architecture/network/onnx/export

Export-owned execution and payload types for NeatapticTS ONNX serialization.

This chapter holds the types that belong to the exporter implementation: export options, build/setup contexts, recurrent and Conv heuristics, and the dense/per-neuron layer-emission payloads used by the export helpers.

Shared runtime bridge types such as NodeInternals remain root-owned in network.onnx.utils.types.ts, and the persisted wire-format schema stays in schema/network.onnx.schema.types.ts.

flowchart LR
  Options[OnnxExportOptions] --> Setup[Setup and build contexts]
  Setup --> Heuristics[Conv and recurrent heuristics]
  Heuristics --> Layers[Layer emission payloads]
  Layers --> Model[Schema model and tensors]

architecture/network/onnx/export/network.onnx.export.types.ts

ActivationSquashFunction

ActivationSquashFunction(
  x: number,
  derivate: boolean | undefined,
): number

Activation function signature used by ONNX layer emission helpers.

ConvInferenceEvaluationContext

Width and shape evaluation context used by Conv inference helpers.

ConvInferenceKernelEvaluationContext

Kernel candidate context for one Conv inference evaluation pass.

ConvInferenceResult

Collected inferred Conv metadata payload.

ConvInferenceTraversalContext

Traversal context for one hidden layer during Conv inference.

ConvKernelConsistencyContext

Context for kernel-coordinate consistency checks at one output position.

ConvLayerPairContext

Context for one resolved Conv mapping layer pair.

ConvOutputCoordinate

Coordinate for one Conv output neuron position.

ConvRepresentativeKernelContext

Context for representative Conv kernel collection per output channel.

ConvSharingValidationContext

Context for validating Conv sharing across all declared mappings.

ConvSharingValidationResult

Result of Conv sharing validation across declared mappings.

DenseActivationContext

Dense activation emission context.

DenseActivationNodePayload

Strongly typed activation node payload used by dense export helpers.

DenseGemmNodePayload

Strongly typed Gemm node payload used by dense export helpers.

DenseGraphNames

Dense graph tensor names.

DenseInitializerValues

Dense initializer value arrays.

DenseLayerContext

Dense layer context enriched with resolved activation function.

DenseLayerParams

Parameters for dense layer emission.

DenseOrderedNodePayload

Dense node payload union used by ordered append helpers.

DenseTensorNames

Dense initializer tensor names.

DenseWeightBuildContext

Context for building dense layer initializers from two adjacent layers.

DenseWeightBuildResult

Dense layer initializer fold output.

DenseWeightRow

One collected dense row before fold to flattened initializers.

DenseWeightRowCollectionContext

Context for collecting one dense row.

DiagonalRecurrentBuildContext

Context for building a diagonal recurrent matrix from self-connections.

ExportNodeIndexAssignmentContext

Context for assigning a stable export index to one node.

FlattenAfterPoolingContext

Flatten emission context after optional pooling.

FusedRecurrentEmissionExecutionContext

Shared execution context for emitting one fused recurrent layer payload.

FusedRecurrentGraphNames

Context for ONNX fused recurrent node payload names.

FusedRecurrentInitializerNames

Context for ONNX fused recurrent initializer names.

GruEmissionContext

Context for heuristic GRU emission when a layer matches expected shape.

HiddenLayerHeuristicContext

Context for one hidden layer during heuristic recurrent emission.

IndexedMetadataAppendContext

Append-an-index metadata context for JSON-array metadata keys.

LayerActivationContext

Activation analysis context for one layer.

LayerBuildContext

Layer build context used while emitting one ONNX graph layer segment.

LayerRecurrentDecisionContext

Context used to decide recurrent emission branch usage.

LayerTraversalContext

Layer traversal context with adjacent layers and output classification.

LstmCandidateContext

Candidate context for validating one LSTM-like hidden layer pattern.

LstmEmissionContext

Context for heuristic LSTM emission when a layer matches expected shape.

LstmLayerTraversalContext

Traversal context for one hidden layer during LSTM stub collection.

LstmPatternStub

Heuristic LSTM pattern stub for metadata output.

OnnxBaseModelBuildContext

Context for constructing a base ONNX model shell.

OnnxBuildResolvedOptions

Resolved options used by ONNX model build orchestration.

OnnxConvEmissionContext

Context used after resolving Conv mapping for one layer.

