trustformers_core/compression/

pipeline.rs

//! Compression Pipeline for combining multiple compression techniques

#![allow(unused_variables)] // Compression pipeline

use crate::compression::{distillation::DistillationConfig, pruning::PruningConfig};
use anyhow::{anyhow, Result};
use std::time::Instant;

/// Compression stage in the pipeline
#[derive(Debug, Clone)]
pub enum CompressionStage {
    /// Pruning stage
    Pruning {
        strategy: String,
        config: PruningConfig,
    },
    /// Quantization stage
    Quantization { bits: u8, symmetric: bool },
    /// Distillation stage
    Distillation {
        teacher_model: String,
        config: DistillationConfig,
    },
    /// Fine-tuning stage
    FineTuning { epochs: usize, learning_rate: f32 },
    /// Custom stage
    Custom {
        name: String,
        params: std::collections::HashMap<String, String>,
    },
}

/// Compression pipeline configuration
#[derive(Debug, Clone)]
pub struct CompressionConfig {
    /// Pipeline stages to execute
    pub stages: Vec<CompressionStage>,
    /// Target compression ratio
    pub target_ratio: f32,
    /// Maximum acceptable accuracy loss
    pub max_accuracy_loss: f32,
    /// Whether to validate after each stage
    pub validate_stages: bool,
    /// Output directory for intermediate models
    pub output_dir: Option<std::path::PathBuf>,
}

impl Default for CompressionConfig {
    fn default() -> Self {
        Self {
            stages: vec![],
            target_ratio: 10.0,
            max_accuracy_loss: 0.01,
            validate_stages: true,
            output_dir: None,
        }
    }
}

/// Result of compression pipeline
#[derive(Debug, Clone)]
pub struct CompressionResult<M>
where
    M: crate::traits::Model,
{
    /// Final compressed model
    pub model: M,
    /// Original model size in bytes
    pub original_size: usize,
    /// Compressed model size in bytes
    pub compressed_size: usize,
    /// Compression ratio achieved
    pub compression_ratio: f32,
    /// Accuracy retention (0-1)
    pub accuracy_retention: f32,
    /// Time taken for compression
    pub compression_time_seconds: u64,
    /// Stage-wise results
    pub stage_results: Vec<StageResult>,
}

#[derive(Debug, Clone)]
pub struct StageResult {
    pub stage_name: String,
    pub model_size: usize,
    pub accuracy: f32,
    pub time_seconds: u64,
}

/// Compression report
#[derive(Debug, Clone)]
pub struct CompressionReport {
    pub summary: String,
    pub detailed_metrics: std::collections::HashMap<String, f32>,
    pub recommendations: Vec<String>,
}

/// Main compression pipeline
pub struct CompressionPipeline {
    // Temporarily commented out due to trait object issues
    // stages: Vec<Box<dyn CompressionStageExecutor>>,
    config: CompressionConfig,
}

impl CompressionPipeline {
    pub fn new(config: CompressionConfig) -> Self {
        Self {
            // stages: vec![], // Temporarily commented out
            config,
        }
    }

    /// Execute the compression pipeline
    pub async fn compress<M>(&self, model: &M) -> Result<CompressionResult<M>>
    where
        M: crate::traits::Model + Clone,
    {
        let start_time = Instant::now();
        let mut current_model = model.clone();
        let original_size = model.num_parameters() * 4; // Assuming FP32
        let mut stage_results = Vec::new();

        // Execute each stage in the pipeline
        for (stage_idx, stage) in self.config.stages.iter().enumerate() {
            let stage_start = Instant::now();
            let stage_name = self.get_stage_name(stage);

            println!(
                "Executing compression stage {}: {}",
                stage_idx + 1,
                stage_name
            );

            // Apply the compression stage
            current_model = self.apply_compression_stage(&current_model, stage).await?;

            // Calculate stage metrics
            let stage_size = current_model.num_parameters() * 4; // Simplified size calculation
            let stage_time = stage_start.elapsed().as_secs();

