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crates/training/rtx-transformers/src/layers/expert_dropout.rs

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+//! Expert Dropout for Mixture of Experts
+//!
+//! This module implements expert dropout strategies for MoE layers, providing:
+//! - Random expert dropout during training
+//! - Structured dropout patterns (block, random, progressive)
+//! - Load-aware dropout (drop less-utilized experts)
+//! - Dropout scheduling over training
+//! - Expert importance scoring
+//! - Integration with MoE routing
+//!
+//! # TDD Implementation
+//! 
+//! This implementation follows strict Test-Driven Development:
+//! 1. Red phase: Write failing tests first
+//! 2. Green phase: Minimal implementation to pass tests
+//! 3. Refactor phase: Optimize while maintaining tests
+
+use crate::{Result, TransformerError};
+use crate::layers::{MoEConfig, RoutingInfo};
+use rtx_tensor::{Tensor, Device, DType};
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use rand::{Rng, thread_rng};
+
+/// Expert dropout strategy types
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub enum DropoutStrategy {
+    /// Random dropout of experts
+    Random,
+    /// Block-wise dropout (drop contiguous blocks of experts)
+    Block,
+    /// Progressive dropout that changes over training
+    Progressive,
+    /// Load-aware dropout that considers expert utilization
+    LoadAware,
+}
+
+/// Configuration for expert dropout
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ExpertDropoutConfig {
+    /// Dropout rate (0.0 to 1.0)
+    pub dropout_rate: f32,
+    /// Dropout strategy to use
+    pub strategy: DropoutStrategy,
+    /// Block size for block dropout strategy
+    pub block_size: Option<usize>,
+    /// Minimum number of experts to keep active
+    pub min_active_experts: usize,
+    /// Whether to enable load-aware adjustments
+    pub load_aware: bool,
+}
+
+impl Default for ExpertDropoutConfig {
+    fn default() -> Self {
+        Self {
+            dropout_rate: 0.1,
+            strategy: DropoutStrategy::Random,
+            block_size: None,
+            min_active_experts: 1,
+            load_aware: false,
+        }
+    }
+}
+
+impl ExpertDropoutConfig {
+    /// Create a new expert dropout configuration
+    pub fn new(dropout_rate: f32, strategy: DropoutStrategy) -> Self {
+        Self {
+            dropout_rate,
+            strategy,
+            ..Default::default()
+        }
+    }
+
+    /// Validate the configuration
+    pub fn validate(&self) -> Result<()> {
+        if self.dropout_rate < 0.0 || self.dropout_rate > 1.0 {
+            return Err(TransformerError::config(
+                format!("dropout_rate must be between 0.0 and 1.0, got {}", self.dropout_rate)
+            ));
+        }
+
+        if self.min_active_experts == 0 {
+            return Err(TransformerError::config(
+                "min_active_experts must be greater than 0".to_string()
+            ));
+        }
+
+        if matches!(self.strategy, DropoutStrategy::Block) && self.block_size.is_none() {
+            return Err(TransformerError::config(
+                "block_size must be specified for Block dropout strategy".to_string()
+            ));
+        }
+
+        Ok(())
+    }
+}
+
+/// Statistics for expert dropout operations
+#[derive(Debug, Clone, Default)]
+pub struct DropoutStatistics {
+    pub total_experts: usize,
+    pub dropped_experts: usize,
+    pub active_experts: usize,
+    pub dropout_rate: f32,
+    pub cumulative_dropped: usize,
+    pub cumulative_total: usize,
+}
+
+impl DropoutStatistics {
+    pub fn new() -> Self {
+        Default::default()
+    }
+
+    pub fn update(&mut self, total: usize, dropped: usize) {
+        self.total_experts = total;
+        self.dropped_experts = dropped;
+        self.active_experts = total - dropped;
+        self.dropout_rate = if total > 0 { dropped as f32 / total as f32 } else { 0.0 };
+        self.cumulative_dropped += dropped;
+        self.cumulative_total += total;
+    }
+}
+
+/// Expert outputs container
+#[derive(Debug)]
+pub struct ExpertOutputs {
+    pub outputs: Vec<Tensor>,