OnnxConvEmissionParams

Parameters accepted by Conv layer emission.

OnnxConvParameters

Flattened Conv parameters for ONNX initializers.

OnnxConvTensorNames

Tensor names generated for Conv parameters.

OnnxExportOptions

Options controlling ONNX-like export.

These options trade off strictness, portability, and fidelity:

• Strict (default-ish) export tries to keep the graph easy to interpret: layered topology, homogeneous activations per layer, and fully-connected layers.

• Relaxed export (allowPartialConnectivity / allowMixedActivations) can represent more networks, but it may generate graphs that are primarily meant for NeatapticTS’s importer (and may be less friendly to external ONNX tooling).

• Recurrent export (allowRecurrent) is intentionally conservative and currently focuses on a constrained single-step representation and optional fused heuristics.

Key fields (high-level):

• includeMetadata: includes metadata_props with architecture hints.
• opset: numeric opset version stored in the exported model metadata (default is resolved by the exporter; commonly 18 in this codebase).
• legacyNodeOrdering: keeps older node ordering for backward compatibility.
• conv2dMappings / pool2dMappings: encode conv/pool semantics for fully-connected layers via explicit mapping declarations.

OnnxGraphDimensionBuildContext

Context for constructing input/output ONNX graph dimensions.

OnnxGraphDimensions

Output dimensions used by ONNX graph input/output value info payloads.

OnnxLayerEmissionContext

Context for emitting non-input layers during model build.

OnnxLayerEmissionResult

Result of emitting non-input export layers.

OnnxModelMetadataContext

Context for applying optional ONNX model metadata.

OnnxPostProcessingContext

Context for post-processing and export metadata finalization.

OnnxRecurrentCollectionContext

Context for collecting recurrent layer indices during model build.

OnnxRecurrentInputValueInfoContext

Context for constructing one recurrent previous-state graph input payload.

OnnxRecurrentLayerProcessingContext

Execution context for processing one hidden recurrent layer.

OnnxRecurrentLayerTraversalContext

Traversal context for one hidden layer during recurrent-input collection.

OptionalLayerOutputParams

Shared parameters for optional pooling/flatten output emission.

OptionalPoolingAndFlattenParams

Parameters for optional pooling + flatten emission after a layer output.

PerNeuronConcatNodePayload

Per-neuron concat node payload.

PerNeuronGraphNames

Per-neuron graph tensor names.

PerNeuronLayerContext

Per-neuron layer context alias.

PerNeuronLayerParams

Parameters for per-neuron layer emission.

PerNeuronNodeContext

Per-neuron normalized node context.

PerNeuronSubgraphContext

Per-neuron subgraph emission context.

PerNeuronTensorNames

Per-neuron initializer tensor names.

PoolingAttributes

Pooling tensor attributes for ONNX node payloads.

PoolingEmissionContext

Pooling emission context resolved for one layer output.

RecurrentActivationEmissionContext

Context for selecting and emitting recurrent activation node payload.

RecurrentGateBlockCollectionContext

Context for collecting one gate parameter block.

RecurrentGateParameterCollectionResult

Flattened recurrent gate parameter vectors for one fused operator.

RecurrentGateRow

One recurrent gate row payload before flatten fold.

RecurrentGateRowCollectionContext

Context for collecting one recurrent gate row (one neuron).

RecurrentGemmEmissionContext

Context for emitting one Gemm node for recurrent single-step export.

RecurrentGraphNames

Derived graph names for one recurrent single-step layer payload.

RecurrentHeuristicEmissionContext

Context for heuristic recurrent operator emission traversal.

RecurrentInitializerEmissionContext

Context for pushing recurrent initializers into ONNX graph state.

RecurrentInitializerNames

Initializer tensor names for one single-step recurrent layer.

RecurrentInitializerValues

Collected initializer vectors for one single-step recurrent layer.

RecurrentLayerEmissionContext

Derived execution context for single-step recurrent layer emission.

RecurrentLayerEmissionParams

Parameters for single-step recurrent layer emission.

RecurrentRowCollectionContext

Context for collecting one recurrent matrix row.

SharedActivationNodeBuildParams

Shared parameters for constructing an activation node payload.

SharedGemmNodeBuildParams

Shared parameters for constructing a Gemm node payload.

SpecMetadataAppendContext

Append-a-spec metadata context for JSON-array metadata keys.

