skainet-lang-core/sk.ainet.context

Package-level declarations

Types

annotation class ContextDsl

class DefaultDataExecutionContext(val tensorDataFactory: TensorDataFactory = DenseTensorDataFactory(), val phase: Phase = Phase.EVAL, _hooks: ForwardHooks? = null) : ExecutionContext

ExecutionContext

interface ExecutionContext

ExecutionObserver

interface ExecutionObserver

Observer that can tap into execution events produced by an ExecutionContext.

ExecutionObserverRegistry

class ExecutionObserverRegistry

Registry maintaining execution observers and providing helper methods to broadcast events.

ExecutionStats

data class ExecutionStats(val operationsExecuted: Long = 0, val totalExecutionTime: Long = 0, val averageExecutionTime: Double = if (operationsExecuted > 0) totalExecutionTime.toDouble() / operationsExecuted else 0.0, val tensorsCreated: Long = 0, val peakMemoryUsage: Long = 0)

Execution statistics

MemoryInfo

data class MemoryInfo(val totalMemory: Long, val usedMemory: Long, val freeMemory: Long = totalMemory - usedMemory, val usagePercentage: Double = (usedMemory.toDouble() / totalMemory) * 100.0)

Memory usage information

Phase

enum Phase : Enum<Phase>

Execution phase indicating whether modules should behave as training or evaluation.

PhaseOverridingExecutionContext

class PhaseOverridingExecutionContext(base: ExecutionContext, val phase: Phase) : ExecutionContext

Wrapper that delegates to a base ExecutionContext but overrides the phase field.

ResettableExecutionObserver

interface ResettableExecutionObserver : ExecutionObserver

TrainingExecutionContext

interface TrainingExecutionContext : ExecutionContext

Extension of ExecutionContext that supports gradient recording and backward pass. This interface allows high-level training utilities to remain backend-agnostic while leveraging autograd capabilities when available.

Functions

createDataMap

fun createDataMap(executionContext: ExecutionContext = DefaultDataExecutionContext(), content: DataContextDsl.(executionContext: ExecutionContext) -> Unit): Map<String, Tensor<*, *>>

data

fun data(executionContext: ExecutionContext = DefaultDataExecutionContext(), content: DataContextDsl.(executionContext: ExecutionContext) -> Unit)

inline fun <T : DType, V> data(executionContext: ExecutionContext = DefaultDataExecutionContext(), noinline content: TypedDataContextDsl<T, V>.(executionContext: ExecutionContext) -> Tensor<T, V>): Tensor<T, V>

eval

inline fun <R> eval(ctx: ExecutionContext, block: (ExecutionContext) -> R): R

Execute the given block with the phase set to EVAL, using a delegating context wrapper.

train

inline fun <R> train(ctx: ExecutionContext, block: (ExecutionContext) -> R): R

Execute the given block with the phase set to TRAIN, using a delegating context wrapper.