Refactor/cleanup model loaders

src/main/java/org/beehive/gpullama3/LlamaApp.java

@@ -1,10 +1,8 @@
 package org.beehive.gpullama3;
 
-import org.beehive.gpullama3.aot.AOT;
 import org.beehive.gpullama3.auxiliary.LastRunMetrics;
 import org.beehive.gpullama3.inference.sampler.Sampler;
 import org.beehive.gpullama3.model.Model;
-import org.beehive.gpullama3.model.loader.ModelLoader;
 
 import java.io.IOException;
 

src/main/java/org/beehive/gpullama3/inference/weights/tornado/FP16Weights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/fp16/FP16Weights.java

@@ -1,6 +1,7 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.fp16;
 
 import org.beehive.gpullama3.core.model.GGMLType;
+import org.beehive.gpullama3.inference.weights.tornado.TornadoWeights;
 import uk.ac.manchester.tornado.api.types.arrays.FloatArray;
 import uk.ac.manchester.tornado.api.types.arrays.HalfFloatArray;
 

src/main/java/org/beehive/gpullama3/inference/weights/tornado/LlamaTornadoWeights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/fp16/LlamaTornadoWeights.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.fp16;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import uk.ac.manchester.tornado.api.types.arrays.FloatArray;

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Phi3TornadoWeights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/fp16/Phi3TornadoWeights.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.fp16;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import uk.ac.manchester.tornado.api.types.arrays.FloatArray;

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Qwen2TornadoWeights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/fp16/Qwen2TornadoWeights.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.fp16;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import uk.ac.manchester.tornado.api.types.arrays.FloatArray;

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Qwen3TornadoWeights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/fp16/Qwen3TornadoWeights.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.fp16;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import uk.ac.manchester.tornado.api.types.arrays.FloatArray;

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Phi3TornadoWeightsQ8_0.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/q8_0/Phi3TornadoWeightsQ8_0.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.q8_0;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import org.beehive.gpullama3.core.model.tensor.Q8_0QuantizedTensor;

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Q8_0Weights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/q8_0/Q8_0Weights.java

@@ -1,7 +1,8 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.q8_0;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import org.beehive.gpullama3.core.model.tensor.Q8_0QuantizedTensor;
+import org.beehive.gpullama3.inference.weights.tornado.TornadoWeights;
 import uk.ac.manchester.tornado.api.types.arrays.FloatArray;
 
 public class Q8_0Weights implements TornadoWeights {

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Qwen2TornadoWeightsQ8_0.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/q8_0/Qwen2TornadoWeightsQ8_0.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.q8_0;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import org.beehive.gpullama3.core.model.tensor.Q8_0QuantizedTensor;

src/main/java/org/beehive/gpullama3/inference/weights/tornado/Qwen3Q8_0TornadoWeights.java → src/main/java/org/beehive/gpullama3/inference/weights/tornado/q8_0/Qwen3Q8_0TornadoWeights.java

@@ -1,4 +1,4 @@
-package org.beehive.gpullama3.inference.weights.tornado;
+package org.beehive.gpullama3.inference.weights.tornado.q8_0;
 
 import org.beehive.gpullama3.core.model.GGMLType;
 import org.beehive.gpullama3.core.model.tensor.Q8_0QuantizedTensor;

