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crates/training/rtx-transformers/src/optimizers/mod.rs

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+//! Optimizers for transformer training
+//!
+//! This module provides high-performance optimizers including Adam and `AdamW`
+//! with support for distributed training and revolutionary enhancements.
+
+use crate::Result;
+use rtx_tensor::Tensor;
+use std::collections::HashMap;
+use tracing::debug;
+
+// ============================================================================
+// PHASE 1: CORE OPTIMIZERS (Essential functionality)
+// ============================================================================
+
+pub mod adam;
+pub mod adamw;
+
+#[cfg(all(test, feature = "disabled_tests"))]
+pub mod adam_test;
+
+// Re-export main optimizer types
+pub use adam::AdamState;
+
+// ============================================================================
+// PHASE 2: ADVANCED OPTIMIZERS (Temporarily disabled)
+// ============================================================================
+// These will be re-enabled once core optimizers are stable
+
+// pub mod ademamix;          // TODO: Re-enable in Phase 2
+// pub mod adabound;          // TODO: Re-enable in Phase 2
+// pub mod lion;              // TODO: Re-enable in Phase 2
+// pub mod sophia;            // TODO: Re-enable in Phase 2
+// pub mod shampoo;           // TODO: Re-enable in Phase 2
+// pub mod kfac;              // TODO: Re-enable in Phase 2
+// pub mod kfac_advanced;     // TODO: Re-enable in Phase 2
+// pub mod kfac_example;      // TODO: Re-enable in Phase 2
+// pub mod ranger;            // TODO: Re-enable in Phase 2
+// pub mod novograd;          // TODO: Re-enable in Phase 2
+// pub mod natural_gradient;  // TODO: Re-enable in Phase 2
+// pub mod trust_region;      // TODO: Re-enable in Phase 2
+// pub mod lbfgs;             // TODO: Re-enable in Phase 2
+
+pub mod matrix_utils;
+
+// Re-export tensor bridge traits for all optimizer modules
+pub use crate::tensor_bridge::{TensorBridge, TensorBridgeStatic, TensorCompat};
+
+// Test modules temporarily disabled due to missing dependencies
+// #[cfg(test)]
+// pub mod parameter_update_tests;
+
+// All test modules temporarily disabled
+// Will be re-enabled after implementing missing optimizer types
+
+// ============================================================================
+// PHASE 1: CORE OPTIMIZER EXPORTS (Only enabled optimizers)
+// ============================================================================
+
+pub use adam::AdamOptimizer;
+pub use adamw::AdamWOptimizer;
+
+// ============================================================================
+// PHASE 2: ADVANCED OPTIMIZER EXPORTS (Temporarily disabled)
+// ============================================================================
+// These will be re-enabled when the modules are uncommented
+
+// pub use ademamix::{AdEMAMixOptimizer, AdEMAMixConfig};
+// pub use adabound::{AdaBoundOptimizer, AdaBoundConfig};
+// pub use lion::{LionOptimizer, LionConfig};
+// pub use sophia::{SophiaOptimizer, SophiaConfig};
+// pub use shampoo::{ShampooOptimizer, ShampooConfig};
+// pub use kfac::{KFacOptimizer, KFacConfig, LayerType};
+// pub use kfac_advanced::{AdvancedKFacOptimizer, AdvancedKFacConfig, ConvergenceMetrics};
+// pub use ranger::{RangerOptimizer, RangerConfig};
+// pub use novograd::{NovoGradOptimizer, NovoGradConfig};
+// pub use natural_gradient::{NaturalGradientOptimizer, NaturalGradientConfig, FisherApproximation};
+// pub use trust_region::{TrustRegionOptimizer, TrustRegionConfig, SubproblemSolver};
+// pub use lbfgs::{LBFGSOptimizer, LBFGSConfig};
+
+/// Base optimizer implementation with gradient storage
+#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
+pub struct BaseOptimizer {
+    /// Stored gradients for batch processing
+    #[serde(skip)]
+    pub stored_gradients: HashMap<String, Tensor>,
+    /// Current learning rate
+    pub learning_rate: f64,
+}
+
+impl BaseOptimizer {
+    /// Create a new base optimizer
+    #[must_use] 
+    pub fn new(learning_rate: f64) -> Self {
+        Self {
+            stored_gradients: HashMap::new(),
+            learning_rate,
+        }
+    }
+
