Abstract base class for neural network components. More...

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

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Collaboration diagram for Mila::Dnn::Component< TDeviceType, TPrecision >:

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Public Member Functions
	Component (const std::string &name)
	Construct component with required name identifier.
virtual	~Component ()=default
virtual void	build (const BuildContext &context) final
	Build the component with the provided BuildContext (canonical overload).
virtual DeviceId	getDeviceId () const =0
	Get the compute device id associated with this component.
virtual std::vector< ITensor * >	getGradients () const =0
	Return non-owning pointers to parameter gradient tensors.
virtual MemoryStats	getMemoryStats () const =0
	Return the current memory allocation breakdown for this component.
const std::string	getName () const
	Get the component's name identifier.
virtual std::vector< std::string >	getParameterNames () const
	List all available parameter names for this component.
virtual std::vector< ITensor * >	getParameters () const =0
	Return non-owning pointers to parameter tensors.
RuntimeMode	getRuntimeMode () const noexcept
	Convenience accessor — true if currently in Eval mode.
TrainingMode	getTrainingMode () const noexcept
	The current runtime behavioral mode of this Component.
virtual const ComponentType	getType () const =0
	Get the component type identifier.
virtual bool	isBuilt () const final
	Returns true if build() has completed successfully.
bool	isInferenceMode () const noexcept
bool	isTrainingMode () const noexcept
virtual void	loadParameter (const std::string &name, const Serialization::ITensorBlob &blob)
	Load a parameter from serialized tensor data.
virtual size_t	parameterCount () const =0
	Return number of trainable parameters.
virtual void	save_ (ModelArchive &archive, SerializationMode mode) const =0
void	setTrainingMode (TrainingMode mode)
	Set the runtime behavioral mode for this Component.
virtual void	synchronize ()=0
	Wait for outstanding device work submitted by this component.
virtual std::string	toString () const =0
	Produce a short, human-readable description of the component.
virtual void	zeroGradients ()
	Clear all model-owned gradients for this component.

Static Public Member Functions
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 Member Functions
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.
virtual void	onBuilding (const BuildContext &config)
	Hook invoked by build() to allocate component buffers.
virtual void	onExecutionContextSet ()
	Lifecycle hook: Called immediately after ExecutionContext is set.
virtual void	onTrainingModeChanging (TrainingMode mode)
	Hook called before TrainingMode transitions.
void	setExecutionContext (IExecutionContext *context)
	Set the execution context for this component.

Protected Attributes
BuildContext	build_context_ { shape_t{ 1 }, RuntimeMode::Training }
	The BuildContext stored at build time.

Private Member Functions
void	ensureBuilt (const char *method) const
	Throws if the component has not yet been built.

Static Private Member Functions
static bool	isIdentifier (const std::string &s) noexcept
	Checks if a string is a valid component identifier.
static const std::string &	validateName (const std::string &name)
	Validates the component name.

Private Attributes
bool	built_ { false }
IExecutionContext *	exec_context_ { nullptr }
std::string	name_
TrainingMode	training_mode_ { TrainingMode::Normal }
std::mutex	training_mode_mutex_

Friends
template<DeviceType, TensorDataType>
class	CompositeComponent
template<DeviceType, TensorDataType>
class	Network
std::ostream &	operator<< (std::ostream &os, const Component &component)
	Stream output uses toString() to provide a human-readable description of the component.

Detailed Description

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

Abstract base class for neural network components.

Component enforces a single ownership model: all components receive a non-owning pointer to an IExecutionContext that is owned by the parent (Network or test fixture). This ensures consistent resource sharing and eliminates dual constructor patterns.

Ownership model

Components NEVER own ExecutionContext.
Parent (Network/CompositeComponent) owns and provides shared context.
Tests explicitly create context and pass raw pointer to components.

Component build lifecycle

Components progress through a well-defined lifecycle. Each stage has a single responsibility and a designated hook for subclass extension.

Stage 1 — Construction

The component is constructed with its name and component config. No device resources are allocated. No ExecutionContext is required.

auto linear = std::make_unique<Linear>( "fc", config, context );

Stage 2 — Build [ onBuilding() ]

build() is called with a BuildContext carrying the leading shape { B, T, ... } and the ExecutionMode that governs buffer allocation.

