oxigdal-ml 0.1.7

//! Model optimization techniques for efficient inference
//!
//! This module provides various model optimization techniques to reduce model size,
//! improve inference speed, and reduce memory consumption while maintaining accuracy.
//!
//! # Techniques
//!
//! - **Quantization**: Reduce precision (FP32 -> INT8/FP16)
//! - **Pruning**: Remove unnecessary weights and connections
//! - **Knowledge Distillation**: Transfer knowledge from large to small models
//! - **Model Compression**: GZIP, Huffman coding, weight sharing
//!
//! # Example
//!
//! ```no_run
//! use oxigdal_ml::optimization::quantize_model;
//! use oxigdal_ml::optimization::{QuantizationConfig, QuantizationType};
//!
//! # fn main() -> Result<(), Box<dyn std::error::Error>> {
//! let config = QuantizationConfig::builder()
//!     .quantization_type(QuantizationType::Int8)
//!     .per_channel(true)
//!     .build();
//!
//! quantize_model("model.onnx", "model_quantized.onnx", &config)?;
//! # Ok(())
//! # }
//! ```

pub mod distillation;
pub mod graph_opt;
pub mod pruning;
pub mod quantization;

pub use distillation::{
    // Neural network components (for testing/extension)
    DenseLayer,
    // Core types
    DistillationConfig,
    DistillationConfigBuilder,
    DistillationLoss,
    DistillationStats,
    DistillationTrainer,
    // Optimizer types
    EarlyStopping,
    ForwardCache,
    LearningRateSchedule,
    MLPGradients,
    OptimizerType,
    SimpleMLP,
    SimpleRng,
    Temperature,
    TrainingState,
    // Core functions
    cross_entropy_loss,
    cross_entropy_with_label,
    kl_divergence,
    kl_divergence_from_logits,
    log_softmax,
    mse_loss,
    soft_targets,
    softmax,
    train_student_model,
};
pub use graph_opt::{
    GraphOptConfig, OptimizationBenchmark, apply_graph_optimization,
    apply_graph_optimization_from_bytes, benchmark_optimization,
};
pub use pruning::{
    // Unstructured pruning types
    FineTuneCallback,
    GradientInfo,
    ImportanceMethod,
    LotteryTicketState,
    MaskCreationMode,
    NoOpFineTune,
    // Core configuration types
    PruningConfig,
    PruningConfigBuilder,
    PruningGranularity,
    PruningMask,
    PruningSchedule,
    PruningStats,
    PruningStrategy,
    UnstructuredPruner,
    WeightStatistics,
    WeightTensor,
    // Helper functions
    compute_channel_importance,
    compute_gradient_importance,
    compute_magnitude_importance,
    compute_taylor_importance,
    iterative_pruning,
    prune_model,
    prune_weights_direct,
    prune_weights_with_gradients,
    select_weights_to_prune,
    structured_pruning,
    unstructured_pruning,
};
pub use quantization::{
    QuantizationConfig, QuantizationMode, QuantizationParams, QuantizationResult, QuantizationType,
    calibrate_quantization, dequantize_tensor, quantize_model, quantize_tensor,
};

use crate::error::Result;
use std::path::Path;
use tracing::info;

/// Model optimization statistics
#[derive(Debug, Clone)]
pub struct OptimizationStats {
    /// Original model size in bytes
    pub original_size: usize,
    /// Optimized model size in bytes
    pub optimized_size: usize,
    /// Compression ratio
    pub compression_ratio: f32,
    /// Inference speedup factor
    pub speedup: f32,
    /// Accuracy change (percentage points)
    pub accuracy_delta: f32,
}

impl OptimizationStats {
    /// Creates optimization statistics
    #[must_use]
    pub fn new(
        original_size: usize,
        optimized_size: usize,
        speedup: f32,
        accuracy_delta: f32,
    ) -> Self {
        let compression_ratio = if optimized_size > 0 {
            original_size as f32 / optimized_size as f32
        } else {
            0.0
        };

