entrenar 0.7.10

//! Prune-finetune trainer implementation.
//!
//! Provides the main trainer that orchestrates the full pruning pipeline.

use crate::prune::calibrate::CalibrationCollector;
use crate::prune::data_loader::CalibrationDataLoader;
use crate::prune::pipeline::{PruneFinetunePipeline, PruningMetrics, PruningStage};

use super::config::PruneTrainerConfig;

/// Prune-finetune trainer that orchestrates the full pipeline.
///
/// # Example
///
/// ```ignore
/// use entrenar::prune::{PruneTrainer, PruneTrainerConfig, PruningConfig};
///
/// fn example() -> Result<(), Box<dyn std::error::Error>> {
///     let config = PruneTrainerConfig::new()
///         .with_pruning(PruningConfig::default().with_target_sparsity(0.5))
///         .with_finetune_epochs(3);
///
///     let mut trainer = PruneTrainer::new(config);
///     trainer.run()?;
///     Ok(())
/// }
/// ```
#[derive(Debug)]
pub struct PruneTrainer {
    /// Configuration.
    config: PruneTrainerConfig,
    /// Pipeline state.
    pipeline: PruneFinetunePipeline,
    /// Calibration data loader.
    data_loader: CalibrationDataLoader,
    /// Calibration collector.
    pub(crate) calibration: Option<CalibrationCollector>,
    /// Current epoch in fine-tuning.
    current_epoch: usize,
}

impl PruneTrainer {
    /// Create a new prune trainer.
    pub fn new(config: PruneTrainerConfig) -> Self {
        let pipeline = PruneFinetunePipeline::new(config.pruning.clone());
        let data_loader = CalibrationDataLoader::new(config.calibration.clone());

        Self { config, pipeline, data_loader, calibration: None, current_epoch: 0 }
    }

    /// Get the configuration.
    pub fn config(&self) -> &PruneTrainerConfig {
        &self.config
    }

    /// Get the current pipeline state.
    pub fn pipeline(&self) -> &PruneFinetunePipeline {
        &self.pipeline
    }

    /// Get mutable access to the pipeline.
    pub fn pipeline_mut(&mut self) -> &mut PruneFinetunePipeline {
        &mut self.pipeline
    }

    /// Get the current stage.
    pub fn stage(&self) -> PruningStage {
        self.pipeline.stage()
    }

    /// Get the metrics.
    pub fn metrics(&self) -> &PruningMetrics {
        self.pipeline.metrics()
    }

    /// Get the current fine-tuning epoch.
    pub fn current_epoch(&self) -> usize {
        self.current_epoch
    }

    /// Check if the trainer is complete.
    pub fn is_complete(&self) -> bool {
        self.pipeline.is_complete()
    }

    /// Check if training succeeded.
    pub fn succeeded(&self) -> bool {
        self.pipeline.succeeded()
    }

    /// Get the error message if failed.
    pub fn error(&self) -> Option<&str> {
        self.pipeline.error()
    }

    /// Initialize the trainer and load calibration data.
    pub fn initialize(&mut self) -> Result<(), String> {
        // Validate configuration
        self.config.validate()?;

        // Load calibration data
        self.data_loader.load()?;

        // Initialize calibration collector if needed
        if self.config.pruning.requires_calibration() {
            self.calibration = Some(CalibrationCollector::new(
                crate::prune::calibrate::CalibrationConfig::new()
                    .with_num_samples(self.config.calibration.num_samples()),
            ));
        }

        Ok(())
    }

    /// Run calibration phase.
    ///
    /// In a real implementation, this would:
    /// 1. Run forward passes through the model
    /// 2. Collect activation statistics for each layer
    /// 3. Store statistics in the calibration collector
    pub fn calibrate(&mut self) -> Result<(), String> {
        if self.pipeline.stage() != PruningStage::Idle
            && self.pipeline.stage() != PruningStage::Calibrating
        {
            return Err("Cannot calibrate in current stage".to_string());
        }

        // Initialize calibration if not done
        if self.calibration.is_none() && self.config.pruning.requires_calibration() {
            self.calibration = Some(CalibrationCollector::new(
                crate::prune::calibrate::CalibrationConfig::new()
                    .with_num_samples(self.config.calibration.num_samples()),
            ));
        }

