TAREA 00: ML Framework - Core Machine Learning Infrastructure¶

Status: 🔴 PLANNING - Architecture defined, ready for implementation

🎯 Purpose¶

Provides the foundational infrastructure for all machine learning operations in AudioLab, including model loading, inference execution, optimization, and deployment across multiple platforms and hardware accelerators.

🏗️ Architecture¶

Core Components¶

05_26_00_ml_framework/
├── include/
│   ├── MLFramework.h              # Main framework interface
│   ├── ModelLoader.h              # Model loading/deserialization
│   ├── InferenceEngine.h          # Inference execution abstraction
│   ├── TensorOps.h                # Tensor operations
│   ├── Quantization.h             # Model quantization (INT8, FP16)
│   ├── ModelOptimizer.h           # Model optimization pipeline
│   └── HardwareAccelerator.h      # CPU/GPU/NPU abstraction
├── src/
│   ├── ModelLoader.cpp
│   ├── ONNXInferenceEngine.cpp    # ONNX Runtime backend
│   ├── TFLiteInferenceEngine.cpp  # TensorFlow Lite backend
│   ├── TorchInferenceEngine.cpp   # LibTorch backend
│   ├── TensorOps.cpp
│   └── Quantization.cpp
├── tests/
│   ├── test_model_loader.cpp
│   ├── test_inference_engine.cpp
│   ├── test_quantization.cpp
│   └── benchmark_inference.cpp
├── examples/
│   ├── simple_inference.cpp       # Basic model loading and inference
│   ├── multi_backend_comparison.cpp
│   └── real_time_inference.cpp
└── models/
    └── test_models/               # Sample models for testing

🔑 Key Features¶

1. Multi-Backend Support¶

ONNX Runtime: Cross-platform, production-ready
TensorFlow Lite: Mobile and embedded deployment
LibTorch: Full PyTorch C++ API support
OpenVINO: Intel CPU/GPU optimization (optional)

2. Hardware Acceleration¶

CPU: Optimized BLAS operations, SIMD vectorization
GPU: CUDA, DirectML, Metal support
NPU: Neural Processing Unit support (Apple Neural Engine, Intel VPU)

3. Model Formats¶

ONNX (.onnx)
TensorFlow Lite (.tflite)
TorchScript (.pt)
CoreML (.mlmodel) - Apple platforms
Custom binary format for optimized models

4. Optimization Pipeline¶

Quantization: FP32 → FP16 → INT8
Pruning: Remove redundant weights
Fusion: Operator fusion for faster inference
Graph Optimization: Constant folding, dead code elimination

📋 Implementation Plan¶

Phase 1: Core Infrastructure (Week 1-2)¶

1.1 Model Loader¶

class IModelLoader {
public:
    virtual ~IModelLoader() = default;
    virtual bool loadModel(const std::string& path) = 0;
    virtual bool loadFromMemory(const void* data, size_t size) = 0;
    virtual ModelMetadata getMetadata() const = 0;
};

class ONNXModelLoader : public IModelLoader {
    // ONNX-specific loading
};

class TFLiteModelLoader : public IModelLoader {
    // TFLite-specific loading
};

1.2 Inference Engine Interface¶

class IInferenceEngine {
public:
    virtual ~IInferenceEngine() = default;

    // Model lifecycle
    virtual bool initialize(const ModelConfig& config) = 0;
    virtual bool loadModel(const std::string& path) = 0;
    virtual void shutdown() = 0;

    // Inference
    virtual bool run(const TensorMap& inputs, TensorMap& outputs) = 0;
    virtual bool runAsync(const TensorMap& inputs,
                          std::function<void(TensorMap&)> callback) = 0;

    // Metadata
    virtual std::vector<TensorInfo> getInputInfo() const = 0;
    virtual std::vector<TensorInfo> getOutputInfo() const = 0;
    virtual PerformanceStats getStats() const = 0;
};

1.3 Tensor Abstraction¶

class Tensor {
public:
    Tensor(const std::vector<int64_t>& shape, DataType dtype);

    // Data access
    template<typename T>
    T* data();

    template<typename T>
    const T* data() const;