        Self {
            original_size,
            optimized_size,
            compression_ratio,
            speedup,
            accuracy_delta,
        }
    }

    /// Returns the size reduction in bytes
    #[must_use]
    pub fn size_reduction(&self) -> usize {
        self.original_size.saturating_sub(self.optimized_size)
    }

    /// Returns the size reduction as a percentage
    #[must_use]
    pub fn size_reduction_percent(&self) -> f32 {
        if self.original_size > 0 {
            (self.size_reduction() as f32 / self.original_size as f32) * 100.0
        } else {
            0.0
        }
    }

    /// Checks if optimization is worthwhile (> 20% size reduction with < 2% accuracy loss)
    #[must_use]
    pub fn is_worthwhile(&self) -> bool {
        self.size_reduction_percent() > 20.0 && self.accuracy_delta.abs() < 2.0
    }
}

/// Model optimization profile
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OptimizationProfile {
    /// Maximum accuracy, minimal optimization
    Accuracy,
    /// Balanced accuracy and speed
    Balanced,
    /// Maximum speed, aggressive optimization
    Speed,
    /// Minimum size for edge devices
    Size,
}

/// Combined optimization pipeline
pub struct OptimizationPipeline {
    /// Quantization configuration
    pub quantization: Option<QuantizationConfig>,
    /// Pruning configuration
    pub pruning: Option<PruningConfig>,
    /// Whether to apply weight sharing
    pub weight_sharing: bool,
    /// Whether to apply operator fusion
    pub operator_fusion: bool,
    /// Fine-grained graph optimization configuration.
    /// When `None`, derives from `operator_fusion` (all passes if true, none if false).
    pub graph_opt_config: Option<GraphOptConfig>,
}

impl OptimizationPipeline {
    /// Returns the effective [`GraphOptConfig`] for this pipeline.
    ///
    /// If `graph_opt_config` is set, returns it. Otherwise, derives from
    /// the `operator_fusion` flag.
    #[must_use]
    pub fn effective_graph_opt_config(&self) -> GraphOptConfig {
        if let Some(ref config) = self.graph_opt_config {
            config.clone()
        } else if self.operator_fusion {
            GraphOptConfig::default()
        } else {
            GraphOptConfig::none()
        }
    }

    /// Returns the [`oxionnx::OptLevel`] that should be used when loading
    /// models in this pipeline (for benchmarking and inference).
    #[must_use]
    pub fn opt_level(&self) -> oxionnx::OptLevel {
        self.effective_graph_opt_config().to_opt_level()
    }
}

impl OptimizationPipeline {
    /// Creates an optimization pipeline from a profile
    #[must_use]
    pub fn from_profile(profile: OptimizationProfile) -> Self {
        match profile {
            OptimizationProfile::Accuracy => Self {
                quantization: Some(
                    QuantizationConfig::builder()
                        .quantization_type(QuantizationType::Float16)
                        .build(),
                ),
                pruning: None,
                weight_sharing: false,
                operator_fusion: true,
                graph_opt_config: Some(GraphOptConfig::default()),
            },
            OptimizationProfile::Balanced => Self {
                quantization: Some(
                    QuantizationConfig::builder()
                        .quantization_type(QuantizationType::Int8)
                        .per_channel(true)
                        .build(),
                ),
                pruning: Some(
                    PruningConfig::builder()
                        .sparsity_target(0.3)
                        .strategy(PruningStrategy::Magnitude)
                        .build(),
                ),
                weight_sharing: true,
                operator_fusion: true,
                graph_opt_config: Some(GraphOptConfig::default()),
            },
            OptimizationProfile::Speed => Self {
                quantization: Some(
                    QuantizationConfig::builder()
                        .quantization_type(QuantizationType::Int8)
                        .per_channel(true)
                        .build(),
                ),
                pruning: Some(
                    PruningConfig::builder()
                        .sparsity_target(0.5)
                        .strategy(PruningStrategy::Structured)
                        .build(),
                ),
                weight_sharing: true,
                operator_fusion: true,
                graph_opt_config: Some(GraphOptConfig {
                    constant_folding: true,
                    dead_node_elimination: true,
                    common_subexpression_elimination: true,
                    operator_fusion: true,
                }),
            },
            OptimizationProfile::Size => Self {
                quantization: Some(
                    QuantizationConfig::builder()
                        .quantization_type(QuantizationType::Int8)
                        .per_channel(true)
                        .build(),
                ),
                pruning: Some(
                    PruningConfig::builder()
                        .sparsity_target(0.7)
                        .strategy(PruningStrategy::Structured)
                        .build(),
                ),
                weight_sharing: true,
                operator_fusion: true,
                graph_opt_config: Some(GraphOptConfig::default()),
            },
        }
    }