Mode	allocationSeqLen()	Gradient buffers
Inference	1	never allocated
Training	leading_shape[1]	allocated on demand

Allocated in onBuilding():

output_ forward output buffer sized by allocationSeqLen()
decode_output_ decode output buffer (decode-capable components)
kv_cache_ KV cache (decode-capable components)
operation_ buffers via operation_->build()

NOT allocated in onBuilding():

gradient buffers (deferred to first setEvaluation( false ))
backward state (deferred to first setEvaluation( false ))

The same BuildContext is cascaded unchanged through CompositeComponent and Network to all child components.

BuildContext config( shape_t{ batch_size, seq_length } );
config.withExecutionMode( ExecutionMode::Training );
model->build( config );

Stage 3 — Evaluation mode [ onEvaluationChanging() ]

Only valid for Training-built components. setEvaluation( false ) triggers gradient buffer allocation on the first call. Subsequent calls zero existing buffers without reallocating. setEvaluation( true ) zeros gradient buffers and disables the backward path.

Allocated on first setEvaluation( false ):

input_grad_ input gradient buffer
weight_grad_ weight gradient buffer
bias_grad_ bias gradient buffer

model->setEvaluation( true );    // suspend backward — eval checkpoint
generateSample( model );
model->setEvaluation( false );   // resume training

Stage 4 — Forward / Decode / Backward

Runtime dimensions are read from the input tensor shape on each call. No shape information is cached from build time beyond what is in build_config_.

Lifecycle invariants

build() requires ExecutionContext to be set setEvaluation() requires build() to have completed setEvaluation() requires ExecutionMode::Training forward() requires build() to have completed backward() requires isTrainingMode() == true decode() requires build() to have completed

Base class provides

build() / lifecycle management with protected onBuilding() hook
execution mode query: isInferenceMode(), isTrainingMode()
evaluation mode transitions with serialized onEvaluationChanging() hook
parameter and gradient access
synchronization and serialization
short human-readable diagnostics via toString()

Template Parameters

TDeviceType	Compile-time device identifier for this component.
TPrecision	Tensor data precision for this component.

Trusted Collaborators

This class grants private access to:

CompositeComponent: Parent components that manage child execution contexts and aggregate parameters from child components.
Network: Top-level graph coordinator that collects parameters and gradients for optimization and serialization.

Examples: /__w/Mila/Mila/Mila/Src/Dnn/Components/Activations/Gelu/Gelu.ixx, and /__w/Mila/Mila/Mila/Src/Dnn/Components/Normalization/Softmax.ixx.

Constructor & Destructor Documentation

◆ Component()

template<DeviceType TDeviceType, TensorDataType TPrecision>

Mila::Dnn::Component< TDeviceType, TPrecision >::Component ( const std::string & name )

inlineexplicitexport

Construct component with required name identifier.

The name is used for identification, logging, and serialization. Names must be valid identifiers: start with a letter, contain only letters, digits, '.', '_', '-', and be 1-128 characters long.

Parameters

name	Component name identifier (mandatory).

Exceptions

std::invalid_argument if name is not a valid identifier.

◆ ~Component()

template<DeviceType TDeviceType, TensorDataType TPrecision>

virtual Mila::Dnn::Component< TDeviceType, TPrecision >::~Component ( )

exportvirtualdefault

Member Function Documentation

◆ build()

template<DeviceType TDeviceType, TensorDataType TPrecision>

virtual void Mila::Dnn::Component< TDeviceType, TPrecision >::build ( const BuildContext & context )

inlinefinalexportvirtual

Build the component with the provided BuildContext (canonical overload).

Validates the config, stores it as build_config_, then invokes the onBuilding() hook for component-specific buffer allocation and initialization.

After onBuilding() returns without throwing, the component is marked built and isBuilt() returns true. If onBuilding() throws, built_ remains false and build() may be retried — but only if the onBuilding() implementation leaves component state coherent on failure.

The stored BuildContext is accessible to derived classes via the protected build_config_ member throughout the component lifetime.

Parameters

config Build-time configuration carrying the leading shape { B, T, ... }, ExecutionMode, and optional micro-batching settings.

Exceptions

std::runtime_error	if the component is already built.
std::runtime_error	if no ExecutionContext has been set.
std::invalid_argument	if config.validate() fails.
Any	exception from onBuilding().

◆ ensureBuilt()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::Component< TDeviceType, TPrecision >::ensureBuilt ( const char * method ) const

inlineexportprivate

Throws if the component has not yet been built.

