oxirs-chat 0.2.4

//! Neural Architecture Search (NAS) Module
//!
//! Provides automated neural architecture search capabilities for finding optimal
//! model configurations, layer arrangements, and hyperparameter combinations.

use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::{
    collections::HashMap,
    sync::Arc,
    time::{Duration, SystemTime},
};
use tokio::sync::RwLock;

/// Neural architecture search configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArchitectureSearchConfig {
    pub search_id: String,
    pub target_task: TaskType,
    pub performance_metric: PerformanceMetric,
    pub search_strategy: SearchStrategy,
    pub constraints: ArchitectureConstraints,
    pub search_budget: SearchBudget,
    pub validation_dataset: String,
}

/// Type of task for architecture optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TaskType {
    LanguageModeling,
    QuestionAnswering,
    TextGeneration,
    Summarization,
    Translation,
    Classification,
    SparqlGeneration,
    ConversationalAI,
}

/// Performance metrics for architecture evaluation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PerformanceMetric {
    Perplexity,
    Accuracy,
    F1Score,
    BleuScore,
    RougeScore,
    Latency,
    ThroughputPerSecond,
    MemoryEfficiency,
    CompositeScore,
}

/// Search strategies for architecture discovery
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SearchStrategy {
    RandomSearch,
    BayesianOptimization,
    EvolutionarySearch,
    ReinforcementLearning,
    DifferentiableNAS,
    ProgressiveSearch,
    HybridApproach,
}

/// Constraints for architecture search space
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArchitectureConstraints {
    pub max_parameters: Option<u64>,
    pub max_memory_mb: Option<f32>,
    pub max_latency_ms: Option<f32>,
    pub min_accuracy: Option<f32>,
    pub layer_type_constraints: Vec<LayerTypeConstraint>,
    pub depth_constraints: DepthConstraints,
    pub width_constraints: WidthConstraints,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerTypeConstraint {
    pub layer_type: LayerType,
    pub min_count: usize,
    pub max_count: Option<usize>,
    pub required_positions: Vec<usize>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DepthConstraints {
    pub min_layers: usize,
    pub max_layers: usize,
    pub prefer_depth_range: Option<(usize, usize)>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WidthConstraints {
    pub min_hidden_size: usize,
    pub max_hidden_size: usize,
    pub hidden_size_multiples: Option<usize>,
}

/// Search budget and resource allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchBudget {
    pub max_search_time: Duration,
    pub max_evaluations: usize,
    pub max_parallel_evaluations: usize,
    pub early_stopping_patience: usize,
    pub resource_allocation: ResourceAllocation,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocation {
    pub cpu_cores: Option<usize>,
    pub memory_gb: Option<f32>,
    pub gpu_devices: Vec<String>,
    pub storage_gb: Option<f32>,
}

/// Model architecture representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelArchitecture {
    pub architecture_id: String,
    pub layers: Vec<LayerConfig>,
    pub connections: Vec<ConnectionConfig>,
    pub hyperparameters: ArchitectureHyperparameters,
    pub estimated_metrics: EstimatedMetrics,
}

/// Layer configuration in the architecture
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerConfig {
    pub layer_id: String,
    pub layer_type: LayerType,
    pub parameters: LayerParameters,
    pub position: usize,
    pub activation: ActivationFunction,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerType {
    Embedding,
    TransformerBlock,
    Attention,
    FeedForward,
    Normalization,
    Dropout,
    Linear,
    Convolution,
    Pooling,
    Residual,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerParameters {
    pub hidden_size: Option<usize>,
    pub num_heads: Option<usize>,
    pub intermediate_size: Option<usize>,
    pub dropout_rate: Option<f32>,
    pub kernel_size: Option<usize>,
    pub stride: Option<usize>,
    pub custom_params: HashMap<String, serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActivationFunction {
    ReLU,
    GELU,
    Swish,
    Tanh,
    Sigmoid,
    LeakyReLU,
    Custom(String),
}

/// Connection configuration between layers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConnectionConfig {
    pub from_layer: String,
    pub to_layer: String,
    pub connection_type: ConnectionType,
    pub weight: Option<f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConnectionType {
    Sequential,
    Residual,
    Dense,
    Attention,
    Custom(String),
}

