[Phase 3] Implement Program Synthesis and Code Generation

Open ooples opened this issue 4 months ago • 1 comments

Problem

COMPLETELY MISSING: Neural program synthesis and code generation capabilities.

Missing Implementations

Code Models (CRITICAL):

CodeBERT (code understanding)
GraphCodeBERT (with data flow)
CodeT5 (encoder-decoder for code)

Program Synthesis (HIGH):

Neural program synthesis
Program induction from examples
Differentiable programming

Code Generation (HIGH):

Code completion
Code translation (language-to-language)
Code summarization
Bug detection

Use Cases

AI-assisted coding
Automated debugging
Test generation
Code review

Architecture

src/ProgramSynthesis/
Interface: ICodeModel, IProgramSynthesizer
Integration with transformer architectures

Success Criteria

CodeXGLUE benchmarks
HumanEval dataset
Program correctness metrics
Execution-based evaluation

Nov 07 '25 03:11 ooples

Issue #404: Junior Developer Implementation Guide

Neural Architecture Search (NAS) - DARTS and ENAS

Understanding Neural Architecture Search
DARTS: Differentiable Architecture Search
ENAS: Efficient Neural Architecture Search
Search Spaces and Operations
Implementation Strategy
Testing Strategy
Step-by-Step Implementation Guide

Understanding Neural Architecture Search

What is Neural Architecture Search?

Neural Architecture Search (NAS) is the process of automatically designing neural network architectures instead of manually engineering them.

Traditional Approach:

Human Expert → Design Architecture → Train → Evaluate → Iterate
                     ↑ Requires deep expertise and intuition

NAS Approach:

Search Algorithm → Generate Architecture → Train → Evaluate → Update Search
                         ↑ Automated discovery of optimal architectures

Why NAS?

Removes Human Bias: Discovers architectures humans might not imagine
Task-Specific Optimization: Finds architectures tailored to specific datasets/tasks
Saves Time: Automates the trial-and-error process
Better Performance: Often outperforms hand-designed architectures

The NAS Problem

Goal: Find the best architecture A* from search space A:

A* = argmax_{A ∈ A} Accuracy(A, D_val)
subject to: Latency(A) < T_max
            Params(A) < P_max

Where:

A = Set of possible architectures
D_val = Validation dataset
T_max = Maximum inference latency constraint
P_max = Maximum parameter count constraint

NAS Components

Search Space: Set of possible architectures
Search Strategy: Algorithm to explore the search space
Performance Estimation: How to evaluate architectures efficiently

DARTS: Differentiable Architecture Search

Core Idea

Instead of searching discrete architectures, relax the search space to be continuous and use gradient descent to optimize architecture parameters.

Key Innovation

Discrete Search (Slow):

Try architecture A1 → Evaluate → Try A2 → Evaluate → ...

Continuous Relaxation (Fast):

Mix all operations → Use gradients to find optimal mixture weights

Mathematical Foundation

1. Continuous Relaxation

Instead of choosing one operation, use a weighted sum of all operations:

Traditional (discrete):
o^(i,j)(x) = operation_k(x)  where k is selected from {conv3x3, conv5x5, maxpool, ...}

DARTS (continuous):
o^(i,j)(x) = Σ_k α_k * operation_k(x)

Where:

α_k = Importance weight for operation k (architecture parameters)
Operations are mixed with softmax: α_k = exp(α_k) / Σ_j exp(α_j)

2. Bilevel Optimization

DARTS optimizes two sets of parameters simultaneously:

min_α L_val(w*(α), α)          [Architecture optimization]
s.t. w*(α) = argmin_w L_train(w, α)  [Weight optimization]

Where:

α = Architecture parameters (which operations to use)
w = Network weights (learned via backprop)
L_train = Training loss
L_val = Validation loss

Intuition:

Train weights w to minimize training loss (given architecture α)
Update architecture α to minimize validation loss (given weights w)

3. Gradient-Based Search

Both w and α are optimized using gradient descent:

// Step 1: Update weights w (standard training)
w ← w - ξ * ∇_w L_train(w, α)

// Step 2: Update architecture α (architecture search)
α ← α - η * ∇_α L_val(w, α)

4. Approximation of ∇_α

Computing the exact gradient ∇_α L_val(w*(α), α) requires computing w*(α) (expensive).