WeightToleranceComparisonContext

Context for comparing two scalar weights with numeric tolerance.

architecture/network/onnx/export/network.onnx.export-flow.utils.ts

runOnnxExportFlow

runOnnxExportFlow(
  network: default,
  options: OnnxExportOptions,
): OnnxModel

Execute the complete ONNX export flow for one network instance.

High-level behavior:

1. Rebuild runtime connection caches and assign stable export indices.
2. Infer layered ordering and collect recurrent-pattern stubs.
3. Validate structural constraints for the requested export options.
4. Build ONNX graph payload and append inference-oriented metadata.

Parameters:

• network - Source network to serialize.
• options - Optional ONNX export controls.

Returns: ONNX-like model payload.

architecture/network/onnx/export/network.onnx.export-build.utils.ts

buildOnnxModel

buildOnnxModel(
  network: default,
  layers: default[][],
  options: OnnxExportOptions,
): OnnxModel

Construct ONNX graph (initializers + nodes) from validated layered network structure.

Parameters:

• network - Source network (retained for API compatibility).
• layers - Layered nodes including input and output layers.
• options - Export options.

Returns: ONNX model.

architecture/network/onnx/export/network.onnx.export-setup.utils.ts

appendRecurrentGraphInput

appendRecurrentGraphInput(
  model: OnnxModel,
  traversalContext: OnnxRecurrentLayerTraversalContext,
): void

Append one recurrent previous-state graph input for a hidden layer.

Parameters:

• model - Target ONNX model.
• traversalContext - Hidden layer traversal context.

Returns: Nothing.

appendRecurrentLayerIndex

appendRecurrentLayerIndex(
  recurrentLayerIndices: number[],
  traversalContext: OnnxRecurrentLayerTraversalContext,
): void

Append one recurrent layer index to the collected index list.

Parameters:

• recurrentLayerIndices - Collected recurrent layer indices.
• traversalContext - Hidden layer traversal context.

Returns: Nothing.

applyModelMetadata

applyModelMetadata(
  context: OnnxModelMetadataContext,
): void

Attach producer and opset metadata to a model when metadata emission is enabled.

Parameters:

• context - Metadata application context.

Returns: Nothing.

collectRecurrentLayerIndices

collectRecurrentLayerIndices(
  context: OnnxRecurrentCollectionContext,
): number[]

Detect hidden layers with self-recurrence and add matching previous-state graph inputs.

Parameters:

• context - Recurrent collection context.

Returns: Export-layer indices with recurrent self-connections.

createBaseModel

createBaseModel(
  context: OnnxBaseModelBuildContext,
): OnnxModel

Create the base ONNX model shell with graph input/output declarations.

Parameters:

• context - Base model build context.

Returns: Initialized ONNX model with empty initializer/node lists.

createGraphDimensions

createGraphDimensions(
  context: OnnxGraphDimensionBuildContext,
): OnnxGraphDimensions

Build tensor dimensions for model input and output, optionally with symbolic batch dimension.

Parameters:

• context - Dimension construction context.

Returns: Input and output dimension arrays for ONNX value info.

createGraphValueInfo

createGraphValueInfo(
  valueName: string,
  dimensions: OnnxDimension[],
): OnnxValueInfo

Create ONNX value info payload for one graph boundary tensor.

Parameters:

• valueName - Tensor value name.
• dimensions - Tensor dimensions.

Returns: ONNX value info payload.

createHiddenLayerIndices

createHiddenLayerIndices(
  totalLayerCount: number,
): number[]

Build hidden layer indices excluding input and output layers.

Parameters:

• totalLayerCount - Total number of network layers.

Returns: Hidden layer indices.

createHiddenLayerTraversalContexts

createHiddenLayerTraversalContexts(
  context: OnnxRecurrentCollectionContext,
): OnnxRecurrentLayerTraversalContext[]

Build traversal contexts for all hidden layers.

Parameters:

• context - Recurrent collection context.

Returns: Hidden layer traversal contexts.

createRecurrentInputValueInfo

createRecurrentInputValueInfo(
  context: OnnxRecurrentInputValueInfoContext,
): OnnxValueInfo

Build one recurrent previous-state graph input payload.

Parameters:

• context - Recurrent input value-info context.