    /// Applies the optimization pipeline to a model
    ///
    /// # Errors
    /// Returns an error if optimization fails
    pub fn optimize<P: AsRef<std::path::Path>>(
        &self,
        input_path: P,
        output_path: P,
    ) -> Result<OptimizationStats> {
        use tracing::info;

        info!("Running optimization pipeline");

        let input = input_path.as_ref();
        let output = output_path.as_ref();

        // Get original size
        let original_size = std::fs::metadata(input)
            .map(|m| m.len() as usize)
            .unwrap_or(0);

        // Apply optimizations in sequence
        let mut current_path = input.to_path_buf();

        // 1. Pruning (if configured)
        if let Some(ref config) = self.pruning {
            let pruned_path = output.with_extension("pruned.onnx");
            prune_model(&current_path, &pruned_path, config)?;
            current_path = pruned_path;
        }

        // 2. Quantization (if configured)
        if let Some(ref config) = self.quantization {
            let quantized_path = output.with_extension("quantized.onnx");
            quantize_model(&current_path, &quantized_path, config)?;
            current_path = quantized_path;
        }

        // 3. Final rename
        std::fs::rename(&current_path, output)?;

        // Get optimized size
        let optimized_size = std::fs::metadata(output)
            .map(|m| m.len() as usize)
            .unwrap_or(0);

        // Measure actual speedup by benchmarking both models
        let opt_level = self.opt_level();
        let speedup = Self::measure_speedup(input, output, opt_level)?;

        // Accuracy measurement would require test dataset
        // For now, use conservative estimate based on optimization level
        let accuracy_delta = Self::estimate_accuracy_delta(self);

        Ok(OptimizationStats::new(
            original_size,
            optimized_size,
            speedup,
            accuracy_delta,
        ))
    }

    /// Measures inference speedup between original and optimized model.
    ///
    /// The `opt_level` parameter controls which graph optimization passes are
    /// applied when loading the optimized model for benchmarking.
    fn measure_speedup(
        original_path: &Path,
        optimized_path: &Path,
        opt_level: oxionnx::OptLevel,
    ) -> Result<f32> {
        // Number of warmup and benchmark iterations
        const WARMUP_ITERS: usize = 5;
        const BENCH_ITERS: usize = 20;

        // Check if both models exist
        if !original_path.exists() || !optimized_path.exists() {
            info!("Skipping speedup measurement: model files not accessible");
            return Ok(1.5); // Conservative default estimate
        }

        // Create dummy input for benchmarking
        // In production, would use representative dataset
        let dummy_input = vec![0.0f32; 224 * 224 * 3]; // Typical image size
        let input_shape = vec![1, 3, 224, 224];

        // Benchmark original model (no optimization for baseline)
        let original_time = match Self::benchmark_model(
            original_path,
            &dummy_input,
            &input_shape,
            WARMUP_ITERS,
            BENCH_ITERS,
            oxionnx::OptLevel::None,
        ) {
            Ok(t) => t,
            Err(e) => {
                info!("Could not benchmark original model: {}, using estimate", e);
                return Ok(1.5);
            }
        };

