trustformers-mobile 0.1.1

//! Efficient On-Device Training for Mobile Deployment
//!
//! This module provides advanced on-device training capabilities with extreme
//! memory efficiency, including gradient checkpointing, model sharding,
//! and incremental training strategies optimized for mobile constraints.

use crate::{
    battery::MobileBatteryManager,
    device_info::{MobileDeviceInfo, PerformanceTier},
    optimization::{
        advanced_quantization::MobileQuantizationEngine, memory_pool::MobileMemoryPool,
    },
    thermal_power::ThermalPowerManager,
    Result,
};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use trustformers_core::errors::invalid_config;
use trustformers_core::Tensor;

/// Mobile-optimized training engine
pub struct MobileTrainingEngine {
    config: MobileTrainingConfig,
    device_info: MobileDeviceInfo,
    memory_pool: Arc<MobileMemoryPool>,
    gradient_manager: GradientManager,
    checkpoint_manager: CheckpointManager,
    battery_manager: Option<Arc<MobileBatteryManager>>,
    thermal_manager: Option<Arc<ThermalPowerManager>>,
    quantization_engine: Option<MobileQuantizationEngine>,
    training_state: Arc<Mutex<TrainingState>>,
}

/// Mobile training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MobileTrainingConfig {
    /// Training method selection
    pub training_method: MobileTrainingMethod,
    /// Memory optimization level
    pub memory_optimization: MemoryOptimizationLevel,
    /// Gradient management strategy
    pub gradient_strategy: GradientStrategy,
    /// Checkpoint strategy
    pub checkpoint_strategy: CheckpointStrategy,
    /// Maximum memory usage (MB)
    pub max_memory_mb: usize,
    /// Learning rate schedule
    pub learning_rate_schedule: LearningRateSchedule,
    /// Batch size strategy
    pub batch_strategy: BatchStrategy,
    /// Enable mixed precision training
    pub enable_mixed_precision: bool,
    /// Enable gradient compression
    pub enable_gradient_compression: bool,
    /// Enable incremental learning
    pub enable_incremental_learning: bool,
    /// Training quality vs efficiency trade-off
    pub quality_efficiency_ratio: f32,
    /// Enable thermal-aware training
    pub thermal_aware: bool,
    /// Enable battery-aware training
    pub battery_aware: bool,
}

/// Mobile training methods optimized for efficiency
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum MobileTrainingMethod {
    /// Low-Rank Adaptation with mobile optimizations
    LoRAMobile {
        rank: usize,
        alpha: f32,
        dropout: f32,
        target_modules: TargetModules,
    },
    /// Adapter-based training with compression
    AdapterMobile {
        bottleneck_size: usize,
        skip_connection: bool,
        layer_norm: bool,
    },
    /// Prefix tuning with minimal memory
    PrefixTuningMobile {
        prefix_length: usize,
        num_virtual_tokens: usize,
        reparameterization: bool,
    },
    /// Prompt tuning for minimal training
    PromptTuning {
        num_prompts: usize,
        prompt_length: usize,
        init_strategy: PromptInitStrategy,
    },
    /// BitFit - bias-only fine-tuning
    BitFit { target_bias_types: Vec<BiasType> },
    /// Differential privacy training
    DifferentialPrivate {
        noise_multiplier: f32,
        max_grad_norm: f32,
        delta: f64,
    },
}

/// Target modules for LoRA
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum TargetModules {
    QueryValue,
    QueryKeyValue,
    AllLinear,
    AttentionOnly,
    Custom(u32), // Bitmask for custom selection
}

/// Prompt initialization strategies
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum PromptInitStrategy {
    Random,
    Vocabulary,
    TaskSpecific,
    ClassLabel,
}

/// Bias types for BitFit
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum BiasType {
    Attention,
    FeedForward,
    LayerNorm,
    All,
}

/// Memory optimization levels
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum MemoryOptimizationLevel {
    /// Maximum memory efficiency, minimal quality loss
    Extreme,
    /// High efficiency with good quality
    Aggressive,
    /// Balanced efficiency and quality
    Balanced,
    /// Prefer quality over efficiency
    Conservative,
}

/// Gradient management strategies
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum GradientStrategy {
    /// Accumulate gradients over multiple steps
    Accumulation { steps: usize },
    /// Checkpoint gradients to reduce memory
    Checkpointing { layers: usize },
    /// Compress gradients during training
    Compression { ratio: f32 },
    /// Combined strategy
    Hybrid,
}