            // Estimate accuracy retention (simplified - in practice would need validation)
            let accuracy = self.estimate_accuracy_retention(stage, stage_idx);

            stage_results.push(StageResult {
                stage_name: stage_name.clone(),
                model_size: stage_size,
                accuracy,
                time_seconds: stage_time,
            });

            // Validate if configured
            if self.config.validate_stages {
                let accuracy_loss = 1.0 - accuracy;
                if accuracy_loss > self.config.max_accuracy_loss {
                    return Err(anyhow!(
                        "Stage '{}' exceeded maximum accuracy loss: {:.2}% > {:.2}%",
                        stage_name,
                        accuracy_loss * 100.0,
                        self.config.max_accuracy_loss * 100.0
                    ));
                }
            }

            // Save intermediate model if output directory is specified
            if let Some(ref output_dir) = self.config.output_dir {
                let model_path = output_dir.join(format!("model_stage_{}.bin", stage_idx + 1));
                // In a real implementation, you would serialize the model here
                println!("Would save intermediate model to: {:?}", model_path);
            }
        }

        // Calculate final metrics
        let compressed_size = current_model.num_parameters() * 4;
        let compression_ratio = original_size as f32 / compressed_size as f32;
        let total_time = start_time.elapsed().as_secs();

        // Calculate overall accuracy retention
        let final_accuracy = stage_results
            .iter()
            .map(|r| r.accuracy)
            .fold(1.0, |acc, stage_acc| acc * stage_acc);

        // Check if target compression ratio was achieved
        if compression_ratio < self.config.target_ratio {
            println!(
                "Warning: Target compression ratio {:.2}x not achieved (got {:.2}x)",
                self.config.target_ratio, compression_ratio
            );
        }

        Ok(CompressionResult {
            model: current_model,
            original_size,
            compressed_size,
            compression_ratio,
            accuracy_retention: final_accuracy,
            compression_time_seconds: total_time,
            stage_results,
        })
    }

    async fn apply_compression_stage<M>(&self, model: &M, stage: &CompressionStage) -> Result<M>
    where
        M: crate::traits::Model + Clone,
    {
        match stage {
            CompressionStage::Pruning { strategy, config } => {
                // Apply pruning (simplified implementation)
                println!("Applying pruning with strategy: {}", strategy);
                // In practice, you would implement actual pruning logic here
                Ok(model.clone())
            },
            CompressionStage::Quantization { bits, symmetric } => {
                println!(
                    "Applying quantization: {} bits, symmetric: {}",
                    bits, symmetric
                );
                // In practice, you would implement quantization logic here
                Ok(model.clone())
            },
            CompressionStage::Distillation {
                teacher_model,
                config,
            } => {
                println!("Applying distillation with teacher: {}", teacher_model);
                // In practice, you would implement distillation logic here
                Ok(model.clone())
            },
            CompressionStage::FineTuning {
                epochs,
                learning_rate,
            } => {
                println!(
                    "Applying fine-tuning: {} epochs, lr: {}",
                    epochs, learning_rate
                );
                // In practice, you would implement fine-tuning logic here
                Ok(model.clone())
            },
            CompressionStage::Custom { name, params } => {
                println!(
                    "Applying custom stage: {} with {} params",
                    name,
                    params.len()
                );
                // In practice, you would implement custom compression logic here
                Ok(model.clone())
            },
        }
    }

    fn get_stage_name(&self, stage: &CompressionStage) -> String {
        match stage {
            CompressionStage::Pruning { strategy, .. } => format!("Pruning ({})", strategy),
            CompressionStage::Quantization { bits, .. } => format!("Quantization ({}bit)", bits),
            CompressionStage::Distillation { .. } => "Distillation".to_string(),
            CompressionStage::FineTuning { .. } => "Fine-tuning".to_string(),
            CompressionStage::Custom { name, .. } => format!("Custom ({})", name),
        }
    }

    fn estimate_accuracy_retention(&self, stage: &CompressionStage, _stage_idx: usize) -> f32 {
        // Simplified accuracy estimation - in practice would need actual evaluation
        match stage {
            CompressionStage::Pruning { .. } => 0.98, // 2% accuracy loss typical for pruning
            CompressionStage::Quantization { bits, .. } => {
                match bits {
                    8 => 0.99, // INT8 usually has minimal accuracy loss
                    4 => 0.95, // INT4 has more significant loss
                    _ => 0.97, // Other bit widths
                }
            },
            CompressionStage::Distillation { .. } => 0.96, // Distillation can be lossy but effective
            CompressionStage::FineTuning { .. } => 1.02, // Fine-tuning can actually improve accuracy
            CompressionStage::Custom { .. } => 0.98,     // Conservative estimate for custom stages
        }
    }

