Fix erro in loading GGUF fully connected model

Author: michalharakal

MNISTDemo/.java-version

@@ -0,0 +1 @@
+21.0

MNISTDemo/composeApp/src/commonMain/kotlin/sk/ai/net/samples/kmp/mnist/demo/DrawingScreenViewModel.kt

@@ -25,8 +25,8 @@ class DrawingScreenViewModel(handleSource: () -> Source) : ViewModel() {
     @Suppress("UnusedPrivateMember")
     private val handleSourceFn = handleSource
 
-    // Selected model (default to CNN). Exposed to UI for selection.
-    var selectedModelId by mutableStateOf(ModelId.CNN_MNIST)
+    // Selected model (default to MLP). Exposed to UI for selection.
+    var selectedModelId by mutableStateOf(ModelId.MLP_MNIST)
         private set
 
     // Current model status

MNISTDemo/composeApp/src/commonMain/kotlin/sk/ai/net/samples/kmp/mnist/demo/settings/AppSettings.kt

@@ -18,7 +18,7 @@ enum class ModelStatus {
  * Minimal solution to support model selection from Settings.
  */
 object AppSettings {
-    private val _selectedModelId = MutableStateFlow(ModelId.CNN_MNIST)
+    private val _selectedModelId = MutableStateFlow(ModelId.MLP_MNIST)
     val selectedModelId: StateFlow<ModelId> = _selectedModelId.asStateFlow()
 
     // Status for each model

MNISTDemo/shared/src/androidMain/kotlin/sk/ainet/lang/model/WeightLoader.android.kt

@@ -0,0 +1,35 @@
+package sk.ainet.lang.model
+
+import kotlinx.io.Buffer
+import kotlinx.io.Source
+import sk.ainet.io.gguf.GGUFReader
+import sk.ainet.lang.nn.Module
+import sk.ainet.lang.types.FP32
+
+/**
+ * Android implementation using reflection-based GGUF loader.
+ * Same approach as JVM since Android supports reflection.
+ */
+actual fun loadWeightsFromBytes(module: Module<FP32, Float>, bytes: ByteArray) {
+    val source: Source = Buffer().apply { write(bytes) }
+    val reader = GGUFReader(source)
+    val tensorMap = reader.tensors.associateBy { it.name }
+
+    module.trainableParameters().forEach { param ->
+        val readerTensor = tensorMap[param.name]
+        if (readerTensor != null) {
+            val tensorData = param.value.data
+            // Use reflection to access the underlying buffer array
+            val bufferField = tensorData::class.java.declaredFields.firstOrNull {
+                it.type.isArray && it.type.componentType == Float::class.javaPrimitiveType
+            }
+            if (bufferField != null) {
+                bufferField.isAccessible = true
+                val array = bufferField.get(tensorData) as FloatArray
+                readerTensor.data.forEachIndexed { idx, value ->
+                    array[idx] = (value as Number).toFloat()
+                }
+            }
+        }
+    }
+}

MNISTDemo/shared/src/commonMain/kotlin/sk/ainet/clean/framework/inference/CnnInferenceModuleAdapter.kt

@@ -3,9 +3,7 @@ package sk.ainet.clean.framework.inference
 import sk.ainet.clean.data.image.GrayScale28To28Image
 import sk.ainet.lang.model.createMNISTCNN
 import sk.ainet.lang.model.classifyImage
-import sk.ainet.lang.model.loader.loadModelWeights
-import kotlinx.io.Buffer
-import kotlinx.io.Source
+import sk.ainet.lang.model.loadWeightsFromBytes
 import sk.ainet.clean.domain.port.InferenceModule
 import sk.ainet.lang.nn.Module
 import sk.ainet.lang.types.FP32
@@ -30,8 +28,7 @@ class CnnInferenceModuleAdapter(
         fun fromModule(module: Module<FP32, Float>): CnnInferenceModuleAdapter {
             return CnnInferenceModuleAdapter(
                 loadFn = { bytes ->
-                    val src: Source = Buffer().apply { write(bytes) }
-                    loadModelWeights(module, src)
+                    loadWeightsFromBytes(module, bytes)
                 },
                 inferFn = { image -> classifyImage(module, image) }
             )

