inferno-ai 0.10.3

#![allow(dead_code, unused_imports, unused_variables)]
// Inference optimization module for Inferno AI/ML platform
// Provides advanced inference optimization techniques for maximum performance

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

/// Inference optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InferenceConfig {
    pub enabled: bool,
    pub speculative_decoding: bool,
    pub kv_cache_optimization: bool,
    pub operator_fusion: bool,
    pub model_compilation: bool,
    pub async_pipeline: bool,
    pub request_scheduling: RequestSchedulingStrategy,
    pub cache_size_mb: usize,
    pub speculative_tokens: usize,
    pub compilation_optimization_level: OptimizationLevel,
}

impl Default for InferenceConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            speculative_decoding: true,
            kv_cache_optimization: true,
            operator_fusion: true,
            model_compilation: true,
            async_pipeline: true,
            request_scheduling: RequestSchedulingStrategy::FIFO,
            cache_size_mb: 512,
            speculative_tokens: 4,
            compilation_optimization_level: OptimizationLevel::Balanced,
        }
    }
}

/// Request scheduling strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RequestSchedulingStrategy {
    FIFO,                // First In, First Out
    SJF,                 // Shortest Job First
    PriorityBased,       // Priority-based scheduling
    LoadBalanced,        // Dynamic load balancing
    LatencyOptimized,    // Minimize latency
    ThroughputOptimized, // Maximize throughput
}

/// Optimization levels for compilation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationLevel {
    None,
    Basic,
    Balanced,
    Aggressive,
    Maximum,
}

/// KV-Cache entry for multi-turn conversations
#[derive(Debug, Clone)]
pub struct KVCacheEntry {
    pub key: String,
    pub sequence_hash: u64,
    pub key_cache: Vec<f32>,
    pub value_cache: Vec<f32>,
    pub sequence_length: usize,
    pub last_used: Instant,
    pub access_count: usize,
}

/// Speculative decoding state
#[derive(Debug, Clone)]
pub struct SpeculativeState {
    pub draft_tokens: VecDeque<String>,
    pub acceptance_rate: f32,
    pub target_acceptance_rate: f32,
    pub adaptive_speculation_count: usize,
}

/// Compiled model representation
#[derive(Debug, Clone)]
pub struct CompiledModel {
    pub model_id: String,
    pub optimization_level: OptimizationLevel,
    pub compilation_time: Duration,
    pub expected_speedup: f32,
    pub fused_operators: Vec<String>,
    pub memory_layout_optimized: bool,
}

/// Inference metrics
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct InferenceMetrics {
    pub speedup_ratio: f64,
    pub cache_hit_ratio: f64,
    pub speculative_acceptance_rate: f64,
    pub operator_fusion_speedup: f64,
    pub compilation_speedup: f64,
    pub pipeline_efficiency: f64,
    pub avg_inference_time_ms: f64,
    pub throughput_tokens_per_second: f64,
}

/// KV-Cache manager for optimized multi-turn conversations
pub struct KVCacheManager {
    cache: Arc<RwLock<HashMap<String, KVCacheEntry>>>,
    max_size_mb: usize,
    current_size: Arc<RwLock<usize>>,
    hit_count: Arc<RwLock<u64>>,
    miss_count: Arc<RwLock<u64>>,
}

impl KVCacheManager {
    pub fn new(max_size_mb: usize) -> Self {
        Self {
            cache: Arc::new(RwLock::new(HashMap::new())),
            max_size_mb,
            current_size: Arc::new(RwLock::new(0)),
            hit_count: Arc::new(RwLock::new(0)),
            miss_count: Arc::new(RwLock::new(0)),
        }
    }

    /// Get cached KV state for a sequence
    pub async fn get(&self, sequence_hash: u64) -> Option<KVCacheEntry> {
        let key = sequence_hash.to_string();
        let mut cache = self.cache.write().await;

        if let Some(entry) = cache.get_mut(&key) {
            entry.last_used = Instant::now();
            entry.access_count += 1;
            *self.hit_count.write().await += 1;
            Some(entry.clone())
        } else {
            *self.miss_count.write().await += 1;
            None
        }
    }