+    /// Get stored gradients
+    #[must_use] 
+    pub fn stored_gradients(&self) -> &HashMap<String, Tensor> {
+        &self.stored_gradients
+    }
+
+    /// Clear stored gradients
+    pub fn clear_gradients(&mut self) {
+        self.stored_gradients.clear();
+    }
+
+    /// Set learning rate
+    pub fn set_learning_rate(&mut self, lr: f64) {
+        self.learning_rate = lr;
+    }
+
+    /// Store gradients
+    pub fn store_gradients(&mut self, gradients: HashMap<String, Tensor>) {
+        self.stored_gradients.clear();
+        self.stored_gradients.extend(gradients);
+    }
+}
+
+/// Configuration for different optimizer types (Phase 1: Core optimizers only)
+#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
+pub enum OptimizerConfig {
+    /// Adam optimizer configuration
+    Adam(AdamConfig),
+    /// `AdamW` optimizer configuration
+    AdamW(AdamWConfig),
+    // ========================================================================
+    // PHASE 2: ADVANCED OPTIMIZERS (Temporarily disabled)
+    // ========================================================================
+    // These will be re-enabled once the corresponding modules are uncommented
+
+    // /// AdEMAMix optimizer configuration
+    // AdEMAMix(AdEMAMixConfig),
+    // /// AdaBound optimizer configuration
+    // AdaBound(AdaBoundConfig),
+    // /// Lion optimizer configuration
+    // Lion(LionConfig),
+    // /// Sophia optimizer configuration
+    // Sophia(SophiaConfig),
+    // /// Shampoo optimizer configuration
+    // Shampoo(ShampooConfig),
+    // /// K-FAC optimizer configuration
+    // KFac(KFacConfig),
+    // /// Advanced K-FAC optimizer configuration
+    // AdvancedKFac(AdvancedKFacConfig),
+    // /// Ranger optimizer configuration (RAdam + Lookahead)
+    // Ranger(RangerConfig),
+    // /// NovoGrad optimizer configuration (layer-wise gradient normalization)
+    // NovoGrad(NovoGradConfig),
+    // /// Natural Gradient optimizer configuration (Fisher Information Matrix preconditioning)
+    // NaturalGradient(NaturalGradientConfig),
+    // /// Trust Region optimizer configuration (second-order with adaptive radius)
+    // TrustRegion(TrustRegionConfig),
+    // /// L-BFGS optimizer configuration (quasi-Newton with limited memory)
+    // LBFGS(LBFGSConfig),
+}
+
+/// Adam optimizer configuration
+#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
+pub struct AdamConfig {
+    /// Learning rate
+    pub learning_rate: f64,
+    /// Beta1 parameter (momentum)
+    pub beta1: f64,
+    /// Beta2 parameter (`RMSprop`)
+    pub beta2: f64,
+    /// Epsilon for numerical stability
+    pub epsilon: f64,
+    /// Weight decay coefficient
+    pub weight_decay: f64,
+    /// Whether to use `AMSGrad` variant
+    pub amsgrad: bool,
+}
+
+impl Default for AdamConfig {
+    fn default() -> Self {
+        Self {
+            learning_rate: 1e-3,
+            beta1: 0.9,
+            beta2: 0.999,
+            epsilon: 1e-8,
+            weight_decay: 0.0,
+            amsgrad: false,
+        }
+    }
+}
+
+/// `AdamW` optimizer configuration
+#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
+pub struct AdamWConfig {
+    /// Learning rate
+    pub learning_rate: f64,
+    /// Beta1 parameter (momentum)
+    pub beta1: f64,
+    /// Beta2 parameter (`RMSprop`)
+    pub beta2: f64,
+    /// Epsilon for numerical stability
+    pub epsilon: f64,
+    /// Weight decay coefficient
+    pub weight_decay: f64,
+    /// Whether to use bias correction
+    pub amsgrad: bool,
+}
+
+impl Default for AdamWConfig {
+    fn default() -> Self {
+        Self {
+            learning_rate: 1e-3,
+            beta1: 0.9,
+            beta2: 0.999,
+            epsilon: 1e-8,
+            weight_decay: 1e-2,
+            amsgrad: false,
+        }
+    }
+}
+
+/// Create an optimizer from configuration (Phase 1: Core optimizers only)
+pub fn create_optimizer(
+    config: OptimizerConfig,
+    _parameters: HashMap<String, Tensor>,
+) -> Result<Box<dyn Optimizer>> {
+    match config {
+        OptimizerConfig::Adam(adam_config) => {
+            debug!("Creating Adam optimizer with config: {:?}", adam_config);
+            Ok(Box::new(AdamOptimizer::new(adam_config)?))