    // Shape operations
    std::vector<int64_t> shape() const;
    int64_t size() const;
    int64_t bytes() const;

    // Type conversion
    Tensor cast(DataType target_dtype) const;
    Tensor quantize(QuantizationType type) const;
};

using TensorMap = std::unordered_map<std::string, Tensor>;

Phase 2: ONNX Backend (Week 3)¶

2.1 ONNX Runtime Integration¶

class ONNXInferenceEngine : public IInferenceEngine {
private:
    Ort::Env env_;
    Ort::Session session_;
    Ort::SessionOptions session_options_;

public:
    bool initialize(const ModelConfig& config) override;
    bool loadModel(const std::string& path) override;
    bool run(const TensorMap& inputs, TensorMap& outputs) override;

private:
    void setupExecutionProvider(ExecutionProvider provider);
    void optimizeSession();
};

2.2 Execution Providers¶

CPU: Default, optimized for multi-core
CUDA: NVIDIA GPU acceleration
DirectML: Windows GPU (AMD, Intel, NVIDIA)
CoreML: Apple Neural Engine

Phase 3: TensorFlow Lite Backend (Week 4)¶

3.1 TFLite Integration¶

class TFLiteInferenceEngine : public IInferenceEngine {
private:
    std::unique_ptr<tflite::FlatBufferModel> model_;
    std::unique_ptr<tflite::Interpreter> interpreter_;

public:
    bool initialize(const ModelConfig& config) override;
    bool loadModel(const std::string& path) override;
    bool run(const TensorMap& inputs, TensorMap& outputs) override;

private:
    void setupDelegates();  // GPU, NNAPI, CoreML
    void allocateTensors();
};

Phase 4: LibTorch Backend (Week 5)¶

4.1 PyTorch C++ API¶

class TorchInferenceEngine : public IInferenceEngine {
private:
    torch::jit::script::Module module_;
    torch::Device device_;

public:
    bool initialize(const ModelConfig& config) override;
    bool loadModel(const std::string& path) override;
    bool run(const TensorMap& inputs, TensorMap& outputs) override;
};

Phase 5: Quantization & Optimization (Week 6)¶

5.1 Quantization¶

class ModelQuantizer {
public:
    // Post-training quantization
    bool quantizeModel(const std::string& input_model,
                      const std::string& output_model,
                      QuantizationType type);

    // Calibration-based quantization
    bool quantizeWithCalibration(const std::string& input_model,
                                const CalibrationDataset& dataset,
                                const std::string& output_model);
};

enum class QuantizationType {
    FP16,           // Half precision
    INT8_SYMMETRIC, // Symmetric quantization
    INT8_ASYMMETRIC,// Asymmetric quantization
    DYNAMIC         // Dynamic quantization (weights only)
};

5.2 Model Optimization¶

class ModelOptimizer {
public:
    // Operator fusion
    bool fuseOperators(const std::string& input_model,
                      const std::string& output_model);

    // Constant folding
    bool foldConstants(const std::string& model_path);

    // Dead code elimination
    bool eliminateDeadCode(const std::string& model_path);
};

🧪 Testing Strategy¶

Unit Tests¶

Model loading from file and memory
Tensor operations (shape, dtype conversion)
Inference correctness (compare with reference outputs)
Quantization accuracy

Integration Tests¶

Multi-backend comparison (same model, different backends)
Real-time inference benchmarks
Memory usage profiling

Performance Tests¶

Latency measurements (p50, p95, p99)
Throughput testing (models/sec)
CPU/GPU utilization

📊 Performance Targets¶

Latency¶

Small models (< 1MB): < 1ms
Medium models (1-10MB): < 5ms
Large models (> 10MB): < 20ms

Throughput¶

CPU: > 100 inferences/sec (small model)
GPU: > 500 inferences/sec (small model)

Memory¶

Model footprint: < 100MB per loaded model
Runtime overhead: < 50MB

🔧 Configuration¶

Model Configuration¶

struct ModelConfig {
    std::string model_path;
    ModelFormat format;          // ONNX, TFLite, TorchScript
    ExecutionProvider provider;  // CPU, CUDA, DirectML, CoreML
    int num_threads = 4;
    bool enable_profiling = false;
    QuantizationType quantization = QuantizationType::NONE;
};