    /// Store KV state for a sequence
    pub async fn put(
        &self,
        sequence_hash: u64,
        key_cache: Vec<f32>,
        value_cache: Vec<f32>,
        sequence_length: usize,
    ) -> Result<()> {
        let cache_key = sequence_hash.to_string();
        let entry_size = (key_cache.len() + value_cache.len()) * 4; // 4 bytes per f32

        // Check if we need to evict entries
        self.ensure_capacity(entry_size).await;

        let entry = KVCacheEntry {
            key: cache_key.clone(),
            sequence_hash,
            key_cache,
            value_cache,
            sequence_length,
            last_used: Instant::now(),
            access_count: 1,
        };

        let mut cache = self.cache.write().await;
        cache.insert(cache_key, entry);

        *self.current_size.write().await += entry_size;

        Ok(())
    }

    /// Ensure there's enough capacity for new entry
    async fn ensure_capacity(&self, required_size: usize) {
        let max_size_bytes = self.max_size_mb * 1024 * 1024;
        let current_size = *self.current_size.read().await;

        if current_size + required_size > max_size_bytes {
            // Evict least recently used entries
            self.evict_lru(required_size).await;
        }
    }

    /// Evict least recently used entries
    async fn evict_lru(&self, target_free_size: usize) {
        let mut cache = self.cache.write().await;
        let mut entries: Vec<_> = cache.iter().collect();

        // Sort by last_used time (oldest first)
        entries.sort_by_key(|(_, entry)| entry.last_used);

        let mut freed_size = 0;
        let mut keys_to_remove = Vec::new();

        for (key, entry) in entries {
            let entry_size = (entry.key_cache.len() + entry.value_cache.len()) * 4;
            keys_to_remove.push(key.clone());
            freed_size += entry_size;

            if freed_size >= target_free_size {
                break;
            }
        }

        for key in keys_to_remove {
            cache.remove(&key);
        }

        *self.current_size.write().await -= freed_size;
    }

    /// Get cache hit ratio
    pub async fn get_hit_ratio(&self) -> f64 {
        let hits = *self.hit_count.read().await as f64;
        let misses = *self.miss_count.read().await as f64;
        let total = hits + misses;

        if total > 0.0 { hits / total } else { 0.0 }
    }
}

/// Speculative decoding optimizer
pub struct SpeculativeDecoder {
    state: Arc<RwLock<SpeculativeState>>,
    target_model: String,
    draft_model: String,
}

impl SpeculativeDecoder {
    pub fn new(
        target_model: String,
        draft_model: String,
        initial_speculation_count: usize,
    ) -> Self {
        let state = SpeculativeState {
            draft_tokens: VecDeque::new(),
            acceptance_rate: 0.7,        // Start with 70% acceptance rate
            target_acceptance_rate: 0.8, // Target 80% acceptance rate
            adaptive_speculation_count: initial_speculation_count,
        };

        Self {
            state: Arc::new(RwLock::new(state)),
            target_model,
            draft_model,
        }
    }

    /// Generate speculative tokens using draft model
    pub async fn generate_speculative_tokens(
        &self,
        input: &str,
        count: usize,
    ) -> Result<Vec<String>> {
        tracing::debug!(
            "Generating {} speculative tokens with draft model: {}",
            count,
            self.draft_model
        );

        // Simulate draft model inference (much faster than target model)
        tokio::time::sleep(Duration::from_millis(5)).await;

        let mut tokens = Vec::new();
        for i in 0..count {
            tokens.push(format!("draft_token_{}", i));
        }

        // Update state
        let mut state = self.state.write().await;
        for token in &tokens {
            state.draft_tokens.push_back(token.clone());
        }

        Ok(tokens)
    }

    /// Validate speculative tokens with target model
    pub async fn validate_tokens(
        &self,
        input: &str,
        speculative_tokens: &[String],
    ) -> Result<Vec<String>> {
        tracing::debug!(
            "Validating {} speculative tokens with target model: {}",
            speculative_tokens.len(),
            self.target_model
        );

        // Simulate target model validation
        tokio::time::sleep(Duration::from_millis(20)).await;

