//! On-device training optimizations module
//!
//! This module provides optimizations for training neural networks on edge devices
//! with limited compute and memory resources.

pub mod memory_efficient_training;
pub mod gradient_checkpointing;
pub mod quantization_aware_training;
pub mod sparse_training;
// pub mod adaptive_computation; // Module not implemented
pub mod model_compression;
pub use memory_efficient_training::{MemoryEfficientTrainer, GradientAccumulation};
pub use gradient_checkpointing::{CheckpointStrategy, GradientCheckpointing};
pub use quantization_aware_training::{QATConfig, QuantizationAwareTraining};
pub use sparse_training::{SparseTrainer, SparsitySchedule};
// pub use adaptive_computation::{AdaptiveCompute, EarlyExitStrategy}; // Module not implemented
pub use model_compression::{CompressionStrategy, ModelCompressor};
use crate::error::Result;
use crate::models::sequential::Sequential;
use scirs2_core::ndarray::prelude::*;
use scirs2_core::ndarray::ArrayView1;
/// Configuration for on-device training
#[derive(Debug, Clone)]
pub struct OnDeviceConfig {
    /// Maximum memory budget in MB
    pub memory_budget_mb: usize,
    /// Target inference latency in ms
    pub target_latency_ms: f32,
    /// Enable gradient checkpointing
    pub gradient_checkpointing: bool,
    /// Enable mixed precision training
    pub mixed_precision: bool,
    /// Enable model quantization
    pub quantization: bool,
    /// Quantization bits (8, 4, 2, 1)
    pub quantization_bits: u8,
    /// Enable sparse training
    pub sparse_training: bool,
    /// Target sparsity level (0.0 to 1.0)
    pub target_sparsity: f32,
    /// Enable adaptive computation
    pub adaptive_computation: bool,
    /// Batch size for training
    pub batch_size: usize,
    /// Gradient accumulation steps
    pub gradient_accumulation_steps: usize,
    /// Learning rate
    pub learning_rate: f32,
    /// Enable knowledge distillation
    pub knowledge_distillation: bool,
    /// Temperature for distillation
    pub distillation_temperature: f32,
}
impl Default for OnDeviceConfig {
    fn default() -> Self {
        Self {
            memory_budget_mb: 512,
            target_latency_ms: 50.0,
            gradient_checkpointing: true,
            mixed_precision: true,
            quantization: true,
            quantization_bits: 8,
            sparse_training: false,
            target_sparsity: 0.9,
            adaptive_computation: false,
            batch_size: 1,
            gradient_accumulation_steps: 4,
            learning_rate: 0.001,
            knowledge_distillation: false,
            distillation_temperature: 3.0,
        }
    }
/// On-device training optimizer
pub struct OnDeviceOptimizer {
    config: OnDeviceConfig,
    memory_tracker: MemoryTracker,
    performance_monitor: PerformanceMonitor,
impl OnDeviceOptimizer {
    /// Create a new on-device optimizer
    pub fn new(config: OnDeviceConfig) -> Self {
            config,
            memory_tracker: MemoryTracker::new(_config.memory_budget_mb),
            performance_monitor: PerformanceMonitor::new(),
    
    /// Optimize model for on-device training
    pub fn optimize_model(&mut self, model: &mut Sequential<f32>) -> Result<OptimizationReport> {
        let start_memory = self.memory_tracker.current_usage();
        let start_params = self.count_parameters(model);
        
        let mut optimizations_applied = Vec::new();
        // Apply gradient checkpointing
        if self._config.gradient_checkpointing {
            self.apply_gradient_checkpointing(model)?;
            optimizations_applied.push("Gradient Checkpointing");
        // Apply quantization
        if self._config.quantization {
            self.apply_quantization(model)?;
            optimizations_applied.push("Quantization");
        // Apply sparsity
        if self._config.sparse_training {
            self.apply_sparsity(model)?;
            optimizations_applied.push("Sparse Training");
        // Apply adaptive computation
        if self._config.adaptive_computation {
            self.apply_adaptive_computation(model)?;
            optimizations_applied.push("Adaptive Computation");
        let end_memory = self.memory_tracker.current_usage();
        let end_params = self.count_parameters(model);
        Ok(OptimizationReport {
            initial_memory_mb: start_memory,
            optimized_memory_mb: end_memory,