Approximation (first-order):

∇_α L_val(w, α) ≈ ∇_α L_val(w - ξ * ∇_w L_train(w, α), α)

Algorithm:

1. Compute ∇_w L_train(w, α)
2. Create temporary weights: w' = w - ξ * ∇_w L_train(w, α)
3. Compute ∇_α L_val(w', α)  [gradient of architecture params]
4. Update α using this gradient

DARTS Search Space

Cell-Based Search:

Define a cell (a small subgraph)
Stack multiple cells to form the full network
Search for optimal cell structure

Node Representation:

Cell = {node_0, node_1, ..., node_N}

node_j = Σ_{i<j} Σ_o softmax(α_{i,j})_o * operation_o(node_i)

Where:

Each node is a feature map
node_0, node_1 = Input nodes (from previous cells)
node_j = Intermediate node (for j >= 2)
operation_o = One of {conv3x3, conv5x5, dilated_conv, sep_conv, max_pool, avg_pool, skip, zero}

Example Cell:

Input 0 ──┐
          ├→ [Mixed Op] → Node 2 ─┐
Input 1 ──┤                       ├→ [Mixed Op] → Node 3 → Output
          └→ [Mixed Op] → Node 2 ─┘

DARTS Algorithm

Algorithm: DARTS
Input: Training data D_train, validation data D_val
Output: Best architecture α*

1. Initialize:
   - Architecture parameters α randomly
   - Network weights w randomly

2. For each search epoch t = 1..T:
   a. Sample mini-batch from D_train
   b. Update w: w ← w - ξ * ∇_w L_train(w, α)

   c. Sample mini-batch from D_val
   d. Compute w' = w - ξ * ∇_w L_train(w, α)  [virtual step]
   e. Update α: α ← α - η * ∇_α L_val(w', α)

3. Discretization:
   For each edge (i, j):
      Select operation with highest α: o* = argmax_o α_{i,j,o}

4. Return discretized architecture α*

Implementation Sketch

public class DARTSSearcher<T> where T : struct
{
    private Tensor<T> _architectureParams;  // α parameters
    private NeuralNetwork<T> _searchNetwork;  // Mixed network
    private IOptimizer<T> _weightOptimizer;
    private IOptimizer<T> _archOptimizer;

    public void Search(int numEpochs)
    {
        for (int epoch = 0; epoch < numEpochs; epoch++)
        {
            // Phase 1: Update network weights w
            var trainBatch = SampleBatch(_trainData);
            var trainLoss = _searchNetwork.Forward(trainBatch.X, trainBatch.Y);
            var wGradients = _searchNetwork.Backward();
            _weightOptimizer.Update(_searchNetwork.Weights, wGradients);

            // Phase 2: Update architecture parameters α
            // 2a. Compute virtual weights w'
            var wPrime = ComputeVirtualWeights();

            // 2b. Evaluate on validation data
            var valBatch = SampleBatch(_valData);
            _searchNetwork.SetWeights(wPrime);  // Temporarily use w'
            var valLoss = _searchNetwork.Forward(valBatch.X, valBatch.Y);

            // 2c. Compute ∇_α L_val(w', α)
            var alphaGradients = ComputeArchitectureGradients(valLoss);
            _archOptimizer.Update(_architectureParams, alphaGradients);

            // Restore original weights
            _searchNetwork.SetWeights(_originalWeights);

            // Log progress
            if (epoch % 10 == 0)
                LogTopOperations();
        }

        // Discretization: select best operations
        return DiscretizeArchitecture();
    }

    private Tensor<T> ComputeVirtualWeights()
    {
        // w' = w - ξ * ∇_w L_train(w, α)
        var gradients = _searchNetwork.ComputeGradients(_trainData);
        var virtualWeights = _searchNetwork.Weights.Clone();

        for (int i = 0; i < virtualWeights.Length; i++)
        {
            var step = NumOps<T>.Multiply(
                gradients[i],
                NumOps<T>.FromDouble(_weightOptimizer.LearningRate)
            );
            virtualWeights[i] = NumOps<T>.Subtract(virtualWeights[i], step);
        }

        return virtualWeights;
    }
}

ENAS: Efficient Neural Architecture Search

Core Idea

Observation: Training each architecture from scratch is wasteful.

Solution: Use parameter sharing across architectures.