/// Architecture-level hyperparameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArchitectureHyperparameters {
    pub learning_rate: f32,
    pub batch_size: usize,
    pub sequence_length: usize,
    pub optimizer: OptimizerConfig,
    pub regularization: RegularizationConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizerConfig {
    pub optimizer_type: OptimizerType,
    pub weight_decay: f32,
    pub momentum: Option<f32>,
    pub beta1: Option<f32>,
    pub beta2: Option<f32>,
    pub epsilon: Option<f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizerType {
    Adam,
    AdamW,
    SGD,
    RMSprop,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegularizationConfig {
    pub dropout_rate: f32,
    pub attention_dropout: f32,
    pub layer_norm_eps: f32,
    pub label_smoothing: Option<f32>,
}

/// Estimated metrics for architecture evaluation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EstimatedMetrics {
    pub parameter_count: u64,
    pub memory_usage_mb: f32,
    pub inference_latency_ms: f32,
    pub training_time_hours: f32,
    pub flops: u64,
    pub predicted_performance: f32,
}

/// Search result containing evaluated architecture
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchResult {
    pub architecture: ModelArchitecture,
    pub evaluation_metrics: EvaluationMetrics,
    pub search_metadata: SearchMetadata,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EvaluationMetrics {
    pub primary_metric: f32,
    pub secondary_metrics: HashMap<String, f32>,
    pub efficiency_metrics: EfficiencyMetrics,
    pub validation_score: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EfficiencyMetrics {
    pub parameters_per_performance: f32,
    pub memory_efficiency: f32,
    pub latency_efficiency: f32,
    pub energy_efficiency: Option<f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchMetadata {
    pub evaluation_time: Duration,
    pub search_iteration: usize,
    pub parent_architectures: Vec<String>,
    pub mutation_history: Vec<String>,
    pub confidence_score: f32,
}

/// Neural Architecture Search Engine
pub struct ArchitectureSearch {
    searches: Arc<RwLock<HashMap<String, SearchState>>>,
    evaluator: Arc<ArchitectureEvaluator>,
    optimizer: Arc<ArchitectureOptimizer>,
}

#[derive(Debug, Clone)]
pub struct SearchState {
    config: ArchitectureSearchConfig,
    status: SearchStatus,
    current_best: Option<SearchResult>,
    evaluated_architectures: Vec<SearchResult>,
    search_history: Vec<SearchStep>,
    started_at: SystemTime,
    completed_at: Option<SystemTime>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SearchStatus {
    Queued,
    Initializing,
    Searching,
    Evaluating,
    Completed,
    Failed(String),
    Cancelled,
}

#[derive(Debug, Clone)]
struct SearchStep {
    iteration: usize,
    architecture_id: String,
    generation_strategy: String,
    evaluation_result: f32,
    improvement: bool,
}

/// Architecture evaluator for performance assessment
pub struct ArchitectureEvaluator {
    evaluation_cache: Arc<RwLock<HashMap<String, EvaluationMetrics>>>,
}

/// Architecture optimizer for generating new candidates
pub struct ArchitectureOptimizer {
    search_history: Arc<RwLock<Vec<(ModelArchitecture, f32)>>>,
}

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

impl ArchitectureSearch {
    /// Create new architecture search engine
    pub fn new() -> Self {
        Self {
            searches: Arc::new(RwLock::new(HashMap::new())),
            evaluator: Arc::new(ArchitectureEvaluator::new()),
            optimizer: Arc::new(ArchitectureOptimizer::new()),
        }
    }

    /// Start a new architecture search
    pub async fn start_search(&self, config: ArchitectureSearchConfig) -> Result<String> {
        let search_id = config.search_id.clone();

        let search_state = SearchState {
            config: config.clone(),
            status: SearchStatus::Queued,
            current_best: None,
            evaluated_architectures: Vec::new(),
            search_history: Vec::new(),
            started_at: SystemTime::now(),
            completed_at: None,
        };

        {
            let mut searches = self.searches.write().await;
            searches.insert(search_id.clone(), search_state);
        }

