Table of Contents

Class GraphCodeBERT<T>

Namespace
AiDotNet.ProgramSynthesis.Engines
Assembly
AiDotNet.dll

GraphCodeBERT extends CodeBERT by incorporating data flow analysis.

public class GraphCodeBERT<T> : CodeModelBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable, ICodeModel<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations (e.g., double, float).

Inheritance
GraphCodeBERT<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

GraphCodeBERT combines source code with data flow information to better understand code semantics. It uses graph neural networks to model the relationships between variables, functions, and data dependencies in code.

For Beginners: GraphCodeBERT understands how data flows through code.

While CodeBERT reads code like text, GraphCodeBERT also understands:

  • Which variables depend on which others
  • How data flows from one function to another
  • The relationships and connections in code structure

Think of it like understanding a city:

  • CodeBERT sees the streets and buildings (structure)
  • GraphCodeBERT also sees how traffic flows and which roads connect (data flow)

This deeper understanding helps with tasks like bug detection and code optimization.

Constructors

GraphCodeBERT(CodeSynthesisArchitecture<T>, ILossFunction<T>?, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>?, ITokenizer?)

Initializes a new instance of the GraphCodeBERT<T> class.

public GraphCodeBERT(CodeSynthesisArchitecture<T> architecture, ILossFunction<T>? lossFunction = null, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, ITokenizer? tokenizer = null)

Parameters

architecture CodeSynthesisArchitecture<T>

The architecture configuration (should have UseDataFlow=true).

lossFunction ILossFunction<T>

Optional loss function.

optimizer IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>

Optional optimizer.

tokenizer ITokenizer

Optional tokenizer (defaults to a safe built-in tokenizer).

Remarks

Creates a new GraphCodeBERT model with data flow analysis capabilities. The architecture should have UseDataFlow set to true to enable graph-based processing.

For Beginners: This creates a new GraphCodeBERT model.

Similar to CodeBERT, but with extra capabilities to understand data flow. Make sure the architecture has UseDataFlow enabled to get the full benefit.

Properties

UsesDataFlow

Gets whether this model uses data flow analysis.

public bool UsesDataFlow { get; }

Property Value

bool

Remarks

GraphCodeBERT's key differentiator is its use of data flow graphs to understand code beyond just sequential structure.

For Beginners: This shows whether the model tracks how data moves.

When true, the model doesn't just read code line by line - it builds a map of how data flows between different parts of the code, giving deeper understanding.

Methods

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

GetModelMetadata()

Gets the metadata for this neural network model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

InitializeLayers()

Initializes the layers of the neural network based on the architecture.

protected override void InitializeLayers()

Remarks

For Beginners: This method sets up all the layers in your neural network according to the architecture you've defined. It's like assembling the parts of your network before you can use it.

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

Train(Tensor<T>, Tensor<T>)

Trains the neural network on a single input-output pair.

public override void Train(Tensor<T> input, Tensor<T> expectedOutput)

Parameters

input Tensor<T>

The input data.

expectedOutput Tensor<T>

The expected output for the given input.

Remarks

This method performs one training step on the neural network using the provided input and expected output. It updates the network's parameters to reduce the error between the network's prediction and the expected output.

For Beginners: This is how your neural network learns. You provide: - An input (what the network should process) - The expected output (what the correct answer should be)

The network then:

  1. Makes a prediction based on the input
  2. Compares its prediction to the expected output
  3. Calculates how wrong it was (the loss)
  4. Adjusts its internal values to do better next time

After training, you can get the loss value using the GetLastLoss() method to see how well the network is learning.

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