Returns: ONNX value info payload for recurrent state input.

createRecurrentInputValueInfoContext

createRecurrentInputValueInfoContext(
  traversalContext: OnnxRecurrentLayerTraversalContext,
): OnnxRecurrentInputValueInfoContext

Build recurrent input context for one hidden recurrent layer.

Parameters:

• traversalContext - Hidden layer traversal context.

Returns: Recurrent input value-info context.

createTensorDimensions

createTensorDimensions(
  width: number,
  batchDimension: boolean,
): OnnxDimension[]

Build one tensor shape dimension payload for dense vectors.

Parameters:

• width - Vector width.
• batchDimension - Whether symbolic batch dimension is enabled.

Returns: ONNX dimensions for the vector payload.

hasLayerSelfRecurrence

hasLayerSelfRecurrence(
  hiddenLayerNodes: default[],
): boolean

Detect whether a hidden layer contains at least one self-recurrent node.

Parameters:

• hiddenLayerNodes - Hidden layer nodes.

Returns: True when a node has a self-connection.

isRecurrentCollectionEnabled

isRecurrentCollectionEnabled(
  context: OnnxRecurrentCollectionContext,
): boolean

Determine whether recurrent layer collection should execute.

Parameters:

• context - Recurrent collection context.

Returns: True when recurrent collection is enabled.

processHiddenLayerRecurrence

processHiddenLayerRecurrence(
  context: OnnxRecurrentLayerProcessingContext,
): void

Process one hidden layer for recurrent self-connections.

Parameters:

• context - Hidden layer recurrent processing context.

Returns: Nothing.

architecture/network/onnx/export/network.onnx.export-postprocess.utils.ts

appendConvLayerValidationResult

appendConvLayerValidationResult(
  result: ConvSharingValidationResult,
  layerIndex: number,
  isConsistent: boolean,
): void

Append one Conv-layer validation outcome and optional warning.

appendConvSharingMetadata

appendConvSharingMetadata(
  model: OnnxModel,
  result: ConvSharingValidationResult,
): void

Append Conv-sharing validation metadata arrays.

appendFusedRecurrentInitializers

appendFusedRecurrentInitializers(
  model: OnnxModel,
  initializerNames: FusedRecurrentInitializerNames,
  parameters: RecurrentGateParameterCollectionResult,
  gateCount: number,
  unitSize: number,
  previousSize: number,
): void

Append fused recurrent initializer tensors to the ONNX graph.

appendFusedRecurrentNode

appendFusedRecurrentNode(
  graph: OnnxGraph,
  operatorType: "LSTM" | "GRU",
  previousOutputName: string,
  initializerNames: FusedRecurrentInitializerNames,
  graphNames: FusedRecurrentGraphNames,
  unitSize: number,
): void

Append fused recurrent operator node to the ONNX graph.

appendIndexMetadata

appendIndexMetadata(
  model: OnnxModel,
  key: string,
  layerIndex: number,
): void

Append a unique layer index to metadata array key.

appendMetadataProperty

appendMetadataProperty(
  model: OnnxModel,
  metadataProperty: OnnxMetadataProperty,
): void

Append metadata property to model metadata_props list.

appendRecurrentSingleStepMetadata

appendRecurrentSingleStepMetadata(
  model: OnnxModel,
  recurrentLayerIndices: number[],
): void

Append recurrent single-step metadata when recurrent layers exist.

areWeightsWithinTolerance

areWeightsWithinTolerance(
  context: WeightToleranceComparisonContext,
): boolean

Compare two scalar weights using configured tolerance.

asNodeInternals

asNodeInternals(
  node: default,
): NodeInternals

Resolve runtime node internals in one typed helper.

buildFusedGruExecutionContext

buildFusedGruExecutionContext(
  context: GruEmissionContext,
): FusedRecurrentEmissionExecutionContext

Build shared fused-recurrent execution context for GRU.

buildFusedLstmExecutionContext

buildFusedLstmExecutionContext(
  context: LstmEmissionContext,
): FusedRecurrentEmissionExecutionContext

Build shared fused-recurrent execution context for LSTM.

buildFusedRecurrentGraphNames

buildFusedRecurrentGraphNames(
  nodePrefix: string,
  outputSuffix: string,
  layerIndex: number,
): FusedRecurrentGraphNames

Build fused recurrent graph names for node and output.