All architectures share the same set of weights
Searching becomes selecting which subgraph to use
Train weights once, search over subgraphs

Controller-Based Search

ENAS uses a controller RNN to generate architectures:

Controller RNN → Generates architecture A → Evaluate A → Update controller
                            ↑ Shares weights with all other architectures

Mathematical Foundation

1. Search Space as DAG

Represent architectures as Directed Acyclic Graphs (DAGs):

Nodes: {0, 1, 2, ..., N}
Edges: (i, j) with operation o_{i,j}

2. Controller Policy

The controller samples architectures from a policy π(A; θ):

θ = Controller parameters (LSTM weights)
A = Architecture sampled from π(θ)

Architecture Encoding:

For each node j = 2..N:
    1. Sample previous node: i ~ Categorical(π_i)
    2. Sample operation: o ~ Categorical(π_o)

Architecture A = {(i, j, o) for all j}

3. Reward Signal

Train controller to maximize expected reward:

J(θ) = E_{A~π(θ)}[R(A)]

Where:

R(A) = Accuracy of architecture A on validation set
Maximize via REINFORCE algorithm

4. REINFORCE Update

∇_θ J(θ) = E_{A~π(θ)}[R(A) * ∇_θ log π(A; θ)]

Approximated by sampling:
∇_θ J(θ) ≈ (1/M) * Σ_m R(A_m) * ∇_θ log π(A_m; θ)

With Baseline (reduce variance):

∇_θ J(θ) ≈ (1/M) * Σ_m (R(A_m) - b) * ∇_θ log π(A_m; θ)

where b = exponential moving average of past rewards

ENAS Algorithm

Algorithm: ENAS
Input: Training data D_train, validation data D_val
Output: Best architecture A*

1. Initialize:
   - Shared weights w randomly
   - Controller parameters θ randomly
   - Baseline b = 0

2. For each search epoch t = 1..T:

   a. Train shared weights:
      For k = 1..K:
         - Sample architecture A from π(θ)
         - Sample mini-batch from D_train
         - Compute gradients ∇_w L(A, w) for architecture A
         - Update w using gradients

   b. Train controller:
      For m = 1..M:
         - Sample architecture A_m from π(θ)
         - Evaluate A_m on D_val to get reward R_m
         - Compute policy gradient:
           ∇_θ J ≈ (R_m - b) * ∇_θ log π(A_m; θ)
         - Update θ
         - Update baseline: b ← 0.95 * b + 0.05 * R_m

3. Return architecture with highest reward: A* = argmax_A R(A)

Implementation Sketch

public class ENASSearcher<T> where T : struct
{
    private NeuralNetwork<T> _sharedWeights;  // Shared across all architectures
    private LSTMController<T> _controller;     // Generates architectures
    private double _baseline;                  // For variance reduction

    public void Search(int numEpochs)
    {
        for (int epoch = 0; epoch < numEpochs; epoch++)
        {
            // Phase 1: Train shared weights
            TrainSharedWeights(numIterations: 50);

            // Phase 2: Train controller
            TrainController(numSamples: 10);

            if (epoch % 10 == 0)
                EvaluateTopArchitectures();
        }

        return FindBestArchitecture();
    }

    private void TrainSharedWeights(int numIterations)
    {
        for (int i = 0; i < numIterations; i++)
        {
            // Sample random architecture from controller
            var arch = _controller.SampleArchitecture();

            // Build network using sampled architecture
            var network = BuildNetwork(arch, _sharedWeights);

            // Train on mini-batch
            var batch = SampleBatch(_trainData);
            var loss = network.Forward(batch.X, batch.Y);
            var gradients = network.Backward();

            // Update ONLY the weights used in this architecture
            UpdateActiveWeights(arch, gradients);
        }
    }

    private void TrainController(int numSamples)
    {
        var rewards = new List<double>();
        var logProbs = new List<Tensor<T>>();

        // Sample multiple architectures
        for (int i = 0; i < numSamples; i++)
        {
            var (arch, logProb) = _controller.SampleWithLogProb();

            // Evaluate architecture
            var network = BuildNetwork(arch, _sharedWeights);
            var accuracy = EvaluateOnValidation(network);
            rewards.Add(accuracy);
            logProbs.Add(logProb);
        }

        // Compute policy gradient with baseline
        for (int i = 0; i < numSamples; i++)
        {
            var advantage = rewards[i] - _baseline;
            var gradient = logProbs[i].Multiply(NumOps<T>.FromDouble(advantage));
            _controller.ApplyGradient(gradient);
        }

        // Update baseline (exponential moving average)
        _baseline = 0.95 * _baseline + 0.05 * rewards.Average();
    }
}

Controller Architecture

public class LSTMController<T> where T : struct
{
    private LSTM<T> _lstm;
    private DenseLayer<T> _nodeSelector;     // Selects which previous node
    private DenseLayer<T> _operationSelector;  // Selects which operation

    public (Architecture, Tensor<T>) SampleWithLogProb()
    {
        var architecture = new Architecture();
        var totalLogProb = 0.0;
        var hiddenState = _lstm.InitialState();