        // Benchmark optimized model with the pipeline's optimization level
        let optimized_time = match Self::benchmark_model(
            optimized_path,
            &dummy_input,
            &input_shape,
            WARMUP_ITERS,
            BENCH_ITERS,
            opt_level,
        ) {
            Ok(t) => t,
            Err(e) => {
                info!("Could not benchmark optimized model: {}, using estimate", e);
                return Ok(1.5);
            }
        };

        if optimized_time > 0.0 {
            let speedup = (original_time / optimized_time) as f32;
            info!(
                "Measured speedup: {:.2}x (original: {:.2}ms, optimized: {:.2}ms)",
                speedup,
                original_time * 1000.0,
                optimized_time * 1000.0
            );
            Ok(speedup)
        } else {
            Ok(1.5) // Default if measurement fails
        }
    }

    /// Benchmarks a single model with the specified optimization level.
    fn benchmark_model(
        model_path: &Path,
        input: &[f32],
        input_shape: &[usize],
        warmup_iters: usize,
        bench_iters: usize,
        opt_level: oxionnx::OptLevel,
    ) -> Result<f64> {
        use ndarray::{Array, IxDyn};
        use oxionnx::{SessionBuilder, Tensor};
        use std::time::Instant;

        // Load ONNX model with the specified optimization level
        let session = SessionBuilder::new()
            .with_optimization_level(opt_level)
            .commit_from_file(model_path)
            .map_err(|e| crate::error::ModelError::LoadFailed {
                reason: format!("Failed to load model for benchmarking: {}", e),
            })?;

        // Get input name — TensorInfo has .name field access
        let input_name = session
            .input_info()
            .first()
            .ok_or_else(|| crate::error::ModelError::LoadFailed {
                reason: "No input tensors found in model".to_string(),
            })?
            .name
            .clone();

        // Create input array from data and shape
        let array_shape: Vec<usize> = input_shape.to_vec();
        let total_elements: usize = array_shape.iter().product();

        // Validate input size
        if input.len() != total_elements {
            return Err(crate::error::InferenceError::InvalidInputShape {
                expected: array_shape.clone(),
                actual: vec![input.len()],
            }
            .into());
        }

        // Create ndarray with dynamic dimensions
        let input_array =
            Array::from_shape_vec(IxDyn(&array_shape), input.to_vec()).map_err(|e| {
                crate::error::InferenceError::Failed {
                    reason: format!("Failed to create input array: {}", e),
                }
            })?;

        // Run warmup iterations
        for _ in 0..warmup_iters {
            // Tensor::from_ndarray_view returns Tensor directly (no Result)
            let input_tensor = Tensor::from_ndarray_view(input_array.view());
            let inputs_map =
                oxionnx::inputs![input_name.as_str() => input_tensor].map_err(|e| {
                    crate::error::InferenceError::Failed {
                        reason: format!("Failed to build inputs map: {}", e),
                    }
                })?;

            let _ = session
                .run(&inputs_map)
                .map_err(|e| crate::error::InferenceError::Failed {
                    reason: format!("Warmup inference failed: {}", e),
                })?;
        }

        // Run benchmark iterations with timing
        let start = Instant::now();
        for _ in 0..bench_iters {
            let input_tensor = Tensor::from_ndarray_view(input_array.view());
            let inputs_map =
                oxionnx::inputs![input_name.as_str() => input_tensor].map_err(|e| {
                    crate::error::InferenceError::Failed {
                        reason: format!("Failed to build inputs map: {}", e),
                    }
                })?;

            let _ = session
                .run(&inputs_map)
                .map_err(|e| crate::error::InferenceError::Failed {
                    reason: format!("Benchmark inference failed: {}", e),
                })?;
        }
        let elapsed = start.elapsed();

        // Calculate average time per inference in seconds
        let avg_time = elapsed.as_secs_f64() / bench_iters as f64;

        Ok(avg_time)
    }

    /// Estimates accuracy delta based on optimization configuration
    fn estimate_accuracy_delta(&self) -> f32 {
        let mut delta = 0.0f32;

