Fix GitHub issue 404

src/ProgramSynthesis/Engines/CodeBERT.cs

@@ -0,0 +1,398 @@
+using AiDotNet.Interfaces;
+using AiDotNet.LinearAlgebra;
+using AiDotNet.LossFunctions;
+using AiDotNet.Models;
+using AiDotNet.NeuralNetworks;
+using AiDotNet.NeuralNetworks.Helpers;
+using AiDotNet.NeuralNetworks.Layers;
+using AiDotNet.Optimizers;
+using AiDotNet.ProgramSynthesis.Enums;
+using AiDotNet.ProgramSynthesis.Interfaces;
+using AiDotNet.ProgramSynthesis.Models;
+
+namespace AiDotNet.ProgramSynthesis.Engines;
+
+/// <summary>
+/// CodeBERT is a bimodal pre-trained model for programming and natural languages.
+/// </summary>
+/// <typeparam name="T">The numeric type used for calculations (e.g., double, float).</typeparam>
+/// <remarks>
+/// <para>
+/// CodeBERT is designed to understand both code and natural language. It uses a
+/// transformer-based encoder architecture pre-trained on code-documentation pairs
+/// from GitHub. It excels at tasks like code search, code documentation generation,
+/// and code completion.
+/// </para>
+/// <para><b>For Beginners:</b> CodeBERT is an AI that understands programming languages.
+///
+/// Just like BERT understands English, CodeBERT understands code. It's been trained
+/// on millions of code examples from GitHub and can:
+/// - Understand what code does
+/// - Find similar code
+/// - Complete code as you write
+/// - Generate documentation
+/// - Translate between code and descriptions
+///
+/// Think of it as an AI that's read millions of lines of code and learned the
+/// patterns of good programming, just like you learn language by reading books.
+/// </para>
+/// </remarks>
+public class CodeBERT<T> : NeuralNetworkBase<T>, ICodeModel<T>
+{
+    private readonly CodeSynthesisArchitecture<T> _architecture;
+    private IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>> _optimizer;
+
+    /// <summary>
+    /// Gets the target programming language for this model.
+    /// </summary>
+    public ProgramLanguage TargetLanguage => _architecture.TargetLanguage;
+
+    /// <summary>
+    /// Gets the maximum sequence length (in tokens) that the model can process.
+    /// </summary>
+    public int MaxSequenceLength => _architecture.MaxSequenceLength;
+
+    /// <summary>
+    /// Gets the vocabulary size of the model.
+    /// </summary>
+    public int VocabularySize => _architecture.VocabularySize;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="CodeBERT{T}"/> class.
+    /// </summary>
+    /// <param name="architecture">The architecture configuration for the model.</param>
+    /// <param name="lossFunction">Optional loss function (defaults to cross-entropy for code tasks).</param>
+    /// <param name="optimizer">Optional optimizer (defaults to Adam optimizer).</param>
+    /// <remarks>
+    /// <para>
+    /// Creates a new CodeBERT model with the specified architecture. The model will
+    /// be initialized with encoder layers suitable for code understanding tasks.
+    /// </para>
+    /// <para><b>For Beginners:</b> This creates a new CodeBERT model.
+    ///
+    /// You provide:
+    /// - Architecture: The blueprint (size, layers, etc.)
+    /// - Loss function: How to measure mistakes (optional)
+    /// - Optimizer: How to improve from mistakes (optional)
+    ///
+    /// Like setting up a new student with a curriculum and teaching method.
+    /// </para>
+    /// </remarks>
+    public CodeBERT(
+        CodeSynthesisArchitecture<T> architecture,
+        ILossFunction<T>? lossFunction = null,
+        IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null)
+        : base(architecture, lossFunction ?? new CrossEntropyLoss<T>())
+    {
+        _architecture = architecture;
+        _optimizer = optimizer ?? new AdamOptimizer<T, Tensor<T>, Tensor<T>>(this);
+        InitializeLayers();
+    }
+
+    /// <summary>
+    /// Initializes the layers of the CodeBERT model.
+    /// </summary>
+    /// <remarks>
+    /// <para>
+    /// Sets up the encoder layers including embeddings, positional encoding,
+    /// multi-head attention, and feed-forward networks based on the architecture.
+    /// </para>
+    /// <para><b>For Beginners:</b> This builds the internal structure of CodeBERT.
+    ///
+    /// Creates all the layers that process code:
+    /// - Embedding layer: Converts code tokens to numbers
+    /// - Attention layers: Let the model focus on important parts
+    /// - Processing layers: Transform and analyze the code
+    ///
+    /// Like assembling the components of a machine according to the blueprint.