        // For each node in the cell
        for (int nodeIdx = 2; nodeIdx < NumNodes; nodeIdx++)
        {
            // Step 1: Select previous node index
            var nodeLogits = _nodeSelector.Forward(hiddenState);
            var nodeProbs = Softmax(nodeLogits);
            var selectedNode = SampleCategorical(nodeProbs);
            totalLogProb += Math.Log(nodeProbs[selectedNode]);

            // Step 2: Select operation type
            var opLogits = _operationSelector.Forward(hiddenState);
            var opProbs = Softmax(opLogits);
            var selectedOp = SampleCategorical(opProbs);
            totalLogProb += Math.Log(opProbs[selectedOp]);

            // Add to architecture
            architecture.AddEdge(selectedNode, nodeIdx, (OperationType)selectedOp);

            // Update LSTM state
            var input = CreateEmbedding(selectedNode, selectedOp);
            hiddenState = _lstm.Step(input, hiddenState);
        }

        return (architecture, NumOps<T>.FromDouble(totalLogProb));
    }
}

Search Spaces and Operations

Primitive Operations

Common operations used in NAS search spaces:

public enum OperationType
{
    // Convolutions
    Conv3x3,           // 3x3 convolution
    Conv5x5,           // 5x5 convolution
    Conv1x1,           // 1x1 convolution (bottleneck)

    // Separable Convolutions (efficient)
    SepConv3x3,        // 3x3 separable convolution
    SepConv5x5,        // 5x5 separable convolution

    // Dilated Convolutions (larger receptive field)
    DilConv3x3,        // 3x3 dilated convolution
    DilConv5x5,        // 5x5 dilated convolution

    // Pooling
    MaxPool3x3,        // 3x3 max pooling
    AvgPool3x3,        // 3x3 average pooling

    // Identity and Zero
    Identity,          // Skip connection (no operation)
    Zero               // No connection
}

Operation Implementations

public class OperationFactory<T> where T : struct
{
    public IOperation<T> CreateOperation(OperationType type, int channels, int stride)
    {
        return type switch
        {
            OperationType.Conv3x3 => new Conv2DLayer<T>(channels, channels, 3, stride, padding: 1),
            OperationType.Conv5x5 => new Conv2DLayer<T>(channels, channels, 5, stride, padding: 2),
            OperationType.SepConv3x3 => CreateSeparableConv(channels, 3, stride),
            OperationType.DilConv3x3 => CreateDilatedConv(channels, 3, stride, dilation: 2),
            OperationType.MaxPool3x3 => new MaxPooling2D<T>(3, stride, padding: 1),
            OperationType.AvgPool3x3 => new AveragePooling2D<T>(3, stride, padding: 1),
            OperationType.Identity => new IdentityLayer<T>(),
            OperationType.Zero => new ZeroLayer<T>(),
            _ => throw new ArgumentException($"Unknown operation type: {type}")
        };
    }

    private IOperation<T> CreateSeparableConv(int channels, int kernel, int stride)
    {
        // Depthwise + Pointwise convolution
        return new SequentialLayer<T>(
            new DepthwiseConv2D<T>(channels, kernel, stride, padding: kernel / 2),
            new Conv2DLayer<T>(channels, channels, kernelSize: 1, stride: 1)
        );
    }

    private IOperation<T> CreateDilatedConv(int channels, int kernel, int stride, int dilation)
    {
        return new DilatedConv2D<T>(channels, channels, kernel, stride, dilation);
    }
}

Mixed Operation (DARTS)

public class MixedOperation<T> : IOperation<T> where T : struct
{
    private readonly List<IOperation<T>> _operations;
    private Tensor<T> _alphas;  // Softmax weights

    public MixedOperation(List<IOperation<T>> operations, Tensor<T> alphas)
    {
        _operations = operations;
        _alphas = alphas;
    }

    public Tensor<T> Forward(Tensor<T> input)
    {
        // Compute softmax of architecture parameters
        var weights = Softmax(_alphas);

