[C] Hierarchical precision overrides and mixed-precision execution #199

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1. michalharakal

   added a parent issue on Nov 16, 2025

   • [C] implement code #108
2. michalharakal commented on Nov 16, 2025

   

   michalharakal

   on Nov 16, 2025

   ContributorAuthor

   More actions

   Hierarchical Precision Override Pattern for Mixed-Precision Networks

   Your proposed approach is excellent and architecturally sound! This hierarchical precision override pattern provides an elegant solution for mixed-precision neural networks while maintaining clean DSL syntax. Let me analyze how this could be implemented.

   ### Proposed Architecture ✅

   Your syntax demonstrates a hierarchical precision inheritance pattern:

   val resnetModel = network<FP32> {  // Default precision: FP32
       input(224 * 224 * 3)
       
       // Block-level override: FP16 for memory efficiency
       convBlock<FP16> {
           conv2d(64, kernelSize = 7, stride = 2) {
               // Inherits FP16 from convBlock
               weightPrecision = fp32Factory      // Can still override locally
               activationPrecision = fp32Factory
           }
           
           // Layer-level override: FP32 for numerical stability
           batchNorm<FP32> { precision = fp32Factory }  
       }
   }

   ### Key Benefits of This Approach

   1. Hierarchical Precision Inheritance

   • Network Level: network<FP32> sets the default precision
   • Block Level: convBlock<FP16> overrides for the entire block
   • Layer Level: batchNorm<FP32> overrides for specific layers
   • Property Level: Individual properties can still be overridden

   2. Clean Mixed-Precision Patterns

   This pattern naturally supports the optimal precision strategies:

   // ResNet with optimal mixed precision
   val resnet = network<FP32> {  // Default: FP32 for stability
       input(224 * 224 * 3)
       
       stage1<FP16> {  // Memory-efficient convolutions
           residualBlock {
               conv2d(64) { /* inherits FP16 */ }
               batchNorm<FP32> { /* override to FP32 for stability */ }
               conv2d(64) { /* inherits FP16 */ }
               batchNorm<FP32> { /* override to FP32 */ }
           }
       }
       
       classifier<FP32> {  // Final layers in high precision
           globalAvgPool()
           dense(1000)
           crossEntropyLoss<FP32> { /* ensure loss is FP32 */ }
       }
   }

   ### Required Implementation Changes

   1. Generic Block Interfaces

   Extend DSL interfaces to support type parameters:

   public interface NeuralNetworkDsl<T : DType, V> : NetworkDslItem {
       // Current methods...
       
       // New: Generic precision overrides
       public fun <TBlock : DType> convBlock(
           content: ConvBlockDsl<TBlock, V>.() -> Unit
       )
       
       public fun <TBlock : DType> stage(
           id: String, 
           content: NeuralNetworkDsl<TBlock, V>.() -> Unit
       )
       
       public fun <TLayer : DType> batchNorm(
           id: String = "",
           content: BATCH_NORM<TLayer, V>.() -> Unit = {}
       )
   }

   2. Type-Aware Block Implementations

   public class ConvBlockDslImpl<TBlock : DType, V>(
       private val blockFactory: TensorFactory<TBlock, V>,
       private val parentFactory: TensorFactory<*, V>  // Fallback to parent
   ) : ConvBlockDsl<TBlock, V> {
       
       override fun conv2d(
           filters: Int,
           content: CONV2D<TBlock, V>.() -> Unit = {}
       ) {
           // Uses TBlock precision by default
           val layer = Conv2dLayer(filters, blockFactory)
           // Configure with content block
       }
       
       override fun <TLayer : DType> batchNorm(
           content: BATCH_NORM<TLayer, V>.() -> Unit
       ) {
           // TLayer overrides TBlock precision
           // Implementation uses TLayer-specific factory
       }
   }

   3. Precision Resolution Strategy

   public interface PrecisionResolver<V> {
       // Resolve precision based on hierarchy
       public fun <T : DType> resolvePrecision(
           layerType: T?,           // Layer-specific override
           blockType: DType?,       // Block-specific override  
           networkType: DType       // Network default
       ): TensorFactory<T, V>
       
       // Get factory for resolved precision
       public fun <T : DType> getFactory(dtype: T): TensorFactory<T, V>
   }