            memory_reduction_percent: ((start_memory - end_memory) as f32 / start_memory as f32) * 100.0,
            initial_parameters: start_params,
            optimized_parameters: end_params,
            parameter_reduction_percent: ((start_params - end_params) as f32 / start_params as f32) * 100.0,
            optimizations_applied,
            estimated_latency_ms: self.estimate_latency(model)?,
            estimated_energy_mj: self.estimate_energy(model)?,
        })
    /// Apply gradient checkpointing
    fn apply_gradient_checkpointing(&mut self, model: &mut Sequential<f32>) -> Result<()> {
        // Implement gradient checkpointing logic
        // This would modify the model to recompute activations during backward pass
        println!("Applying gradient checkpointing...");
        Ok(())
    /// Apply quantization
    fn apply_quantization(&mut self, model: &mut Sequential<f32>) -> Result<()> {
        // Implement quantization logic
        println!("Applying {}-bit quantization...", self.config.quantization_bits);
    /// Apply sparsity
    fn apply_sparsity(&mut self, model: &mut Sequential<f32>) -> Result<()> {
        // Implement sparse training logic
        println!("Applying sparsity with target {}%...", self.config.target_sparsity * 100.0);
    /// Apply adaptive computation
    fn apply_adaptive_computation(&mut self, model: &mut Sequential<f32>) -> Result<()> {
        // Implement adaptive computation logic
        println!("Applying adaptive computation...");
    /// Count model parameters
    fn count_parameters(&self, model: &Sequential<f32>) -> usize {
        // Simplified parameter counting
        1_000_000 // Placeholder
    /// Estimate inference latency
    fn estimate_latency(&self, model: &Sequential<f32>) -> Result<f32> {
        // Simplified latency estimation
        Ok(25.0) // ms
    /// Estimate energy consumption
    fn estimate_energy(&self, model: &Sequential<f32>) -> Result<f32> {
        // Simplified energy estimation
        Ok(10.0) // mJ
    /// Create training pipeline optimized for device
    pub fn create_training_pipeline(&self, model: &mut Sequential<f32>) -> Result<DeviceTrainingPipeline> {
        let trainer = if self.config.memory_budget_mb < 256 {
            TrainingStrategy::MicroBatch
        } else if self.config.memory_budget_mb < 1024 {
            TrainingStrategy::GradientAccumulation
        } else {
            TrainingStrategy::Standard
        };
        Ok(DeviceTrainingPipeline {
            model: model.clone(),
            strategy: trainer,
            config: self.config.clone(),
/// Optimization report
pub struct OptimizationReport {
    /// Initial memory usage in MB
    pub initial_memory_mb: usize,
    /// Optimized memory usage in MB
    pub optimized_memory_mb: usize,
    /// Memory reduction percentage
    pub memory_reduction_percent: f32,
    /// Initial parameter count
    pub initial_parameters: usize,
    /// Optimized parameter count
    pub optimized_parameters: usize,
    /// Parameter reduction percentage
    pub parameter_reduction_percent: f32,
    /// List of optimizations applied
    pub optimizations_applied: Vec<&'static str>,
    /// Estimated inference latency
    pub estimated_latency_ms: f32,
    /// Estimated energy per inference
    pub estimated_energy_mj: f32,
/// Memory tracker for on-device training
struct MemoryTracker {
    budget_mb: usize,
    current_usage_mb: usize,
    peak_usage_mb: usize,
impl MemoryTracker {
    fn new(_budgetmb: usize) -> Self {
            budget_mb,
            current_usage_mb: 0,
            peak_usage_mb: 0,
    fn current_usage(&self) -> usize {
        self.current_usage_mb
    fn allocate(&mut self, sizemb: usize) -> Result<()> {
        if self.current_usage_mb + size_mb > self._budget_mb {
            return Err(crate::error::NeuralError::ResourceExhausted(
                format!("Memory budget exceeded: {} + {} > {} MB", 
                    self.current_usage_mb, size_mb, self.budget_mb)
            ));
        self.current_usage_mb += size_mb;
        self.peak_usage_mb = self.peak_usage_mb.max(self.current_usage_mb);
    fn deallocate(&mut self, sizemb: usize) {
        self.current_usage_mb = self.current_usage_mb.saturating_sub(size_mb);
/// Performance monitor for on-device execution
struct PerformanceMonitor {
    latency_history: Vec<f32>,
    energy_history: Vec<f32>,
impl PerformanceMonitor {
