Add initial version of slicing DSL and underlaying implementaion

Related-To: #120, #95

Author: michalharakal

skainet-lang/skainet-lang-api/src/commonMain/kotlin/sk/ainet/lang/tensor/CopyMaterializationStrategy.kt

@@ -0,0 +1,209 @@
+package sk.ainet.lang.tensor
+
+import sk.ainet.lang.tensor.data.TensorData
+import sk.ainet.lang.tensor.ops.TensorOps
+import sk.ainet.lang.types.DType
+
+/**
+ * A materialization strategy that immediately copies all data from a tensor view
+ * into a new, standalone tensor with contiguous memory layout.
+ * 
+ * This strategy provides immediate materialization by iterating through all
+ * elements in the view and copying them to a new tensor with the view's shape.
+ * The resulting tensor is completely independent of the parent tensor and can
+ * be used even after the parent tensor is garbage collected.
+ * 
+ * ## Characteristics
+ * 
+ * - **Immediate Execution**: Materialization happens synchronously when called
+ * - **Memory Independent**: Result tensor has no dependencies on parent tensor
+ * - **Contiguous Layout**: Output data is stored in standard row-major order
+ * - **Type Preservation**: Maintains the same data type and value type as the view
+ * 
+ * ## Trade-offs
+ * 
+ * **Benefits:**
+ * - Predictable memory usage and performance
+ * - No ongoing computational overhead for element access
+ * - Enables garbage collection of parent tensors
+ * - Compatible with all downstream operations
+ * 
+ * **Costs:**
+ * - Immediate memory allocation for full tensor size
+ * - Computational cost of copying all elements
+ * - Temporary memory pressure during materialization
+ * 
+ * ## Usage Scenarios
+ * 
+ * This strategy is optimal when:
+ * - The materialized tensor will be accessed frequently
+ * - Memory usage is predictable and acceptable
+ * - The parent tensor can be released after materialization
+ * - Compatibility with external libraries is required
+ * 
+ * @param T the data type constraint extending DType
+ * @param V the actual value type that will be stored and accessed
+ */
+public class CopyMaterializationStrategy<T : DType, V> : MaterializationStrategy<T, V> {
+    
+    override val name: String = "CopyMaterialization"
+    
+    override fun materialize(view: TensorView<T, V>): Tensor<T, V> {
+        val viewShape = view.viewShape
+        val viewVolume = viewShape.volume
+        
+        // Create a new data array to hold the materialized elements
+        val materializedData = createDataArray(view, viewVolume)
+        
+        // Copy all elements from the view to the new array
+        copyViewElements(view, materializedData, viewShape)
+        
+        // Create and return the materialized tensor
+        return createMaterializedTensor(view, materializedData, viewShape)
+    }
+    
+    override fun canMaterialize(view: TensorView<T, V>): Boolean {
+        // CopyMaterializationStrategy can handle any view as long as:
+        // 1. The view has a valid shape
+        // 2. Memory is available for allocation
+        return try {
+            view.viewShape.volume >= 0
+        } catch (e: Exception) {
+            false
+        }
+    }
+    
+    override fun estimateMemoryOverhead(view: TensorView<T, V>): Long {
+        // Estimate memory required for a copy of the view data
+        val viewVolume = view.viewShape.volume
+        val bytesPerElement = estimateBytesPerElement(view.dtype)
+        return viewVolume.toLong() * bytesPerElement
+    }
+    
+    /**
+     * Creates a data array suitable for storing the materialized view elements.
+     * 
+     * This method needs to create an appropriate array type based on the
+     * tensor's value type. Since we don't have direct access to the tensor
+     * factory here, we'll need to work with the existing data structure.
+     */
+    @Suppress("UNCHECKED_CAST")
+    private fun createDataArray(view: TensorView<T, V>, volume: Int): Array<V?> {
+        return arrayOfNulls<Any>(volume) as Array<V?>
+    }
+    
+    /**
+     * Copies all elements from the tensor view to the materialized data array.
