Table of Contents

Class HybridShardedOptimizer<T, TInput, TOutput>

Namespace
AiDotNet.DistributedTraining
Assembly
AiDotNet.dll

Implements 3D Parallelism optimizer - coordinates across data, tensor, and pipeline dimensions.

public class HybridShardedOptimizer<T, TInput, TOutput> : ShardedOptimizerBase<T, TInput, TOutput>, IShardedOptimizer<T, TInput, TOutput>, IOptimizer<T, TInput, TOutput>, IModelSerializer

Type Parameters

T

The numeric type

TInput

The input type for the model

TOutput

The output type for the model

Inheritance
ShardedOptimizerBase<T, TInput, TOutput>
HybridShardedOptimizer<T, TInput, TOutput>
Implements
IShardedOptimizer<T, TInput, TOutput>
IOptimizer<T, TInput, TOutput>
Inherited Members
Extension Methods

Remarks

Strategy Overview: 3D Parallelism optimizer coordinates optimization across all three parallelism dimensions: - Data parallel: synchronizes gradients across data-parallel replicas - Tensor parallel: synchronizes within tensor-parallel groups - Pipeline parallel: handles gradient accumulation across micro-batches

This requires managing separate communication groups for each dimension and ensuring proper synchronization order to maintain correctness and efficiency.

For Beginners: This is the most complex optimizer, coordinating all three types of parallelism. It needs to handle: 1. Averaging gradients across data-parallel replicas (GPUs processing different batches) 2. Synchronizing tensor-parallel groups (GPUs sharing layer computations) 3. Accumulating gradients from pipeline micro-batches

Think of it like coordinating a massive team split into departments (pipeline stages), work groups (tensor parallel), and shifts (data parallel) - all need to sync at the right times.

Use Cases: - Frontier-scale models (100B+ parameters) - 100s to 1000s of GPUs - Works with HybridShardedModel

Trade-offs: - Memory: Excellent - exploits all dimensions - Communication: Complex - multiple sync patterns - Complexity: Very High - most complex optimizer - Best for: Largest scale training

Constructors

HybridShardedOptimizer(IOptimizer<T, TInput, TOutput>, IShardingConfiguration<T>, int, int, int) 

public HybridShardedOptimizer(IOptimizer<T, TInput, TOutput> wrappedOptimizer, IShardingConfiguration<T> config, int pipelineParallelSize = 1, int tensorParallelSize = 1, int dataParallelSize = -1)

Parameters

wrappedOptimizer IOptimizer<T, TInput, TOutput>
config IShardingConfiguration<T>
pipelineParallelSize int
tensorParallelSize int
dataParallelSize int

Methods

Deserialize(byte[])

Loads a previously serialized model from binary data.

public override void Deserialize(byte[] data)

Parameters

data byte[]

The byte array containing the serialized model data.

Remarks

This method takes binary data created by the Serialize method and uses it to restore a model to its previous state.

For Beginners: This is like opening a saved file to continue your work.

When you call this method:

  • You provide the binary data (bytes) that was previously created by Serialize
  • The model rebuilds itself using this data
  • After deserializing, the model is exactly as it was when serialized
  • It's ready to make predictions without needing to be trained again

For example:

  • You download a pre-trained model file for detecting spam emails
  • You deserialize this file into your application
  • Immediately, your application can detect spam without any training
  • The model has all the knowledge that was built into it by its original creator

This is particularly useful when:

  • You want to use a model that took days to train
  • You need to deploy the same model across multiple devices
  • You're creating an application that non-technical users will use

Think of it like installing the brain of a trained expert directly into your application.

Optimize(OptimizationInputData<T, TInput, TOutput>)

Performs the optimization process to find the best parameters for a model.

public override OptimizationResult<T, TInput, TOutput> Optimize(OptimizationInputData<T, TInput, TOutput> inputData)

Parameters

inputData OptimizationInputData<T, TInput, TOutput>

The data needed for optimization, including the objective function, initial parameters, and any constraints.

Returns

OptimizationResult<T, TInput, TOutput>

The result of the optimization process, including the optimized parameters and performance metrics.

Remarks

This method takes input data and attempts to find the optimal parameters that minimize or maximize the objective function.

For Beginners: This is where the actual "learning" happens. The optimizer looks at your data and tries different parameter values to find the ones that make your model perform best.

The process typically involves:

  1. Evaluating how well the current parameters perform
  2. Calculating how to change the parameters to improve performance
  3. Updating the parameters
  4. Repeating until the model performs well enough or reaches a maximum number of attempts

Serialize()

Converts the current state of a machine learning model into a binary format.

public override byte[] Serialize()

Returns

byte[]

A byte array containing the serialized model data.

Remarks

This method captures all the essential information about a trained model and converts it into a sequence of bytes that can be stored or transmitted.

For Beginners: This is like exporting your work to a file.

When you call this method:

  • The model's current state (all its learned patterns and parameters) is captured
  • This information is converted into a compact binary format (bytes)
  • You can then save these bytes to a file, database, or send them over a network

For example:

  • After training a model to recognize cats vs. dogs in images
  • You can serialize the model to save all its learned knowledge
  • Later, you can use this saved data to recreate the model exactly as it was
  • The recreated model will make the same predictions as the original

Think of it like taking a snapshot of your model's brain at a specific moment in time.

SynchronizeOptimizerState()

Synchronizes optimizer state (like momentum buffers) across all processes.

public override void SynchronizeOptimizerState()

Remarks

For Beginners: Some optimizers (like Adam) keep track of past gradients to make smarter updates. This method makes sure all processes have the same optimizer state, so they stay coordinated. It's like making sure all team members are reading from the same playbook.

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