        // Weighted sum of all operations
        Tensor<T>? output = null;

        for (int i = 0; i < _operations.Count; i++)
        {
            var opOutput = _operations[i].Forward(input);
            var weighted = opOutput.Multiply(weights[i]);

            output = output == null ? weighted : output.Add(weighted);
        }

        return output!;
    }

    public Tensor<T> Backward(Tensor<T> gradOutput)
    {
        // Backpropagate through all operations
        var weights = Softmax(_alphas);
        Tensor<T>? gradInput = null;

        for (int i = 0; i < _operations.Count; i++)
        {
            var opGrad = _operations[i].Backward(gradOutput.Multiply(weights[i]));
            gradInput = gradInput == null ? opGrad : gradInput.Add(opGrad);
        }

        // Also compute gradient w.r.t. alphas
        ComputeAlphaGradients(gradOutput);

        return gradInput!;
    }
}

Cell-Based Search Space

public class SearchCell<T> : ILayer<T> where T : struct
{
    private readonly int _numNodes;
    private readonly List<MixedOperation<T>> _mixedOps;
    private Tensor<T> _architectureParams;

    public SearchCell(int numNodes, int channels)
    {
        _numNodes = numNodes;
        _mixedOps = new List<MixedOperation<T>>();

        // Create mixed operations for all possible edges
        int numEdges = numNodes * (numNodes - 1) / 2;
        _architectureParams = InitializeArchitectureParams(numEdges, NumOperations);

        // Build mixed operations
        for (int j = 0; j < numNodes; j++)
        {
            for (int i = 0; i < j; i++)
            {
                var operations = CreateAllOperations(channels);
                var alphas = GetAlphasForEdge(i, j);
                _mixedOps.Add(new MixedOperation<T>(operations, alphas));
            }
        }
    }

    public Tensor<T> Forward(Tensor<T> input0, Tensor<T> input1)
    {
        // Nodes: [input0, input1, node2, node3, ..., nodeN]
        var nodes = new List<Tensor<T>> { input0, input1 };

        int edgeIdx = 0;
        for (int j = 2; j < _numNodes; j++)
        {
            Tensor<T>? nodeOutput = null;

            // Aggregate from all previous nodes
            for (int i = 0; i < j; i++)
            {
                var edgeOutput = _mixedOps[edgeIdx++].Forward(nodes[i]);
                nodeOutput = nodeOutput == null ? edgeOutput : nodeOutput.Add(edgeOutput);
            }

            nodes.Add(nodeOutput!);
        }

        // Concatenate all intermediate nodes (skip inputs)
        return ConcatenateNodes(nodes.Skip(2).ToList());
    }

    public Architecture Discretize()
    {
        // Select top-k operations for each node
        var architecture = new Architecture();
        int edgeIdx = 0;

        for (int j = 2; j < _numNodes; j++)
        {
            var topEdges = new List<(int node, int opIdx, double weight)>();

            for (int i = 0; i < j; i++)
            {
                var alphas = GetAlphasForEdge(i, j);
                var maxIdx = GetMaxIndex(alphas);
                var maxWeight = alphas[maxIdx];
                topEdges.Add((i, maxIdx, Convert.ToDouble(maxWeight)));
                edgeIdx++;
            }

            // Keep top-2 edges for each node
            var selectedEdges = topEdges.OrderByDescending(e => e.weight).Take(2);

            foreach (var (node, opIdx, _) in selectedEdges)
            {
                architecture.AddEdge(node, j, (OperationType)opIdx);
            }
        }

        return architecture;
    }
}

Implementation Strategy

Phase 1: Core Abstractions

Architecture Representation

public class Architecture
{
    public List<ArchitectureEdge> Edges { get; set; } = new List<ArchitectureEdge>();
    public int NumNodes { get; set; }

    public void AddEdge(int fromNode, int toNode, OperationType operation)
    {
        Edges.Add(new ArchitectureEdge
        {
            FromNode = fromNode,
            ToNode = toNode,
            Operation = operation
        });
    }

    public NeuralNetwork<T> Build<T>(int inputChannels, int numClasses) where T : struct
    {
        // Convert architecture to executable network
        var layers = new List<ILayer<T>>();

        // ... build network from edges ...

        return new NeuralNetwork<T>(layers);
    }
}

public class ArchitectureEdge
{
    public int FromNode { get; set; }
    public int ToNode { get; set; }
    public OperationType Operation { get; set; }
}

Search Strategy Interface

public interface IArchitectureSearchStrategy<T> where T : struct
{
    /// <summary>
    /// Execute architecture search and return best architecture.
    /// </summary>
    Architecture Search(SearchConfig config);

