Device-templated Layer Normalization component. More...

Inheritance diagram for Mila::Dnn::LayerNorm< TDeviceType, TPrecision >:

Public Types
using	ComponentBase = Component<TDeviceType, TPrecision>
using	MR = typename DeviceTypeTraits<TDeviceType>::memory_resource
using	TensorType = Tensor<TPrecision, MR>

Public Member Functions
	LayerNorm (const std::string &name, const LayerNormConfig &config, std::optional< DeviceId > device_id=std::nullopt)
	Construct LayerNorm with optional ExecutionContext ownership.
TensorType &	backward (const TensorType &input, const TensorType &output_grad)
	Run backward pass and return a reference to the component-owned input-gradient tensor.
TensorType &	forward (const TensorType &input)
	Run forward pass and return a reference to the component-owned output tensor.
DeviceId	getDeviceId () const override
	Get the compute device id associated with this component.
std::vector< ITensor * >	getGradients () const override
	Return non-owning pointers to parameter gradient tensors.
MemoryStats	getMemoryStats () const override
	Return the current memory allocation breakdown for this component.
std::vector< std::string >	getParameterNames () const override
	List all available parameter names for this component.
std::vector< ITensor * >	getParameters () const override
	Return non-owning pointers to parameter tensors.
const ComponentType	getType () const override
	Get the component type identifier.
void	loadParameter (const std::string &name, const ITensorBlob &blob) override
	Load a parameter from serialized tensor data.
dim_t	parameterCount () const override
	Return number of trainable parameters.
void	save_ (ModelArchive &archive, SerializationMode mode) const override
void	saveFlatTensors (Serialization::SafeTensorsWriter &writer, const std::string &prefix, Serialization::TensorSavePass pass) const override
	Drive this component's tensors through one pass of a flat safetensors save.
void	synchronize () override
std::string	toString () const override
	Produce a short, human-readable description of the component.
void	zeroGradients () override
	Clear all model-owned gradients for this component.
Public Member Functions inherited from Mila::Dnn::Component< TDeviceType, TPrecision >
	Component (const std::string &name)
	Construct component with required name identifier.
virtual void	build (const BuildContext &context) final
	Build the component with the provided BuildContext (canonical overload).
const std::string	getName () const
	Get the component's name identifier.
virtual MemoryStats	getRequiredMemory (const BuildContext &context) const
	Report what build( context ) would allocate, without allocating it.
TrainingMode	getTrainingMode () const noexcept
	The current runtime behavioral mode of this Component.
virtual bool	isBuilt () const final
	Returns true if build() has completed successfully.
virtual void	load_ (ModelArchive &archive, SerializationMode mode)
	Restore this component's parameters from its archive scope.
virtual void	requireSerializableParameters () const
	Verify this component can serialize whatever parameters it owns.
void	setTrainingMode (TrainingMode mode)
	Set the runtime behavioral mode for this Component.

Protected Member Functions
void	onBuilding (const BuildContext &context) override
	Hook invoked during build() to initialize component with input shape.
void	onExecutionContextSet () override
	Hook invoked after ExecutionContext is set.
void	onTrainingModeChanging (TrainingMode training_mode) override
	Hook invoked when training mode is about to change.
Protected Member Functions inherited from Mila::Dnn::Component< TDeviceType, TPrecision >
IExecutionContext *	getExecutionContext () const
	Get the shared execution context.
bool	hasExecutionContext () const noexcept
	Check if execution context has been set.
template<TensorDataType TParameterPrecision, typename TMemoryResource>
void	loadParameterFromBlob (const std::string &param_name, const Serialization::ITensorBlob &blob, Tensor< TParameterPrecision, TMemoryResource > &target, const shape_t &expected_shape)
	Load a tensor blob into a parameter tensor with validation.
template<TensorDataType TParameterPrecision, typename TMemoryResource>
void	saveParameterToArchive (ModelArchive &archive, const std::string &parameter_name, const Tensor< TParameterPrecision, TMemoryResource > &parameter) const
	Write one parameter tensor into the archive under "tensors/<name>".
template<TensorDataType TParameterPrecision, typename TMemoryResource>
void	saveParameterToWriter (Serialization::SafeTensorsWriter &writer, const std::string &flat_name, const Tensor< TParameterPrecision, TMemoryResource > &parameter, Serialization::TensorSavePass pass) const
	Drive one parameter through one pass of a flat safetensors save.
void	setExecutionContext (IExecutionContext *context)
	Set the execution context for this component.