buildFusedRecurrentInitializerNames

buildFusedRecurrentInitializerNames(
  operatorType: "LSTM" | "GRU",
  layerIndex: number,
): FusedRecurrentInitializerNames

Build fused recurrent initializer names for the current layer.

buildGruEmissionContext

buildGruEmissionContext(
  context: HiddenLayerHeuristicContext,
): GruEmissionContext

Build GRU emission context from one hidden-layer traversal record.

buildHiddenLayerHeuristicContext

buildHiddenLayerHeuristicContext(
  context: RecurrentHeuristicEmissionContext,
  layerIndex: number,
): HiddenLayerHeuristicContext

Build one hidden-layer traversal context.

buildLstmEmissionContext

buildLstmEmissionContext(
  context: HiddenLayerHeuristicContext,
): LstmEmissionContext

Build LSTM emission context from one hidden-layer traversal record.

buildMetadataProperty

buildMetadataProperty(
  key: string,
  value: unknown,
): OnnxMetadataProperty

Build a metadata key/value property with JSON string serialization.

buildRecurrentHeuristicEmissionContext

buildRecurrentHeuristicEmissionContext(
  model: OnnxModel,
  layers: default[][],
  previousOutputName: string,
): RecurrentHeuristicEmissionContext

Build reusable context for recurrent heuristic traversal.

collectConvKernelCoordinates

collectConvKernelCoordinates(
  convSpec: Conv2DMapping,
): OnnxConvKernelCoordinate[]

Collect kernel coordinates for one Conv kernel traversal.

collectConvOutputCoordinates

collectConvOutputCoordinates(
  convSpec: Conv2DMapping,
): ConvOutputCoordinate[]

Collect output coordinates for full Conv traversal.

collectGruGateNodeGroups

collectGruGateNodeGroups(
  context: GruEmissionContext,
): default[][]

Collect GRU gate node groups in canonical export order.

collectHiddenLayerIndices

collectHiddenLayerIndices(
  layers: default[][],
): number[]

Collect hidden-layer indices for recurrent traversal.

collectLstmGateNodeGroups

collectLstmGateNodeGroups(
  context: LstmEmissionContext,
): default[][]

Collect LSTM gate node groups in canonical export order.

collectRecurrentGateBlockParameters

collectRecurrentGateBlockParameters(
  context: RecurrentGateBlockCollectionContext,
): RecurrentGateParameterCollectionResult

Collect flattened parameter vectors for one gate node block.

collectRecurrentGateRow

collectRecurrentGateRow(
  context: RecurrentGateRowCollectionContext,
): RecurrentGateRow

Collect one recurrent gate row payload (inputs, recurrent slice, and bias).

collectRepresentativeKernelForChannel

collectRepresentativeKernelForChannel(
  context: ConvRepresentativeKernelContext,
): number[]

Collect one representative kernel by reading the first output position for a channel.

collectRepresentativeKernels

collectRepresentativeKernels(
  context: ConvLayerPairContext,
): number[][]

Collect representative kernels for each output channel.

collectRepresentativeKernelWeight

collectRepresentativeKernelWeight(
  convSpec: Conv2DMapping,
  previousLayerNodes: default[],
  representativeInternal: NodeInternals,
  kernelCoordinate: OnnxConvKernelCoordinate,
): number

Collect representative kernel value using top-left receptive field indexing.

emitFallbackRecurrentPatternMetadata

emitFallbackRecurrentPatternMetadata(
  context: HiddenLayerHeuristicContext,
): void

Emit fallback metadata for recurrent-size ambiguity.

emitFusedRecurrentHeuristics

emitFusedRecurrentHeuristics(
  model: OnnxModel,
  layers: default[][],
  allowRecurrent: boolean | undefined,
  previousOutputName: string,
): void

Emit heuristic fused recurrent operators (LSTM/GRU) when recurrent export is enabled.

Parameters:

• model - Target ONNX model.
• layers - Layered network nodes.
• allowRecurrent - Whether recurrent export is enabled.
• previousOutputName - Current graph output name (kept for backward-compatible emission semantics).