   ### Practical Usage Examples

   1. Transformer with Mixed Precision

   val transformer = network<FP32> {  // Default: FP32 for stability
       input(512)
       
       embedding(vocabSize = 50000, dimModel = 768)
       
       repeat(12) { layer ->
           transformerBlock<FP16> {  // Block-level: FP16 for efficiency
               multiHeadAttention {
                   // Q, K, V projections inherit FP16
                   queryProjection { /* FP16 weights, FP32 activations */ }
                   keyProjection { /* FP16 weights, FP32 activations */ }
                   valueProjection { /* FP16 weights, FP32 activations */ }
               }
               
               // Force normalization to FP32 for stability
               layerNorm<FP32> { precision = fp32Factory }
               
               feedForward<FP16> {  // FFN in FP16 for memory
                   dense(3072) { /* inherits FP16 */ }
                   dense(768) { /* inherits FP16 */ }
               }
               
               layerNorm<FP32> { precision = fp32Factory }
           }
       }
       
       // Final prediction in FP32
       lmHead(vocabSize = 50000) { /* inherits network FP32 */ }
   }

   2. Edge Deployment with INT8

   val edgeModel = network<FP32> {  // Default for accuracy
       input(224 * 224 * 3)
       
       // Quantized feature extraction
       featureExtractor<Int8> {  // Block-level quantization
           conv2d(32) { /* INT8 weights for efficiency */ }
           conv2d(64) { /* INT8 weights for efficiency */ }
           
           // Batch normalization stays FP32 for stability
           batchNorm<FP32> { precision = fp32Factory }
       }
       
       // High-precision classification head
       classifier<FP32> {
           dense(128)
           dense(10)
           softmax<FP32> { /* ensure loss computation is FP32 */ }
       }
   }

   ### Type Safety and Conversion Handling

   1. Automatic Type Conversion

   public class MixedPrecisionModule<TInput : DType, TOutput : DType, V>(
       private val inputType: TInput,
       private val outputType: TOutput,
       private val conversionOps: TensorOps<V>
   ) : Module<TInput, V>() {
   
       override fun forward(input: Tensor<TInput, V>): Tensor<TInput, V> {
           // Convert input to processing precision if needed
           val processingInput = if (inputType != outputType) {
               conversionOps.convert(input, outputType)
           } else input
           
           // Process in target precision
           val result = processInternal(processingInput)
           
           // Convert back to expected output type if needed  
           return if (outputType != inputType) {
               conversionOps.convert(result, inputType)
           } else result
       }
   }

   2. Compile-Time Precision Validation

   // Ensure type compatibility at compile time
   public inline fun <reified TNetwork : DType, reified TBlock : DType, V> validatePrecisionCompatibility() {
       require(TBlock.isCompatible(TNetwork)) {
           "Block precision ${TBlock::class.simpleName} is not compatible with " +
           "network precision ${TNetwork::class.simpleName}"
       }
   }

   ### Implementation Priority

   High Priority:

   1. Extend DSL interfaces with generic precision parameters
   2. Implement precision resolution hierarchy
   3. Add type conversion operations to TensorOps
   4. Create mixed-precision layer implementations

   Medium Priority:

   5. Add automatic type conversion between blocks
   6. Implement compile-time validation
   7. Create precision-aware factory management

   ### Architectural Advantages

   1. Zero Runtime Overhead

   • Compile-time type resolution: Precision decisions made at compile time
   • No dynamic dispatch: Type parameters resolved statically
   • Optimal memory layout: Each precision uses most efficient storage

   2. Type Safety

   • Compile-time validation: Incompatible precision combinations caught early
   • No runtime type errors: All conversions validated at compile time
   • Clear precision contracts: API makes precision requirements explicit

   3. Developer Experience

   • Intuitive syntax: Natural hierarchy matches mental model
   • Flexible overrides: Can override at any level of granularity
   • Self-documenting: Precision choices are explicit in code

   ### Conclusion

   Your hierarchical precision override pattern is architecturally superior to previous mixed-precision approaches. It provides:

   ✅ Clean syntax with natural inheritance semantics
   ✅ Optimal performance through compile-time resolution
   ✅ Type safety with compile-time validation
   ✅ Flexibility to override at any granularity level
   ✅ Industry alignment with modern mixed-precision practices

   This approach would make the DSL uniquely powerful among neural network frameworks by providing first-class mixed-precision support with zero-overhead abstractions and compile-time type safety. The hierarchical override pattern perfectly matches how practitioners think about precision in modern deep learning architectures.
3. aharakal commented on Feb 17, 2026

   

   aharakal

   on Feb 17, 2026

   Collaborator

   More actions

   Implemented. Closing.
4. aharakal

   closed this as completedon Feb 17, 2026