Additional Inherited Members
Static Public Member Functions inherited from Mila::Dnn::Component< TDeviceType, TPrecision >
static constexpr DeviceType	getDeviceType ()
	Compile-time device type for this component instance.
static constexpr TensorDataType	getPrecision () noexcept
	Compile-time tensor precision for this component instance.
Protected Types inherited from Mila::Dnn::Component< TDeviceType, TPrecision >
using	HostStagingMemoryResource
	Host memory a device-resident parameter stages through.
Protected Attributes inherited from Mila::Dnn::Component< TDeviceType, TPrecision >
BuildContext	build_context_ { shape_t{ 1 }, RuntimeMode::Training }
	The BuildContext stored at build time.

Detailed Description

template<DeviceType TDeviceType, TensorDataType TPrecision>
requires PrecisionSupportedOnDevice<TPrecision, TDeviceType>
class Mila::Dnn::LayerNorm< TDeviceType, TPrecision >

Device-templated Layer Normalization component.

Provides forward and backward APIs that operate on concrete Tensor types. Delegates heavy compute to a UnaryOperation backend. Parameters (weight/bias) and parameter gradients are owned by the component.

Constructor & Destructor Documentation

◆ LayerNorm()

template<DeviceType TDeviceType, TensorDataType TPrecision>

Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::LayerNorm	(	const std::string &	name,
		const LayerNormConfig &	config,
		std::optional< DeviceId >	device_id = std::nullopt )

inlineexplicit

Construct LayerNorm with optional ExecutionContext ownership.

Parameters

name	Component name (used for tensor names).
config	LayerNorm configuration (normalized_shape, axis, epsilon, bias).
device_id	If provided, component creates and owns an ExecutionContext bound to this device; otherwise a parent must supply one before building.

Exceptions

std::invalid_argument if provided device_id type does not match template.

Member Function Documentation

◆ backward()

template<DeviceType TDeviceType, TensorDataType TPrecision>

TensorType & Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::backward	(	const TensorType &	input,
		const TensorType &	output_grad )

inline

Run backward pass and return a reference to the component-owned input-gradient tensor.

The returned reference refers to a Tensor owned by this component. The backend operation_->backward will write/accumulate into the provided input-gradient tensor.

Preconditions:

Component must be built and in training mode.
Backend operation must be initialized.
Component-owned input-gradient buffer must be allocated (done during build or on training start).

Parameters

input	Original forward input tensor (device-bound).
output_grad	Gradient with respect to the component output (device-bound).

Returns: Reference to the component-owned input-gradient Tensor.

Exceptions

std::runtime_error on precondition violations.

◆ forward()

template<DeviceType TDeviceType, TensorDataType TPrecision>

TensorType & Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::forward ( const TensorType & input )

inline

Run forward pass and return a reference to the component-owned output tensor.

The returned reference refers to a Tensor owned by this component. The backend operation_->forward will write into the provided output tensor.

Preconditions:

Component must be built.
Backend operation must be initialized.
Component-owned output buffer must be allocated (done during build).

Parameters

input Input Tensor bound to the component device.

Returns: Reference to the component-owned output Tensor.

Exceptions

std::runtime_error on precondition violations.

◆ getDeviceId()

template<DeviceType TDeviceType, TensorDataType TPrecision>

DeviceId Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::getDeviceId ( ) const

inlineoverridevirtual

Get the compute device id associated with this component.

