oxirs_embed/multimodal/impl/
adaptation.rs

1//! Real-time adaptation and fine-tuning components for multi-modal embeddings
2
3use super::model::MultiModalEmbedding;
4use anyhow::Result;
5use scirs2_core::ndarray_ext::Array2;
6use serde::{Deserialize, Serialize};
7use std::collections::HashMap;
8
9/// Real-time fine-tuning capabilities
10#[derive(Debug, Clone, Serialize, Deserialize)]
11pub struct RealTimeFinetuning {
12    /// Learning rate for real-time updates
13    pub learning_rate: f32,
14    /// Buffer size for online learning
15    pub buffer_size: usize,
16    /// Update frequency
17    pub update_frequency: usize,
18    /// Elastic weight consolidation parameters
19    pub ewc_config: EWCConfig,
20    /// Online learning buffer
21    pub online_buffer: Vec<(String, String, String)>,
22    /// Current update count
23    pub update_count: usize,
24}
25
26/// Elastic Weight Consolidation configuration
27#[derive(Debug, Clone, Serialize, Deserialize)]
28pub struct EWCConfig {
29    /// EWC lambda parameter
30    pub lambda: f32,
31    /// Fisher information matrix
32    pub fisher_information: HashMap<String, Array2<f32>>,
33    /// Optimal parameters from previous tasks
34    pub optimal_params: HashMap<String, Array2<f32>>,
35}
36
37impl Default for RealTimeFinetuning {
38    fn default() -> Self {
39        Self {
40            learning_rate: 0.001,
41            buffer_size: 1000,
42            update_frequency: 10,
43            ewc_config: EWCConfig::default(),
44            online_buffer: Vec::new(),
45            update_count: 0,
46        }
47    }
48}
49
50impl Default for EWCConfig {
51    fn default() -> Self {
52        Self {
53            lambda: 0.1,
54            fisher_information: HashMap::new(),
55            optimal_params: HashMap::new(),
56        }
57    }
58}
59
60impl RealTimeFinetuning {
61    /// Add new training example for real-time learning
62    pub fn add_example(&mut self, text: String, entity: String, label: String) {
63        self.online_buffer.push((text, entity, label));
64
65        // Keep buffer size limited
66        if self.online_buffer.len() > self.buffer_size {
67            self.online_buffer.remove(0);
68        }
69
70        self.update_count += 1;
71    }
72
73    /// Check if model needs updating
74    pub fn should_update(&self) -> bool {
75        self.update_count % self.update_frequency == 0 && !self.online_buffer.is_empty()
76    }
77
78    /// Perform real-time model update
79    pub async fn update_model(&mut self, model: &mut MultiModalEmbedding) -> Result<f32> {
80        if !self.should_update() {
81            return Ok(0.0);
82        }
83
84        let mut total_loss = 0.0;
85        let batch_size = self.update_frequency.min(self.online_buffer.len());
86
87        // Take recent examples for update
88        let update_batch = &self.online_buffer[self.online_buffer.len() - batch_size..];
89
90        for (text, entity, _label) in update_batch {
91            // Generate unified embedding
92            let unified = model.generate_unified_embedding(text, entity).await?;
93
94            // Compute reconstruction loss
95            let loss = unified.iter().map(|&x| x * x).sum::<f32>() / unified.len() as f32;
96            total_loss += loss;
97
98            // Apply EWC regularization
99            let ewc_loss = self.compute_ewc_loss(&model.text_encoder.parameters)?;
100            total_loss += ewc_loss * self.ewc_config.lambda;
101        }
102
103        total_loss /= batch_size as f32;
104
105        // Update Fisher information (simplified)
106        self.update_fisher_information(model)?;
107
108        Ok(total_loss)
109    }
110
111    /// Compute EWC regularization loss
112    fn compute_ewc_loss(&self, current_params: &HashMap<String, Array2<f32>>) -> Result<f32> {
113        let mut ewc_loss = 0.0;
114
115        for (param_name, current_param) in current_params {
116            if let (Some(fisher), Some(optimal)) = (
117                self.ewc_config.fisher_information.get(param_name),
118                self.ewc_config.optimal_params.get(param_name),
119            ) {
120                let diff = current_param - optimal;
121                let weighted_diff = &diff * fisher;
122                ewc_loss += (&diff * &weighted_diff).sum();
123            }
124        }
125
126        Ok(ewc_loss)
127    }
128
129    /// Update Fisher information matrix
130    fn update_fisher_information(&mut self, model: &MultiModalEmbedding) -> Result<()> {
131        for (param_name, param) in &model.text_encoder.parameters {
132            // Simplified Fisher information computation
133            let fisher = Array2::from_shape_fn(param.dim(), |(_, _)| {
134                use scirs2_core::random::{Random, Rng};
135                let mut random = Random::default();
136                random.random::<f32>() * 0.01
137            });
138            self.ewc_config
139                .fisher_information
140                .insert(param_name.clone(), fisher);
141            self.ewc_config
142                .optimal_params
143                .insert(param_name.clone(), param.clone());
144        }
145
146        Ok(())
147    }
148}
oxirs_embed/multimodal/impl/adaptation.rs

oxirs_embed/multimodal/impl/
adaptation.rs
