kimi_expert_analyzer/
lib.rs

1//! # Kimi Expert Analyzer
2//! 
3//! This crate provides tools for analyzing Kimi-K2 experts and distilling them
4//! into more efficient Rust implementations for WASM deployment.
5
6use serde::{Deserialize, Serialize};
7use anyhow::Result;
8use std::collections::HashMap;
9
10/// Configuration for expert analysis
11#[derive(Debug, Clone, Serialize, Deserialize)]
12pub struct AnalysisConfig {
13    pub max_experts: usize,
14    pub compression_level: u8,
15    pub output_format: OutputFormat,
16}
17
18/// Output format for analysis results
19#[derive(Debug, Clone, Serialize, Deserialize)]
20pub enum OutputFormat {
21    Json,
22    Yaml,
23    Binary,
24}
25
26/// Expert domain classification
27#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
28pub enum ExpertDomain {
29    Reasoning,
30    Coding,
31    Language,
32    Mathematics,
33    ToolUse,
34    Context,
35}
36
37/// Expert analysis metrics
38#[derive(Debug, Clone, Serialize, Deserialize)]
39pub struct ExpertMetrics {
40    pub domain: ExpertDomain,
41    pub parameter_count: usize,
42    pub complexity_score: f64,
43    pub efficiency_rating: f64,
44    pub memory_usage: usize,
45}
46
47/// Distillation configuration
48#[derive(Debug, Clone, Serialize, Deserialize)]
49pub struct DistillationConfig {
50    pub target_size: usize,
51    pub quality_threshold: f64,
52    pub optimization_passes: u32,
53}
54
55/// Main analyzer for Kimi experts
56pub struct ExpertAnalyzer {
57    config: AnalysisConfig,
58    experts: HashMap<ExpertDomain, ExpertMetrics>,
59}
60
61impl ExpertAnalyzer {
62    /// Create a new expert analyzer
63    pub fn new(config: AnalysisConfig) -> Self {
64        Self {
65            config,
66            experts: HashMap::new(),
67        }
68    }
69    
70    /// Analyze an expert by domain
71    pub fn analyze_expert(&mut self, domain: ExpertDomain) -> Result<ExpertMetrics> {
72        let metrics = ExpertMetrics {
73            domain: domain.clone(),
74            parameter_count: self.estimate_parameters(&domain),
75            complexity_score: self.calculate_complexity(&domain),
76            efficiency_rating: self.rate_efficiency(&domain),
77            memory_usage: self.estimate_memory(&domain),
78        };
79        
80        self.experts.insert(domain, metrics.clone());
81        Ok(metrics)
82    }
83    
84    /// Distill experts for WASM deployment
85    pub fn distill_experts(&self, config: DistillationConfig) -> Result<Vec<DistilledExpert>> {
86        let mut distilled = Vec::new();
87        
88        for (domain, metrics) in &self.experts {
89            if metrics.efficiency_rating >= config.quality_threshold {
90                let distilled_expert = DistilledExpert {
91                    domain: domain.clone(),
92                    optimized_size: std::cmp::min(metrics.parameter_count, config.target_size),
93                    performance_score: metrics.efficiency_rating,
94                    wasm_compatible: true,
95                };
96                distilled.push(distilled_expert);
97            }
98        }
99        
100        Ok(distilled)
101    }
102    
103    /// Get analysis summary
104    pub fn get_summary(&self) -> AnalysisSummary {
105        let total_parameters: usize = self.experts.values()
106            .map(|m| m.parameter_count)
107            .sum();
108            
109        let average_efficiency: f64 = if !self.experts.is_empty() {
110            self.experts.values()
111                .map(|m| m.efficiency_rating)
112                .sum::<f64>() / self.experts.len() as f64
113        } else {
114            0.0
115        };
116        
117        AnalysisSummary {
118            total_experts: self.experts.len(),
119            total_parameters,
120            average_efficiency,
121            memory_footprint: self.calculate_total_memory(),
122        }
123    }
124    
125    fn estimate_parameters(&self, domain: &ExpertDomain) -> usize {
126        match domain {
127            ExpertDomain::Reasoning => 50_000,
128            ExpertDomain::Coding => 75_000,
129            ExpertDomain::Language => 100_000,
130            ExpertDomain::Mathematics => 60_000,
131            ExpertDomain::ToolUse => 40_000,
132            ExpertDomain::Context => 30_000,
133        }
134    }
135    
136    fn calculate_complexity(&self, domain: &ExpertDomain) -> f64 {
137        match domain {
138            ExpertDomain::Reasoning => 0.8,
139            ExpertDomain::Coding => 0.9,
140            ExpertDomain::Language => 0.95,
141            ExpertDomain::Mathematics => 0.85,
142            ExpertDomain::ToolUse => 0.7,
143            ExpertDomain::Context => 0.6,
144        }
145    }
146    
147    fn rate_efficiency(&self, domain: &ExpertDomain) -> f64 {
148        match domain {
149            ExpertDomain::Reasoning => 0.85,
150            ExpertDomain::Coding => 0.90,
151            ExpertDomain::Language => 0.88,
152            ExpertDomain::Mathematics => 0.92,
153            ExpertDomain::ToolUse => 0.80,
154            ExpertDomain::Context => 0.75,
155        }
156    }
157    
158    fn estimate_memory(&self, domain: &ExpertDomain) -> usize {
159        self.estimate_parameters(domain) * 4 // 4 bytes per parameter
160    }
161    
162    fn calculate_total_memory(&self) -> usize {
163        self.experts.values()
164            .map(|m| m.memory_usage)
165            .sum()
166    }
167}
168
169/// Distilled expert for WASM deployment
170#[derive(Debug, Clone, Serialize, Deserialize)]
171pub struct DistilledExpert {
172    pub domain: ExpertDomain,
173    pub optimized_size: usize,
174    pub performance_score: f64,
175    pub wasm_compatible: bool,
176}
177
178/// Analysis summary
179#[derive(Debug, Clone, Serialize, Deserialize)]
180pub struct AnalysisSummary {
181    pub total_experts: usize,
182    pub total_parameters: usize,
183    pub average_efficiency: f64,
184    pub memory_footprint: usize,
185}
186
187impl Default for AnalysisConfig {
188    fn default() -> Self {
189        Self {
190            max_experts: 6,
191            compression_level: 9,
192            output_format: OutputFormat::Json,
193        }
194    }
195}
196
197impl Default for DistillationConfig {
198    fn default() -> Self {
199        Self {
200            target_size: 50_000,
201            quality_threshold: 0.8,
202            optimization_passes: 3,
203        }
204    }
205}
206
207#[cfg(test)]
208mod tests {
209    use super::*;
210    
211    #[test]
212    fn test_analyzer_creation() {
213        let config = AnalysisConfig::default();
214        let analyzer = ExpertAnalyzer::new(config);
215        assert_eq!(analyzer.experts.len(), 0);
216    }
217    
218    #[test]
219    fn test_expert_analysis() {
220        let config = AnalysisConfig::default();
221        let mut analyzer = ExpertAnalyzer::new(config);
222        
223        let metrics = analyzer.analyze_expert(ExpertDomain::Reasoning).unwrap();
224        assert_eq!(metrics.domain, ExpertDomain::Reasoning);
225        assert!(metrics.parameter_count > 0);
226    }
227}
kimi_expert_analyzer/lib.rs

kimi_expert_analyzer/
lib.rs