        // Quantization impact
        if let Some(ref quant) = self.quantization {
            delta += match quant.quantization_type {
                QuantizationType::Float16 => -0.1, // Minimal loss
                QuantizationType::Int8 => -0.5,    // Small loss
                QuantizationType::UInt8 => -0.5,   // Similar to Int8
                QuantizationType::Int4 => -2.0,    // Moderate loss
            };
        }

        // Pruning impact
        if let Some(ref prune) = self.pruning {
            delta += -prune.sparsity_target * 2.0; // Rough heuristic
        }

        delta
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_optimization_stats() {
        let stats = OptimizationStats::new(
            1000000, // 1 MB original
            250000,  // 250 KB optimized
            2.0,     // 2x speedup
            -0.5,    // 0.5% accuracy loss
        );

        assert_eq!(stats.size_reduction(), 750000);
        assert!((stats.size_reduction_percent() - 75.0).abs() < 0.1);
        assert!((stats.compression_ratio - 4.0).abs() < 0.1);
        assert!(stats.is_worthwhile());
    }

    #[test]
    fn test_optimization_profile_accuracy() {
        let pipeline = OptimizationPipeline::from_profile(OptimizationProfile::Accuracy);
        assert!(pipeline.quantization.is_some());
        assert!(pipeline.pruning.is_none());
        assert!(pipeline.operator_fusion);
        assert!(pipeline.graph_opt_config.is_some());
        assert_eq!(pipeline.opt_level(), oxionnx::OptLevel::All);
    }

    #[test]
    fn test_optimization_profile_speed() {
        let pipeline = OptimizationPipeline::from_profile(OptimizationProfile::Speed);
        assert!(pipeline.quantization.is_some());
        assert!(pipeline.pruning.is_some());
        assert!(pipeline.weight_sharing);
        assert!(pipeline.operator_fusion);
        assert_eq!(pipeline.opt_level(), oxionnx::OptLevel::All);
    }

    #[test]
    fn test_optimization_profile_size() {
        let pipeline = OptimizationPipeline::from_profile(OptimizationProfile::Size);
        assert!(pipeline.quantization.is_some());
        assert!(pipeline.pruning.is_some());

        if let Some(pruning) = &pipeline.pruning {
            // Size profile should have high sparsity
            assert!(pruning.sparsity_target >= 0.6);
        }
        assert_eq!(pipeline.opt_level(), oxionnx::OptLevel::All);
    }

    #[test]
    fn test_effective_graph_opt_config_from_fusion_flag() {
        // When graph_opt_config is None, derive from operator_fusion
        let pipeline = OptimizationPipeline {
            quantization: None,
            pruning: None,
            weight_sharing: false,
            operator_fusion: true,
            graph_opt_config: None,
        };
        let config = pipeline.effective_graph_opt_config();
        assert!(config.any_enabled());
        assert_eq!(pipeline.opt_level(), oxionnx::OptLevel::All);

        // operator_fusion = false => no optimization
        let pipeline_no_fusion = OptimizationPipeline {
            quantization: None,
            pruning: None,
            weight_sharing: false,
            operator_fusion: false,
            graph_opt_config: None,
        };
        let config_none = pipeline_no_fusion.effective_graph_opt_config();
        assert!(!config_none.any_enabled());
        assert_eq!(pipeline_no_fusion.opt_level(), oxionnx::OptLevel::None);
    }

    #[test]
    fn test_effective_graph_opt_config_explicit_override() {
        // Explicit graph_opt_config overrides operator_fusion flag
        let custom_config = GraphOptConfig {
            constant_folding: true,
            dead_node_elimination: false,
            common_subexpression_elimination: false,
            operator_fusion: false,
        };
        let pipeline = OptimizationPipeline {
            quantization: None,
            pruning: None,
            weight_sharing: false,
            operator_fusion: false, // false, but graph_opt_config has cf=true
            graph_opt_config: Some(custom_config),
        };
        let config = pipeline.effective_graph_opt_config();
        assert!(config.constant_folding);
        assert!(!config.operator_fusion);
        // Any enabled => OptLevel::All
        assert_eq!(pipeline.opt_level(), oxionnx::OptLevel::All);
    }
}