        // Start calibration stage if at Idle
        if self.pipeline.stage() == PruningStage::Idle {
            if let Some(cal) = self.calibration.take() {
                self.pipeline.start_calibration(cal);
            } else {
                // No calibration needed, advance from Idle to Calibrating
                self.pipeline.advance();
            }
        }

        // Process calibration batches
        for _batch in &self.data_loader {
            // In real implementation:
            // 1. Forward pass through model
            // 2. Extract activations at each layer
            // 3. Update calibration statistics
        }

        // Advance from Calibrating to ComputingImportance
        if self.pipeline.stage() == PruningStage::Calibrating {
            self.pipeline.advance();
        }

        Ok(())
    }

    /// Run the pruning phase.
    ///
    /// In a real implementation, this would:
    /// 1. Compute importance scores using calibration data
    /// 2. Generate sparsity masks
    /// 3. Apply masks to model weights
    pub fn prune(&mut self) -> Result<(), String> {
        // Advance through importance computation
        while self.pipeline.stage() == PruningStage::ComputingImportance {
            // Compute importance scores
            self.pipeline.advance();
        }

        if self.pipeline.stage() != PruningStage::Pruning {
            return Err(format!("Cannot prune in stage {:?}", self.pipeline.stage()));
        }

        // In real implementation:
        // 1. Generate masks based on importance and target sparsity
        // 2. Apply masks to model weights
        // 3. Record metrics

        let target_sparsity = self.config.pruning.target_sparsity();
        self.pipeline.metrics_mut().target_sparsity = target_sparsity;
        self.pipeline.metrics_mut().achieved_sparsity = target_sparsity;

        self.pipeline.advance();
        Ok(())
    }

    /// Run the fine-tuning phase.
    ///
    /// In a real implementation, this would:
    /// 1. Set up optimizer with fine-tuning learning rate
    /// 2. Run training epochs
    /// 3. Track loss and metrics
    pub fn finetune(&mut self) -> Result<(), String> {
        if self.pipeline.stage() != PruningStage::FineTuning {
            return Err(format!("Cannot finetune in stage {:?}", self.pipeline.stage()));
        }

        for epoch in 0..self.config.finetune_epochs {
            self.current_epoch = epoch;

            // In real implementation:
            // 1. Run training epoch
            // 2. Track loss
            // 3. Optionally save checkpoint

            // Simulate loss decrease
            let loss = 1.0 / (epoch + 1) as f32;
            self.pipeline.metrics_mut().record_finetune_loss(loss);
        }

        self.pipeline.advance();
        Ok(())
    }

    /// Run evaluation phase.
    pub fn evaluate(&mut self) -> Result<(), String> {
        if self.pipeline.stage() != PruningStage::Evaluating {
            return Err(format!("Cannot evaluate in stage {:?}", self.pipeline.stage()));
        }

        // In real implementation:
        // 1. Run evaluation on validation set
        // 2. Compute perplexity/accuracy
        // 3. Record metrics

        self.pipeline.advance();
        Ok(())
    }

    /// Run export phase.
    pub fn export(&mut self) -> Result<(), String> {
        if self.pipeline.stage() != PruningStage::Exporting {
            return Err(format!("Cannot export in stage {:?}", self.pipeline.stage()));
        }

        // In real implementation:
        // 1. Save pruned model
        // 2. Export to desired format (SafeTensors, GGUF, etc.)

        self.pipeline.advance();
        Ok(())
    }

    /// Run the full prune-finetune pipeline.
    pub fn run(&mut self) -> Result<PruningMetrics, String> {
        self.initialize()?;
        self.calibrate()?;
        self.prune()?;

        if self.config.pruning.fine_tune_after_pruning() {
            self.finetune()?;
        }

        if self.config.evaluate_pre_post {
            self.evaluate()?;
        }

        self.export()?;

        Ok(self.metrics().clone())
    }

    /// Reset the trainer to initial state.
    pub fn reset(&mut self) {
        self.pipeline.reset();
        self.calibration = None;
        self.current_epoch = 0;
        self.data_loader.reset();
    }
}

impl Clone for PruneTrainer {
    fn clone(&self) -> Self {
        Self {
            config: self.config.clone(),
            pipeline: self.pipeline.clone(),
            data_loader: self.data_loader.clone(),
            calibration: self.calibration.clone(),
            current_epoch: self.current_epoch,
        }
    }
}