+        }
+        OptimizerConfig::AdamW(adamw_config) => {
+            debug!("Creating AdamW optimizer with config: {:?}", adamw_config);
+            Ok(Box::new(AdamWOptimizer::new(
+                adamw_config.learning_rate,
+                adamw_config.beta1,
+                adamw_config.beta2,
+                adamw_config.epsilon,
+                adamw_config.weight_decay,
+                adamw_config.amsgrad, // Using amsgrad field as decoupled flag for now
+            )?))
+        } // ====================================================================
+          // PHASE 2: ADVANCED OPTIMIZERS (Temporarily disabled)
+          // ====================================================================
+          // These cases will be re-enabled when the optimizer modules are uncommented
+
+          // OptimizerConfig::AdEMAMix(ademamix_config) => {
+          //     debug!("Creating AdEMAMix optimizer with config: {:?}", ademamix_config);
+          //     Ok(Box::new(AdEMAMixOptimizer::new(ademamix_config)?))
+          // }
+
+          // All other optimizer cases are temporarily disabled since the modules are commented out
+    }
+}
+
+/// Trait for all optimizers in the transformer training infrastructure
+pub trait Optimizer: Send + Sync {
+    /// Perform a single optimization step for a parameter
+    fn step_param(&mut self, param_name: &str, param: &Tensor, grad: &Tensor) -> Result<Tensor>;
+
+    /// Get the current learning rate
+    fn learning_rate(&self) -> f64;
+
+    /// Set the learning rate
+    fn set_learning_rate(&mut self, lr: f64) -> Result<()>;
+
+    /// Check if optimizer has state for a parameter
+    fn has_state(&self, param_name: &str) -> bool;
+
+    /// Reset state for a specific parameter
+    fn reset_state(&mut self, param_name: &str) -> Result<()>;
+
+    /// Reset all optimizer state
+    fn reset_all_state(&mut self);
+
+    /// Get step count for a parameter
+    fn get_step_count(&self, param_name: &str) -> Result<i64>;
+
+    /// Get optimizer type name
+    fn optimizer_type(&self) -> &'static str;
+
+    /// Get mutable reference as Any for downcasting
+    fn as_any_mut(&mut self) -> &mut dyn std::any::Any;
+
+    /// Zero all gradients (clear stored gradients)
+    fn zero_grad(&mut self) {
+        // Default implementation clears stored gradients
+        if let Ok(()) = self.store_gradients_internal(HashMap::new()) {
+            // Gradients cleared successfully
+        }
+    }
+
+    /// Store gradients for batch processing (used by trainer)
+    fn set_gradients(&mut self, gradients: HashMap<String, Tensor>) -> Result<()> {
+        // Default implementation stores gradients in internal map
+        self.store_gradients_internal(gradients)
+    }
+
+    /// Perform optimization step on all stored gradients with given learning rate
+    fn step(&mut self, learning_rate: f64) -> Result<HashMap<String, Tensor>> {
+        // Default implementation processes stored gradients
+        self.process_stored_gradients(learning_rate)
+    }
+
+    /// Internal method to store gradients (default implementation)
+    fn store_gradients_internal(&mut self, gradients: HashMap<String, Tensor>) -> Result<()>;
+
+    /// Internal method to process stored gradients (default implementation)
+    fn process_stored_gradients(&mut self, learning_rate: f64) -> Result<HashMap<String, Tensor>>;
+}
+
+/// Optimizer type enumeration
+#[derive(Debug, Clone)]
+pub enum OptimizerType {
+    /// Adam optimizer
+    Adam,
+    /// `AdamW` optimizer with decoupled weight decay
+    AdamW,
+    /// `AdEMAMix` optimizer with adaptive EMA mixture
+    AdEMAMix,
+    /// `AdaBound` optimizer with dynamic learning rate bounds
+    AdaBound,
+    /// Lion optimizer with evolved sign momentum
+    Lion,
+    /// Sophia optimizer with second-order clipped stochastic optimization
+    Sophia,
+    /// Shampoo optimizer with full matrix preconditioning
+    Shampoo,
+    /// K-FAC optimizer with Kronecker-factored approximation
+    KFac,
+    /// Ranger optimizer combining `RAdam` with Lookahead
+    Ranger,
+    /// `NovoGrad` optimizer with layer-wise gradient normalization
+    NovoGrad,
+    /// Natural Gradient optimizer with Fisher Information Matrix preconditioning
+    NaturalGradient,
+    /// Trust Region optimizer with adaptive radius and second-order approximation
+    TrustRegion,
+    /// L-BFGS optimizer with limited-memory quasi-Newton approximation
+    LBFGS,
+}