+     * 
+     * This method iterates through the view's coordinate space and copies
+     * each element to the corresponding position in the output array using
+     * row-major order.
+     */
+    private fun copyViewElements(view: TensorView<T, V>, data: Array<V?>, shape: Shape) {
+        val dimensions = shape.dimensions
+        val indices = IntArray(dimensions.size)
+        
+        fun copyRecursive(dimension: Int, flatIndex: Int): Int {
+            var currentIndex = flatIndex
+            
+            if (dimension == dimensions.size) {
+                // Base case: copy the element at this coordinate
+                val element = view.data.get(*indices)
+                data[currentIndex] = element
+                return currentIndex + 1
+            }
+            
+            // Recursive case: iterate through this dimension
+            for (i in 0 until dimensions[dimension]) {
+                indices[dimension] = i
+                currentIndex = copyRecursive(dimension + 1, currentIndex)
+            }
+            
+            return currentIndex
+        }
+        
+        copyRecursive(0, 0)
+    }
+    
+    /**
+     * Creates a materialized tensor from the copied data.
+     * 
+     * This method constructs a new Tensor instance using the copied data
+     * and the view's shape and data type information.
+     */
+    private fun createMaterializedTensor(
+        view: TensorView<T, V>, 
+        data: Array<V?>, 
+        shape: Shape
+    ): Tensor<T, V> {
+        // Create a simple tensor implementation that wraps our materialized data
+        return MaterializedTensor(
+            data = MaterializedTensorData<T, V>(shape, data),
+            ops = view.ops,
+            dtype = view.dtype
+        )
+    }
+    
+    /**
+     * Estimates the number of bytes per element for the given data type.
+     */
+    private fun estimateBytesPerElement(dtype: DType): Int {
+        return when (dtype.name) {
+            "FP32" -> 4
+            "FP16" -> 2
+            "Int32" -> 4
+            "Int8" -> 1
+            "Int4" -> 1 // Packed, but estimate 1 byte for simplicity
+            "Ternary" -> 1 // Packed, but estimate 1 byte for simplicity
+            else -> 4 // Default to 4 bytes
+        }
+    }
+    
+    /**
+     * Simple tensor data implementation for materialized tensors.
+     */
+    private class MaterializedTensorData<T : DType, V>(
+        override val shape: Shape,
+        private val data: Array<V?>
+    ) : TensorData<T, V> {
+        
+        override fun get(vararg indices: Int): V {
+            val flatIndex = calculateFlatIndex(indices)
+            return data[flatIndex] ?: throw IllegalStateException("Null data at index $flatIndex")
+        }
+        
+        override fun set(vararg indices: Int, value: V) {
+            val flatIndex = calculateFlatIndex(indices)
+            data[flatIndex] = value
+        }
+        
+        private fun calculateFlatIndex(indices: IntArray): Int {
+            require(indices.size == shape.dimensions.size) {
+                "Expected ${shape.dimensions.size} indices, got ${indices.size}"
+            }
+            
+            var flatIndex = 0
+            var stride = 1
+            
+            // Calculate flat index using row-major order
+            for (i in shape.dimensions.size - 1 downTo 0) {
+                require(indices[i] >= 0 && indices[i] < shape.dimensions[i]) {
+                    "Index ${indices[i]} out of bounds for dimension $i with size ${shape.dimensions[i]}"
+                }
+                flatIndex += indices[i] * stride
+                stride *= shape.dimensions[i]
+            }
+            
+            return flatIndex
+        }
+    }
+    
+    /**
+     * Simple tensor implementation for materialized tensors.
+     */
+    private class MaterializedTensor<T : DType, V>(
+        override val data: TensorData<T, V>,
+        override val ops: TensorOps<V>,
+        override val dtype: T
+    ) : Tensor<T, V>
+}