Returns: Nothing.

emitFusedRecurrentLayer

emitFusedRecurrentLayer(
  context: FusedRecurrentEmissionExecutionContext,
): void

Emit shared fused recurrent payload (initializers, node, metadata).

ensureMetadataProps

ensureMetadataProps(
  model: OnnxModel,
): OnnxMetadataProperty[]

Ensure metadata_props array exists and return it.

finalizeExportMetadata

finalizeExportMetadata(
  model: OnnxModel,
  layers: default[][],
  options: OnnxExportOptions,
  includeMetadata: boolean,
  hiddenSizesMetadata: number[],
  recurrentLayerIndices: number[],
): void

Finalize export metadata and optional conv-sharing validation.

Parameters:

• model - Target ONNX model.
• layers - Layered network nodes.
• options - Export options.
• includeMetadata - Whether metadata emission is enabled.
• hiddenSizesMetadata - Hidden-layer sizes collected during emission.
• recurrentLayerIndices - Recurrent layer indices.

Returns: Nothing.

findMetadataPropertyIndex

findMetadataPropertyIndex(
  metadataProperties: OnnxMetadataProperty[],
  key: string,
): number

Find metadata property index by key.

foldRecurrentGateBlocks

foldRecurrentGateBlocks(
  gateParameterBlocks: RecurrentGateParameterCollectionResult[],
): RecurrentGateParameterCollectionResult

Fold gate blocks into a single fused parameter payload.

foldRecurrentGateRows

foldRecurrentGateRows(
  gateRows: RecurrentGateRow[],
): RecurrentGateParameterCollectionResult

Fold recurrent gate rows into flattened ONNX initializer vectors.

isConvLayerPairConsistent

isConvLayerPairConsistent(
  context: ConvLayerPairContext,
): boolean

Validate one Conv layer pair against representative kernel sharing.

isEligibleForGruHeuristic

isEligibleForGruHeuristic(
  currentSize: number,
): boolean

Check GRU heuristic eligibility by size and gate divisibility.

isEligibleForLstmHeuristic

isEligibleForLstmHeuristic(
  currentSize: number,
): boolean

Check LSTM heuristic eligibility by size and gate divisibility.

isFallbackRecurrentPatternSize

isFallbackRecurrentPatternSize(
  currentSize: number,
): boolean

Check whether hidden size should emit recurrent fallback metadata.

isInputPositionInsideBounds

isInputPositionInsideBounds(
  convSpec: Conv2DMapping,
  inputRow: number,
  inputColumn: number,
): boolean

Check whether input row/column falls inside Conv input bounds.

isKernelCoordinateConsistent

isKernelCoordinateConsistent(
  context: ConvKernelConsistencyContext,
): boolean

Validate one kernel coordinate against its representative channel value.

isOutputCoordinateConsistent

isOutputCoordinateConsistent(
  context: ConvLayerPairContext,
  outputCoordinate: ConvOutputCoordinate,
  representativeKernels: number[][],
  tolerance: number,
): boolean

Validate one output coordinate against channel representative kernel weights.

parseMetadataLayerIndices

parseMetadataLayerIndices(
  metadataValue: string,
): number[]

Parse metadata JSON value into a numeric layer-index array.

resolveConvLayerPairContext

resolveConvLayerPairContext(
  layers: default[][],
  layerIndex: number,
  convSpec: Conv2DMapping,
): ConvLayerPairContext | undefined

Resolve one Conv mapping layer pair or return undefined for invalid layout.

resolveGruPreviousOutputName

resolveGruPreviousOutputName(
  layerIndex: number,
): string

Resolve previous output naming semantics for GRU heuristic emission.

resolveIncomingWeight

resolveIncomingWeight(
  targetNodeInternal: NodeInternals,
  sourceNode: default,
): number

Resolve incoming connection weight from a specific source node.

resolveInputPosition

resolveInputPosition(
  context: ConvKernelConsistencyContext,
): { inputRow: number; inputColumn: number; }

Resolve input row/column projected by output and kernel coordinates.

resolveNeuronInternalAtOutputCoordinate

resolveNeuronInternalAtOutputCoordinate(
  context: ConvLayerPairContext,
  outputCoordinate: ConvOutputCoordinate,
): NodeInternals | undefined

Resolve runtime internals for output coordinate neuron, if present.

resolveRecurrentRowWeight

resolveRecurrentRowWeight(
  context: RecurrentGateRowCollectionContext,
  columnIndex: number,
): number

Resolve one recurrent row value at the requested column.

resolveSelfConnectionWeight

resolveSelfConnectionWeight(
  targetNodeInternal: NodeInternals,
): number

Resolve self-connection weight for diagonal recurrent matrix entries.