    /// Generate compression report
    pub fn generate_report<M>(&self, result: &CompressionResult<M>) -> CompressionReport
    where
        M: crate::traits::Model,
    {
        let summary = format!(
            "Compression Summary:\n\
             - Original size: {} MB\n\
             - Compressed size: {} MB\n\
             - Compression ratio: {:.2}x\n\
             - Accuracy retention: {:.2}%\n\
             - Total time: {} seconds",
            result.original_size / 1_000_000,
            result.compressed_size / 1_000_000,
            result.compression_ratio,
            result.accuracy_retention * 100.0,
            result.compression_time_seconds
        );

        let mut detailed_metrics = std::collections::HashMap::new();
        detailed_metrics.insert("compression_ratio".to_string(), result.compression_ratio);
        detailed_metrics.insert("accuracy_retention".to_string(), result.accuracy_retention);
        detailed_metrics.insert(
            "size_reduction".to_string(),
            1.0 - (result.compressed_size as f32 / result.original_size as f32),
        );

        let recommendations = self.generate_recommendations(result);

        CompressionReport {
            summary,
            detailed_metrics,
            recommendations,
        }
    }

    // Temporarily commented out helper methods due to trait object issues
    /*
    fn execute_pruning<M>(&self, model: &M, strategy: &str, config: &PruningConfig) -> Result<M>
    where M: crate::traits::Model + Clone,
    {
        // Implementation would use actual pruning strategies
        Ok(model.clone())
    }

    fn execute_quantization<M>(&self, model: &M, bits: u8, symmetric: bool) -> Result<M>
    where M: crate::traits::Model + Clone,
    {
        // Implementation would use quantization module
        Ok(model.clone())
    }
    */

    // All helper methods temporarily commented out due to trait object issues
    /*
    async fn execute_distillation<M>(&self, model: &M, teacher_model: &str, config: &DistillationConfig) -> Result<M>
    where M: crate::traits::Model + Clone,
    {
        // Implementation would use distillation module
        Ok(model.clone())
    }

    fn execute_finetuning<M>(&self, model: &M, epochs: usize, learning_rate: f32) -> Result<M>
    where M: crate::traits::Model + Clone,
    {
        // Implementation would use training module
        Ok(model.clone())
    }

    fn execute_custom<M>(&self, model: &M, name: &str, params: &std::collections::HashMap<String, String>) -> Result<M>
    where M: crate::traits::Model + Clone,
    {
        // Implementation would use custom compression methods
        Ok(model.clone())
    }

    fn estimate_model_size<M>(&self, model: &M) -> usize
    where M: crate::traits::Model,
    {
        // Estimate based on parameter count and data type
        1_000_000 // Placeholder
    }

    fn evaluate_accuracy<M>(&self, model: &M) -> Result<f32>
    where M: crate::traits::Model,
    {
        // Would evaluate on validation set
        Ok(0.95)
    }
    */

    #[allow(dead_code)]
    fn validate_stage_result(&self, result: &StageResult) -> Result<()> {
        if result.accuracy < (1.0 - self.config.max_accuracy_loss) {
            return Err(anyhow!(
                "Stage {} resulted in too much accuracy loss: {:.2}%",
                result.stage_name,
                (1.0 - result.accuracy) * 100.0
            ));
        }
        Ok(())
    }

    fn generate_recommendations<M>(&self, result: &CompressionResult<M>) -> Vec<String>
    where
        M: crate::traits::Model,
    {
        let mut recommendations = Vec::new();

        if result.compression_ratio < self.config.target_ratio {
            recommendations.push(format!(
                "Target compression ratio {:.1}x not achieved. Consider more aggressive pruning or quantization.",
                self.config.target_ratio
            ));
        }

        if result.accuracy_retention < 0.95 {
            recommendations.push(
                "Significant accuracy loss detected. Consider using knowledge distillation or fine-tuning.".to_string()
            );
        }

