CUDA implementation of the Layer Normalization operation for neural networks. More...

Inheritance diagram for Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >:

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Collaboration diagram for Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >:

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Public Types
using	MR = typename CudaDevice::MR

using	UnaryOperationBase = UnaryOperation< DeviceType::Cuda, TInput, TOutput >

Public Types inherited from Mila::Dnn::Compute::UnaryOperation< TDeviceType, TInput, TOutput >
using	MR = std::conditional_t< TDeviceType==DeviceType::Cuda, CudaMemoryResource, HostMemoryResource >
	Memory resource type based on device type.

Public Member Functions
	CudaLayerNormOp (const LayerNormConfig &config)
	Constructs a new CUDA Layer Normalization operation with the default device context.

	CudaLayerNormOp (std::shared_ptr< DeviceContext > context, const LayerNormConfig &config)
	Constructs a new CUDA Layer Normalization operation with a specific device context.

void	backward (const Tensor< TInput, MR > &input, const Tensor< TOutput, MR > &output, const Tensor< TOutput, MR > &output_gradient, const std::vector< std::shared_ptr< Tensor< TInput, MR > > > &parameters, std::vector< std::shared_ptr< Tensor< TInput, MR > > > &parameter_gradients, Tensor< TInput, MR > &input_gradient, const OperationAttributes &properties, const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &output_state) const
	Performs the backward pass of the Layer Normalization operation.

void	forward (const Tensor< TInput, MR > &input, const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &parameters, const OperationAttributes &properties, Tensor< TOutput, MR > &output, std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &output_state) const override
	Performs the forward pass of the Layer Normalization operation on CUDA.

std::string	getName () const override
	Gets the name of this operation.

Public Member Functions inherited from Mila::Dnn::Compute::UnaryOperation< TDeviceType, TInput, TOutput >
	UnaryOperation (OperationType operation_type)
	Constructs a UnaryOperation with the specified operation type.

	UnaryOperation (OperationType operation_type, std::shared_ptr< DeviceContext > context)
	Constructs a UnaryOperation with the specified operation type and device context.

virtual	~UnaryOperation ()=default
	Virtual destructor for proper cleanup of derived classes.

virtual void	backward (const Tensor< TInput, MR > &grad, const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &parameters, std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &output_grads) const
	Executes the backward pass of a unary operation.

virtual void	backward (const Tensor< TInput, MR > &input, const Tensor< TOutput, MR > &output_grad, const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &parameters, std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &parameter_grads, Tensor< TInput, MR > &input_grad, const OperationAttributes &properties, const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &output_state) const
	Executes the comprehensive backward pass of a unary operation.

virtual void	forward (const Tensor< TInput, MR > &input, const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &parameters, const OperationAttributes &properties, Tensor< TOutput, MR > &output, std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &output_state) const =0
	Executes the forward pass of a unary operation.

Public Member Functions inherited from Mila::Dnn::Compute::OperationBase< TDeviceType, TInput1, TInput2, TOutput >
	OperationBase (OperationType operation_type, std::shared_ptr< DeviceContext > context)
	Constructs an OperationBase object with a specific device context and compute precision.

virtual	~OperationBase ()=default
	Virtual destructor for the OperationBase class.

std::shared_ptr< DeviceContext >	getDeviceContext () const
	Gets the device context associated with this operation.

DeviceType	getDeviceType () const
	Gets the device type for this operation.

OperationType	getOperationType () const
	Gets the operation type enumeration value.

Private Attributes
LayerNormConfig	config_
	Configuration for the layer normalization operation.

Detailed Description

template<typename TInput = float, typename TOutput = TInput>
class Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >

CUDA implementation of the Layer Normalization operation for neural networks.

This class provides a CUDA-based implementation of the Layer Normalization operation, which normalizes the activations of a layer for each example in a batch, usually applied before the activation function. Layer normalization helps stabilize the learning process and reduce the training time required to learn the parameters of neural networks.

The normalization is applied across the last dimension (feature dimension) and includes learnable scale (gamma) and shift (beta) parameters. The implementation is optimized for NVIDIA GPUs using CUDA for high-performance computation.

Template Parameters

TPrecision	The data type of the input tensor elements.
TDataType	The data type of the output tensor elements (defaults to the input type).