+}
+
+/// Output of expert dropout layer
+#[derive(Debug)]
+pub struct ExpertDropoutOutput {
+    pub active_experts: Vec<bool>,
+    pub dropout_stats: DropoutStatistics,
+    pub modified_outputs: Option<ExpertOutputs>,
+}
+
+/// Modified routing information after dropout
+#[derive(Debug)]
+pub struct ModifiedRoutingInfo {
+    pub active_expert_mask: Vec<bool>,
+    pub original_routing: RoutingInfo,
+    pub dropout_stats: DropoutStatistics,
+}
+
+/// Scheduler for dropout rate over training
+#[derive(Debug, Clone)]
+pub struct DropoutScheduler {
+    initial_rate: f32,
+    final_rate: f32,
+    total_steps: usize,
+}
+
+impl DropoutScheduler {
+    pub fn new(initial_rate: f32, final_rate: f32, total_steps: usize) -> Self {
+        Self {
+            initial_rate,
+            final_rate,
+            total_steps,
+        }
+    }
+
+    pub fn get_dropout_rate(&self, current_step: usize) -> f32 {
+        if current_step >= self.total_steps {
+            return self.final_rate;
+        }
+
+        let progress = current_step as f32 / self.total_steps as f32;
+        self.initial_rate + (self.final_rate - self.initial_rate) * progress
+    }
+}
+
+/// Expert importance scorer
+#[derive(Debug, Clone)]
+pub struct ExpertImportanceScorer {
+    num_experts: usize,
+    importance_scores: Vec<f32>,
+    alpha: f32, // EMA decay factor
+}
+
+impl ExpertImportanceScorer {
+    pub fn new(num_experts: usize) -> Self {
+        Self {
+            num_experts,
+            importance_scores: vec![1.0 / num_experts as f32; num_experts],
+            alpha: 0.9,
+        }
+    }
+
+    pub fn get_importance_scores(&self) -> &Vec<f32> {
+        &self.importance_scores
+    }
+
+    pub fn update_scores(&mut self, routing_weights: &[f32]) {
+        assert_eq!(routing_weights.len(), self.num_experts);
+        
+        for (i, &weight) in routing_weights.iter().enumerate() {
+            self.importance_scores[i] = self.alpha * self.importance_scores[i] + 
+                                       (1.0 - self.alpha) * weight;
+        }
+        
+        // Normalize scores
+        let sum: f32 = self.importance_scores.iter().sum();
+        if sum > 0.0 {
+            for score in &mut self.importance_scores {
+                *score /= sum;
+            }
+        }
+    }
+}
+
+/// Main expert dropout layer
+#[derive(Debug)]
+pub struct ExpertDropoutLayer {
+    config: ExpertDropoutConfig,
+    moe_config: MoEConfig,
+    device: Device,
+    training: bool,
+    training_step: usize,
+    expert_loads: Vec<f32>,
+    importance_scorer: ExpertImportanceScorer,
+    scheduler: Option<DropoutScheduler>,
+}
+
+impl ExpertDropoutLayer {
+    /// Create a new expert dropout layer
+    pub fn new(
+        config: ExpertDropoutConfig,
+        moe_config: MoEConfig,
+        device: &Device,
+    ) -> Result<Self> {
+        config.validate()?;
+        moe_config.validate()?;
+
+        let importance_scorer = ExpertImportanceScorer::new(moe_config.num_experts);
+        let expert_loads = vec![0.0; moe_config.num_experts];
+
+        Ok(Self {
+            config,
+            moe_config,
+            device: device.clone(),
+            training: false,
+            training_step: 0,
+            expert_loads,
+            importance_scorer,
+            scheduler: None,
+        })
+    }
+
+    /// Set dropout scheduler
+    pub fn set_scheduler(&mut self, scheduler: DropoutScheduler) {
+        self.scheduler = Some(scheduler);
+    }
+
+    /// Get current configuration
+    pub fn config(&self) -> &ExpertDropoutConfig {
+        &self.config
+    }
+
+    /// Get MoE configuration
+    pub fn moe_config(&self) -> &MoEConfig {
+        &self.moe_config
+    }
+
+    /// Get number of experts
+    pub fn num_experts(&self) -> usize {
+        self.moe_config.num_experts
+    }
+
+    /// Check if in training mode
+    pub fn is_training(&self) -> bool {
+        self.training
+    }
+
+    /// Set training mode
+    pub fn set_training(&mut self, training: bool) {
+        self.training = training;
+    }
+
+    /// Set current training step
+    pub fn set_training_step(&mut self, step: usize) {