Inference Configuration¶

struct InferenceConfig {
    bool enable_batching = false;
    int max_batch_size = 1;
    float timeout_ms = 100.0f;
    bool async_mode = false;
};

📖 Usage Examples¶

Example 1: Simple Inference¶

#include "MLFramework.h"

int main() {
    // Create inference engine
    auto engine = createInferenceEngine(ModelFormat::ONNX);

    // Configure
    ModelConfig config;
    config.model_path = "models/audio_classifier.onnx";
    config.provider = ExecutionProvider::CPU;
    config.num_threads = 4;

    // Initialize
    if (!engine->initialize(config)) {
        return -1;
    }

    // Load model
    if (!engine->loadModel(config.model_path)) {
        return -1;
    }

    // Prepare input
    TensorMap inputs;
    inputs["audio_input"] = Tensor({1, 1, 44100}, DataType::FLOAT32);
    // ... fill with audio data

    // Run inference
    TensorMap outputs;
    if (engine->run(inputs, outputs)) {
        auto predictions = outputs["predictions"].data<float>();
        // Process results
    }

    engine->shutdown();
    return 0;
}

Example 2: Quantized Model¶

// Quantize model
ModelQuantizer quantizer;
quantizer.quantizeModel(
    "models/audio_classifier_fp32.onnx",
    "models/audio_classifier_int8.onnx",
    QuantizationType::INT8_SYMMETRIC
);

// Load quantized model
ModelConfig config;
config.model_path = "models/audio_classifier_int8.onnx";
config.quantization = QuantizationType::INT8_SYMMETRIC;

auto engine = createInferenceEngine(ModelFormat::ONNX);
engine->initialize(config);

Example 3: GPU Acceleration¶

ModelConfig config;
config.model_path = "models/source_separation.onnx";
config.provider = ExecutionProvider::CUDA;  // Use NVIDIA GPU

auto engine = createInferenceEngine(ModelFormat::ONNX);
engine->initialize(config);

🔗 Dependencies¶

External Libraries¶

# ONNX Runtime
find_package(onnxruntime REQUIRED)

# TensorFlow Lite
find_package(TensorFlowLite REQUIRED)

# LibTorch (PyTorch C++)
find_package(Torch REQUIRED)

# Optional: OpenVINO
find_package(OpenVINO)

Build Configuration¶

add_library(ml_framework
    src/ModelLoader.cpp
    src/ONNXInferenceEngine.cpp
    src/TFLiteInferenceEngine.cpp
    src/TorchInferenceEngine.cpp
    src/TensorOps.cpp
    src/Quantization.cpp
)

target_link_libraries(ml_framework
    PUBLIC
        onnxruntime::onnxruntime
        tensorflow::tensorflowlite
        torch
)

📚 Research References¶

ONNX Runtime - Microsoft, cross-platform ML inference
TensorFlow Lite - Google, mobile/embedded ML
LibTorch - PyTorch C++ API
Quantization Aware Training (Jacob et al., 2018)
Neural Network Distillation (Hinton et al., 2015)

🚀 Roadmap¶

Week 1-2: Foundation¶

Architecture design
Core interfaces (IModelLoader, IInferenceEngine)
Tensor abstraction
Basic unit tests

Week 3: ONNX Backend¶

ONNX Runtime integration
CPU execution provider
GPU execution provider (CUDA/DirectML)
Performance benchmarks

Week 4: TFLite Backend¶

TensorFlow Lite integration
Mobile GPU delegate
CoreML delegate (Apple)
Benchmarks vs ONNX

Week 5: LibTorch Backend¶

PyTorch C++ API integration
TorchScript loading
GPU support
Comparison with other backends

Week 6: Optimization¶

Quantization pipeline (FP16, INT8)
Model pruning
Operator fusion
Final benchmarks

Last Updated: 2025-10-15 Status: 🔴 Ready for implementation Priority: 🔥 Critical - Foundation for all ML features