Used as a precondition guard in public methods that require build() to have completed.

Parameters

method Caller name for the error message.

Exceptions

std::runtime_error if !built_.

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◆ getDeviceId()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

Get the compute device id associated with this component.

Must return the device on which parameters and operations execute.

◆ getDeviceType()

template<DeviceType TDeviceType, TensorDataType TPrecision>

constexpr DeviceType Mila::Dnn::Component< TDeviceType, TPrecision >::getDeviceType ( )

inlinestaticconstexprexport

Compile-time device type for this component instance.

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◆ getExecutionContext()

template<DeviceType TDeviceType, TensorDataType TPrecision>

IExecutionContext * Mila::Dnn::Component< TDeviceType, TPrecision >::getExecutionContext ( ) const

inlineexportprotected

Get the shared execution context.

Provides access to the execution context for derived classes to:

Query device information
Create tensors on the correct device
Pass to backend operations
Synchronize device work

Returns: Non-owning pointer to execution context (guaranteed non-null).

Exceptions

std::runtime_error if context has not been set.

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◆ getGradients()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

Return non-owning pointers to parameter gradient tensors.

Only valid when isTraining() is true.

Exceptions

std::runtime_error if called when not in training mode or before the component has been built.

◆ getMemoryStats()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

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 — parameters only After build( Inference ) — parameters + T=1 state buffers After build( Training ) — parameters + T=full state buffers After setEvaluation( false ) — 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.

◆ getName()

template<DeviceType TDeviceType, TensorDataType TPrecision>

const std::string Mila::Dnn::Component< TDeviceType, TPrecision >::getName ( ) const

inlineexport

Get the component's name identifier.

The name is used for logging, diagnostics, and serialization.

Returns: Component full hierarchical path.

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◆ getParameterNames()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

inlineexportvirtual

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().

◆ getParameters()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

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).

◆ getPrecision()

template<DeviceType TDeviceType, TensorDataType TPrecision>

constexpr TensorDataType Mila::Dnn::Component< TDeviceType, TPrecision >::getPrecision ( )

inlinestaticconstexprexportnoexcept

Compile-time tensor precision for this component instance.

◆ getRuntimeMode()

template<DeviceType TDeviceType, TensorDataType TPrecision>

RuntimeMode Mila::Dnn::Component< TDeviceType, TPrecision >::getRuntimeMode ( ) const

inlineexportnoexcept

Convenience accessor — true if currently in Eval mode.

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

Returns: true if in Eval mode.

◆ getTrainingMode()

template<DeviceType TDeviceType, TensorDataType TPrecision>

TrainingMode Mila::Dnn::Component< TDeviceType, TPrecision >::getTrainingMode ( ) const

inlineexportnoexcept

The current runtime behavioral mode of this Component.

Returns the current TrainingMode for Components built with RuntimeMode::Training. For Components built with RuntimeMode::Inference the return value is always TrainingMode::Eval — inference components never compute gradients.

Returns: Current TrainingMode.

◆ getType()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

Get the component type identifier.

Used for serialization and runtime type identification.

Returns: Component type enum value.

◆ hasExecutionContext()

template<DeviceType TDeviceType, TensorDataType TPrecision>

bool Mila::Dnn::Component< TDeviceType, TPrecision >::hasExecutionContext ( ) const

inlineexportprotectednoexcept

Check if execution context has been set.

Returns: true if context is set, false otherwise.

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◆ isBuilt()

template<DeviceType TDeviceType, TensorDataType TPrecision>

virtual bool Mila::Dnn::Component< TDeviceType, TPrecision >::isBuilt ( ) const

inlinefinalexportvirtual

Returns true if build() has completed successfully.

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◆ isIdentifier()

template<DeviceType TDeviceType, TensorDataType TPrecision>

bool Mila::Dnn::Component< TDeviceType, TPrecision >::isIdentifier ( const std::string & s )

inlinestaticexportprivatenoexcept

Checks if a string is a valid component identifier.

Rules: start with A-Za-z, then allow A-Za-z0-9._- ; length 1..128.

Parameters

s	String to check.

Returns: true if valid identifier, false otherwise.