/// Checkpoint strategies for training
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum CheckpointStrategy {
    /// No checkpointing
    None,
    /// Checkpoint every N layers
    EveryNLayers { n: usize },
    /// Checkpoint based on memory pressure
    MemoryBased { threshold_mb: usize },
    /// Adaptive checkpointing
    Adaptive,
}

/// Learning rate scheduling for mobile training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LearningRateSchedule {
    /// Initial learning rate
    pub initial_lr: f32,
    /// Schedule type
    pub schedule_type: ScheduleType,
    /// Warmup steps
    pub warmup_steps: usize,
    /// Decay factor
    pub decay_factor: f32,
    /// Minimum learning rate
    pub min_lr: f32,
}

/// Learning rate schedule types
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum ScheduleType {
    Constant,
    Linear,
    Cosine,
    Exponential,
    StepDecay { step_size: usize },
    AdaptiveBattery, // Adjust based on battery level
    AdaptiveThermal, // Adjust based on thermal state
}

/// Batch size strategies
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum BatchStrategy {
    /// Fixed batch size
    Fixed { size: usize },
    /// Dynamic batch size based on memory
    Dynamic { min_size: usize, max_size: usize },
    /// Gradient accumulation simulation
    Simulated {
        effective_size: usize,
        micro_batch: usize,
    },
    /// Adaptive based on device state
    Adaptive,
}

/// Training state tracking
#[derive(Debug, Clone)]
pub struct TrainingState {
    /// Current epoch
    pub current_epoch: usize,
    /// Current step
    pub current_step: usize,
    /// Training loss history
    pub loss_history: VecDeque<f32>,
    /// Learning rate history
    pub lr_history: VecDeque<f32>,
    /// Memory usage history
    pub memory_history: VecDeque<usize>,
    /// Training start time
    pub start_time: Instant,
    /// Current learning rate
    pub current_lr: f32,
    /// Is training paused
    pub is_paused: bool,
    /// Pause reason
    pub pause_reason: Option<PauseReason>,
}

/// Reasons for training pause
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum PauseReason {
    BatteryLow,
    ThermalThrottling,
    MemoryPressure,
    UserRequest,
    BackgroundMode,
}

/// Gradient manager for efficient gradient handling
pub struct GradientManager {
    compression_enabled: bool,
    accumulation_steps: usize,
    checkpointing_enabled: bool,
    gradient_buffer: Arc<Mutex<HashMap<String, Vec<f32>>>>,
    compression_ratio: f32,
}

/// Checkpoint manager for training state
pub struct CheckpointManager {
    strategy: CheckpointStrategy,
    checkpoint_interval: Duration,
    max_checkpoints: usize,
    checkpoint_history: VecDeque<TrainingCheckpoint>,
    memory_pool: Arc<MobileMemoryPool>,
}

/// Training checkpoint data
#[derive(Debug, Clone)]
pub struct TrainingCheckpoint {
    /// Checkpoint ID
    pub id: String,
    /// Model state
    pub model_state: Vec<u8>,
    /// Optimizer state
    pub optimizer_state: Vec<u8>,
    /// Training metadata
    pub metadata: CheckpointMetadata,
    /// Creation timestamp
    pub timestamp: Instant,
}

/// Checkpoint metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CheckpointMetadata {
    /// Epoch number
    pub epoch: usize,
    /// Step number
    pub step: usize,
    /// Loss value
    pub loss: f32,
    /// Learning rate
    pub learning_rate: f32,
    /// Memory usage at checkpoint
    pub memory_usage_mb: usize,
}

impl MobileTrainingEngine {
    /// Create a new mobile training engine
    pub fn new(
        config: MobileTrainingConfig,
        device_info: MobileDeviceInfo,
        memory_pool: Arc<MobileMemoryPool>,
    ) -> Result<Self> {
        let gradient_manager = GradientManager::new(&config)?;
        let checkpoint_manager =
            CheckpointManager::new(config.checkpoint_strategy, memory_pool.clone())?;

        let training_state = TrainingState {
            current_epoch: 0,
            current_step: 0,
            loss_history: VecDeque::with_capacity(1000),
            lr_history: VecDeque::with_capacity(1000),
            memory_history: VecDeque::with_capacity(1000),
            start_time: Instant::now(),
            current_lr: config.learning_rate_schedule.initial_lr,
            is_paused: false,
            pause_reason: None,
        };

