[Inference Optimization] Implement Kernel Optimization and Custom Operators

[ooples]

Problem

MISSING: Low-level kernel optimization for critical operations, enabling hardware-specific acceleration.

Missing Implementations

Custom Kernels (HIGH):

• Optimized matrix multiplication (GEMM)
• Fused attention kernels
• Depthwise separable convolution
• Group convolution
• Custom activation functions

SIMD/Vectorization (HIGH):

• AVX2/AVX-512 on x86
• NEON on ARM
• Explicit vectorization
• Auto-vectorization hints

GPU Optimization (HIGH):

• CUDA kernel implementations
• Shared memory utilization
• Warp-level primitives
• Tensor cores usage (Ampere+)

CPU Optimization (MEDIUM):

• Cache-aware algorithms
• Loop tiling
• Prefetching
• Parallel decomposition

Integration Points

• Custom operator registration
• Fallback to reference implementation
• Platform detection
• Performance profiling

Architecture

Success Criteria

• 2-5x speedup on critical operations
• Hardware-specific optimization
• Graceful fallback
• Benchmarks vs MKL, cuBLAS

[ooples]

Junior Developer Implementation Guide: Issue #412

Overview

Issue: Dynamic Batching Engine Goal: Implement intelligent batching for inference optimization Difficulty: Advanced Estimated Time: 12-14 hours

What is Dynamic Batching?

Dynamic batching groups multiple inference requests into a single batch to maximize GPU/CPU utilization:

• Problem: Single requests underutilize hardware
• Solution: Wait briefly to accumulate requests, process together
• Benefit: Higher throughput, better hardware utilization

Key Metrics

Latency = Queue Wait Time + Processing Time
Throughput = Requests / Second
Utilization = (Batch Size / Max Batch Size) * 100%

Implementation Strategy

Core Components

// File: C:\Users\cheat\source\repos\AiDotNet\src\Batching\DynamicBatcher.cs
namespace AiDotNet.Batching
{
    public class DynamicBatcher<TInput, TOutput>
    {
        private readonly int _maxBatchSize;
        private readonly TimeSpan _maxWaitTime;
        private readonly Queue<BatchRequest<TInput, TOutput>> _queue;
        private readonly SemaphoreSlim _semaphore;

        public DynamicBatcher(int maxBatchSize = 32, int maxWaitMs = 10)
        {
            _maxBatchSize = maxBatchSize;
            _maxWaitTime = TimeSpan.FromMilliseconds(maxWaitMs);
            _queue = new Queue<BatchRequest<TInput, TOutput>>();
            _semaphore = new SemaphoreSlim(1);
        }

        public async Task<TOutput> PredictAsync(TInput input, Func<TInput[], TOutput[]> batchPredictor)
        {
            var request = new BatchRequest<TInput, TOutput>(input);

            await _semaphore.WaitAsync();
            _queue.Enqueue(request);
            _semaphore.Release();

            // Start batch processor if needed
            _ = ProcessBatchesAsync(batchPredictor);

            // Wait for result
            return await request.CompletionSource.Task;
        }

        private async Task ProcessBatchesAsync(Func<TInput[], TOutput[]> batchPredictor)
        {
            await Task.Delay(_maxWaitTime);

            await _semaphore.WaitAsync();

            var batchRequests = new List<BatchRequest<TInput, TOutput>>();
            while (_queue.Count > 0 && batchRequests.Count < _maxBatchSize)
            {
                batchRequests.Add(_queue.Dequeue());
            }

            _semaphore.Release();

            if (batchRequests.Count == 0)
                return;

            // Process batch
            var inputs = batchRequests.Select(r => r.Input).ToArray();
            var outputs = batchPredictor(inputs);

            // Complete requests
            for (int i = 0; i < batchRequests.Count; i++)
            {
                batchRequests[i].CompletionSource.SetResult(outputs[i]);
            }
        }
    }

    public class BatchRequest<TInput, TOutput>
    {
        public TInput Input { get; }
        public TaskCompletionSource<TOutput> CompletionSource { get; }

        public BatchRequest(TInput input)
        {
            Input = input;
            CompletionSource = new TaskCompletionSource<TOutput>();
        }
    }
}

Adaptive Batching

public class AdaptiveDynamicBatcher<TInput, TOutput>
{
    private int _currentMaxBatchSize;
    private readonly int _minBatchSize = 1;
    private readonly int _maxBatchSize = 128;
    private readonly MovingAverage _latencyTracker;

    public void AdaptBatchSize(double currentLatency, double targetLatency)
    {
        if (currentLatency > targetLatency * 1.1)
        {
            // Latency too high, reduce batch size
            _currentMaxBatchSize = Math.Max(_minBatchSize, _currentMaxBatchSize - 4);
        }
        else if (currentLatency < targetLatency * 0.9)
        {
            // Latency acceptable, try larger batch
            _currentMaxBatchSize = Math.Min(_maxBatchSize, _currentMaxBatchSize + 4);
        }
    }
}

Testing Strategy

[Fact]
public async Task DynamicBatcher_CombinesMultipleRequests()
{
    var batcher = new DynamicBatcher<int, int>(maxBatchSize: 4, maxWaitMs: 50);

    int batchCount = 0;
    Func<int[], int[]> predictor = inputs =>
    {
        batchCount++;
        return inputs.Select(x => x * 2).ToArray();
    };

    // Send 4 requests simultaneously
    var tasks = new[]
    {
        batcher.PredictAsync(1, predictor),
        batcher.PredictAsync(2, predictor),
        batcher.PredictAsync(3, predictor),
        batcher.PredictAsync(4, predictor)
    };

    var results = await Task.WhenAll(tasks);

    // All requests should be in one batch
    Assert.Equal(1, batchCount);
    Assert.Equal(new[] { 2, 4, 6, 8 }, results);
}

Advanced Features

Priority Queuing

public class PriorityBatchRequest<TInput, TOutput>
{
    public TInput Input { get; set; }
    public int Priority { get; set; } // Higher = more important
    public DateTime SubmitTime { get; set; }
}

Batching Strategies

1. Time-based: Wait max N milliseconds 2. Size-based: Wait until batch size reaches N 3. Hybrid: Whichever comes first 4. Adaptive: Adjust based on latency metrics

Learning Resources

• NVIDIA Triton Inference Server: https://github.com/triton-inference-server/server
• TensorFlow Serving Batching: https://www.tensorflow.org/tfx/serving/serving_config#batching_configuration

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Good luck! Dynamic batching is essential for production inference servers handling high request rates.