oxirs-embed 0.2.4

//! Module for vision-language-graph integration

use super::*;
use anyhow::Result;
use scirs2_core::ndarray_ext::{Array1, Array2};
use scirs2_core::random::{Random, RngExt};
use std::collections::HashMap;
#[derive(Debug)]
pub struct MultiModalTransformer {
    pub config: MultiModalTransformerConfig,
    /// Cross-attention parameters
    pub cross_attention_params: HashMap<String, Array2<f32>>,
    /// Fusion parameters
    pub fusion_params: HashMap<String, Array2<f32>>,
    /// Modality embeddings
    pub modality_embeddings: Array2<f32>,
}

impl MultiModalTransformer {
    pub fn new(config: MultiModalTransformerConfig) -> Self {
        let mut cross_attention_params = HashMap::new();
        let mut fusion_params = HashMap::new();

        // Initialize cross-attention parameters
        for layer in 0..config.num_fusion_layers {
            for modality_pair in &["vision_language", "language_graph", "vision_graph"] {
                let mut random = Random::default();
                cross_attention_params.insert(
                    format!("{modality_pair}_{layer}"),
                    Array2::from_shape_fn((config.unified_dim, config.unified_dim), |_| {
                        (random.random::<f32>() - 0.5) * 0.1
                    }),
                );
            }
        }

        // Initialize fusion parameters
        let mut random = Random::default();
        fusion_params.insert(
            "tri_modal_fusion".to_string(),
            Array2::from_shape_fn((config.unified_dim, config.unified_dim * 3), |_| {
                (random.random::<f32>() - 0.5) * 0.1
            }),
        );

        // Modality embeddings
        let mut random = Random::default();
        let modality_embeddings = Array2::from_shape_fn(
            (3, config.unified_dim), // vision, language, graph
            |_| (random.random::<f32>() - 0.5) * 0.1,
        );

        Self {
            config,
            cross_attention_params,
            fusion_params,
            modality_embeddings,
        }
    }

    /// Fuse multi-modal embeddings
    pub fn fuse_embeddings(
        &self,
        vision_emb: &Array1<f32>,
        language_emb: &Array1<f32>,
        graph_emb: &Array1<f32>,
    ) -> Result<Array1<f32>> {
        match self.config.fusion_strategy {
            FusionStrategy::EarlyFusion => self.early_fusion(vision_emb, language_emb, graph_emb),
            FusionStrategy::CrossAttention => {
                self.cross_attention_fusion(vision_emb, language_emb, graph_emb)
            }
            FusionStrategy::TensorFusion => self.tensor_fusion(vision_emb, language_emb, graph_emb),
            _ => self.early_fusion(vision_emb, language_emb, graph_emb),
        }
    }

    /// Early fusion by concatenation
    fn early_fusion(
        &self,
        vision_emb: &Array1<f32>,
        language_emb: &Array1<f32>,
        graph_emb: &Array1<f32>,
    ) -> Result<Array1<f32>> {
        let mut concatenated = Vec::new();
        concatenated.extend_from_slice(vision_emb.as_slice().expect("array should be contiguous"));
        concatenated
            .extend_from_slice(language_emb.as_slice().expect("array should be contiguous"));
        concatenated.extend_from_slice(graph_emb.as_slice().expect("array should be contiguous"));

        let concat_array = Array1::from_vec(concatenated);

        if let Some(fusion_matrix) = self.fusion_params.get("tri_modal_fusion") {
            Ok(fusion_matrix.dot(&concat_array))
        } else {
            // Simple average if no fusion matrix
            let avg_len = vision_emb
                .len()
                .min(language_emb.len())
                .min(graph_emb.len());
            let mut averaged = Array1::zeros(avg_len);

            for i in 0..avg_len {
                averaged[i] = (vision_emb[i] + language_emb[i] + graph_emb[i]) / 3.0;
            }

            Ok(averaged)
        }
    }

    /// Cross-attention fusion
    fn cross_attention_fusion(
        &self,
        vision_emb: &Array1<f32>,
        language_emb: &Array1<f32>,
        graph_emb: &Array1<f32>,
    ) -> Result<Array1<f32>> {
        // Simplified cross-attention
        let mut fused = vision_emb.clone();

        // Vision-Language attention
        if let Some(vl_attention) = self.cross_attention_params.get("vision_language_0") {
            let vl_attended = vl_attention.dot(language_emb);
            fused = &fused + &vl_attended;
        }

        // Vision-Graph attention
        if let Some(vg_attention) = self.cross_attention_params.get("vision_graph_0") {
            let vg_attended = vg_attention.dot(graph_emb);
            fused = &fused + &vg_attended;
        }

        // Normalize
        let norm = fused.dot(&fused).sqrt();
        if norm > 0.0 {
            fused /= norm;
        }

        Ok(fused)
    }

    /// Tensor fusion
    fn tensor_fusion(
        &self,
        vision_emb: &Array1<f32>,
        language_emb: &Array1<f32>,
        graph_emb: &Array1<f32>,
    ) -> Result<Array1<f32>> {
        // Simplified tensor fusion using outer products
        let min_dim = vision_emb
            .len()
            .min(language_emb.len())
            .min(graph_emb.len());
        let mut fused = Array1::zeros(min_dim);

        for i in 0..min_dim {
            fused[i] = vision_emb[i] * language_emb[i] * graph_emb[i];
        }

        Ok(fused)
    }
}