    fn new() -> Self {
            latency_history: Vec::new(),
            energy_history: Vec::new(),
    fn record_inference(&mut self, latency_ms: f32, energymj: f32) {
        self.latency_history.push(latency_ms);
        self.energy_history.push(energy_mj);
    fn average_latency(&self) -> f32 {
        if self.latency_history.is_empty() {
            0.0
            self.latency_history.iter().sum::<f32>() / self.latency_history.len() as f32
    fn average_energy(&self) -> f32 {
        if self.energy_history.is_empty() {
            self.energy_history.iter().sum::<f32>() / self.energy_history.len() as f32
/// Training strategy for devices
#[derive(Debug, Clone, Copy, PartialEq)]
enum TrainingStrategy {
    Standard,
    GradientAccumulation,
    MicroBatch,
/// Device-optimized training pipeline
pub struct DeviceTrainingPipeline {
    model: Sequential<f32>,
    strategy: TrainingStrategy,
impl DeviceTrainingPipeline {
    /// Train on a batch of data
    pub fn train_batch(
        &mut self,
        data: &ArrayView2<f32>,
        labels: &ArrayView1<usize>,
    ) -> Result<f32> {
        match self.strategy {
            TrainingStrategy::Standard => self.standard_training(data, labels),
            TrainingStrategy::GradientAccumulation => self.gradient_accumulation_training(data, labels),
            TrainingStrategy::MicroBatch => self.micro_batch_training(data, labels),
    fn standard_training(
        // Standard training implementation
        Ok(0.5) // Placeholder loss
    fn gradient_accumulation_training(
        // Gradient accumulation implementation
        let micro_batch_size = self.config.batch_size / self.config.gradient_accumulation_steps;
        let mut total_loss = 0.0;
        for i in 0..self.config.gradient_accumulation_steps {
            let start = i * micro_batch_size;
            let end = ((i + 1) * micro_batch_size).min(data.shape()[0]);
            
            // Process micro-batch
            total_loss += 0.1; // Placeholder
        Ok(total_loss / self.config.gradient_accumulation_steps as f32)
    fn micro_batch_training(
        // Process one sample at a time
        for i in 0..data.shape()[0] {
            // Process single sample
        Ok(total_loss / data.shape()[0] as f32)
/// Edge device profiles
pub struct DeviceProfile {
    pub name: String,
    pub cpu_cores: usize,
    pub cpu_freq_mhz: u32,
    pub ram_mb: usize,
    pub has_gpu: bool,
    pub has_npu: bool,
    pub battery_mah: Option<u32>,
impl DeviceProfile {
    /// Common device profiles
    pub fn raspberry_pi_4() -> Self {
            name: "Raspberry Pi 4".to_string(),
            cpu_cores: 4,
            cpu_freq_mhz: 1500,
            ram_mb: 4096,
            has_gpu: true,
            has_npu: false,
            battery_mah: None,
    pub fn nvidia_jetson_nano() -> Self {
            name: "NVIDIA Jetson Nano".to_string(),
            cpu_freq_mhz: 1430,
    pub fn google_coral() -> Self {
            name: "Google Coral Dev Board".to_string(),
            ram_mb: 1024,
            has_gpu: false,
            has_npu: true,
    pub fn smartphone() -> Self {
            name: "Generic Smartphone".to_string(),
            cpu_cores: 8,
            cpu_freq_mhz: 2840,
            ram_mb: 8192,
            battery_mah: Some(4000),
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_on_device_config_default() {
        let config = OnDeviceConfig::default();
        assert_eq!(config.memory_budget_mb, 512);
        assert!(config.gradient_checkpointing);
        assert!(config.mixed_precision);
    fn test_memory_tracker() {
        let mut tracker = MemoryTracker::new(100);
        assert!(tracker.allocate(50).is_ok());
        assert_eq!(tracker.current_usage(), 50);
        assert!(tracker.allocate(40).is_ok());
        assert_eq!(tracker.current_usage(), 90);
        assert!(tracker.allocate(20).is_err()); // Exceeds budget
        tracker.deallocate(30);
        assert_eq!(tracker.current_usage(), 60);
    fn test_device_profiles() {
        let rpi = DeviceProfile::raspberry_pi_4();
        assert_eq!(rpi.cpu_cores, 4);
        assert_eq!(rpi.ram_mb, 4096);
        let jetson = DeviceProfile::nvidia_jetson_nano();
        assert!(jetson.has_gpu);
        let coral = DeviceProfile::google_coral();
        assert!(coral.has_npu);
        let phone = DeviceProfile::smartphone();
        assert!(phone.battery_mah.is_some());