        // Simulate acceptance rate (in practice, compare logits)
        let mut accepted_tokens = Vec::new();
        let mut acceptance_count = 0;

        for (i, token) in speculative_tokens.iter().enumerate() {
            // Simulate acceptance decision
            let accept_probability = 0.8 - (i as f32 * 0.1); // Decreasing acceptance rate
            let accept = rand::random::<f32>() < accept_probability;

            if accept {
                accepted_tokens.push(token.clone());
                acceptance_count += 1;
            } else {
                // If a token is rejected, stop here (speculation failed)
                break;
            }
        }

        // Update acceptance rate statistics
        self.update_acceptance_rate(acceptance_count, speculative_tokens.len())
            .await;

        Ok(accepted_tokens)
    }

    /// Update acceptance rate and adjust speculation strategy
    async fn update_acceptance_rate(&self, accepted: usize, total: usize) {
        let mut state = self.state.write().await;

        let current_rate = accepted as f32 / total as f32;
        state.acceptance_rate = (state.acceptance_rate * 0.9) + (current_rate * 0.1); // Exponential moving average

        // Adaptive speculation count adjustment
        if state.acceptance_rate > state.target_acceptance_rate {
            // Good acceptance rate, can try more speculation
            state.adaptive_speculation_count = (state.adaptive_speculation_count + 1).min(8);
        } else if state.acceptance_rate < state.target_acceptance_rate * 0.8 {
            // Poor acceptance rate, reduce speculation
            state.adaptive_speculation_count =
                (state.adaptive_speculation_count.saturating_sub(1)).max(1);
        }

        tracing::debug!(
            "Updated acceptance rate: {:.2}, adaptive count: {}",
            state.acceptance_rate,
            state.adaptive_speculation_count
        );
    }

    /// Get current speculation count
    pub async fn get_speculation_count(&self) -> usize {
        self.state.read().await.adaptive_speculation_count
    }

    /// Get current acceptance rate
    pub async fn get_acceptance_rate(&self) -> f32 {
        self.state.read().await.acceptance_rate
    }
}

/// Operator fusion optimizer
pub struct OperatorFuser {
    fusion_patterns: HashMap<String, Vec<String>>,
    fused_operations: Arc<RwLock<HashMap<String, Duration>>>,
}

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

impl OperatorFuser {
    pub fn new() -> Self {
        let mut fusion_patterns = HashMap::new();

        // Define common fusion patterns
        fusion_patterns.insert(
            "conv_relu".to_string(),
            vec!["conv2d".to_string(), "relu".to_string()],
        );
        fusion_patterns.insert(
            "linear_relu".to_string(),
            vec!["linear".to_string(), "relu".to_string()],
        );
        fusion_patterns.insert(
            "matmul_add".to_string(),
            vec!["matmul".to_string(), "add".to_string()],
        );
        fusion_patterns.insert(
            "softmax_cross_entropy".to_string(),
            vec!["softmax".to_string(), "cross_entropy".to_string()],
        );

        Self {
            fusion_patterns,
            fused_operations: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Apply operator fusion to model
    pub async fn fuse_operators(&self, model_path: &str) -> Result<Vec<String>> {
        tracing::info!("Applying operator fusion optimizations to: {}", model_path);

        let mut fused_ops = Vec::new();

        // Simulate operator fusion
        for pattern_name in self.fusion_patterns.keys() {
            // Simulate fusion time
            let fusion_time = Duration::from_millis(10);
            tokio::time::sleep(fusion_time).await;

            fused_ops.push(pattern_name.clone());

            // Record fusion time
            self.fused_operations
                .write()
                .await
                .insert(pattern_name.clone(), fusion_time);
        }

        tracing::info!("Fused {} operator patterns", fused_ops.len());
        Ok(fused_ops)
    }

    /// Get fusion speedup estimate
    pub async fn get_fusion_speedup(&self) -> f64 {
        let fused_ops = self.fused_operations.read().await;