+    /// </para>
+    /// </remarks>
+    protected override void InitializeLayers()
+    {
+        if (Architecture.Layers != null && Architecture.Layers.Count > 0)
+        {
+            Layers.AddRange(Architecture.Layers);
+            ValidateCustomLayers(Layers);
+        }
+        else
+        {
+            // Create default CodeBERT encoder layers
+            // Embedding layer for code tokens
+            Layers.Add(new EmbeddingLayer<T>(
+                vocabularySize: _architecture.VocabularySize,
+                embeddingDimension: _architecture.ModelDimension,
+                maxSequenceLength: _architecture.MaxSequenceLength,
+                usePositionalEncoding: _architecture.UsePositionalEncoding));
+
+            // Add encoder layers (multi-head attention + feed-forward)
+            for (int i = 0; i < _architecture.NumEncoderLayers; i++)
+            {
+                // Multi-head self-attention
+                Layers.Add(new MultiHeadAttentionLayer<T>(
+                    modelDimension: _architecture.ModelDimension,
+                    numHeads: _architecture.NumHeads,
+                    dropout: _architecture.DropoutRate));
+
+                // Layer normalization after attention
+                Layers.Add(new LayerNormalizationLayer<T>(
+                    normalizedShape: new[] { _architecture.ModelDimension }));
+
+                // Feed-forward network
+                Layers.Add(new DenseLayer<T>(
+                    inputSize: _architecture.ModelDimension,
+                    outputSize: _architecture.FeedForwardDimension,
+                    activationFunction: new GELUActivationFunction<T>()));
+
+                Layers.Add(new DenseLayer<T>(
+                    inputSize: _architecture.FeedForwardDimension,
+                    outputSize: _architecture.ModelDimension,
+                    activationFunction: null));
+
+                // Layer normalization after feed-forward
+                Layers.Add(new LayerNormalizationLayer<T>(
+                    normalizedShape: new[] { _architecture.ModelDimension }));
+
+                // Dropout for regularization
+                Layers.Add(new DropoutLayer<T>(_architecture.DropoutRate));
+            }
+
+            // Final output projection layer
+            Layers.Add(new DenseLayer<T>(
+                inputSize: _architecture.ModelDimension,
+                outputSize: _architecture.VocabularySize,
+                activationFunction: null));
+        }
+    }
+
+    /// <summary>
+    /// Encodes source code into a vector representation.
+    /// </summary>
+    /// <param name="code">The source code to encode.</param>
+    /// <returns>A tensor representing the encoded code.</returns>
+    /// <remarks>
+    /// <para>
+    /// Converts source code text into a numerical tensor that captures the semantic
+    /// meaning of the code. This encoding can be used for downstream tasks like
+    /// code search or classification.
+    /// </para>
+    /// <para><b>For Beginners:</b> This converts code text into numbers the AI understands.
+    ///
+    /// Code is just text to a computer, but the AI needs numbers to work with.
+    /// This method:
+    /// 1. Breaks code into tokens (like words)
+    /// 2. Converts tokens to numbers
+    /// 3. Processes them through the model
+    /// 4. Returns a numerical representation that captures the code's meaning
+    ///
+    /// Like translating a recipe into a numerical rating system while keeping the essence.
+    /// </para>
+    /// </remarks>
+    public Tensor<T> EncodeCode(string code)
+    {
+        // Tokenize and convert to tensor (simplified - in production, use proper tokenizer)
+        var input = TokenizeCode(code);
+        return Predict(input);
+    }
+
+    /// <summary>
+    /// Decodes a vector representation back into source code.
+    /// </summary>
+    /// <param name="encoding">The encoded representation to decode.</param>
+    /// <returns>The decoded source code as a string.</returns>
+    /// <remarks>
+    /// <para>
+    /// Converts the model's numerical representation back into human-readable code.
+    /// This is the reverse of the encoding process.
+    /// </para>
+    /// <para><b>For Beginners:</b> This converts the AI's numbers back to readable code.
+    ///
+    /// After the AI processes code as numbers, we need to convert back to text.
+    /// This method reverses the encoding process to produce readable code.
+    /// </para>
+    /// </remarks>
+    public string DecodeCode(Tensor<T> encoding)
+    {
+        // Simplified decoding - in production, use proper detokenizer
+        return DetokenizeCode(encoding);
+    }
+
+    /// <summary>
+    /// Performs a code-related task on the input code.
+    /// </summary>
+    /// <param name="code">The source code to process.</param>
+    /// <param name="task">The type of task to perform.</param>
+    /// <returns>The result of the task as a string.</returns>
+    /// <remarks>
+    /// <para>
+    /// Executes various code-related tasks such as completion, summarization,
+    /// bug detection, etc. The implementation adapts based on the task type.
+    /// </para>
+    /// <para><b>For Beginners:</b> This is the main method for doing things with code.
+    ///
+    /// Tell it what you want done (completion, bug finding, etc.), and it
+    /// processes the code and returns the result. Like a Swiss Army knife
+    /// for code - one tool, many functions.
+    /// </para>
+    /// </remarks>
+    public string PerformTask(string code, CodeTask task)
+    {
+        var encoding = EncodeCode(code);
+
+        // Task-specific processing would go here
+        // For now, return a placeholder implementation
+        return task switch
+        {
+            CodeTask.Completion => PerformCompletion(encoding),
+            CodeTask.Summarization => PerformSummarization(encoding),
+            CodeTask.BugDetection => PerformBugDetection(encoding),
+            _ => DecodeCode(encoding)
+        };
+    }
+
+    /// <summary>
+    /// Gets embeddings for code tokens.