MNISTDemo/shared/src/commonMain/kotlin/sk/ainet/clean/framework/inference/MlpInferenceModuleAdapter.kt

@@ -3,9 +3,7 @@ package sk.ainet.clean.framework.inference
 import sk.ainet.clean.data.image.GrayScale28To28Image
 import sk.ainet.lang.model.createMNISTMLP
 import sk.ainet.lang.model.classifyImage
-import sk.ainet.lang.model.loader.loadModelWeights
-import kotlinx.io.Buffer
-import kotlinx.io.Source
+import sk.ainet.lang.model.loadWeightsFromBytes
 import sk.ainet.clean.domain.port.InferenceModule
 import sk.ainet.lang.nn.Module
 
@@ -29,8 +27,7 @@ class MlpInferenceModuleAdapter(
         fun fromModule(module: Module<FP32, Float>): MlpInferenceModuleAdapter {
             return MlpInferenceModuleAdapter(
                 loadFn = { bytes ->
-                    val src: Source = Buffer().apply { write(bytes) }
-                    loadModelWeights(module, src)
+                    loadWeightsFromBytes(module, bytes)
                 },
                 inferFn = { image -> classifyImage(module, image) }
             )

MNISTDemo/shared/src/commonMain/kotlin/sk/ainet/lang/model/WeightLoader.kt

@@ -0,0 +1,11 @@
+package sk.ainet.lang.model
+
+import sk.ainet.lang.nn.Module
+import sk.ainet.lang.types.FP32
+
+/**
+ * Platform-specific weight loader for GGUF files.
+ * On JVM, uses reflection-based loader that correctly handles PyTorch-exported GGUF.
+ * On other platforms, falls back to library loader.
+ */
+expect fun loadWeightsFromBytes(module: Module<FP32, Float>, bytes: ByteArray)

MNISTDemo/shared/src/jsMain/kotlin/sk/ainet/lang/model/WeightLoader.js.kt

@@ -0,0 +1,16 @@
+package sk.ainet.lang.model
+
+import kotlinx.io.Buffer
+import kotlinx.io.Source
+import sk.ainet.lang.model.loader.loadModelWeights
+import sk.ainet.lang.nn.Module
+import sk.ainet.lang.types.FP32
+
+/**
+ * JS implementation using library loader.
+ * Note: This may not work correctly with PyTorch-exported GGUF files.
+ */
+actual fun loadWeightsFromBytes(module: Module<FP32, Float>, bytes: ByteArray) {
+    val source: Source = Buffer().apply { write(bytes) }
+    loadModelWeights(module, source)
+}

MNISTDemo/shared/src/jvmMain/kotlin/sk/ainet/lang/model/WeightLoader.jvm.kt

@@ -0,0 +1,12 @@
+package sk.ainet.lang.model
+
+import sk.ainet.lang.nn.Module
+import sk.ainet.lang.types.FP32
+
+/**
+ * JVM implementation using reflection-based GGUF loader.
+ * This correctly loads PyTorch-exported GGUF weights.
+ */
+actual fun loadWeightsFromBytes(module: Module<FP32, Float>, bytes: ByteArray) {
+    loadGgufWeights(module, bytes)
+}

MNISTDemo/shared/src/wasmJsMain/kotlin/sk/ainet/lang/model/WeightLoader.wasmJs.kt

@@ -0,0 +1,16 @@
+package sk.ainet.lang.model
+
+import kotlinx.io.Buffer
+import kotlinx.io.Source
+import sk.ainet.lang.model.loader.loadModelWeights
+import sk.ainet.lang.nn.Module
+import sk.ainet.lang.types.FP32
+
+/**
+ * WasmJS implementation using library loader.
+ * Note: This may not work correctly with PyTorch-exported GGUF files.
+ */
+actual fun loadWeightsFromBytes(module: Module<FP32, Float>, bytes: ByteArray) {
+    val source: Source = Buffer().apply { write(bytes) }
+    loadModelWeights(module, source)
+}