        Ok(Self {
            config,
            device_info,
            memory_pool,
            gradient_manager,
            checkpoint_manager,
            battery_manager: None,
            thermal_manager: None,
            quantization_engine: None,
            training_state: Arc::new(Mutex::new(training_state)),
        })
    }

    /// Set battery manager for battery-aware training
    pub fn set_battery_manager(&mut self, battery_manager: Arc<MobileBatteryManager>) {
        self.battery_manager = Some(battery_manager);
    }

    /// Set thermal manager for thermal-aware training
    pub fn set_thermal_manager(&mut self, thermal_manager: Arc<ThermalPowerManager>) {
        self.thermal_manager = Some(thermal_manager);
    }

    /// Start training with mobile optimizations
    pub async fn start_training(
        &mut self,
        model_id: &str,
        training_data: &[TrainingSample],
        validation_data: Option<&[TrainingSample]>,
    ) -> Result<TrainingResult> {
        // Check device readiness for training
        self.check_training_readiness()?;

        // Initialize training state
        {
            let mut state = self.training_state.lock().expect("Operation failed");
            state.start_time = Instant::now();
            state.is_paused = false;
            state.pause_reason = None;
        }

        // Prepare training data with mobile optimizations
        let prepared_data = self.prepare_training_data(training_data)?;

        // Start training loop
        let mut training_result = TrainingResult::default();

        for epoch in 0..self.get_max_epochs() {
            // Check if training should be paused
            if self.should_pause_training()? {
                self.pause_training()?;
                break;
            }

            // Update learning rate
            self.update_learning_rate(epoch)?;

            // Train one epoch
            let epoch_result = self.train_epoch(&prepared_data, epoch).await?;
            training_result.epoch_results.push(epoch_result);

            // Validate if validation data is provided
            if let Some(val_data) = validation_data {
                let val_result = self.validate_epoch(val_data, epoch).await?;
                training_result.validation_results.push(val_result);
            }

            // Create checkpoint
            self.create_checkpoint(epoch)?;

            // Update training state
            {
                let mut state = self.training_state.lock().expect("Operation failed");
                state.current_epoch = epoch;
            }
        }

        Ok(training_result)
    }

    /// Train one epoch with mobile optimizations
    async fn train_epoch(
        &mut self,
        data: &[PreparedTrainingSample],
        epoch: usize,
    ) -> Result<EpochResult> {
        let mut epoch_loss = 0.0;
        let mut step_count = 0;
        let epoch_start = Instant::now();

        for batch in self.create_batches(data)? {
            // Check device state before each batch
            if self.should_pause_training()? {
                break;
            }

            // Process batch with memory optimization
            let batch_result = self.process_training_batch(&batch).await?;
            epoch_loss += batch_result.loss;
            step_count += 1;

            // Update training state
            {
                let mut state = self.training_state.lock().expect("Operation failed");
                state.current_step += 1;
                state.loss_history.push_back(batch_result.loss);
                state.memory_history.push_back(batch_result.memory_usage_mb);

                // Keep history size manageable
                if state.loss_history.len() > 1000 {
                    state.loss_history.pop_front();
                }
                if state.memory_history.len() > 1000 {
                    state.memory_history.pop_front();
                }
            }

            // Apply gradient accumulation if enabled
            if step_count % self.get_gradient_accumulation_steps() == 0 {
                self.apply_accumulated_gradients()?;
            }
        }

        Ok(EpochResult {
            epoch,
            average_loss: epoch_loss / step_count as f32,
            step_count,
            duration: epoch_start.elapsed(),
            memory_peak_mb: self.get_peak_memory_usage(),
        })
    }

    /// Process a single training batch
    async fn process_training_batch(&mut self, batch: &TrainingBatch) -> Result<BatchResult> {
        let batch_start = Instant::now();
        let memory_before = self.memory_pool.get_stats().current_memory_bytes;

        // Forward pass with gradient checkpointing if enabled
        let (loss, gradients) = if self.config.checkpoint_strategy != CheckpointStrategy::None {
            self.forward_pass_with_checkpointing(batch).await?
        } else {
            self.forward_pass_standard(batch).await?
        };