        // Start search execution
        let searches_clone = self.searches.clone();
        let evaluator_clone = self.evaluator.clone();
        let optimizer_clone = self.optimizer.clone();

        tokio::spawn(async move {
            Self::execute_search(search_id, searches_clone, evaluator_clone, optimizer_clone).await
        });

        Ok(config.search_id)
    }

    /// Get search status and results
    pub async fn get_search_status(&self, search_id: &str) -> Result<SearchState> {
        let searches = self.searches.read().await;
        searches
            .get(search_id)
            .cloned()
            .ok_or_else(|| anyhow!("Search not found: {}", search_id))
    }

    /// Execute architecture search
    async fn execute_search(
        search_id: String,
        searches: Arc<RwLock<HashMap<String, SearchState>>>,
        evaluator: Arc<ArchitectureEvaluator>,
        optimizer: Arc<ArchitectureOptimizer>,
    ) -> Result<()> {
        // Update status to initializing
        {
            let mut searches_lock = searches.write().await;
            if let Some(search) = searches_lock.get_mut(&search_id) {
                search.status = SearchStatus::Initializing;
            }
        }

        // Generate initial population
        let initial_architectures =
            Self::generate_initial_population(&search_id, &searches).await?;

        // Update status to searching
        {
            let mut searches_lock = searches.write().await;
            if let Some(search) = searches_lock.get_mut(&search_id) {
                search.status = SearchStatus::Searching;
            }
        }

        // Main search loop
        for iteration in 0..100 {
            // Mock iteration count
            // Generate candidate architectures
            let candidates = optimizer
                .generate_candidates(&initial_architectures, iteration)
                .await?;

            // Evaluate candidates
            for candidate in candidates {
                let evaluation = evaluator.evaluate_architecture(&candidate).await?;

                let result = SearchResult {
                    architecture: candidate,
                    evaluation_metrics: evaluation,
                    search_metadata: SearchMetadata {
                        evaluation_time: Duration::from_secs(30),
                        search_iteration: iteration,
                        parent_architectures: Vec::new(),
                        mutation_history: Vec::new(),
                        confidence_score: 0.8,
                    },
                };

                // Update search state with result
                {
                    let mut searches_lock = searches.write().await;
                    if let Some(search) = searches_lock.get_mut(&search_id) {
                        search.evaluated_architectures.push(result.clone());

                        // Update best result if improved
                        if search.current_best.as_ref().map_or(true, |best| {
                            result.evaluation_metrics.primary_metric
                                > best.evaluation_metrics.primary_metric
                        }) {
                            search.current_best = Some(result);
                        }
                    }
                }
            }

            // Simulate search time
            tokio::time::sleep(Duration::from_millis(100)).await;
        }

        // Mark search as completed
        {
            let mut searches_lock = searches.write().await;
            if let Some(search) = searches_lock.get_mut(&search_id) {
                search.status = SearchStatus::Completed;
                search.completed_at = Some(SystemTime::now());
            }
        }

        Ok(())
    }

    /// Generate initial population of architectures
    async fn generate_initial_population(
        search_id: &str,
        _searches: &Arc<RwLock<HashMap<String, SearchState>>>,
    ) -> Result<Vec<ModelArchitecture>> {
        let mut architectures = Vec::new();

        // Generate diverse initial architectures
        for i in 0..10 {
            let architecture = ModelArchitecture {
                architecture_id: format!("arch_{search_id}_{i}"),
                layers: Self::generate_random_layers(i),
                connections: Vec::new(),
                hyperparameters: ArchitectureHyperparameters {
                    learning_rate: 2e-5,
                    batch_size: 32,
                    sequence_length: 512,
                    optimizer: OptimizerConfig {
                        optimizer_type: OptimizerType::AdamW,
                        weight_decay: 0.01,
                        momentum: None,
                        beta1: Some(0.9),
                        beta2: Some(0.999),
                        epsilon: Some(1e-8),
                    },
                    regularization: RegularizationConfig {
                        dropout_rate: 0.1,
                        attention_dropout: 0.1,
                        layer_norm_eps: 1e-12,
                        label_smoothing: Some(0.1),
                    },
                },
                estimated_metrics: EstimatedMetrics {
                    parameter_count: 100_000_000 + i as u64 * 10_000_000,
                    memory_usage_mb: 1000.0 + i as f32 * 100.0,
                    inference_latency_ms: 50.0 + i as f32 * 10.0,
                    training_time_hours: 2.0 + i as f32 * 0.5,
                    flops: 1_000_000_000 + i as u64 * 100_000_000,
                    predicted_performance: 0.8 + i as f32 * 0.02,
                },
            };

