Dnn.Modules.Linear Module Reference

Exported Modules
module	Compute.UnaryOperation
module	Compute.DeviceContext
module	Compute.CudaMemoryResource
module	Compute.MemoryResource
module	Compute.DeviceType
module	Dnn.Module
module	Dnn.TensorTraits
module	Compute.OperationRegistry
module	Compute.Precision
module	Dnn.TensorHelpers
module	Dnn.Tensor
module	Serialization.ModelArchive
module	Compute.ComputeDevice
module	Compute.CpuMemoryResource
module	Compute.OperationBase
module	Compute.OperationAttributes

Classes
class	Mila::Dnn::Linear< TDeviceType, TInput, TOutput >
	A class representing a linear transformation module. More...
class	Mila::Dnn::LinearConfig
	Configuration class for Linear module. More...

Typedefs
template<typename TInput = float, typename TOutput = TInput>
using	Mila::Dnn::CpuLinear = Linear< DeviceType::Cpu, TInput, TOutput >
	Type alias for CPU-based linear module with customizable tensor types.
template<typename TInput = float, typename TOutput = TInput>
using	Mila::Dnn::CudaLinear = Linear< DeviceType::Cuda, TInput, TOutput >
	Type alias for CUDA-based linear module with customizable tensor types.
using	ModuleBase = Module< TDeviceType, TInput, TOutput >
	Alias for base module type.
using	MR = std::conditional_t< TDeviceType==DeviceType::Cuda, CudaMemoryResource, CpuMemoryResource >
	Memory resource type used for tensors, selected based on device type.

Functions
	Linear (const std::string &device_name, const LinearConfig &config)
	Constructs a new Linear module with a device name.
	Linear (std::shared_ptr< DeviceContext > device_context, const LinearConfig &config)
	Constructs a new Linear module with a provided device context.
void	backward (const Tensor< TInput, MR > &input, const Tensor< TOutput, MR > &output_grad, Tensor< TInput, MR > &input_grad)
	Performs the backward pass of the Linear operation.
void	createOperation ()
	Creates the appropriate Linear operation based on the current device context.
void	forward (const Tensor< TInput, MR > &input, Tensor< TOutput, MR > &output)
	Performs the forward pass of the Linear operation.
std::optional< std::shared_ptr< Tensor< TOutput, MR > > >	getBias ()
	Retrieves the bias tensor if present.
std::shared_ptr< Tensor< TOutput, MR > >	getWeight ()
	Retrieves the weight tensor for this linear layer.
bool	hasBias () const
	Checks whether the module has a bias tensor.
void	initializeParameterGradients ()
	Initializes gradient tensors for parameters.
void	initializeParameters ()
	Initializes the tensors needed for the Linear operation.
void	load (ModelArchive &archive) override
	Deserializes the module state from a ZIP archive.
size_t	parameterCount () const override
	Gets the number of trainable parameters in this module.
void	save (ModelArchive &zip) const override
	Serializes the module state to a ZIP archive.
std::string	toString () const override
	Converts the module information to a human-readable string.

Variables
std::shared_ptr< Tensor< TOutput, MR > >	bias_ { nullptr }
	The bias tensor added after the matrix multiplication.
LinearConfig	config_
	Configuration for the Linear module.
std::shared_ptr< UnaryOperation< TDeviceType, TInput, TOutput > >	operation_ { nullptr }
	The underlying operation that implements the Linear transformation.
std::vector< std::shared_ptr< Tensor< TOutput, MR > > >	output_state_
	Cache of intermediate tensors needed for backward pass.
std::vector< std::shared_ptr< Tensor< TOutput, MR > > >	parameter_grads_
	Gradients for the parameters of this module.
std::vector< std::shared_ptr< Tensor< TOutput, MR > > >	parameters_
	Collection of trainable parameters for this module.
OperationAttributes	properties_
	Additional configuration options for the linear operation.
std::shared_ptr< Tensor< TOutput, MR > >	weight_ { nullptr }
	The weight tensor for the linear transformation.

Files
file	/home/runner/work/Mila/Mila/Mila/Src/Dnn/Modules/Layers/Linear.ixx
	Implementation of the Linear (fully connected) module for neural networks.
file	/home/runner/work/Mila/Mila/Mila/Src/Dnn/Modules/Layers/LinearConfig.ixx
	Configuration interface for the Linear module in the Mila DNN framework.