resolveSourceNodeAtInputPosition

resolveSourceNodeAtInputPosition(
  convSpec: Conv2DMapping,
  previousLayerNodes: default[],
  inChannelIndex: number,
  inputRow: number,
  inputColumn: number,
): default | undefined

Resolve source node by Conv input position coordinates.

shouldValidateConvSharing

shouldValidateConvSharing(
  options: OnnxExportOptions,
): boolean

Determine whether Conv2D sharing validation is enabled and configured.

tryEmitFusedGru

tryEmitFusedGru(
  context: HiddenLayerHeuristicContext,
): void

Try emitting heuristic fused GRU node and metadata.

tryEmitFusedLstm

tryEmitFusedLstm(
  context: HiddenLayerHeuristicContext,
): void

Try emitting heuristic fused LSTM node and metadata.

upsertLayerIndexMetadataValue

upsertLayerIndexMetadataValue(
  metadataProperties: OnnxMetadataProperty[],
  metadataIndex: number,
  layerIndex: number,
): void

Upsert one layer index into metadata array-like JSON value.

validateConvSharingAcrossMappings

validateConvSharingAcrossMappings(
  context: ConvSharingValidationContext,
): ConvSharingValidationResult

Validate Conv2D sharing across all declared Conv mappings.

architecture/network/onnx/export/network.onnx.export-orchestrators.utils.ts

appendConvInferenceMetadata

appendConvInferenceMetadata(
  model: OnnxModel,
  layers: default[][],
  options: OnnxExportOptions,
): void

Append heuristic conv inference metadata when requested.

Parameters:

• model - Target ONNX model.
• layers - Layered network nodes.
• options - Export options.

Returns: Nothing.

appendLstmPatternStubMetadata

appendLstmPatternStubMetadata(
  model: OnnxModel,
  lstmPatternStubs: LstmPatternStub[],
): void

Append LSTM pattern stub metadata.

Parameters:

• model - Target ONNX model.
• lstmPatternStubs - Pattern stubs.

Returns: Nothing.

appendMetadataProperties

appendMetadataProperties(
  model: OnnxModel,
  metadataProperties: OnnxMetadataProperty[],
): void

Append metadata properties in a single, normalized path.

Parameters:

• model - Target ONNX model.
• metadataProperties - Metadata properties to append.

Returns: Nothing.

applyExportNodeIndexAssignments

applyExportNodeIndexAssignments(
  assignmentContexts: ExportNodeIndexAssignmentContext[],
): void

Apply prepared node/index assignment contexts.

Parameters:

• assignmentContexts - Prepared contexts.

Returns: Nothing.

applySingleExportNodeIndexAssignment

applySingleExportNodeIndexAssignment(
  assignmentContext: ExportNodeIndexAssignmentContext,
): void

Apply one export index assignment.

Parameters:

• assignmentContext - Assignment context.

Returns: Nothing.

assignExportNodeIndices

assignExportNodeIndices(
  network: default,
): void

Assign stable index values to nodes for export diagnostics.

Parameters:

• network - Source network.

Returns: Nothing.

collectInferredConvMetadata

collectInferredConvMetadata(
  context: { layers: default[][]; declaredMappings: Conv2DMapping[] | undefined; },
): ConvInferenceResult

Collect inferred Conv metadata from hidden-layer traversals.

Parameters:

• context - Conv traversal context.

Returns: Inferred Conv metadata result.

collectLstmPatternStubs

collectLstmPatternStubs(
  layers: default[][],
  allowRecurrent: boolean | undefined,
): LstmPatternStub[]

Collect heuristic LSTM grouping stubs from hidden layers.

Parameters:

• layers - Layered network nodes.
• allowRecurrent - Whether recurrent export heuristics are enabled.

Returns: Candidate LSTM pattern stubs.

collectLstmPatternStubsFromLayers

collectLstmPatternStubsFromLayers(
  layers: default[][],
): LstmPatternStub[]

Collect LSTM pattern stubs from hidden layers.

Parameters:

• layers - Layered network nodes.

Returns: LSTM pattern stubs.

createConvInferenceEvaluationContext

createConvInferenceEvaluationContext(
  traversalContext: ConvInferenceTraversalContext,
): ConvInferenceEvaluationContext

Create width/square-evaluation context for Conv inference.