+        self.training_step = step;
+    }
+
+    /// Update expert load statistics
+    pub fn update_expert_loads(&mut self, loads: &[f32]) {
+        assert_eq!(loads.len(), self.moe_config.num_experts);
+        self.expert_loads = loads.to_vec();
+    }
+
+    /// Forward pass with expert dropout
+    pub fn forward(&mut self, expert_outputs: &ExpertOutputs) -> Result<ExpertDropoutOutput> {
+        assert_eq!(expert_outputs.outputs.len(), self.moe_config.num_experts);
+
+        if !self.training {
+            // In inference mode, keep all experts active
+            let active_experts = vec![true; self.moe_config.num_experts];
+            let mut stats = DropoutStatistics::new();
+            stats.update(self.moe_config.num_experts, 0);
+            
+            return Ok(ExpertDropoutOutput {
+                active_experts,
+                dropout_stats: stats,
+                modified_outputs: None,
+            });
+        }
+
+        let dropout_rate = self.get_effective_dropout_rate();
+        let active_experts = self.compute_dropout_mask(dropout_rate)?;
+        
+        let dropped_count = active_experts.iter().filter(|&&x| !x).count();
+        let mut stats = DropoutStatistics::new();
+        stats.update(self.moe_config.num_experts, dropped_count);
+
+        Ok(ExpertDropoutOutput {
+            active_experts,
+            dropout_stats: stats,
+            modified_outputs: None,
+        })
+    }
+
+    /// Apply dropout to MoE routing information
+    pub fn apply_to_routing(&mut self, routing_info: &RoutingInfo) -> Result<ModifiedRoutingInfo> {
+        let dropout_rate = self.get_effective_dropout_rate();
+        let active_mask = self.compute_dropout_mask(dropout_rate)?;
+        
+        let dropped_count = active_mask.iter().filter(|&&x| !x).count();
+        let mut stats = DropoutStatistics::new();
+        stats.update(self.moe_config.num_experts, dropped_count);
+
+        Ok(ModifiedRoutingInfo {
+            active_expert_mask: active_mask,
+            original_routing: routing_info.clone(),
+            dropout_stats: stats,
+        })
+    }
+
+    /// Get effective dropout rate considering scheduling
+    fn get_effective_dropout_rate(&self) -> f32 {
+        if let Some(ref scheduler) = self.scheduler {
+            scheduler.get_dropout_rate(self.training_step)
+        } else {
+            self.config.dropout_rate
+        }
+    }
+
+    /// Compute dropout mask based on strategy
+    fn compute_dropout_mask(&self, dropout_rate: f32) -> Result<Vec<bool>> {
+        let num_experts = self.moe_config.num_experts;
+        let mut mask = vec![true; num_experts];
+        let mut rng = thread_rng();
+
+        let num_to_drop = ((num_experts as f32 * dropout_rate).round() as usize)
+            .min(num_experts - self.config.min_active_experts);
+
+        if num_to_drop == 0 {
+            return Ok(mask);
+        }
+
+        match self.config.strategy {
+            DropoutStrategy::Random => {
+                let mut indices: Vec<usize> = (0..num_experts).collect();
+                for _ in 0..num_to_drop {
+                    if !indices.is_empty() {
+                        let idx = rng.gen_range(0..indices.len());
+                        let expert_idx = indices.swap_remove(idx);
+                        mask[expert_idx] = false;
+                    }
+                }
+            }
+            DropoutStrategy::Block => {
+                let block_size = self.config.block_size.unwrap_or(1);
+                let num_blocks_to_drop = (num_to_drop + block_size - 1) / block_size;
+                let max_start_idx = if num_experts >= block_size {
+                    num_experts - block_size + 1
+                } else {
+                    1
+                };
+
+                for _ in 0..num_blocks_to_drop {
+                    if max_start_idx > 0 {
+                        let start_idx = rng.gen_range(0..max_start_idx);
+                        for i in 0..block_size.min(num_experts - start_idx) {
+                            if start_idx + i < num_experts {
+                                mask[start_idx + i] = false;