Must return the device on which parameters and operations execute.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ getGradients()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::vector< ITensor * > Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::getGradients ( ) const

inlineoverridevirtual

Return non-owning pointers to parameter gradient tensors.

Gradient buffers are allocated only when the component is built in training mode, so a component built for inference returns an empty vector. Stateless components return empty in either mode. This is the accessor counterpart to getParameters() and does not throw on mode.

Returns: Vector of gradient pointers; empty when built for inference.

Exceptions

std::runtime_error if called before the component has been built.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ getMemoryStats()

template<DeviceType TDeviceType, TensorDataType TPrecision>

MemoryStats Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::getMemoryStats ( ) const

inlineoverridevirtual

Return the current memory allocation breakdown for this component.

Reflects allocations at the moment of the call. The returned stats naturally track the component lifecycle:

After construction – nothing; construction allocates none After build( Inference ) – parameters + T=1 state buffers After build( Training ) – parameters + T=full state buffers After setTrainingMode( Train ) – parameters + state + gradients

For CompositeComponent and Network, the returned stats are the recursive aggregate of all child components.

May be called at any time – no lifecycle preconditions.

Returns: MemoryStats reflecting current allocations.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ getParameterNames()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::vector< std::string > Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::getParameterNames ( ) const

inlineoverridevirtual

List all available parameter names for this component.

Returns an empty vector by default. Leaf components with parameters should override to return their canonical parameter name list in the same stable order used by save_() and loadParameter().

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ getParameters()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::vector< ITensor * > Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::getParameters ( ) const

inlineoverridevirtual

Return non-owning pointers to parameter tensors.

The returned tensor pointers remain valid for the lifetime of the component. Order should be canonical (weights before biases).

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ getType()

template<DeviceType TDeviceType, TensorDataType TPrecision>

const ComponentType Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::getType ( ) const

inlineoverridevirtual

Get the component type identifier.

Used for serialization and runtime type identification.

Returns: Component type enum value.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ loadParameter()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::loadParameter	(	const std::string &	name,
		const ITensorBlob &	blob )

inlineoverridevirtual

Load a parameter from serialized tensor data.

Loads raw tensor bytes directly into an existing parameter tensor, handling precision conversion and device upload as needed.

The component validates that the blob's shape matches the parameter's expected shape, then delegates to the backend to perform:

Precision conversion (blob dtype -> parameter dtype)
Device upload (CPU bytes -> target device)

Takes the parameter name used to locate the target tensor, and a blob holding serialized tensor metadata and raw bytes.

Exceptions

std::runtime_error	if component has no parameters to load.
std::runtime_error	if blob shape doesn't match parameter shape.

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ onBuilding()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::onBuilding ( const BuildContext & context )

inlineoverrideprotectedvirtual

Hook invoked during build() to initialize component with input shape.

Validates input shape, allocates parameters if needed, binds parameters to the backend operation, triggers backend build, and allocates the component-owned forward output and input-gradient tensors.

Output buffer – allocated at the full input shape.

LayerNorm is a general component with no knowledge of sequence dimensions or inference decode paths. The parent Network or Transformer is responsible for passing the correct input shape via BuildContext:

Training – full sequence shape e.g. [B, T, features] Inference – decode shape e.g. [1, 1, features] for decode path or prefill shape e.g. [1, T_chunk, features] for prefill

In all cases LayerNorm simply allocates at inputShape() – no special casing for inference or sequence dimensions.

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ onExecutionContextSet()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::onExecutionContextSet ( )

inlineoverrideprotectedvirtual

Hook invoked after ExecutionContext is set.

Creates the backend operation and performs any eager parameter allocation if normalized_shape was supplied at construction time.

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ onTrainingModeChanging()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::onTrainingModeChanging ( TrainingMode training_mode )

inlineoverrideprotectedvirtual

Hook invoked when training mode is about to change.

Propagates training state to the backend operation and allocates or clears parameter gradient buffers as appropriate.