    /// <summary>
    /// Get current search progress.
    /// </summary>
    SearchProgress GetProgress();
}

public class SearchConfig
{
    public int SearchEpochs { get; set; } = 50;
    public int TrainEpochs { get; set; } = 600;
    public double LearningRate { get; set; } = 0.025;
    public double ArchitectureLearningRate { get; set; } = 3e-4;
    public int BatchSize { get; set; } = 64;
    public SearchSpaceType SearchSpace { get; set; } = SearchSpaceType.DARTS;
}

Testing Strategy

Unit Tests

[TestClass]
public class DARTSTests
{
    [TestMethod]
    public void MixedOperation_ForwardPass_AggregatesCorrectly()
    {
        // Arrange: Create mixed operation with 3 primitive ops
        var ops = new List<IOperation<double>>
        {
            new Conv2DLayer<double>(16, 16, 3, 1, padding: 1),
            new MaxPooling2D<double>(3, 1, padding: 1),
            new IdentityLayer<double>()
        };

        var alphas = new Tensor<double>(new[] { 0.5, 0.3, 0.2 });  // Softmax weights
        var mixedOp = new MixedOperation<double>(ops, alphas);

        var input = CreateRandomTensor(1, 16, 32, 32);

        // Act
        var output = mixedOp.Forward(input);

        // Assert: Output shape matches input
        Assert.AreEqual(input.Shape, output.Shape);
    }

    [TestMethod]
    public void SearchCell_Discretization_SelectsTopOperations()
    {
        // Arrange: Create search cell with known alpha values
        var cell = new SearchCell<double>(numNodes: 4, channels: 16);

        // Manually set alphas to prefer certain operations
        cell.SetAlpha(edge: 0, operation: OperationType.Conv3x3, value: 5.0);
        cell.SetAlpha(edge: 0, operation: OperationType.MaxPool3x3, value: 0.1);

        // Act: Discretize
        var architecture = cell.Discretize();

        // Assert: Selected operation is Conv3x3
        Assert.IsTrue(architecture.ContainsOperation(OperationType.Conv3x3));
    }

    [TestMethod]
    public void DARTS_Search_ReducesValidationLoss()
    {
        // Arrange
        var searcher = new DARTSSearcher<double>();
        var (trainX, trainY, valX, valY) = CreateSmallDataset();

        // Act: Run search for 10 epochs
        var config = new SearchConfig { SearchEpochs = 10 };
        var initialLoss = EvaluateLoss(searcher.CurrentArchitecture, valX, valY);

        searcher.Search(config);

        var finalLoss = EvaluateLoss(searcher.BestArchitecture, valX, valY);

        // Assert: Validation loss decreased
        Assert.IsTrue(finalLoss < initialLoss);
    }
}

Integration Tests

[TestClass]
public class NASIntegrationTests
{
    [TestMethod]
    public void DARTS_CIFAR10_FindsCompetitiveArchitecture()
    {
        // Arrange: Load CIFAR-10
        var (trainX, trainY, testX, testY) = LoadCIFAR10();

        var searcher = new DARTSSearcher<double>();

        // Act: Search for 50 epochs
        var config = new SearchConfig
        {
            SearchEpochs = 50,
            LearningRate = 0.025,
            ArchitectureLearningRate = 3e-4
        };

        var architecture = searcher.Search(config);

        // Train discovered architecture from scratch
        var network = architecture.Build<double>(inputChannels: 3, numClasses: 10);
        network.Train(trainX, trainY, epochs: 600);

        // Assert: Achieves > 90% accuracy
        var accuracy = network.Evaluate(testX, testY);
        Assert.IsTrue(accuracy > 0.90, $"Expected > 90%, got {accuracy:F2}%");
    }

    [TestMethod]
    public void ENAS_Search_SharesWeightsCorrectly()
    {
        // Arrange
        var searcher = new ENASSearcher<double>();
        var (trainX, trainY, valX, valY) = LoadCIFAR10();

        // Act: Search
        var architecture = searcher.Search(new SearchConfig { SearchEpochs = 50 });

        // Assert: Shared weights were reused
        var totalTrainingTime = searcher.GetTotalTrainingTime();
        var expectedTime = 50 * AverageBatchTime;  // Should be ~50x, not 1000x

        Assert.IsTrue(totalTrainingTime < expectedTime * 2,
            "ENAS should be much faster than training each architecture separately");
    }
}

Step-by-Step Implementation Guide

Phase 1: Primitive Operations (Week 1)

Step 1: Define Operation Interface

File: src/NAS/IOperation.cs

namespace AiDotNet.NAS
{
    public interface IOperation<T> where T : struct
    {
        Tensor<T> Forward(Tensor<T> input);
        Tensor<T> Backward(Tensor<T> gradOutput);
        int GetOutputChannels();
        (int height, int width) GetOutputShape(int inputHeight, int inputWidth);
    }
}