skainet-lang/skainet-lang-api/src/commonMain/kotlin/sk/ainet/lang/tensor/IndexMapper.kt

@@ -0,0 +1,128 @@
+package sk.ainet.lang.tensor
+
+/**
+ * Interface for coordinate transformation between tensor view space and parent tensor space.
+ * 
+ * IndexMapper handles the complex task of translating multidimensional coordinates from
+ * a tensor view's coordinate system to the parent tensor's coordinate system. This enables
+ * efficient zero-copy slicing operations by providing a mapping layer that transforms
+ * element access requests.
+ * 
+ * ## Coordinate Mapping Strategy
+ * 
+ * The mapper operates on the principle of coordinate space transformation:
+ * - **Child Space**: The coordinate system as seen by the tensor view
+ * - **Parent Space**: The coordinate system of the underlying parent tensor
+ * - **Mapping Function**: A transformation that converts child coordinates to parent coordinates
+ * 
+ * ## Performance Considerations
+ * 
+ * IndexMappers are designed with performance in mind and provide optimization hints:
+ * - Contiguity detection for vectorized operations
+ * - Stride information for efficient memory access patterns
+ * - Caching opportunities for repeated coordinate calculations
+ * 
+ * ## Implementation Guidelines
+ * 
+ * Implementations should:
+ * - Validate coordinate bounds before transformation
+ * - Cache expensive calculations when possible
+ * - Provide accurate contiguity and stride information
+ * - Handle edge cases gracefully (empty slices, single elements)
+ * 
+ * @see TensorView for the primary consumer of IndexMapper implementations
+ * @see SliceIndexMapper for a general-purpose implementation
+ * @see NCHWIndexMapper for NCHW layout-optimized implementation
+ */
+public interface IndexMapper {
+    
+    /**
+     * Maps child (view) coordinates to parent tensor coordinates.
+     * 
+     * This method performs the core coordinate transformation, converting indices
+     * from the view's coordinate system to the parent tensor's coordinate system.
+     * The transformation must handle:
+     * - Dimensional mapping (view dimensions to parent dimensions)
+     * - Offset calculations (handling slice starting positions)
+     * - Stride transformations (handling step sizes and memory layout)
+     * 
+     * ## Coordinate Validation
+     * 
+     * Implementations should validate that:
+     * - childIndices.size matches the expected view dimensionality
+     * - All child indices are within valid bounds for the view
+     * - The resulting parent indices are within parent tensor bounds
+     * 
+     * ## Performance Requirements
+     * 
+     * This method is called for every element access operation, so implementations
+     * must be highly optimized. Consider:
+     * - Pre-computing stride multipliers
+     * - Using lookup tables for common patterns
+     * - Minimizing array allocations
+     * 
+     * @param childIndices the coordinates in the view's coordinate system
+     * @return the corresponding coordinates in the parent tensor's coordinate system
+     * @throws IndexOutOfBoundsException if childIndices are invalid or out of bounds
+     * @throws IllegalArgumentException if childIndices has incorrect dimensionality
+     */
+    public fun mapToParent(childIndices: IntArray): IntArray
+    
+    /**
+     * Determines whether the mapped region represents contiguous memory access.
+     * 
+     * This optimization hint indicates whether elements accessed through this
+     * mapper are stored contiguously in memory within the parent tensor.
+     * Contiguous access patterns enable significant performance optimizations:
+     * - Vectorized operations (SIMD)
+     * - Cache-friendly memory access patterns  
+     * - Bulk memory operations (memcpy-style transfers)
+     * - Reduced coordinate calculation overhead
+     * 
+     * ## Contiguity Criteria
+     * 
+     * A mapping is considered contiguous when:
+     * - Sequential view coordinates map to sequential parent coordinates
+     * - No gaps exist between mapped memory locations
+     * - The memory layout follows a predictable stride pattern
+     * 
+     * ## Use Cases
+     * 
+     * Contiguous mappings are common for:
+     * - Full dimension slices: `tensor[:, :, 0:10, :]`
+     * - Batch extraction: `tensor[0:5, :, :, :]`
+     * - Simple range slices without stepping
+     * 
+     * @return true if the mapping represents contiguous memory access, false otherwise
+     */
+    public fun isContiguous(): Boolean
+    
+    /**
+     * Returns the stride pattern for efficient memory access.
+     * 
+     * Strides define the number of elements to skip in memory when moving
+     * one position along each dimension. This information is crucial for:
+     * - Optimizing nested loop access patterns
+     * - Implementing efficient iteration algorithms  
+     * - Cache-aware memory access strategies
+     * - Vectorization opportunity detection
+     * 
+     * ## Stride Calculation
+     * 
+     * For a view with shape [d0, d1, d2, ...], the stride array contains:
+     * - stride[0]: elements to skip when incrementing dimension 0 by 1
+     * - stride[1]: elements to skip when incrementing dimension 1 by 1
+     * - stride[2]: elements to skip when incrementing dimension 2 by 1
+     * - ...
+     * 
+     * ## Memory Layout Considerations
+     * 
+     * Strides must account for the parent tensor's memory layout:
+     * - Row-major (C-style): rightmost dimension has stride 1
+     * - NCHW layout: strides typically [C×H×W, H×W, W, 1]
+     * - Slicing effects: non-unit steps multiply base strides
+     * 
+     * @return array of stride values for each dimension, matching view dimensionality
+     */
+    public fun getStride(): IntArray
+}