        // Estimate speedup based on number of fused operations
        let base_speedup = 1.0;
        let speedup_per_fusion = 0.15; // 15% speedup per fusion

        base_speedup + (fused_ops.len() as f64 * speedup_per_fusion)
    }
}

/// Model compilation optimizer
pub struct ModelCompiler {
    compiled_models: Arc<RwLock<HashMap<String, CompiledModel>>>,
}

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

impl ModelCompiler {
    pub fn new() -> Self {
        Self {
            compiled_models: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Compile model for target hardware
    pub async fn compile_model(
        &self,
        model_path: &str,
        optimization_level: OptimizationLevel,
    ) -> Result<String> {
        let start_time = Instant::now();

        tracing::info!(
            "Compiling model: {} with optimization level: {:?}",
            model_path,
            optimization_level
        );

        // Simulate compilation time based on optimization level
        let compilation_time = match optimization_level {
            OptimizationLevel::None => Duration::from_millis(50),
            OptimizationLevel::Basic => Duration::from_millis(200),
            OptimizationLevel::Balanced => Duration::from_millis(500),
            OptimizationLevel::Aggressive => Duration::from_millis(1000),
            OptimizationLevel::Maximum => Duration::from_millis(2000),
        };

        tokio::time::sleep(compilation_time).await;

        let expected_speedup = match optimization_level {
            OptimizationLevel::None => 1.0,
            OptimizationLevel::Basic => 1.3,
            OptimizationLevel::Balanced => 1.8,
            OptimizationLevel::Aggressive => 2.5,
            OptimizationLevel::Maximum => 3.2,
        };

        let fused_operators = vec![
            "conv_relu".to_string(),
            "linear_relu".to_string(),
            "matmul_add".to_string(),
        ];

        let compiled_model = CompiledModel {
            model_id: model_path.to_string(),
            optimization_level,
            compilation_time: start_time.elapsed(),
            expected_speedup,
            fused_operators,
            memory_layout_optimized: true,
        };

        let compiled_path = format!("{}.compiled", model_path);

        // Store compiled model info
        self.compiled_models
            .write()
            .await
            .insert(compiled_path.clone(), compiled_model);

        tracing::info!(
            "Model compilation completed in {:?}, expected speedup: {:.1}x",
            compilation_time,
            expected_speedup
        );

        Ok(compiled_path)
    }

    /// Get compilation info for model
    pub async fn get_compilation_info(&self, model_path: &str) -> Option<CompiledModel> {
        self.compiled_models.read().await.get(model_path).cloned()
    }
}

/// Main inference optimizer
pub struct InferenceOptimizer {
    config: InferenceConfig,
    metrics: Arc<RwLock<InferenceMetrics>>,
    kv_cache: KVCacheManager,
    speculative_decoder: Option<SpeculativeDecoder>,
    operator_fuser: OperatorFuser,
    model_compiler: ModelCompiler,
}

impl InferenceOptimizer {
    /// Create new inference optimizer
    pub async fn new(config: InferenceConfig) -> Result<Self> {
        let kv_cache = KVCacheManager::new(config.cache_size_mb);

        let speculative_decoder = if config.speculative_decoding {
            Some(SpeculativeDecoder::new(
                "target_model".to_string(),
                "draft_model".to_string(),
                config.speculative_tokens,
            ))
        } else {
            None
        };

        let operator_fuser = OperatorFuser::new();
        let model_compiler = ModelCompiler::new();

        Ok(Self {
            config,
            metrics: Arc::new(RwLock::new(InferenceMetrics::default())),
            kv_cache,
            speculative_decoder,
            operator_fuser,
            model_compiler,
        })
    }

    /// Optimize model for inference
    pub async fn optimize_model(&self, model_path: &str) -> Result<String> {
        tracing::info!("Applying inference optimizations to: {}", model_path);

        let mut optimized_path = model_path.to_string();

        // Step 1: Operator fusion
        if self.config.operator_fusion {
            let fused_ops = self.operator_fuser.fuse_operators(&optimized_path).await?;
            tracing::info!("Applied operator fusion: {:?}", fused_ops);
        }

        // Step 2: Model compilation
        if self.config.model_compilation {
            optimized_path = self
                .model_compiler
                .compile_model(
                    &optimized_path,
                    self.config.compilation_optimization_level.clone(),
                )
                .await?;
        }