        // Backward pass with memory optimization
        self.backward_pass_optimized(&gradients).await?;

        let memory_after = self.memory_pool.get_stats().current_memory_bytes;

        Ok(BatchResult {
            loss,
            duration: batch_start.elapsed(),
            memory_usage_mb: (memory_after - memory_before) / (1024 * 1024),
        })
    }

    /// Check if training should be paused based on device state
    fn should_pause_training(&self) -> Result<bool> {
        // Check battery level
        if let Some(battery_manager) = &self.battery_manager {
            if self.config.battery_aware {
                // Implementation would check battery state
                // For now, simplified check
                return Ok(false);
            }
        }

        // Check thermal state
        if let Some(thermal_manager) = &self.thermal_manager {
            if self.config.thermal_aware {
                // Implementation would check thermal state
                // For now, simplified check
                return Ok(false);
            }
        }

        // Check memory pressure
        let memory_usage_ratio = self.memory_pool.get_usage_ratio();
        if memory_usage_ratio > 0.9 {
            return Ok(true);
        }

        Ok(false)
    }

    /// Helper methods (simplified implementations)
    fn check_training_readiness(&self) -> Result<()> {
        // Check available memory
        if self.memory_pool.get_available_memory() < self.config.max_memory_mb * 1024 * 1024 {
            return Err(invalid_config(
                "MobileTrainingEngine::check_training_readiness",
                "Insufficient memory for training",
            ));
        }

        Ok(())
    }

    fn prepare_training_data(
        &self,
        data: &[TrainingSample],
    ) -> Result<Vec<PreparedTrainingSample>> {
        // Simplified preparation
        Ok(data
            .iter()
            .map(|sample| PreparedTrainingSample {
                input: sample.input.clone(),
                target: sample.target.clone(),
                weight: 1.0,
            })
            .collect())
    }

    fn get_max_epochs(&self) -> usize {
        match self.device_info.performance_scores.overall_tier {
            PerformanceTier::VeryLow => 1,
            PerformanceTier::Low => 2,
            PerformanceTier::Budget => 3,
            PerformanceTier::Medium => 4,
            PerformanceTier::Mid => 5,
            PerformanceTier::High => 10,
            PerformanceTier::VeryHigh => 12,
            PerformanceTier::Flagship => 15, // Premium flagship devices can handle more epochs
        }
    }

    fn update_learning_rate(&mut self, epoch: usize) -> Result<()> {
        let new_lr = self.calculate_learning_rate(epoch);
        let mut state = self.training_state.lock().expect("Operation failed");
        state.current_lr = new_lr;
        state.lr_history.push_back(new_lr);
        if state.lr_history.len() > 1000 {
            state.lr_history.pop_front();
        }
        Ok(())
    }

    fn calculate_learning_rate(&self, epoch: usize) -> f32 {
        let schedule = &self.config.learning_rate_schedule;
        let progress = epoch as f32 / self.get_max_epochs() as f32;

        match schedule.schedule_type {
            ScheduleType::Constant => schedule.initial_lr,
            ScheduleType::Linear => schedule.initial_lr * (1.0 - progress),
            ScheduleType::Cosine => {
                schedule.min_lr
                    + (schedule.initial_lr - schedule.min_lr)
                        * (1.0 + (std::f32::consts::PI * progress).cos())
                        / 2.0
            },
            ScheduleType::Exponential => {
                schedule.initial_lr * schedule.decay_factor.powf(epoch as f32)
            },
            _ => schedule.initial_lr, // Simplified for other types
        }
    }

    // Additional helper methods would be implemented here
    fn create_batches(&self, _data: &[PreparedTrainingSample]) -> Result<Vec<TrainingBatch>> {
        Ok(vec![]) // Simplified
    }

    fn get_gradient_accumulation_steps(&self) -> usize {
        match self.config.gradient_strategy {
            GradientStrategy::Accumulation { steps } => steps,
            _ => 1,
        }
    }

    fn apply_accumulated_gradients(&mut self) -> Result<()> {
        // Implementation would apply accumulated gradients
        Ok(())
    }

    async fn forward_pass_with_checkpointing(
        &mut self,
        _batch: &TrainingBatch,
    ) -> Result<(f32, Vec<f32>)> {
        // Implementation would perform forward pass with checkpointing
        Ok((0.0, vec![]))
    }

