oxirs_embed/vision_language_graph/
transformer.rs

1//! Module for vision-language-graph integration
2
3use super::*;
4use anyhow::Result;
5use scirs2_core::ndarray_ext::{Array1, Array2};
6use scirs2_core::random::{Random, Rng};
7use std::collections::HashMap;
8#[derive(Debug)]
9pub struct MultiModalTransformer {
10    pub config: MultiModalTransformerConfig,
11    /// Cross-attention parameters
12    pub cross_attention_params: HashMap<String, Array2<f32>>,
13    /// Fusion parameters
14    pub fusion_params: HashMap<String, Array2<f32>>,
15    /// Modality embeddings
16    pub modality_embeddings: Array2<f32>,
17}
18
19impl MultiModalTransformer {
20    pub fn new(config: MultiModalTransformerConfig) -> Self {
21        let mut cross_attention_params = HashMap::new();
22        let mut fusion_params = HashMap::new();
23
24        // Initialize cross-attention parameters
25        for layer in 0..config.num_fusion_layers {
26            for modality_pair in &["vision_language", "language_graph", "vision_graph"] {
27                let mut random = Random::default();
28                cross_attention_params.insert(
29                    format!("{modality_pair}_{layer}"),
30                    Array2::from_shape_fn((config.unified_dim, config.unified_dim), |_| {
31                        (random.random::<f32>() - 0.5) * 0.1
32                    }),
33                );
34            }
35        }
36
37        // Initialize fusion parameters
38        let mut random = Random::default();
39        fusion_params.insert(
40            "tri_modal_fusion".to_string(),
41            Array2::from_shape_fn((config.unified_dim, config.unified_dim * 3), |_| {
42                (random.random::<f32>() - 0.5) * 0.1
43            }),
44        );
45
46        // Modality embeddings
47        let mut random = Random::default();
48        let modality_embeddings = Array2::from_shape_fn(
49            (3, config.unified_dim), // vision, language, graph
50            |_| (random.random::<f32>() - 0.5) * 0.1,
51        );
52
53        Self {
54            config,
55            cross_attention_params,
56            fusion_params,
57            modality_embeddings,
58        }
59    }
60
61    /// Fuse multi-modal embeddings
62    pub fn fuse_embeddings(
63        &self,
64        vision_emb: &Array1<f32>,
65        language_emb: &Array1<f32>,
66        graph_emb: &Array1<f32>,
67    ) -> Result<Array1<f32>> {
68        match self.config.fusion_strategy {
69            FusionStrategy::EarlyFusion => self.early_fusion(vision_emb, language_emb, graph_emb),
70            FusionStrategy::CrossAttention => {
71                self.cross_attention_fusion(vision_emb, language_emb, graph_emb)
72            }
73            FusionStrategy::TensorFusion => self.tensor_fusion(vision_emb, language_emb, graph_emb),
74            _ => self.early_fusion(vision_emb, language_emb, graph_emb),
75        }
76    }
77
78    /// Early fusion by concatenation
79    fn early_fusion(
80        &self,
81        vision_emb: &Array1<f32>,
82        language_emb: &Array1<f32>,
83        graph_emb: &Array1<f32>,
84    ) -> Result<Array1<f32>> {
85        let mut concatenated = Vec::new();
86        concatenated.extend_from_slice(vision_emb.as_slice().expect("array should be contiguous"));
87        concatenated
88            .extend_from_slice(language_emb.as_slice().expect("array should be contiguous"));
89        concatenated.extend_from_slice(graph_emb.as_slice().expect("array should be contiguous"));
90
91        let concat_array = Array1::from_vec(concatenated);
92
93        if let Some(fusion_matrix) = self.fusion_params.get("tri_modal_fusion") {
94            Ok(fusion_matrix.dot(&concat_array))
95        } else {
96            // Simple average if no fusion matrix
97            let avg_len = vision_emb
98                .len()
99                .min(language_emb.len())
100                .min(graph_emb.len());
101            let mut averaged = Array1::zeros(avg_len);
102
103            for i in 0..avg_len {
104                averaged[i] = (vision_emb[i] + language_emb[i] + graph_emb[i]) / 3.0;
105            }
106
107            Ok(averaged)
108        }
109    }
110
111    /// Cross-attention fusion
112    fn cross_attention_fusion(
113        &self,
114        vision_emb: &Array1<f32>,
115        language_emb: &Array1<f32>,
116        graph_emb: &Array1<f32>,
117    ) -> Result<Array1<f32>> {
118        // Simplified cross-attention
119        let mut fused = vision_emb.clone();
120
121        // Vision-Language attention
122        if let Some(vl_attention) = self.cross_attention_params.get("vision_language_0") {
123            let vl_attended = vl_attention.dot(language_emb);
124            fused = &fused + &vl_attended;
125        }
126
127        // Vision-Graph attention
128        if let Some(vg_attention) = self.cross_attention_params.get("vision_graph_0") {
129            let vg_attended = vg_attention.dot(graph_emb);
130            fused = &fused + &vg_attended;
131        }
132
133        // Normalize
134        let norm = fused.dot(&fused).sqrt();
135        if norm > 0.0 {
136            fused /= norm;
137        }
138
139        Ok(fused)
140    }
141
142    /// Tensor fusion
143    fn tensor_fusion(
144        &self,
145        vision_emb: &Array1<f32>,
146        language_emb: &Array1<f32>,
147        graph_emb: &Array1<f32>,
148    ) -> Result<Array1<f32>> {
149        // Simplified tensor fusion using outer products
150        let min_dim = vision_emb
151            .len()
152            .min(language_emb.len())
153            .min(graph_emb.len());
154        let mut fused = Array1::zeros(min_dim);
155
156        for i in 0..min_dim {
157            fused[i] = vision_emb[i] * language_emb[i] * graph_emb[i];
158        }
159
160        Ok(fused)
161    }
162}
oxirs_embed/vision_language_graph/transformer.rs

oxirs_embed/vision_language_graph/
transformer.rs