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◆ isInferenceMode()

template<DeviceType TDeviceType, TensorDataType TPrecision>

bool Mila::Dnn::Component< TDeviceType, TPrecision >::isInferenceMode ( ) const

inlineexportnoexcept

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◆ isTrainingMode()

template<DeviceType TDeviceType, TensorDataType TPrecision>

bool Mila::Dnn::Component< TDeviceType, TPrecision >::isTrainingMode ( ) const

inlineexportnoexcept

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◆ loadParameter()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

inlineexportvirtual

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)

Parameters

name	Parameter name used to locate the target tensor.
blob	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 in Mila::Dnn::LayerNorm< TDeviceType, TPrecision >, Mila::Dnn::Linear< TDeviceType, TComputePrecision, TWeightQuant >, Mila::Dnn::Linear< TDeviceType, TPrecision >, Mila::Dnn::Linear< TDeviceType, TPrecision, TWeightQuant >, Mila::Dnn::Linear< TDeviceType, TPrecision, TWeightQuantization >, Mila::Dnn::Lpe< TDeviceType, TIndex, TPrecision >, Mila::Dnn::Lpe< TDeviceType, dtype_t::INT32, TPrecision >, Mila::Dnn::RmsNorm< TDeviceType, TPrecision >, Mila::Dnn::TokenEmbedding< TDeviceType, TIndex, TPrecision >, and Mila::Dnn::TokenEmbedding< TDeviceType, dtype_t::INT32, TPrecision >.

◆ loadParameterFromBlob()

template<DeviceType TDeviceType, TensorDataType TPrecision>

template<TensorDataType TParameterPrecision, typename TMemoryResource>

void Mila::Dnn::Component< TDeviceType, TPrecision >::loadParameterFromBlob	(	const std::string &	param_name,
		const Serialization::ITensorBlob &	blob,
		Tensor< TParameterPrecision, TMemoryResource > &	target,
		const shape_t &	expected_shape )

inlineexportprotected

Load a tensor blob into a parameter tensor with validation.

Validates dtype and shape match then copies blob data into the tensor. Intended for use in loadParameter() overrides.

Parameters

param_name	Parameter name (used in error messages).
blob	Source tensor blob from the model archive.
target	Destination tensor (must be initialized).
expected_shape	Expected tensor shape for validation.

Exceptions

std::invalid_argument if dtype or shape mismatch.

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◆ onBuilding()

template<DeviceType TDeviceType, TensorDataType TPrecision>

virtual void Mila::Dnn::Component< TDeviceType, TPrecision >::onBuilding ( const BuildContext & config )

inlineexportprotectedvirtual

Hook invoked by build() to allocate component buffers.

Receives the stored BuildContext. Implementations must use config.allocationSeqLen() when sizing output buffers — this is the single call that makes Inference and Training allocate the correct buffer sizes automatically without per-component logic.

// Example — Linear component:
shape_t out_shape =
{
    config.batchSize(),
    config.allocationSeqLen(),   // 1 for Inference, T for Training
    config_.getOutputFeatures()
};
output_ = std::make_unique<TensorType>( device, out_shape,
    this->getName() + ".output" );

The default implementation forwards to the legacy onBuilding( const shape_t& ) overload for backwards compatibility. New components should override this overload directly.

Note: Do not call build() or onBuilding() from within this hook.; Implementations should either succeed fully or leave no partial state, as a failed build() may be retried.

Parameters

config Build-time configuration. Use config.allocationSeqLen() to obtain the correct output buffer sequence dimension.

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◆ onExecutionContextSet()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

inlineexportprotectedvirtual

Lifecycle hook: Called immediately after ExecutionContext is set.

Override this to perform initialization that requires a valid ExecutionContext. At the time this is called, getExecutionContext() is guaranteed to return a valid context.

Common uses:

Composite components: Create and configure child components.
Device resource allocation: Query device capabilities.

Default implementation does nothing.

Exceptions

Any	exception thrown will cause setExecutionContext() to fail and restore the component to a "context not set" state.

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◆ onTrainingModeChanging()

template<DeviceType TDeviceType, TensorDataType TPrecision>

virtual void Mila::Dnn::Component< TDeviceType, TPrecision >::onTrainingModeChanging ( TrainingMode mode )

inlineexportprotectedvirtual

Hook called before TrainingMode transitions.

Called by setTrainingMode() after validation and lock acquisition, before the internal state is updated. Derived classes override to respond to the transition — e.g. zeroing gradient buffers on transition to Eval, or re-enabling dropout on transition to Training.