        // Stage-specific recommendations
        for (i, stage_result) in result.stage_results.iter().enumerate() {
            if i > 0 {
                let prev_result = &result.stage_results[i - 1];
                let size_reduction =
                    1.0 - (stage_result.model_size as f32 / prev_result.model_size as f32);

                if size_reduction < 0.1 {
                    recommendations.push(format!(
                        "Stage '{}' achieved minimal size reduction ({:.1}%). Consider adjusting parameters.",
                        stage_result.stage_name,
                        size_reduction * 100.0
                    ));
                }
            }
        }

        recommendations
    }
}

/// Pipeline builder for easy configuration
pub struct PipelineBuilder {
    stages: Vec<CompressionStage>,
    config: CompressionConfig,
}

impl Default for PipelineBuilder {
    fn default() -> Self {
        Self::new()
    }
}

impl PipelineBuilder {
    pub fn new() -> Self {
        Self {
            stages: Vec::new(),
            config: CompressionConfig::default(),
        }
    }

    /// Add pruning stage
    pub fn add_pruning(mut self, sparsity: f32) -> Self {
        self.stages.push(CompressionStage::Pruning {
            strategy: "magnitude".to_string(),
            config: PruningConfig {
                target_sparsity: sparsity,
                ..Default::default()
            },
        });
        self
    }

    /// Add quantization stage
    pub fn add_quantization(mut self, bits: u8) -> Self {
        self.stages.push(CompressionStage::Quantization {
            bits,
            symmetric: true,
        });
        self
    }

    /// Add distillation stage
    pub fn add_distillation(mut self, teacher_model: String, temperature: f32) -> Self {
        self.stages.push(CompressionStage::Distillation {
            teacher_model,
            config: DistillationConfig {
                temperature,
                ..Default::default()
            },
        });
        self
    }

    /// Add fine-tuning stage
    pub fn add_finetuning(mut self, epochs: usize, learning_rate: f32) -> Self {
        self.stages.push(CompressionStage::FineTuning {
            epochs,
            learning_rate,
        });
        self
    }

    /// Set target compression ratio
    pub fn target_ratio(mut self, ratio: f32) -> Self {
        self.config.target_ratio = ratio;
        self
    }

    /// Set maximum accuracy loss
    pub fn max_accuracy_loss(mut self, loss: f32) -> Self {
        self.config.max_accuracy_loss = loss;
        self
    }

    /// Build the pipeline
    pub fn build(mut self) -> CompressionPipeline {
        self.config.stages = self.stages;
        CompressionPipeline::new(self.config)
    }
}

/// Trait for custom compression stage executors
#[allow(dead_code)]
trait CompressionStageExecutor: Send + Sync {
    fn execute<M>(&self, model: &M) -> Result<M>
    where
        M: crate::traits::Model;
    fn name(&self) -> &str;
}

// Mock implementation for demonstration
#[allow(dead_code)]
struct MockModel;

impl crate::traits::Model for MockModel {
    type Config = MockConfig;
    type Input = crate::tensor::Tensor;
    type Output = crate::tensor::Tensor;

    fn forward(&self, input: Self::Input) -> crate::errors::Result<Self::Output> {
        Ok(input)
    }

    fn load_pretrained(&mut self, _reader: &mut dyn std::io::Read) -> crate::errors::Result<()> {
        Ok(())
    }

    fn get_config(&self) -> &Self::Config {
        &MockConfig
    }

    fn num_parameters(&self) -> usize {
        // Mock model with a reasonable parameter count for testing
        800_000
    }
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
#[allow(dead_code)]
struct MockConfig;

impl crate::traits::Config for MockConfig {
    fn architecture(&self) -> &'static str {
        "mock"
    }
}