Table of Contents

Class CodeT5<T>

Namespace
AiDotNet.ProgramSynthesis.Engines
Assembly
AiDotNet.dll

CodeT5 is an encoder-decoder model for code understanding and generation.

public class CodeT5<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
CodeT5<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

CodeT5 is based on the T5 (Text-To-Text Transfer Transformer) architecture adapted for code. It uses an encoder-decoder structure that can handle both code understanding and generation tasks. It's particularly effective for code translation, summarization, and generation from natural language descriptions.

For Beginners: CodeT5 can both understand AND generate code.

Unlike CodeBERT which mainly understands code, CodeT5 can also create it:

  • Understand: Read and analyze code (encoder)
  • Generate: Write new code (decoder)

This makes it powerful for tasks like:

  • Translating Python to Java
  • Generating code from English descriptions
  • Creating documentation from code
  • Fixing bugs by rewriting code

Think of it as both a reader and a writer, not just a reader.

Constructors

CodeT5(CodeSynthesisArchitecture<T>, ILossFunction<T>?, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>?, ITokenizer?)

Initializes a new instance of the CodeT5<T> class.

public CodeT5(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.

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 CodeT5 model with encoder-decoder architecture. The model can both understand existing code and generate new code.

For Beginners: This creates a new CodeT5 model.

CodeT5 needs both encoder and decoder layers, so make sure your architecture specifies both (NumEncoderLayers and NumDecoderLayers).

Properties

NumDecoderLayers

Gets the number of decoder layers.

public int NumDecoderLayers { get; }

Property Value

int

Remarks

The decoder generates the output code based on the encoder's understanding.

For Beginners: Decoder layers write the output.

After understanding the input (encoder), these layers generate the response, like writing an essay based on your understanding.

NumEncoderLayers

Gets the number of encoder layers.

public int NumEncoderLayers { get; }

Property Value

int

Remarks

The encoder processes and understands the input code or text.

For Beginners: Encoder layers read and understand the input.

These layers analyze and comprehend what you give the model, like reading comprehension in school.

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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