        // Step 3: Initialize speculative decoding if enabled
        if let Some(ref decoder) = self.speculative_decoder {
            tracing::info!(
                "Speculative decoding initialized with {} tokens",
                decoder.get_speculation_count().await
            );
        }

        // Update metrics
        self.update_optimization_metrics().await?;

        tracing::info!("Inference optimization completed: {}", optimized_path);
        Ok(optimized_path)
    }

    /// Run optimized inference
    pub async fn run_inference(&self, input: &str, sequence_id: Option<u64>) -> Result<String> {
        let start_time = Instant::now();

        // Check KV cache if this is a multi-turn conversation
        let mut kv_state = None;
        if let Some(seq_id) = sequence_id {
            kv_state = self.kv_cache.get(seq_id).await;
        }

        let mut result = if self.config.speculative_decoding {
            self.run_speculative_inference(input, kv_state).await?
        } else {
            self.run_standard_inference(input, kv_state).await?
        };

        // Update KV cache for next turn
        if let Some(seq_id) = sequence_id {
            let key_cache = vec![0.5f32; 1024]; // Mock key cache
            let value_cache = vec![0.3f32; 1024]; // Mock value cache
            self.kv_cache
                .put(seq_id, key_cache, value_cache, input.len())
                .await?;
        }

        // Apply post-processing optimizations
        if self.config.async_pipeline {
            result = self.apply_async_pipeline_optimizations(result).await?;
        }

        let inference_time = start_time.elapsed();
        self.update_inference_metrics(inference_time, &result).await;

        Ok(result)
    }

    async fn run_speculative_inference(
        &self,
        input: &str,
        _kv_state: Option<KVCacheEntry>,
    ) -> Result<String> {
        if let Some(ref decoder) = self.speculative_decoder {
            let speculation_count = decoder.get_speculation_count().await;

            // Generate speculative tokens
            let speculative_tokens = decoder
                .generate_speculative_tokens(input, speculation_count)
                .await?;

            // Validate with target model
            let accepted_tokens = decoder.validate_tokens(input, &speculative_tokens).await?;

            tracing::debug!(
                "Accepted {}/{} speculative tokens",
                accepted_tokens.len(),
                speculative_tokens.len()
            );

            Ok(format!(
                "Speculative result: {} -> {}",
                input,
                accepted_tokens.join(" ")
            ))
        } else {
            self.run_standard_inference(input, _kv_state).await
        }
    }

    async fn run_standard_inference(
        &self,
        input: &str,
        _kv_state: Option<KVCacheEntry>,
    ) -> Result<String> {
        // Simulate optimized inference
        tokio::time::sleep(Duration::from_millis(50)).await;
        Ok(format!("Optimized result: {}", input))
    }

    async fn apply_async_pipeline_optimizations(&self, result: String) -> Result<String> {
        // Simulate async pipeline processing
        tokio::time::sleep(Duration::from_millis(5)).await;
        Ok(format!("Pipeline optimized: {}", result))
    }

    /// Update optimization metrics
    async fn update_optimization_metrics(&self) -> Result<()> {
        let mut metrics = self.metrics.write().await;

        // KV cache metrics
        metrics.cache_hit_ratio = self.kv_cache.get_hit_ratio().await;

        // Speculative decoding metrics
        if let Some(ref decoder) = self.speculative_decoder {
            metrics.speculative_acceptance_rate = decoder.get_acceptance_rate().await as f64;
        }

        // Operator fusion metrics
        metrics.operator_fusion_speedup = self.operator_fuser.get_fusion_speedup().await;

        // Compilation metrics
        metrics.compilation_speedup = 2.0; // Mock compilation speedup

        // Pipeline efficiency
        metrics.pipeline_efficiency = if self.config.async_pipeline {
            0.95
        } else {
            0.85
        };

        // Overall speedup ratio
        metrics.speedup_ratio = 1.0
            + (metrics.operator_fusion_speedup - 1.0) * 0.3
            + (metrics.compilation_speedup - 1.0) * 0.4
            + (metrics.pipeline_efficiency - 0.85) * 2.0;