+                            }
+                        }
+                    }
+                }
+            }
+            DropoutStrategy::Progressive => {
+                // Progressive strategy: drop different experts based on training step
+                let step_offset = self.training_step % num_experts;
+                for i in 0..num_to_drop {
+                    let expert_idx = (step_offset + i) % num_experts;
+                    mask[expert_idx] = false;
+                }
+            }
+            DropoutStrategy::LoadAware => {
+                // Drop experts with lower loads first
+                let mut expert_load_pairs: Vec<(usize, f32)> = 
+                    self.expert_loads.iter().enumerate().map(|(i, &load)| (i, load)).collect();
+                expert_load_pairs.sort_by(|a, b| a.1.total_cmp(&b.1));
+                
+                for i in 0..num_to_drop {
+                    if i < expert_load_pairs.len() {
+                        mask[expert_load_pairs[i].0] = false;
+                    }
+                }
+            }
+        }
+
+        Ok(mask)
+    }
+}
+
+#[cfg(all(test, feature = "disabled_tests"))]
+mod tests {
+    use super::*;
+    use crate::layers::{MoEConfig, Router};
+    use rtx_tensor::{Tensor, Device, DType};
+    use std::collections::HashMap;
+
+    // Test 1: ExpertDropoutConfig creation and validation
+    #[test]
+    fn test_expert_dropout_config_creation() {
+        // Test default configuration
+        let config = ExpertDropoutConfig::default();
+        assert!(config.validate().is_ok());
+        assert_eq!(config.dropout_rate, 0.1);
+        assert!(matches!(config.strategy, DropoutStrategy::Random));
+
+        // Test custom configuration
+        let config = ExpertDropoutConfig::new(0.2, DropoutStrategy::LoadAware);
+        assert_eq!(config.dropout_rate, 0.2);
+        assert!(matches!(config.strategy, DropoutStrategy::LoadAware));
+        assert!(config.validate().is_ok());
+
+        // Test invalid configurations
+        let mut invalid_config = ExpertDropoutConfig::default();
+        invalid_config.dropout_rate = -0.1; // Invalid: negative dropout
+        assert!(invalid_config.validate().is_err());
+
+        invalid_config.dropout_rate = 1.5; // Invalid: > 1.0 dropout
+        assert!(invalid_config.validate().is_err());
+    }
+
+    // Test 2: DropoutStrategy enum functionality
+    #[test]
+    fn test_dropout_strategy_types() {
+        let strategies = vec![
+            DropoutStrategy::Random,
+            DropoutStrategy::Block,
+            DropoutStrategy::Progressive,
+            DropoutStrategy::LoadAware,
+        ];
+
+        for strategy in strategies {
+            let config = ExpertDropoutConfig::new(0.1, strategy.clone());
+            assert!(config.validate().is_ok());
+        }
+    }
+
+    // Test 3: ExpertDropoutLayer creation
+    #[test]
+    fn test_expert_dropout_layer_creation() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let moe_config = MoEConfig::new(8, 2, 768, 3072);
+        let dropout_config = ExpertDropoutConfig::default();
+
+        let dropout_layer = ExpertDropoutLayer::new(dropout_config, moe_config, &device);
+        assert!(dropout_layer.is_ok());
+
+        let dropout_layer = dropout_layer.unwrap();
+        assert_eq!(dropout_layer.num_experts(), 8);
+        assert!(!dropout_layer.is_training()); // Should default to inference mode
+    }
+
+    // Test 4: Random dropout functionality
+    #[test]
+    fn test_random_dropout_forward() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let moe_config = MoEConfig::new(8, 2, 768, 3072);
+        let dropout_config = ExpertDropoutConfig::new(0.5, DropoutStrategy::Random);
+        let mut dropout_layer = ExpertDropoutLayer::new(dropout_config, moe_config, &device).unwrap();
+        
+        dropout_layer.set_training(true);
+
+        let batch_size = 4;
+        let seq_len = 16;
+        let expert_outputs = create_mock_expert_outputs(batch_size, seq_len, 8, 768, &device);
+        
+        let result = dropout_layer.forward(&expert_outputs);
+        assert!(result.is_ok());
+        
+        let output = result.unwrap();
