get the view of an input variable. | |
| VariableBase * | output_view (const VariableName &) |
| Get the view of an output variable. | |
| template<typename T = BatchTensor> | |
| Variable< T > & | get_input_variable (const VariableName &name) |
| template<typename T = BatchTensor> | |
| const Variable< T > & | get_input_variable (const VariableName &name) const |
| template<typename T = BatchTensor> | |
| const Variable< T > & | get_output_variable (const VariableName &name) |
| template<typename T = BatchTensor> | |
| const Variable< T > & | get_output_variable (const VariableName &name) const |
| LabeledAxis & | input_axis () |
| const LabeledAxis & | input_axis () const |
| LabeledAxis & | output_axis () |
| const LabeledAxis & | output_axis () const |
| Storage< VariableName, VariableBase > & | input_views () |
| const Storage< VariableName, VariableBase > & | input_views () const |
| Storage< VariableName, VariableBase > & | output_views () |
| const Storage< VariableName, VariableBase > & | output_views () const |
| LabeledVector & | input_storage () |
| const LabeledVector & | input_storage () const |
| LabeledVector & | output_storage () |
| const LabeledVector & | output_storage () const |
| LabeledMatrix & | derivative_storage () |
| const LabeledMatrix & | derivative_storage () const |
| LabeledTensor3D & | second_derivative_storage () |
| const LabeledTensor3D & | second_derivative_storage () const |
| Public Member Functions inherited from NonlinearSystem | |
| NonlinearSystem (const OptionSet &options) | |
| virtual void | init_scaling (const bool verbose=false) |
| Compute algebraic Jacobian-based automatic scaling following https://cs.stanford.edu/people/paulliu/files/cs517-project.pdf. | |
| BatchTensor | scale_residual (const BatchTensor &r) const |
| Apply scaling to the residual. | |
| BatchTensor | scale_Jacobian (const BatchTensor &J) const |
| Apply scaling to the Jacobian. | |
| BatchTensor | scale_direction (const BatchTensor &p) const |
| Remove scaling from the search direction, i.e. \( J^{-1} r \). | |
| virtual BatchTensor | solution () const |
| Get the solution vector. | |
| BatchTensor | residual (const BatchTensor &x) |
| Convenient shortcut to set the current solution, assemble and return the system residual. | |
| void | residual () |
| Convenient shortcut to assemble and return the system residual. | |
| BatchTensor | Jacobian (const BatchTensor &x) |
| Convenient shortcut to set the current solution, assemble and return the system Jacobian. | |
| void | Jacobian () |
| Convenient shortcut to assemble and return the system Jacobian. | |
| std::tuple< BatchTensor, BatchTensor > | residual_and_Jacobian (const BatchTensor &x) |
| Convenient shortcut to set the current solution, assemble and return the system residual and Jacobian. | |
| void | residual_and_Jacobian () |
| Convenient shortcut to assemble and return the system residual and Jacobian. | |
| const BatchTensor & | residual_view () const |
| const BatchTensor & | Jacobian_view () const |
| BatchTensor | residual_norm () const |
| The residual norm. | |
Static Public Member Functions | |
| static OptionSet | expected_options () |
| Static Public Member Functions inherited from Model | |
| static OptionSet | expected_options () |
| Static Public Member Functions inherited from Data | |
| static OptionSet | expected_options () |
| Static Public Member Functions inherited from NEML2Object | |
| static OptionSet | expected_options () |
| Static Public Member Functions inherited from NonlinearSystem | |
| static OptionSet | expected_options () |
| static void | disable_automatic_scaling (OptionSet &options) |
| static void | enable_automatic_scaling (OptionSet &options) |
Protected Attributes | |
| Variable< T > & | _p |
| The nonlinear parameter. | |
| Protected Attributes inherited from Model | |
| std::vector< Model * > | _registered_models |
| Models this model may use during its evaluation. | |
| bool | _AD_1st_deriv |
| Whether to use AD to compute 1st derivatives. | |
| bool | _AD_2nd_deriv |
| Whether to use AD to compute 2nd derivatives. | |
| Protected Attributes inherited from Data | |
| std::vector< Data * > | _registered_data |
| Registered Data objects. | |
| Protected Attributes inherited from NonlinearSystem | |
| TorchSize | _ndof |
| Number of degrees of freedom. | |
| BatchTensor | _solution |
| View for the solution of this nonlinear system. | |
| BatchTensor | _residual |
| View for the residual of this nonlinear system. | |
| BatchTensor | _Jacobian |
| View for the Jacobian of this nonlinear system. | |
| BatchTensor | _scaled_residual |
| BatchTensor | _scaled_Jacobian |
| const bool | _autoscale |
| If true, do automatic scaling. | |
| const Real | _autoscale_tol |
| Tolerance for convergence check of the iterative automatic scaling algorithm. | |
| const unsigned int | _autoscale_miter |
| Maximum number of iterations allowed for the iterative automatic scaling algorithm. | |
| bool | _scaling_matrices_initialized |
| Flag to indicate whether scaling matrices have been computed. | |
| BatchTensor | _row_scaling |
| Row scaling "matrix" – since it's a batched diagonal matrix, we are only storing its diagonals. | |
| BatchTensor | _col_scaling |
| Column scaling "matrix" – since it's a batched diagonal matrix, we are only storing its diagonals. | |
Additional Inherited Members | |
| Public Types inherited from Model | |
| enum | Stage { SOLVING , UPDATING } |