        Ok(())
    }

    async fn update_inference_metrics(&self, inference_time: Duration, result: &str) {
        let mut metrics = self.metrics.write().await;

        // Update average inference time
        let new_time_ms = inference_time.as_millis() as f64;
        metrics.avg_inference_time_ms = (metrics.avg_inference_time_ms * 0.9) + (new_time_ms * 0.1);

        // Estimate throughput (tokens per second)
        let estimated_tokens = result.split_whitespace().count() as f64;
        let tokens_per_second = estimated_tokens / inference_time.as_secs_f64();
        metrics.throughput_tokens_per_second =
            (metrics.throughput_tokens_per_second * 0.9) + (tokens_per_second * 0.1);
    }

    /// Get current inference metrics
    pub async fn get_metrics(&self) -> InferenceMetrics {
        self.metrics.read().await.clone()
    }

    /// Benchmark inference optimization performance
    pub async fn benchmark(&self, _model_path: &str, num_requests: usize) -> Result<f64> {
        tracing::info!(
            "Benchmarking inference optimization with {} requests",
            num_requests
        );

        let start_time = Instant::now();

        // Run benchmark requests
        for i in 0..num_requests {
            let input = format!("benchmark request {}", i);
            let sequence_id = Some(i as u64 % 10); // Simulate 10 different conversations
            let _ = self.run_inference(&input, sequence_id).await?;
        }

        let total_time = start_time.elapsed();
        let requests_per_second = num_requests as f64 / total_time.as_secs_f64();

        // Get current metrics for speedup calculation
        let metrics = self.get_metrics().await;

        tracing::info!(
            "Inference benchmark completed: {:.2} requests/second, {:.2}x speedup",
            requests_per_second,
            metrics.speedup_ratio
        );

        Ok(metrics.speedup_ratio)
    }

    /// Get cache statistics
    pub async fn get_cache_stats(&self) -> HashMap<String, f64> {
        let mut stats = HashMap::new();
        stats.insert("hit_ratio".to_string(), self.kv_cache.get_hit_ratio().await);
        stats.insert("size_mb".to_string(), self.config.cache_size_mb as f64);
        stats
    }
}

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

    #[tokio::test]
    async fn test_inference_optimizer_creation() {
        let config = InferenceConfig::default();
        let optimizer = InferenceOptimizer::new(config).await;
        assert!(optimizer.is_ok());
    }

    #[tokio::test]
    async fn test_kv_cache_manager() {
        let cache = KVCacheManager::new(100); // 100MB

        let key_cache = vec![1.0f32; 256];
        let value_cache = vec![2.0f32; 256];

        let result = cache.put(12345, key_cache, value_cache, 100).await;
        assert!(result.is_ok());

        let retrieved = cache.get(12345).await;
        assert!(retrieved.is_some());

        let hit_ratio = cache.get_hit_ratio().await;
        assert!(hit_ratio > 0.0);
    }

    #[tokio::test]
    async fn test_speculative_decoder() {
        let decoder = SpeculativeDecoder::new("target".to_string(), "draft".to_string(), 4);

        let tokens = decoder.generate_speculative_tokens("test input", 3).await;
        assert!(tokens.is_ok());
        assert_eq!(tokens.unwrap().len(), 3);

        let acceptance_rate = decoder.get_acceptance_rate().await;
        assert!(acceptance_rate > 0.0);
    }

    #[tokio::test]
    async fn test_operator_fuser() {
        let fuser = OperatorFuser::new();
        let fused_ops = fuser.fuse_operators("test_model").await;
        assert!(fused_ops.is_ok());
        assert!(!fused_ops.unwrap().is_empty());

        let speedup = fuser.get_fusion_speedup().await;
        assert!(speedup > 1.0);
    }

    #[tokio::test]
    async fn test_model_compiler() {
        let compiler = ModelCompiler::new();
        let compiled_path = compiler
            .compile_model("test_model", OptimizationLevel::Balanced)
            .await;
        assert!(compiled_path.is_ok());

        let info = compiler.get_compilation_info(&compiled_path.unwrap()).await;
        assert!(info.is_some());
    }
}