Member Typedef Documentation

◆ MR

template<typename TInput = float, typename TOutput = TInput>

using Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::MR = typename CudaDevice::MR

◆ UnaryOperationBase

template<typename TInput = float, typename TOutput = TInput>

using Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::UnaryOperationBase = UnaryOperation<DeviceType::Cuda, TInput, TOutput>

Constructor & Destructor Documentation

◆ CudaLayerNormOp() [1/2]

template<typename TInput = float, typename TOutput = TInput>

Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::CudaLayerNormOp ( const LayerNormConfig & config )

inline

Constructs a new CUDA Layer Normalization operation with the default device context.

Initializes the operation with a CUDA device context (defaults to CUDA:0).

◆ CudaLayerNormOp() [2/2]

template<typename TInput = float, typename TOutput = TInput>

Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::CudaLayerNormOp	(	std::shared_ptr< DeviceContext >	context,
		const LayerNormConfig &	config
	)

inline

Constructs a new CUDA Layer Normalization operation with a specific device context.

Parameters

context The device context to use for this operation.

Exceptions

std::runtime_error If the context is not for a CUDA device.

Member Function Documentation

◆ backward()

template<typename TInput = float, typename TOutput = TInput>

void Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::backward	(	const Tensor< TInput, MR > &	input,
		const Tensor< TOutput, MR > &	output,
		const Tensor< TOutput, MR > &	output_gradient,
		const std::vector< std::shared_ptr< Tensor< TInput, MR > > > &	parameters,
		std::vector< std::shared_ptr< Tensor< TInput, MR > > > &	parameter_gradients,
		Tensor< TInput, MR > &	input_gradient,
		const OperationAttributes &	properties,
		const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &	output_state
	)		const

inline

Performs the backward pass of the Layer Normalization operation.

Computes gradients with respect to inputs, weights, and biases.

Parameters

input	Input tensor from the forward pass.
output	Output tensor from the forward pass.
output_gradient	Gradient of the loss with respect to the output.
parameters	Parameters tensor from forward pass [weight, bias].
parameter_gradients	Gradients for parameters [d_weight, d_bias].
input_gradient	Gradient of the loss with respect to the input.
properties	Additional attributes for the operation.
output_state	Cache tensors from forward pass [mean, rstd].

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◆ forward()

template<typename TInput = float, typename TOutput = TInput>

void Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::forward	(	const Tensor< TInput, MR > &	input,
		const std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &	parameters,
		const OperationAttributes &	properties,
		Tensor< TOutput, MR > &	output,
		std::vector< std::shared_ptr< Tensor< TOutput, MR > > > &	output_state
	)		const

inlineoverride

Performs the forward pass of the Layer Normalization operation on CUDA.

Normalizes the input tensor across the feature dimension (last dimension) by:

Computing the mean and standard deviation of each sample
Normalizing the values using these statistics
Applying learnable scale and shift parameters

The computation is performed on the GPU using CUDA kernels for optimal performance.

Parameters

input	Input tensor of shape [B, TDataType, C] to be normalized, where B is batch size, TDataType is sequence length, and C is feature dimension.
parameters	Vector of parameter tensors [weight, bias] where weight (gamma) and bias (beta) are both of shape [C].
properties	Additional attributes for the operation.
output	Output tensor of shape [B, TDataType, C] containing the normalized values.
output_state	Vector containing tensors for intermediate results [mean, rstd], where mean is the mean values and rstd is the reciprocal of standard deviation.

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◆ getName()

template<typename TInput = float, typename TOutput = TInput>

std::string Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::getName ( ) const

inlineoverridevirtual

Gets the name of this operation.

Returns: std::string The name of the operation ("Cuda::LayerNormOp").

Implements Mila::Dnn::Compute::OperationBase< TDeviceType, TInput1, TInput2, TOutput >.

Member Data Documentation

◆ config_

template<typename TInput = float, typename TOutput = TInput>

LayerNormConfig Mila::Dnn::Compute::CudaLayerNormOp< TInput, TOutput >::config_

private

Configuration for the layer normalization operation.

The documentation for this class was generated from the following file:

/home/runner/work/Mila/Mila/Mila/Src/Dnn/Compute/Operations/Cuda/CudaLayerNormOp.ixx

Public Types

Public Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

◆ MR

◆ UnaryOperationBase

Constructor & Destructor Documentation

◆ CudaLayerNormOp() [1/2]

◆ CudaLayerNormOp() [2/2]

Member Function Documentation

◆ backward()

◆ forward()

◆ getName()

Member Data Documentation

◆ config_