trustformers-optim 0.1.0

//! # GENIE: Generalization-ENhancing Iterative Equalizer Optimizer (Stub Implementation)
//!
//! This is a simplified stub implementation of GENIE that compiles correctly.
//! The full implementation with proper tensor operations will be completed
//! after resolving API compatibility issues.

use anyhow::Result;
use std::collections::HashMap;
use trustformers_core::tensor::Tensor;

/// Configuration for GENIE optimizer
#[derive(Debug, Clone)]
pub struct GENIEConfig {
    pub learning_rate: f32,
    pub osgr_momentum: f32,
    pub alignment_weight: f32,
    pub preconditioning_eps: f32,
    pub min_osgr: f32,
    pub max_osgr: f32,
    pub adaptive_alignment: bool,
    pub weight_decay: f32,
    pub normalize_osgr: bool,
    pub warmup_steps: u64,
}

impl Default for GENIEConfig {
    fn default() -> Self {
        Self {
            learning_rate: 1e-3,
            osgr_momentum: 0.9,
            alignment_weight: 0.1,
            preconditioning_eps: 1e-8,
            min_osgr: 1e-6,
            max_osgr: 1e6,
            adaptive_alignment: true,
            weight_decay: 0.0,
            normalize_osgr: true,
            warmup_steps: 100,
        }
    }
}

impl GENIEConfig {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn learning_rate(mut self, lr: f32) -> Self {
        self.learning_rate = lr;
        self
    }

    pub fn osgr_momentum(mut self, momentum: f32) -> Self {
        self.osgr_momentum = momentum;
        self
    }

    pub fn alignment_weight(mut self, weight: f32) -> Self {
        self.alignment_weight = weight;
        self
    }

    pub fn preconditioning_eps(mut self, eps: f32) -> Self {
        self.preconditioning_eps = eps;
        self
    }

    pub fn weight_decay(mut self, decay: f32) -> Self {
        self.weight_decay = decay;
        self
    }

    pub fn build(self) -> Self {
        self
    }
}

/// GENIE optimizer state (simplified)
#[derive(Debug, Clone, Default)]
pub struct GENIEState {
    pub step: u64,
    pub momentum_buffers: HashMap<String, Vec<f32>>,
}

/// Domain generalization statistics
#[derive(Debug, Clone, Default)]
pub struct DomainStats {
    pub domain_losses: Vec<f32>,
    pub domain_variance: f32,
    pub cross_domain_alignment: f32,
}

/// GENIE optimizer (stub implementation)
pub struct GENIE {
    config: GENIEConfig,
    state: GENIEState,
}

impl GENIE {
    pub fn new(config: GENIEConfig) -> Self {
        Self {
            config,
            state: GENIEState::default(),
        }
    }

    pub fn learning_rate(&self) -> f32 {
        self.config.learning_rate
    }

    pub fn set_learning_rate(&mut self, lr: f32) {
        self.config.learning_rate = lr;
    }

    /// Simplified step implementation using parameter data arrays
    pub fn step(
        &mut self,
        parameters: &mut HashMap<String, Tensor>,
        gradients: &HashMap<String, Tensor>,
        _current_loss: f32,
    ) -> Result<()> {
        self.state.step += 1;

        for (param_name, gradient) in gradients.iter() {
            if let Some(parameter) = parameters.get_mut(param_name) {
                // Get parameter and gradient data
                let param_data = parameter.data()?;
                let grad_data = gradient.data()?;

                // Initialize momentum buffer if needed
                if !self.state.momentum_buffers.contains_key(param_name) {
                    self.state
                        .momentum_buffers
                        .insert(param_name.clone(), vec![0.0; param_data.len()]);
                }

                let momentum_buffer = self
                    .state
                    .momentum_buffers
                    .get_mut(param_name)
                    .expect("GENIE momentum buffer must exist after initialization");

                // Simple momentum update (simplified GENIE algorithm)
                let mut updated_params = param_data.clone();
                for i in 0..param_data.len() {
                    // Update momentum
                    momentum_buffer[i] = self.config.osgr_momentum * momentum_buffer[i]
                        + (1.0 - self.config.osgr_momentum) * grad_data[i];

                    // Apply weight decay if configured
                    let effective_grad = if self.config.weight_decay > 0.0 {
                        grad_data[i] + self.config.weight_decay * param_data[i]
                    } else {
                        grad_data[i]
                    };

                    // Simple update with momentum (placeholder for full GENIE algorithm)
                    updated_params[i] = param_data[i]
                        - self.config.learning_rate
                            * (momentum_buffer[i] + self.config.alignment_weight * effective_grad);
                }

                // Update parameter
                *parameter = Tensor::new(updated_params)?;
            }
        }

        Ok(())
    }

    pub fn get_osgr_stats(&self) -> HashMap<String, f32> {
        // Placeholder implementation
        HashMap::new()
    }

    pub fn get_alignment_stats(&self) -> HashMap<String, f32> {
        // Placeholder - return empty HashMap
        HashMap::new()
    }

    pub fn get_domain_stats(&self) -> DomainStats {
        DomainStats {
            domain_losses: Vec::new(),
            domain_variance: 0.0,
            cross_domain_alignment: 0.0,
        }
    }

    pub fn reset_state(&mut self) {
        self.state = GENIEState::default();
    }

    pub fn get_stats(&self) -> GENIEStats {
        GENIEStats {
            step: self.state.step,
            mean_osgr: 1.0,
            mean_alignment: 0.0,
            num_parameters: self.state.momentum_buffers.len(),
            adaptive_alignment_weight: self.config.alignment_weight,
            domain_variance: 0.0,
        }
    }
}

/// GENIE optimizer statistics
#[derive(Debug, Clone)]
pub struct GENIEStats {
    pub step: u64,
    pub mean_osgr: f32,
    pub mean_alignment: f32,
    pub num_parameters: usize,
    pub adaptive_alignment_weight: f32,
    pub domain_variance: f32,
}