    async fn forward_pass_standard(&mut self, _batch: &TrainingBatch) -> Result<(f32, Vec<f32>)> {
        // Implementation would perform standard forward pass
        Ok((0.0, vec![]))
    }

    async fn backward_pass_optimized(&mut self, _gradients: &[f32]) -> Result<()> {
        // Implementation would perform optimized backward pass
        Ok(())
    }

    async fn validate_epoch(
        &mut self,
        _data: &[TrainingSample],
        _epoch: usize,
    ) -> Result<ValidationResult> {
        Ok(ValidationResult::default())
    }

    fn create_checkpoint(&mut self, epoch: usize) -> Result<()> {
        self.checkpoint_manager.create_checkpoint(epoch)
    }

    fn pause_training(&mut self) -> Result<()> {
        let mut state = self.training_state.lock().expect("Operation failed");
        state.is_paused = true;
        Ok(())
    }

    fn get_peak_memory_usage(&self) -> usize {
        self.memory_pool.get_peak_usage() / (1024 * 1024)
    }
}

impl GradientManager {
    fn new(config: &MobileTrainingConfig) -> Result<Self> {
        Ok(Self {
            compression_enabled: config.enable_gradient_compression,
            accumulation_steps: match config.gradient_strategy {
                GradientStrategy::Accumulation { steps } => steps,
                _ => 1,
            },
            checkpointing_enabled: config.checkpoint_strategy != CheckpointStrategy::None,
            gradient_buffer: Arc::new(Mutex::new(HashMap::new())),
            compression_ratio: 0.1, // 10x compression by default
        })
    }
}

impl CheckpointManager {
    fn new(strategy: CheckpointStrategy, memory_pool: Arc<MobileMemoryPool>) -> Result<Self> {
        Ok(Self {
            strategy,
            checkpoint_interval: Duration::from_secs(60), // Default: 1 minute
            max_checkpoints: 3,
            checkpoint_history: VecDeque::new(),
            memory_pool,
        })
    }

    fn create_checkpoint(&mut self, epoch: usize) -> Result<()> {
        // Implementation would create actual checkpoint
        Ok(())
    }
}

// Supporting data structures
#[derive(Debug, Clone)]
pub struct TrainingSample {
    pub input: Tensor,
    pub target: Tensor,
}

#[derive(Debug, Clone)]
pub struct PreparedTrainingSample {
    pub input: Tensor,
    pub target: Tensor,
    pub weight: f32,
}

#[derive(Debug, Clone)]
pub struct TrainingBatch {
    pub samples: Vec<PreparedTrainingSample>,
}

#[derive(Debug, Clone, Default)]
pub struct TrainingResult {
    pub epoch_results: Vec<EpochResult>,
    pub validation_results: Vec<ValidationResult>,
}

#[derive(Debug, Clone)]
pub struct EpochResult {
    pub epoch: usize,
    pub average_loss: f32,
    pub step_count: usize,
    pub duration: Duration,
    pub memory_peak_mb: usize,
}

#[derive(Debug, Clone, Default)]
pub struct ValidationResult {
    pub accuracy: f32,
    pub loss: f32,
}

#[derive(Debug, Clone)]
pub struct BatchResult {
    pub loss: f32,
    pub duration: Duration,
    pub memory_usage_mb: usize,
}

impl Default for MobileTrainingConfig {
    fn default() -> Self {
        Self {
            training_method: MobileTrainingMethod::LoRAMobile {
                rank: 16,
                alpha: 32.0,
                dropout: 0.1,
                target_modules: TargetModules::QueryValue,
            },
            memory_optimization: MemoryOptimizationLevel::Balanced,
            gradient_strategy: GradientStrategy::Accumulation { steps: 4 },
            checkpoint_strategy: CheckpointStrategy::EveryNLayers { n: 2 },
            max_memory_mb: 512,
            learning_rate_schedule: LearningRateSchedule {
                initial_lr: 1e-4,
                schedule_type: ScheduleType::Cosine,
                warmup_steps: 100,
                decay_factor: 0.95,
                min_lr: 1e-6,
            },
            batch_strategy: BatchStrategy::Dynamic {
                min_size: 1,
                max_size: 4,
            },
            enable_mixed_precision: true,
            enable_gradient_compression: true,
            enable_incremental_learning: true,
            quality_efficiency_ratio: 0.8,
            thermal_aware: true,
            battery_aware: true,
        }
    }
}