            architectures.push(architecture);
        }

        Ok(architectures)
    }

    /// Generate random layer configurations
    fn generate_random_layers(seed: usize) -> Vec<LayerConfig> {
        let mut layers = Vec::new();

        // Add embedding layer
        layers.push(LayerConfig {
            layer_id: format!("embedding_{seed}"),
            layer_type: LayerType::Embedding,
            parameters: LayerParameters {
                hidden_size: Some(768),
                num_heads: None,
                intermediate_size: None,
                dropout_rate: Some(0.1),
                kernel_size: None,
                stride: None,
                custom_params: HashMap::new(),
            },
            position: 0,
            activation: ActivationFunction::GELU,
        });

        // Add transformer blocks
        for i in 1..=6 {
            layers.push(LayerConfig {
                layer_id: format!("transformer_{seed}_{i}"),
                layer_type: LayerType::TransformerBlock,
                parameters: LayerParameters {
                    hidden_size: Some(768),
                    num_heads: Some(12),
                    intermediate_size: Some(3072),
                    dropout_rate: Some(0.1),
                    kernel_size: None,
                    stride: None,
                    custom_params: HashMap::new(),
                },
                position: i,
                activation: ActivationFunction::GELU,
            });
        }

        layers
    }
}

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

impl ArchitectureEvaluator {
    /// Create new architecture evaluator
    pub fn new() -> Self {
        Self {
            evaluation_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Evaluate architecture performance
    pub async fn evaluate_architecture(
        &self,
        architecture: &ModelArchitecture,
    ) -> Result<EvaluationMetrics> {
        // Check cache first
        {
            let cache = self.evaluation_cache.read().await;
            if let Some(cached) = cache.get(&architecture.architecture_id) {
                return Ok(cached.clone());
            }
        }

        // Simulate evaluation process
        tokio::time::sleep(Duration::from_millis(500)).await;

        // Generate mock evaluation metrics
        let primary_metric = 0.7 + (architecture.layers.len() as f32 * 0.02);
        let metrics = EvaluationMetrics {
            primary_metric,
            secondary_metrics: {
                let mut map = HashMap::new();
                map.insert("accuracy".to_string(), primary_metric);
                map.insert("f1_score".to_string(), primary_metric * 0.95);
                map.insert("bleu_score".to_string(), primary_metric * 0.9);
                map
            },
            efficiency_metrics: EfficiencyMetrics {
                parameters_per_performance: architecture.estimated_metrics.parameter_count as f32
                    / primary_metric,
                memory_efficiency: primary_metric / architecture.estimated_metrics.memory_usage_mb,
                latency_efficiency: primary_metric
                    / architecture.estimated_metrics.inference_latency_ms,
                energy_efficiency: Some(primary_metric * 0.8),
            },
            validation_score: primary_metric * 0.95,
        };

        // Cache the result
        {
            let mut cache = self.evaluation_cache.write().await;
            cache.insert(architecture.architecture_id.clone(), metrics.clone());
        }

        Ok(metrics)
    }
}

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

impl ArchitectureOptimizer {
    /// Create new architecture optimizer
    pub fn new() -> Self {
        Self {
            search_history: Arc::new(RwLock::new(Vec::new())),
        }
    }

    /// Generate candidate architectures for evaluation
    pub async fn generate_candidates(
        &self,
        _base_architectures: &[ModelArchitecture],
        iteration: usize,
    ) -> Result<Vec<ModelArchitecture>> {
        let mut candidates = Vec::new();