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ parameterCount()

template<DeviceType TDeviceType, TensorDataType TPrecision>

dim_t Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::parameterCount ( ) const

inlineoverridevirtual

Return number of trainable parameters.

For leaf components this is the element count of owned parameter tensors. CompositeComponent and Network implementations should return the recursive aggregate across all children.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ save_()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::save_	(	ModelArchive &	archive,
		SerializationMode	mode ) const

inlineoverridevirtual

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ saveFlatTensors()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::saveFlatTensors	(	Serialization::SafeTensorsWriter &	writer,
		const std::string &	prefix,
		Serialization::TensorSavePass	pass ) const

inlineoverridevirtual

Drive this component's tensors through one pass of a flat safetensors save.

This is the path save_() cannot serve. A quantized weight is packed storage plus a scale companion and ModelArchive has no representation for that pairing, so save_() refuses it; the flat container expresses it as sibling tensors and this writes them.

The vocabulary is the flat format's: a fully qualified dotted component path plus a parameter name, exactly what loadParameters() parses on the way back in. Composites contribute nothing themselves – they recurse and extend the prefix – so the emitted set matches what the reader will route back through loadParameter().

Concrete parameter types, not ITensor, on purpose: a type-erased walk would have to ask at runtime whether a tensor's memory is host-accessible, and MemoryResource::is_host_accessible is a compile-time constant. Keeping concrete types here is what avoids widening an exported core type for a serialization concern.

The default refuses rather than writing nothing. A component that owns parameters and does not implement this would otherwise contribute silently to an artifact that loads, runs, and produces garbage – the same failure mode Phase 0 removed from save_().

Parameters

writer	Writer being driven.
prefix	Fully qualified component path, e.g. "tf_layer_0.qkv_proj".
pass	Declare reserves byte ranges; Write streams bytes in declaration order.

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ synchronize()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::synchronize ( )

inlineoverridevirtual

         @brief Convenience accessor -- true if currently in Eval mode.

         Equivalent to getTrainingMode() == TrainingMode::Eval.
         Valid for both RuntimeMode::Inference and RuntimeMode::Training
         built components.

         @return true if in Eval mode.
        &zwj;/

bool isEvalMode() const noexcept { return getTrainingMode() == TrainingMode::Eval; }

    RuntimeMode getRuntimeMode() const noexcept
    {
        return build_context_.getRuntimeMode();
    }

    bool isInferenceMode() const noexcept
    {
        return build_context_.isInferenceMode();
    }

    bool isTrainingMode() const noexcept
    {
        return build_context_.isTrainingMode();
    }

====================================================================

Synchronization

    /**
       @brief Wait for outstanding device work submitted by this component.

       On CPU this may be a no-op. Use to ensure results are visible to
       the host or to measure synchronous timings.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ toString()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::string Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::toString ( ) const

inlineoverridevirtual

Produce a short, human-readable description of the component.

Implementations should keep output concise and avoid throwing.

Implements Mila::Dnn::Component< TDeviceType, TPrecision >.

◆ zeroGradients()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::LayerNorm< TDeviceType, TPrecision >::zeroGradients ( )

inlineoverridevirtual

Clear all model-owned gradients for this component.

Default implementation is a no-op. Composite components should override to recurse to children. Leaf components should override to zero their parameter and activation gradients using device-aware helpers.

Reimplemented from Mila::Dnn::Component< TDeviceType, TPrecision >.

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

Mila/Src/Dnn/Components/Normalization/LayerNorm/LayerNorm.ixx

Public Types

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ LayerNorm()

Member Function Documentation

◆ backward()

◆ forward()

◆ getDeviceId()

◆ getGradients()

◆ getMemoryStats()

◆ getParameterNames()

◆ getParameters()

◆ getType()

◆ loadParameter()

◆ onBuilding()

◆ onExecutionContextSet()

◆ onTrainingModeChanging()

◆ parameterCount()

◆ save_()

◆ saveFlatTensors()

◆ synchronize()

Synchronization

◆ toString()

◆ zeroGradients()