+        assert_eq!(output.active_experts.len(), 8);
+        assert!(output.dropout_stats.dropped_experts > 0); // Should have dropped some experts
+        assert!(output.dropout_stats.total_experts == 8);
+    }
+
+    // Test 5: Block dropout functionality
+    #[test]
+    fn test_block_dropout_forward() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let moe_config = MoEConfig::new(8, 2, 768, 3072);
+        let mut dropout_config = ExpertDropoutConfig::new(0.25, DropoutStrategy::Block);
+        dropout_config.block_size = Some(2); // Drop in blocks of 2
+        
+        let mut dropout_layer = ExpertDropoutLayer::new(dropout_config, moe_config, &device).unwrap();
+        dropout_layer.set_training(true);
+
+        let batch_size = 4;
+        let seq_len = 16;
+        let expert_outputs = create_mock_expert_outputs(batch_size, seq_len, 8, 768, &device);
+        
+        let result = dropout_layer.forward(&expert_outputs);
+        assert!(result.is_ok());
+        
+        let output = result.unwrap();
+        // In block dropout, experts are dropped in contiguous blocks
+        let dropped_count = output.dropout_stats.dropped_experts;
+        assert!(dropped_count % 2 == 0); // Should be multiple of block_size
+    }
+
+    // Test 6: Load-aware dropout functionality
+    #[test]
+    fn test_load_aware_dropout_forward() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let moe_config = MoEConfig::new(8, 2, 768, 3072);
+        let dropout_config = ExpertDropoutConfig::new(0.3, DropoutStrategy::LoadAware);
+        let mut dropout_layer = ExpertDropoutLayer::new(dropout_config, moe_config, &device).unwrap();
+        
+        dropout_layer.set_training(true);
+        
+        // Simulate load statistics with some experts having higher utilization
+        let expert_loads = vec![0.9, 0.8, 0.1, 0.2, 0.7, 0.05, 0.15, 0.6]; // Expert 2, 5, 6 have low load
+        dropout_layer.update_expert_loads(&expert_loads);
+
+        let batch_size = 4;
+        let seq_len = 16;
+        let expert_outputs = create_mock_expert_outputs(batch_size, seq_len, 8, 768, &device);
+        
+        let result = dropout_layer.forward(&expert_outputs);
+        assert!(result.is_ok());
+        
+        let output = result.unwrap();
+        // Less-utilized experts (2, 5, 6) should be more likely to be dropped
+        assert!(output.dropout_stats.dropped_experts > 0);
+    }
+
+    // Test 7: Progressive dropout functionality
+    #[test]
+    fn test_progressive_dropout_forward() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let moe_config = MoEConfig::new(8, 2, 768, 3072);
+        let dropout_config = ExpertDropoutConfig::new(0.4, DropoutStrategy::Progressive);
+        let mut dropout_layer = ExpertDropoutLayer::new(dropout_config, moe_config, &device).unwrap();
+        
+        dropout_layer.set_training(true);
+
+        let batch_size = 4;
+        let seq_len = 16;
+        let expert_outputs = create_mock_expert_outputs(batch_size, seq_len, 8, 768, &device);
+        
+        // Test progressive dropout at different training steps
+        dropout_layer.set_training_step(100);
+        let result_early = dropout_layer.forward(&expert_outputs);
+        assert!(result_early.is_ok());
+        
+        dropout_layer.set_training_step(1000);
+        let result_late = dropout_layer.forward(&expert_outputs);
+        assert!(result_late.is_ok());
+        
+        // Progressive dropout should change behavior over training steps
+        let early_dropped = result_early.unwrap().dropout_stats.dropped_experts;
+        let late_dropped = result_late.unwrap().dropout_stats.dropped_experts;
+        assert!(early_dropped != late_dropped);
+    }
+
+    // Test 8: Dropout scheduler functionality
+    #[test]
+    fn test_dropout_scheduler() {
+        let initial_rate = 0.5;
+        let final_rate = 0.1;
+        let total_steps = 1000;
+        
+        let scheduler = DropoutScheduler::new(initial_rate, final_rate, total_steps);
+        
+        // Test initial dropout rate
+        assert_eq!(scheduler.get_dropout_rate(0), initial_rate);
+        