Step 2: Implement Primitive Operations

File: src/NAS/Operations/PrimitiveOperations.cs

namespace AiDotNet.NAS.Operations
{
    public class SeparableConv2D<T> : IOperation<T> where T : struct
    {
        private readonly DepthwiseConv2D<T> _depthwise;
        private readonly Conv2DLayer<T> _pointwise;

        public SeparableConv2D(int channels, int kernelSize, int stride, int padding)
        {
            _depthwise = new DepthwiseConv2D<T>(channels, kernelSize, stride, padding);
            _pointwise = new Conv2DLayer<T>(channels, channels, kernelSize: 1, stride: 1);
        }

        public Tensor<T> Forward(Tensor<T> input)
        {
            var depthwiseOut = _depthwise.Forward(input);
            return _pointwise.Forward(depthwiseOut);
        }

        public Tensor<T> Backward(Tensor<T> gradOutput)
        {
            var pointwiseGrad = _pointwise.Backward(gradOutput);
            return _depthwise.Backward(pointwiseGrad);
        }
    }

    public class DilatedConv2D<T> : IOperation<T> where T : struct
    {
        private readonly Conv2DLayer<T> _conv;
        private readonly int _dilation;

        public DilatedConv2D(int inChannels, int outChannels, int kernelSize,
            int stride, int dilation)
        {
            _dilation = dilation;
            int padding = dilation * (kernelSize - 1) / 2;
            _conv = new Conv2DLayer<T>(inChannels, outChannels, kernelSize, stride, padding);
        }

        public Tensor<T> Forward(Tensor<T> input)
        {
            // Apply convolution with dilated kernel
            return _conv.ForwardWithDilation(input, _dilation);
        }
    }

    public class ZeroOperation<T> : IOperation<T> where T : struct
    {
        public Tensor<T> Forward(Tensor<T> input)
        {
            // Return tensor of zeros with same shape
            return new Tensor<T>(input.Shape);
        }

        public Tensor<T> Backward(Tensor<T> gradOutput)
        {
            // No gradient flows through zero operation
            return new Tensor<T>(gradOutput.Shape);
        }
    }
}

Phase 2: DARTS Implementation (Week 2-3)

Step 3: Implement Mixed Operation

File: src/NAS/DARTS/MixedOperation.cs

(See Mixed Operation code in Search Spaces section)

Step 4: Implement Search Cell

File: src/NAS/DARTS/SearchCell.cs

(See SearchCell code in Search Spaces section)

Step 5: Implement DARTS Searcher

File: src/NAS/DARTS/DARTSSearcher.cs

namespace AiDotNet.NAS.DARTS
{
    public class DARTSSearcher<T> : IArchitectureSearchStrategy<T> where T : struct
    {
        private readonly SearchSpace<T> _searchSpace;
        private readonly Tensor<T> _architectureParams;
        private readonly IOptimizer<T> _weightOptimizer;
        private readonly IOptimizer<T> _archOptimizer;

        public DARTSSearcher(SearchConfig config)
        {
            _searchSpace = new SearchSpace<T>(config);
            _architectureParams = InitializeArchitectureParams();
            _weightOptimizer = new SGD<T>(config.LearningRate);
            _archOptimizer = new Adam<T>(config.ArchitectureLearningRate);
        }

        public Architecture Search(SearchConfig config)
        {
            for (int epoch = 0; epoch < config.SearchEpochs; epoch++)
            {
                // Phase 1: Train network weights
                TrainWeights(config);

                // Phase 2: Update architecture parameters
                UpdateArchitecture(config);

                // Log progress
                if (epoch % 10 == 0)
                {
                    LogProgress(epoch);
                }
            }

            // Discretize continuous architecture
            return _searchSpace.Discretize(_architectureParams);
        }

        private void TrainWeights(SearchConfig config)
        {
            var batch = SampleBatch(_trainData, config.BatchSize);

            // Forward pass
            var output = _searchSpace.Forward(batch.X, _architectureParams);
            var loss = ComputeLoss(output, batch.Y);

            // Backward pass
            var gradients = _searchSpace.Backward(loss);

            // Update weights
            _weightOptimizer.Update(_searchSpace.Weights, gradients);
        }

        private void UpdateArchitecture(SearchConfig config)
        {
            // Compute virtual weights: w' = w - ξ∇_w L_train
            var trainGradients = ComputeTrainGradients();
            var virtualWeights = ComputeVirtualStep(_searchSpace.Weights, trainGradients);