src/main/java/org/beehive/gpullama3/model/loader/AbstractModelLoader.java

@@ -0,0 +1,172 @@
+package org.beehive.gpullama3.model.loader;
+
+import org.beehive.gpullama3.core.model.GGUF;
+import org.beehive.gpullama3.core.model.tensor.GGMLTensorEntry;
+import org.beehive.gpullama3.core.types.Pair;
+import org.beehive.gpullama3.inference.weights.Weights;
+import org.beehive.gpullama3.model.Configuration;
+import org.beehive.gpullama3.model.Model;
+import org.beehive.gpullama3.tokenizer.impl.Tokenizer;
+import org.beehive.gpullama3.tokenizer.vocabulary.Vocabulary;
+
+import java.io.IOException;
+import java.nio.channels.FileChannel;
+import java.util.Map;
+
+/**
+ * Abstract base class for model loaders using Template Method pattern. Provides common loading flow with extension points for model-specific logic.
+ *
+ * @param <M>
+ *         The specific Model type to load
+ * @param <C>
+ *         The specific Configuration type for the model
+ */
+public abstract class AbstractModelLoader<M extends Model, C extends Configuration> {
+
+    protected final FileChannel fileChannel;
+    protected final GGUF gguf;
+    protected final int contextLength;
+    protected final boolean loadWeights;
+    protected final boolean useTornadovm;
+
+    protected Vocabulary vocabulary;
+
+    protected AbstractModelLoader(FileChannel fileChannel, GGUF gguf, int contextLength, boolean loadWeights, boolean useTornadovm) {
+        this.fileChannel = fileChannel;
+        this.gguf = gguf;
+        this.contextLength = contextLength;
+        this.loadWeights = loadWeights;
+        this.useTornadovm = useTornadovm;
+    }
+
+    /**
+     * Template method that defines the model loading workflow. Subclasses should not override this method.
+     *
+     * @return The loaded model instance
+     */
+    public final M loadModel() {
+        try {
+            Map<String, Object> metadata = gguf.getMetadata();
+
+            // Step 1: Load vocabulary
+            this.vocabulary = loadVocabulary(metadata);
+
+            // Step 2: Create tokenizer
+            Tokenizer tokenizer = createTokenizer(metadata, vocabulary);
+
+            // Step 3: Create configuration
+            C config = createConfiguration(metadata);
+
+            // Step 4: Load weights (if requested)
+            Weights weights = null;
+            if (loadWeights) {
+                Map<String, GGMLTensorEntry> tensorEntries = GGUF.loadTensors(fileChannel, gguf.getTensorDataOffset(), gguf.getTensorInfos());
+                weights = loadWeights(tensorEntries, config);
+            }
+
+            // Step 5: Create and return model instance
+            return createModel(config, tokenizer, weights);
+
+        } catch (IOException e) {
+            throw new ModelLoadException("Failed to load model", e);
+        }
+    }
+
+    /**
+     * Load the vocabulary from GGUF metadata. Model-specific implementations should override this method.
+     *
+     * @param metadata
+     *         The GGUF metadata map
+     * @return The loaded Vocabulary
+     */
+    protected abstract Vocabulary loadVocabulary(Map<String, Object> metadata);
+
+    /**
+     * Create a tokenizer instance for this model.
+     *
+     * @param metadata
+     *         The GGUF metadata map
+     * @param vocabulary
+     *         The loaded vocabulary
+     * @return The tokenizer instance
+     */
+    protected abstract Tokenizer createTokenizer(Map<String, Object> metadata, Vocabulary vocabulary);
+
+    /**
+     * Create a configuration instance from GGUF metadata.
+     *
+     * @param metadata
+     *         The GGUF metadata map
+     * @return The configuration instance
+     */
+    protected abstract C createConfiguration(Map<String, Object> metadata);
+
+    /**
+     * Load model weights from tensor entries. Default implementation handles common weight loading logic.
+     *
+     * @param tensorEntries
+     *         Map of tensor names to tensor entries
+     * @param config
+     *         The model configuration
+     * @return The loaded weights
+     */
+    public Weights loadWeights(Map<String, GGMLTensorEntry> tensorEntries, C config) {
+        // Precompute RoPE frequencies
+        Pair<float[], float[]> ropeFreqs = precomputeRopeFrequencies(config);
+
+        // Get token embeddings and output weights
+        GGMLTensorEntry tokenEmbeddings = getTokenEmbeddings(tensorEntries);
+        GGMLTensorEntry outputWeight = getOutputWeight(tensorEntries, tokenEmbeddings);
+
+        // Delegate to specific implementation
+        if (useTornadovm) {
+            return createTornadoVMWeights(tensorEntries, config, ropeFreqs, tokenEmbeddings, outputWeight);
+        } else {
+            return createStandardWeights(tensorEntries, config, ropeFreqs, tokenEmbeddings, outputWeight);
+        }
+    }
+
+    /**
+     * Create the final model instance.
+     *
+     * @param config
+     *         The model configuration
+     * @param tokenizer
+     *         The tokenizer
+     * @param weights
+     *         The loaded weights
+     * @return The model instance
+     */
+    protected abstract M createModel(C config, Tokenizer tokenizer, Weights weights);
+
+    /**
+     * Precompute RoPE frequencies for this model. Default implementation can be overridden for custom RoPE configurations.
+     */
+    protected abstract Pair<float[], float[]> precomputeRopeFrequencies(C config);
+
+    /**
+     * Get token embeddings tensor entry. Default implementation can be overridden for different tensor naming.
+     */
+    protected GGMLTensorEntry getTokenEmbeddings(Map<String, GGMLTensorEntry> tensorEntries) {
+        return tensorEntries.get("token_embd.weight");
+    }
+
+    /**
+     * Get output weight tensor entry. Default implementation falls back to token embeddings if output.weight not found.
+     */
+    protected GGMLTensorEntry getOutputWeight(Map<String, GGMLTensorEntry> tensorEntries, GGMLTensorEntry tokenEmbeddings) {
+        return tensorEntries.getOrDefault("output.weight", tokenEmbeddings);
+    }
+
+    /**
+     * Create standard (CPU) weights.
+     */
+    protected abstract Weights createStandardWeights(Map<String, GGMLTensorEntry> tensorEntries, C config, Pair<float[], float[]> ropeFreqs, GGMLTensorEntry tokenEmbeddings,
+            GGMLTensorEntry outputWeight);
+
+    /**
+     * Create TornadoVM (GPU) weights.
+     */
+    protected abstract Weights createTornadoVMWeights(Map<String, GGMLTensorEntry> tensorEntries, C config, Pair<float[], float[]> ropeFreqs, GGMLTensorEntry tokenEmbeddings,
+            GGMLTensorEntry outputWeight);
+}