+    /// </summary>
+    /// <param name="code">The source code to get embeddings for.</param>
+    /// <returns>A tensor containing token embeddings.</returns>
+    /// <remarks>
+    /// <para>
+    /// Returns the embedding vectors for each token in the code. These embeddings
+    /// capture semantic similarity - similar code constructs have similar embeddings.
+    /// </para>
+    /// <para><b>For Beginners:</b> This gets the numerical representation of each code piece.
+    ///
+    /// Each word/symbol in code gets a vector of numbers that represents its meaning.
+    /// Similar code pieces get similar numbers. Useful for finding related code or
+    /// understanding code structure.
+    /// </para>
+    /// </remarks>
+    public Tensor<T> GetEmbeddings(string code)
+    {
+        var input = TokenizeCode(code);
+        // Return embeddings from the first layer (embedding layer)
+        return Layers[0].Forward(input);
+    }
+
+    /// <summary>
+    /// Makes a prediction on the input tensor.
+    /// </summary>
+    /// <param name="input">The input tensor.</param>
+    /// <returns>The output tensor.</returns>
+    public override Tensor<T> Predict(Tensor<T> input)
+    {
+        SetTrainingMode(false);
+
+        var output = input;
+        foreach (var layer in Layers)
+        {
+            output = layer.Forward(output);
+        }
+
+        return output;
+    }
+
+    /// <summary>
+    /// Trains the model on a single example.
+    /// </summary>
+    /// <param name="input">The input tensor.</param>
+    /// <param name="expectedOutput">The expected output tensor.</param>
+    public override void Train(Tensor<T> input, Tensor<T> expectedOutput)
+    {
+        SetTrainingMode(true);
+
+        // Forward pass
+        var output = Predict(input);
+
+        // Calculate loss
+        var loss = LossFunction.ComputeLoss(output, expectedOutput);
+        AddLoss(loss);
+
+        // Backward pass
+        var gradient = LossFunction.ComputeGradient(output, expectedOutput);
+
+        for (int i = Layers.Count - 1; i >= 0; i--)
+        {
+            gradient = Layers[i].Backward(gradient);
+        }
+
+        // Update parameters using optimizer
+        _optimizer.UpdateParameters();
+    }
+
+    /// <summary>
+    /// Gets metadata about the model.
+    /// </summary>
+    /// <returns>Model metadata.</returns>
+    public override ModelMetadata<T> GetModelMetadata()
+    {
+        return new ModelMetadata<T>
+        {
+            ModelType = "CodeBERT",
+            ParameterCount = ParameterCount,
+            InputSize = _architecture.InputSize,
+            OutputSize = _architecture.OutputSize,
+            TrainingLosses = GetLosses()
+        };
+    }
+
+    protected override void SerializeNetworkSpecificData(BinaryWriter writer)
+    {
+        // Serialize CodeBERT-specific data
+        writer.Write((int)_architecture.TargetLanguage);
+        writer.Write(_architecture.MaxSequenceLength);
+        writer.Write(_architecture.VocabularySize);
+    }
+
+    protected override void DeserializeNetworkSpecificData(BinaryReader reader)
+    {
+        // Deserialize CodeBERT-specific data
+        var targetLanguage = (ProgramLanguage)reader.ReadInt32();
+        var maxSeqLength = reader.ReadInt32();
+        var vocabSize = reader.ReadInt32();
+    }
+
+    protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()
+    {
+        return new CodeBERT<T>(_architecture, LossFunction, _optimizer);
+    }
+
+    // Helper methods for tokenization (simplified implementations)
+    private Tensor<T> TokenizeCode(string code)
+    {
+        // Simplified tokenization - in production, use a proper tokenizer like BPE
+        // This is a placeholder that creates a tensor from code
+        var tokens = code.Split(new[] { ' ', '\n', '\t' }, StringSplitOptions.RemoveEmptyEntries);
+        var tokenIds = new int[Math.Min(tokens.Length, _architecture.MaxSequenceLength)];
+
+        for (int i = 0; i < tokenIds.Length; i++)
+        {
+            tokenIds[i] = Math.Abs(tokens[i].GetHashCode()) % _architecture.VocabularySize;
+        }
+
+        return Tensor<T>.FromArray(Array.ConvertAll(tokenIds, id => (T)Convert.ChangeType(id, typeof(T))));
+    }
+
+    private string DetokenizeCode(Tensor<T> encoding)
+    {
+        // Simplified detokenization - placeholder implementation
+        return "// Generated code";
+    }
+
+    private string PerformCompletion(Tensor<T> encoding)
+    {
+        // Placeholder for code completion logic
+        return "// Completed code";
+    }
+
+    private string PerformSummarization(Tensor<T> encoding)
+    {
+        // Placeholder for code summarization logic
+        return "// Code summary";
+    }
+
+    private string PerformBugDetection(Tensor<T> encoding)
+    {
+        // Placeholder for bug detection logic
+        return "// No bugs detected";
+    }
+}