Affine combination output = input * weights + bias (the dense matmul building block). More...

#include <operators.h>

Inheritance diagram for opennn::CombinationOp:

Public Member Functions
void	set (Index new_input_features, Index new_output_features, Type new_weight_type=Type::FP32)
	Configures input/output dimensions and the weight storage dtype.

vector< TensorSpec >	parameter_specs () const override
	Returns the tensor specs of trainable parameters owned by this operator.

void	link_parameters (span< const TensorView > views) override
	Binds parameter views provided by the hosting layer.

void	link_gradients (span< const TensorView > views) override
	Binds gradient views provided by the hosting layer.

void	set_parameters_random () override
	Initializes parameters with random values.

void	set_parameters_glorot () override
	Initializes parameters using Glorot (Xavier) initialization.

void	forward_propagate (ForwardPropagation &fp, size_t layer, bool is_training) noexcept override
	Runs the operator's forward computation.

void	back_propagate (ForwardPropagation &fp, BackPropagation &bp, size_t layer) const noexcept override
	Runs the operator's backward computation, accumulating into gradient/delta buffers.

void	apply (const TensorView &input, TensorView &output, cublasLtEpilogue_t epilogue=CUBLASLT_EPILOGUE_BIAS)
	Computes output = input * weights + bias with an optional fused epilogue (ReLU, bias, etc.).

void	apply_delta (const TensorView &output_delta, const TensorView &input, TensorView &input_delta, bool accumulate_input_delta=false) const
	Computes input_delta from output_delta and updates weight/bias gradients.

Public Member Functions inherited from opennn::Operator
virtual	~Operator ()=default

virtual vector< TensorSpec >	state_specs () const
	Returns the tensor specs of persistent state owned by this operator.

virtual void	link_states (span< const TensorView >)
	Binds state views provided by the hosting layer.

virtual void	to_JSON (JsonWriter &) const
	Serializes the operator configuration to a JSON writer.

virtual void	from_JSON (const Json *)
	Restores the operator configuration from a JSON node.

virtual void	load_state_from_JSON (const Json *)
	Restores persistent state (e.g. running statistics) from a JSON node.

virtual void	destroy_cuda ()
	Releases CUDA resources owned by the operator; called from destructors.

TensorView &	get_input (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept

vector< TensorView > &	get_inputs (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept

TensorView &	get_output (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept

TensorView &	get_output_delta (BackPropagation &bp, size_t layer, size_t i=0) const noexcept

TensorView &	get_input_delta (BackPropagation &bp, size_t layer, size_t i=0) const noexcept

Public Attributes
Index	input_features = 0

Index	output_features = 0

Type	weight_type = Type::FP32

TensorView	weights

TensorView	bias

TensorView	weight_gradient

TensorView	bias_gradient

Public Attributes inherited from opennn::Operator
vector< size_t >	input_slots = {0}

vector< size_t >	output_slots = {1}

vector< size_t >	input_delta_slots = {1}

vector< size_t >	output_delta_slots = {0}

Detailed Description

Affine combination output = input * weights + bias (the dense matmul building block).

Member Function Documentation

◆ apply()

void opennn::CombinationOp::apply	(	const TensorView &	input,
		TensorView &	output,
		cublasLtEpilogue_t	epilogue = CUBLASLT_EPILOGUE_BIAS )

Computes output = input * weights + bias with an optional fused epilogue (ReLU, bias, etc.).

◆ apply_delta()

void opennn::CombinationOp::apply_delta	(	const TensorView &	output_delta,
		const TensorView &	input,
		TensorView &	input_delta,
		bool	accumulate_input_delta = false ) const

Computes input_delta from output_delta and updates weight/bias gradients.

Parameters

output_delta	Gradient w.r.t. the operator's output.
input	Forward-pass input (needed for the weight gradient).
input_delta	Output: gradient w.r.t. the operator's input.
accumulate_input_delta	If true, accumulates into `input_delta` instead of overwriting.

◆ back_propagate()

void opennn::CombinationOp::back_propagate	(	ForwardPropagation &	fp,
		BackPropagation &	bp,
		size_t	layer ) const

overridevirtualnoexcept

Runs the operator's backward computation, accumulating into gradient/delta buffers.

Parameters

fp	Forward propagation workspace (read-only).
bp	Back propagation workspace receiving gradients and deltas.
layer	Index of the hosting layer in the workspace.

Reimplemented from opennn::Operator.

◆ forward_propagate()

void opennn::CombinationOp::forward_propagate	(	ForwardPropagation &	fp,
		size_t	layer,
		bool	is_training )

overridevirtualnoexcept

Runs the operator's forward computation.

Parameters

fp	Forward propagation workspace.
layer	Index of the hosting layer in the workspace.
is_training	If true, enables training-only behavior (e.g. dropout sampling).

Reimplemented from opennn::Operator.

◆ link_gradients()

void opennn::CombinationOp::link_gradients ( span< const TensorView > )

overridevirtual

Binds gradient views provided by the hosting layer.

Reimplemented from opennn::Operator.

◆ link_parameters()

void opennn::CombinationOp::link_parameters ( span< const TensorView > )

overridevirtual

Binds parameter views provided by the hosting layer.

Reimplemented from opennn::Operator.

◆ parameter_specs()

vector< TensorSpec > opennn::CombinationOp::parameter_specs ( ) const

overridevirtual

Returns the tensor specs of trainable parameters owned by this operator.

Reimplemented from opennn::Operator.

◆ set()

void opennn::CombinationOp::set	(	Index	new_input_features,
		Index	new_output_features,
		Type	new_weight_type = Type::FP32 )

Configures input/output dimensions and the weight storage dtype.

Parameters

new_input_features	Number of input features.
new_output_features	Number of output features.
new_weight_type	Storage dtype of the weight matrix.

◆ set_parameters_glorot()

void opennn::CombinationOp::set_parameters_glorot ( )

overridevirtual

Initializes parameters using Glorot (Xavier) initialization.

Reimplemented from opennn::Operator.

◆ set_parameters_random()

void opennn::CombinationOp::set_parameters_random ( )

overridevirtual

Initializes parameters with random values.

Reimplemented from opennn::Operator.

Member Data Documentation

◆ bias

TensorView opennn::CombinationOp::bias

◆ bias_gradient

TensorView opennn::CombinationOp::bias_gradient

◆ input_features

Index opennn::CombinationOp::input_features = 0

◆ output_features

Index opennn::CombinationOp::output_features = 0

◆ weight_gradient

TensorView opennn::CombinationOp::weight_gradient

◆ weight_type

Type opennn::CombinationOp::weight_type = Type::FP32

◆ weights

TensorView opennn::CombinationOp::weights

Public Member Functions

Public Attributes

Detailed Description

Member Function Documentation

◆ apply()

◆ apply_delta()

◆ back_propagate()

◆ forward_propagate()

◆ link_gradients()

◆ link_parameters()

◆ parameter_specs()

◆ set()

◆ set_parameters_glorot()

◆ set_parameters_random()

Member Data Documentation

◆ bias

◆ bias_gradient

◆ input_features

◆ output_features

◆ weight_gradient

◆ weight_type

◆ weights