+        // Test final dropout rate
+        assert_eq!(scheduler.get_dropout_rate(total_steps), final_rate);
+        
+        // Test intermediate rate (should be between initial and final)
+        let mid_rate = scheduler.get_dropout_rate(total_steps / 2);
+        assert!(mid_rate > final_rate);
+        assert!(mid_rate < initial_rate);
+        
+        // Test beyond total steps (should return final rate)
+        assert_eq!(scheduler.get_dropout_rate(total_steps + 100), final_rate);
+    }
+
+    // Test 9: Expert importance scorer
+    #[test]
+    fn test_expert_importance_scorer() {
+        let num_experts = 8;
+        let scorer = ExpertImportanceScorer::new(num_experts);
+        
+        // Test initial scores (should be uniform)
+        let initial_scores = scorer.get_importance_scores();
+        assert_eq!(initial_scores.len(), num_experts);
+        for &score in &initial_scores {
+            assert!(score >= 0.0 && score <= 1.0);
+        }
+        
+        // Test score update with routing weights
+        let routing_weights = vec![0.8, 0.6, 0.1, 0.2, 0.9, 0.05, 0.15, 0.7];
+        let mut updated_scorer = scorer.clone();
+        updated_scorer.update_scores(&routing_weights);
+        
+        let updated_scores = updated_scorer.get_importance_scores();
+        assert_eq!(updated_scores.len(), num_experts);
+        
+        // Expert 4 (index 4) had highest routing weight (0.9)
+        let max_score_idx = updated_scores.iter()
+            .enumerate()
+            .max_by(|(_, a), (_, b)| a.total_cmp(b))
+            .unwrap().0;
+        assert_eq!(max_score_idx, 4);
+    }
+
+    // Test 10: Dropout statistics tracking
+    #[test]
+    fn test_dropout_statistics() {
+        let mut stats = DropoutStatistics::new();
+        
+        // Test initial state
+        assert_eq!(stats.total_experts, 0);
+        assert_eq!(stats.dropped_experts, 0);
+        assert_eq!(stats.active_experts, 0);
+        assert_eq!(stats.dropout_rate, 0.0);
+        
+        // Test update
+        stats.update(8, 3);
+        assert_eq!(stats.total_experts, 8);
+        assert_eq!(stats.dropped_experts, 3);
+        assert_eq!(stats.active_experts, 5);
+        assert_eq!(stats.dropout_rate, 3.0 / 8.0);
+        
+        // Test cumulative tracking
+        stats.update(8, 2);
+        assert_eq!(stats.cumulative_dropped, 5); // 3 + 2
+        assert_eq!(stats.cumulative_total, 16); // 8 + 8
+    }
+
+    // Test 11: Integration with MoE routing
+    #[test]
+    fn test_moe_integration() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let moe_config = MoEConfig::new(8, 2, 768, 3072);
+        let router = Router::new(moe_config.clone(), &device).unwrap();
+        
+        let dropout_config = ExpertDropoutConfig::new(0.2, DropoutStrategy::Random);
+        let mut dropout_layer = ExpertDropoutLayer::new(dropout_config, moe_config.clone(), &device).unwrap();
+        dropout_layer.set_training(true);
+
+        let batch_size = 4;
+        let seq_len = 16;
+        let input = Tensor::randn(&[batch_size, seq_len, moe_config.hidden_dim], DType::F32, &device).unwrap();
+        
+        // Get routing info from MoE router
+        let routing_info = router.route(&input).unwrap();
+        
+        // Apply expert dropout to routing
+        let dropout_result = dropout_layer.apply_to_routing(&routing_info);
+        assert!(dropout_result.is_ok());
+        
+        let modified_routing = dropout_result.unwrap();
+        // Some experts should be masked out
+        assert!(modified_routing.active_expert_mask.len() == moe_config.num_experts);
+    }
+
+    // Helper function to create mock expert outputs
+    fn create_mock_expert_outputs(
+        batch_size: usize,
+        seq_len: usize,
+        num_experts: usize,
+        hidden_dim: usize,
+        device: &Device
+    ) -> ExpertOutputs {
+        let mut outputs = Vec::new();
+        for _ in 0..num_experts {
+            let expert_output = Tensor::randn(&[batch_size, seq_len, hidden_dim], DType::F32, device).unwrap();
+            outputs.push(expert_output);
+        }
+        
+        ExpertOutputs { outputs }
+    }
+}