Parameters:

• traversalContext - Conv traversal context.

Returns: Conv evaluation context.

createConvTraversalContexts

createConvTraversalContexts(
  context: { layers: default[][]; declaredMappings: Conv2DMapping[] | undefined; },
): ConvInferenceTraversalContext[]

Create Conv traversal contexts for hidden layers.

Parameters:

• context - Conv traversal source context.

Returns: Conv traversal contexts.

createExportNodeIndexAssignmentContexts

createExportNodeIndexAssignmentContexts(
  network: default,
): ExportNodeIndexAssignmentContext[]

Create node/index assignment contexts for export diagnostics.

Parameters:

• network - Source network.

Returns: Assignment contexts.

createHiddenLayerTraversalContexts

createHiddenLayerTraversalContexts(
  layers: default[][],
): LstmLayerTraversalContext[]

Create traversal contexts for hidden layers only.

Parameters:

• layers - Layered network nodes.

Returns: Hidden layer contexts.

createLstmCandidateContext

createLstmCandidateContext(
  hiddenLayerContext: LstmLayerTraversalContext,
): LstmCandidateContext

Build LSTM candidate context for one hidden layer.

Parameters:

• hiddenLayerContext - Hidden layer context.

Returns: LSTM candidate context.

hasInferredConvMetadata

hasInferredConvMetadata(
  inferenceResult: ConvInferenceResult,
): boolean

Check whether inferred Conv metadata exists.

Parameters:

• inferenceResult - Inferred Conv result.

Returns: True when inferred metadata exists.

hasRequiredSelfConnectionCount

hasRequiredSelfConnectionCount(
  nodeItem: default,
): boolean

Check whether one node has the required self-connection count.

Parameters:

• nodeItem - Node to inspect.

Returns: True when self-connection count matches requirement.

isDeclaredConvLayer

isDeclaredConvLayer(
  traversalContext: ConvInferenceTraversalContext,
): boolean

Check whether a traversal layer already has declared Conv mapping.

Parameters:

• traversalContext - Conv traversal context.

Returns: True when mapping is already declared.

isInferredConvSpec

isInferredConvSpec(
  specification: (Conv2DMapping & { note?: string | undefined; }) | undefined,
): boolean

Type guard for inferred Conv specifications.

Parameters:

• specification - Conv specification candidate.

Returns: True when specification is defined.

isValidLstmCandidateContext

isValidLstmCandidateContext(
  candidateContext: LstmCandidateContext,
): boolean

Determine whether a candidate context satisfies heuristic LSTM conditions.

Parameters:

• candidateContext - Candidate context.

Returns: True when the candidate is a valid LSTM stub.

mapLstmCandidateToStub

mapLstmCandidateToStub(
  candidateContext: LstmCandidateContext,
): LstmPatternStub

Map a valid candidate context to metadata stub.

Parameters:

• candidateContext - Valid candidate context.

Returns: LSTM pattern stub.

resolveConvInferenceForLayer

resolveConvInferenceForLayer(
  traversalContext: ConvInferenceTraversalContext,
): (Conv2DMapping & { note?: string | undefined; }) | undefined

Resolve inferred Conv specification for one hidden layer.

Parameters:

• traversalContext - Conv traversal context.

Returns: Inferred Conv specification when matched.

resolveConvSpecForKernel

resolveConvSpecForKernel(
  kernelContext: ConvInferenceKernelEvaluationContext,
): (Conv2DMapping & { note?: string | undefined; }) | undefined

Resolve Conv specification for one kernel candidate.

Parameters:

• kernelContext - Kernel-evaluation context.

Returns: Inferred Conv specification when matched.

resolveConvSpecFromKernelCandidates

resolveConvSpecFromKernelCandidates(
  evaluationContext: ConvInferenceEvaluationContext,
): (Conv2DMapping & { note?: string | undefined; }) | undefined

Resolve Conv specification using ordered kernel candidates.

Parameters:

• evaluationContext - Conv evaluation context.

Returns: Inferred Conv specification when matched.

safelyCollectLstmPatternStubs

safelyCollectLstmPatternStubs(
  layers: default[][],
): LstmPatternStub[]

Collect LSTM pattern stubs with heuristic error isolation.

Parameters:

• layers - Layered network nodes.

Returns: LSTM pattern stubs.