The default implementation is a no-op.

Parameters

mode	The incoming TrainingMode.

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◆ parameterCount()

template<DeviceType TDeviceType, TensorDataType TPrecision>

virtual size_t Mila::Dnn::Component< TDeviceType, TPrecision >::parameterCount ( ) const

exportpure virtual

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.

◆ save_()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

◆ setExecutionContext()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::Component< TDeviceType, TPrecision >::setExecutionContext ( IExecutionContext * context )

inlineexportprotected

Set the execution context for this component.

Establishes the device and execution environment. Can only be called once — the execution context is immutable after setting.

Called by:

The component itself (standalone mode with owned context)
Parent composite when adding child (shared context mode)
ComponentFactory during deserialization

After setting the context, the onExecutionContextSet() hook is invoked to allow the component to perform context-dependent initialization.

Parameters

context Non-owning pointer to execution context (must be non-null).

Exceptions

std::invalid_argument	if context is null.
std::runtime_error	if context has already been set.
std::invalid_argument	if context device type doesn't match TDeviceType.
std::runtime_error	if onExecutionContextSet() throws; context is restored to nullptr on failure.

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◆ setTrainingMode()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::Component< TDeviceType, TPrecision >::setTrainingMode ( TrainingMode mode )

inlineexport

Set the runtime behavioral mode for this Component.

Toggles between Training and Eval behavioral states at runtime. Only valid on Components built with RuntimeMode::Training — throws if called on a Component built with RuntimeMode::Inference.

State transitions

From	To	Effect
Training	Eval	Gradients off, dropout off, running stats
Eval	Training	Gradients on, dropout on, batch stats

Derived classes respond to the transition via the onTrainingModeChanging() hook, called before the state is updated.

Parameters

mode	TrainingMode::Normal or TrainingMode::Eval.

Exceptions

std::runtime_error	if the component is not built.
std::runtime_error	if built with RuntimeMode::Inference.

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◆ synchronize()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

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.

◆ toString()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

exportpure virtual

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

Implementations should keep output concise and avoid throwing.

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◆ validateName()

template<DeviceType TDeviceType, TensorDataType TPrecision>

const std::string & Mila::Dnn::Component< TDeviceType, TPrecision >::validateName ( const std::string & name )

inlinestaticexportprivate

Validates the component name.

Enforces identifier rules: must start with a letter and contain only letters, digits, '.', '_', '-' with length between 1 and 128 characters.

Parameters

name	Name to validate.

Exceptions

std::invalid_argument if name is not a valid identifier.

◆ zeroGradients()

template<DeviceType TDeviceType, TensorDataType TPrecision>

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

inlineexportvirtual

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.

◆ operator<<

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::ostream & operator<<	(	std::ostream &	os,
		const Component< TDeviceType, TPrecision > &	component )

friend

Stream output uses toString() to provide a human-readable description of the component.

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

/__w/Mila/Mila/Mila/Src/Dnn/Core/Component.ixx

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Static Private Member Functions

Private Attributes

Friends

Detailed Description

Ownership model

Component build lifecycle

Stage 1 — Construction

Stage 2 — Build [ onBuilding() ]

Stage 3 — Evaluation mode [ onEvaluationChanging() ]

Stage 4 — Forward / Decode / Backward

Lifecycle invariants

Base class provides

Constructor & Destructor Documentation

◆ Component()

◆ ~Component()

Member Function Documentation

◆ build()

◆ ensureBuilt()

◆ getDeviceId()

◆ getDeviceType()

◆ getExecutionContext()

◆ getGradients()

◆ getMemoryStats()

◆ getName()

◆ getParameterNames()

◆ getParameters()

◆ getPrecision()

◆ getRuntimeMode()

◆ getTrainingMode()

◆ getType()

◆ hasExecutionContext()

◆ isBuilt()

◆ isIdentifier()

◆ isInferenceMode()

◆ isTrainingMode()

◆ loadParameter()

◆ loadParameterFromBlob()

◆ onBuilding()

◆ onExecutionContextSet()

◆ onTrainingModeChanging()

◆ parameterCount()

◆ save_()

◆ setExecutionContext()

◆ setTrainingMode()

State transitions

◆ synchronize()

◆ toString()

◆ validateName()

◆ zeroGradients()

Friends And Related Symbol Documentation

◆ operator<<