| Static Public Attributes inherited from Model | |
| static enum neml2::Model::Stage | stage = UPDATING |
| Protected Member Functions inherited from Model | |
| virtual void | setup () override |
| Setup this model. | |
| virtual void | allocate_variables (int deriv_order, bool options_changed) |
| Call VariableStore::allocate_variables recursively on all submodels. | |
| virtual void | setup_input_views () override |
| Call VariableStore::setup_input_views recursively on all submodels. | |
| virtual void | setup_submodel_input_views () |
| virtual void | setup_output_views () override |
| Call VariableStore::setup_output_views recursively on all submodels. | |
| virtual void | setup_submodel_output_views () |
| virtual void | reinit_input_views () override |
| Call VariableStore::reinit_input_views recursively on all submodels. | |
| virtual void | reinit_output_views (bool out, bool dout_din=true, bool d2out_din2=true) override |
| Call VariableStore::reinit_output_views recursively on all submodels. | |
| virtual void | detach_and_zero (bool out, bool dout_din=true, bool d2out_din2=true) override |
| Call VariableStore::detach_and_zero recursively on all submodels. | |
| virtual void | set_solution (const BatchTensor &x) override |
Set x as the current solution of the nonlinear system. | |
| virtual void | set_value (bool out, bool dout_din, bool d2out_din2)=0 |
| The map between input -> output, and optionally its derivatives. | |
| virtual void | cache (TorchShapeRef batch_shape) override |
| Cache the variable's batch shape. | |
| virtual void | cache (const torch::TensorOptions &options) |
| Cache tensor options. | |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<Model, T>>> | |
| T & | register_model (const std::string &name, int extra_deriv_order=0, bool nonlinear=false, bool merge_input=true) |
| Register a model that the current model may use during its evaluation. | |
| virtual void | assemble (bool residual, bool Jacobian) override |
| Compute the residual and Jacobian. | |
| virtual void | allocate_variables (TorchShapeRef batch_shape, const torch::TensorOptions &options, bool in, bool out, bool dout_din, bool d2out_din2) |
| Allocate variable storages given the batch shape and tensor options. | |
| virtual void | cache (TorchShapeRef batch_shape) |
| Cache the variable's batch shape. | |
| Protected Member Functions inherited from Data | |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<Data, T>>> | |
| T & | register_data (const std::string &name) |
| Protected Member Functions inherited from BufferStore | |
| virtual void | send_buffers_to (const torch::TensorOptions &options) |
Send all buffers to options. | |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<BatchTensorBase<T>, T>>> | |
| const T & | declare_buffer (const std::string &name, const T &rawval) |
| Declare a buffer. | |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<BatchTensorBase<T>, T>>> | |
| const T & | declare_buffer (const std::string &name, const std::string &input_option_name) |
| Declare a buffer. | |
| Protected Member Functions inherited from ParameterStore | |
| virtual void | send_parameters_to (const torch::TensorOptions &options) |
| Send parameters to options. | |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<BatchTensorBase<T>, T>>> | |
| const T & | declare_parameter (const std::string &name, const T &rawval) |
| Declare a parameter. | |
| template<typename T , typename = typename std::enable_if_t<std::is_base_of_v<BatchTensorBase<T>, T>>> | |
| const T & | declare_parameter (const std::string &name, const std::string &input_option_name) |
| Declare a parameter. | |
| Protected Member Functions inherited from VariableStore | |
| template<typename T , typename... S> | |
| const Variable< T > & | declare_input_variable (S &&... name) |
| Declare an input variable. | |
| template<typename... S> | |
| const Variable< BatchTensor > & | declare_input_variable (TorchSize sz, S &&... name) |
| Declare an input variable (with unknown base shape at compile time) | |
| template<typename T , typename... S> | |
| const Variable< BatchTensor > & | declare_input_variable_list (TorchSize list_size, S &&... name) |
| Declare an input variable that is a list of tensors of fixed size. | |
| template<typename T , typename... S> | |
| Variable< T > & | declare_output_variable (S &&... name) |
| Declare an output variable. | |
| template<typename... S> | |
| Variable< BatchTensor > & | declare_output_variable (TorchSize sz, S &&... name) |
| Declare an input variable (with unknown base shape at compile time) | |
| template<typename T , typename... S> | |
| Variable< BatchTensor > & | declare_output_variable_list (TorchSize list_size, S &&... name) |
| Declare an output variable that is a list of tensors of fixed size. | |
| template<typename T > | |
| VariableName | declare_variable (LabeledAxis &axis, const VariableName &var) const |
| Declare an item recursively on an axis. | |
| VariableName | declare_variable (LabeledAxis &axis, const VariableName &var, TorchSize sz) const |
| Declare an item (with known storage size) recursively on an axis. | |
| VariableName | declare_subaxis (LabeledAxis &axis, const VariableName &subaxis) const |
| Declare a subaxis recursively on an axis. | |
Constructor & Destructor Documentation
◆ NonlinearParameter()
template<typename T >
| NonlinearParameter | ( | const OptionSet & | options | ) |