            // Temporarily set virtual weights
            var originalWeights = _searchSpace.Weights.Clone();
            _searchSpace.SetWeights(virtualWeights);

            // Evaluate on validation data
            var valBatch = SampleBatch(_valData, config.BatchSize);
            var valOutput = _searchSpace.Forward(valBatch.X, _architectureParams);
            var valLoss = ComputeLoss(valOutput, valBatch.Y);

            // Compute ∇_α L_val(w', α)
            var archGradients = _searchSpace.BackwardArchitecture(valLoss);

            // Update architecture parameters
            _archOptimizer.Update(_architectureParams, archGradients);

            // Restore original weights
            _searchSpace.SetWeights(originalWeights);
        }
    }
}

Phase 3: ENAS Implementation (Week 4-5)

Step 6: Implement Controller

File: src/NAS/ENAS/LSTMController.cs

(See LSTMController code in ENAS section)

Step 7: Implement ENAS Searcher

File: src/NAS/ENAS/ENASSearcher.cs

(See ENASSearcher code in ENAS section)

Phase 4: Testing and Validation (Week 6)

Step 8: Create Test Suite

(See Testing Strategy section for comprehensive tests)

Step 9: Run Experiments

File: examples/NAS/CIFAR10Search.cs

public class CIFAR10SearchExample
{
    public static void Run()
    {
        // Load CIFAR-10
        var (trainX, trainY, testX, testY) = LoadCIFAR10();

        // Configure search
        var config = new SearchConfig
        {
            SearchEpochs = 50,
            LearningRate = 0.025,
            ArchitectureLearningRate = 3e-4,
            BatchSize = 64
        };

        // Run DARTS search
        Console.WriteLine("Starting DARTS search...");
        var dartsSearcher = new DARTSSearcher<double>(config);
        var dartsArch = dartsSearcher.Search(config);

        Console.WriteLine("Discovered architecture:");
        dartsArch.Print();

        // Train from scratch
        var network = dartsArch.Build<double>(inputChannels: 3, numClasses: 10);
        network.Train(trainX, trainY, epochs: 600);

        // Evaluate
        var accuracy = network.Evaluate(testX, testY);
        Console.WriteLine($"Final accuracy: {accuracy:F2}%");
    }
}

Summary

This guide provides:

DARTS: Gradient-based architecture search with continuous relaxation
ENAS: Efficient search using parameter sharing and controller
Search Spaces: Cell-based representations with primitive operations
Implementation: Complete code for both DARTS and ENAS
Testing: Comprehensive unit and integration tests

Key Takeaways:

DARTS is faster but requires more memory (all operations active)
ENAS is more memory-efficient (parameter sharing)
Both discover competitive architectures automatically
Cell-based search spaces generalize well across datasets

Expected Timeline: 6 weeks for full implementation with experiments

Nov 07 '25 04:11 ooples

[Phase 3] Implement Program Synthesis and Code Generation

Problem

Missing Implementations

Use Cases

Architecture

Success Criteria

Issue #404: Junior Developer Implementation Guide

Neural Architecture Search (NAS) - DARTS and ENAS

Table of Contents

Understanding Neural Architecture Search

What is Neural Architecture Search?

Why NAS?

The NAS Problem

NAS Components

DARTS: Differentiable Architecture Search

Core Idea

Key Innovation

Mathematical Foundation

1. Continuous Relaxation

2. Bilevel Optimization

3. Gradient-Based Search

4. Approximation of ∇_α

DARTS Search Space

DARTS Algorithm

Implementation Sketch

ENAS: Efficient Neural Architecture Search

Core Idea

Controller-Based Search

Mathematical Foundation

1. Search Space as DAG

2. Controller Policy

3. Reward Signal

4. REINFORCE Update

ENAS Algorithm

Implementation Sketch

Controller Architecture

Search Spaces and Operations

Primitive Operations

Operation Implementations

Mixed Operation (DARTS)

Cell-Based Search Space

Implementation Strategy

Phase 1: Core Abstractions

Architecture Representation

Search Strategy Interface

Testing Strategy

Unit Tests

Integration Tests

Step-by-Step Implementation Guide

Phase 1: Primitive Operations (Week 1)

Phase 2: DARTS Implementation (Week 2-3)

Phase 3: ENAS Implementation (Week 4-5)

Phase 4: Testing and Validation (Week 6)

Summary