        // Generate 3 candidates per iteration
        for i in 0..3 {
            let candidate = ModelArchitecture {
                architecture_id: format!("candidate_{iteration}_{i}"),
                layers: vec![], // Simplified for mock
                connections: vec![],
                hyperparameters: ArchitectureHyperparameters {
                    learning_rate: 1e-5 + (i as f32 * 1e-6),
                    batch_size: 32,
                    sequence_length: 512,
                    optimizer: OptimizerConfig {
                        optimizer_type: OptimizerType::AdamW,
                        weight_decay: 0.01,
                        momentum: None,
                        beta1: Some(0.9),
                        beta2: Some(0.999),
                        epsilon: Some(1e-8),
                    },
                    regularization: RegularizationConfig {
                        dropout_rate: 0.1,
                        attention_dropout: 0.1,
                        layer_norm_eps: 1e-12,
                        label_smoothing: Some(0.1),
                    },
                },
                estimated_metrics: EstimatedMetrics {
                    parameter_count: 100_000_000,
                    memory_usage_mb: 1000.0,
                    inference_latency_ms: 50.0,
                    training_time_hours: 2.0,
                    flops: 1_000_000_000,
                    predicted_performance: 0.8,
                },
            };

            candidates.push(candidate);
        }

        Ok(candidates)
    }
}

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

    #[tokio::test]
    async fn test_architecture_search_creation() {
        let search = ArchitectureSearch::new();

        let config = ArchitectureSearchConfig {
            search_id: "test_search".to_string(),
            target_task: TaskType::LanguageModeling,
            performance_metric: PerformanceMetric::Perplexity,
            search_strategy: SearchStrategy::RandomSearch,
            constraints: ArchitectureConstraints {
                max_parameters: Some(1_000_000_000),
                max_memory_mb: Some(8192.0),
                max_latency_ms: Some(100.0),
                min_accuracy: Some(0.8),
                layer_type_constraints: vec![],
                depth_constraints: DepthConstraints {
                    min_layers: 6,
                    max_layers: 24,
                    prefer_depth_range: Some((12, 18)),
                },
                width_constraints: WidthConstraints {
                    min_hidden_size: 512,
                    max_hidden_size: 1024,
                    hidden_size_multiples: Some(64),
                },
            },
            search_budget: SearchBudget {
                max_search_time: Duration::from_secs(3600),
                max_evaluations: 100,
                max_parallel_evaluations: 4,
                early_stopping_patience: 10,
                resource_allocation: ResourceAllocation {
                    cpu_cores: Some(8),
                    memory_gb: Some(32.0),
                    gpu_devices: vec!["gpu:0".to_string()],
                    storage_gb: Some(100.0),
                },
            },
            validation_dataset: "validation_data.json".to_string(),
        };

        let search_id = search.start_search(config).await.expect("should succeed");
        assert_eq!(search_id, "test_search");
    }

    #[tokio::test]
    async fn test_architecture_evaluator() {
        let evaluator = ArchitectureEvaluator::new();

        let architecture = ModelArchitecture {
            architecture_id: "test_arch".to_string(),
            layers: vec![],
            connections: vec![],
            hyperparameters: ArchitectureHyperparameters {
                learning_rate: 2e-5,
                batch_size: 32,
                sequence_length: 512,
                optimizer: OptimizerConfig {
                    optimizer_type: OptimizerType::AdamW,
                    weight_decay: 0.01,
                    momentum: None,
                    beta1: Some(0.9),
                    beta2: Some(0.999),
                    epsilon: Some(1e-8),
                },
                regularization: RegularizationConfig {
                    dropout_rate: 0.1,
                    attention_dropout: 0.1,
                    layer_norm_eps: 1e-12,
                    label_smoothing: Some(0.1),
                },
            },
            estimated_metrics: EstimatedMetrics {
                parameter_count: 100_000_000,
                memory_usage_mb: 1000.0,
                inference_latency_ms: 50.0,
                training_time_hours: 2.0,
                flops: 1_000_000_000,
                predicted_performance: 0.8,
            },
        };

        let metrics = evaluator
            .evaluate_architecture(&architecture)
            .await
            .expect("should succeed");
        assert!(metrics.primary_metric > 0.0);
        assert!(metrics.validation_score > 0.0);
    }
}