use crate::optimization::adaptive_inference::InferenceStrategy as AdaptiveStrategy;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use trustformers_core::errors::Result;
use trustformers_core::tensor::Tensor;
#[derive(Debug, Clone)]
pub struct MultiModalConfig {
pub max_video_frames: usize,
pub temporal_window_size: usize,
pub memory_optimization_enabled: bool,
pub cross_modal_attention_threshold: f32,
pub streaming_buffer_size: usize,
pub enable_real_time_processing: bool,
}
impl Default for MultiModalConfig {
fn default() -> Self {
Self {
max_video_frames: 30,
temporal_window_size: 8,
memory_optimization_enabled: true,
cross_modal_attention_threshold: 0.1,
streaming_buffer_size: 4096,
enable_real_time_processing: true,
}
}
}
#[derive(Debug)]
pub struct CrossModalAttentionOptimizer {
config: MultiModalConfig,
attention_cache: Arc<Mutex<HashMap<String, Tensor>>>,
memory_usage: Arc<Mutex<usize>>,
}
impl CrossModalAttentionOptimizer {
pub fn new(config: MultiModalConfig) -> Self {
Self {
config,
attention_cache: Arc::new(Mutex::new(HashMap::new())),
memory_usage: Arc::new(Mutex::new(0)),
}
}
pub fn optimize_cross_modal_attention(
&self,
vision_features: &Tensor,
text_features: &Tensor,
strategy: &AdaptiveStrategy,
) -> Result<Tensor> {
let batch_size = vision_features.shape()[0];
let vision_seq_len = vision_features.shape()[1];
let text_seq_len = text_features.shape()[1];
let hidden_dim = vision_features.shape()[2];
let mut attention_weights = vec![0.0f32; batch_size * vision_seq_len * text_seq_len];
for b in 0..batch_size {
for v in 0..vision_seq_len {
for t in 0..text_seq_len {
let mut score = 0.0f32;
for d in 0..hidden_dim {
let v_idx = b * vision_seq_len * hidden_dim + v * hidden_dim + d;
let t_idx = b * text_seq_len * hidden_dim + t * hidden_dim + d;
if let (Ok(vision_data), Ok(text_data)) =
(vision_features.data(), text_features.data())
{
if v_idx < vision_data.len() && t_idx < text_data.len() {
score += vision_data[v_idx] * text_data[t_idx];
}
}
}
score /= (hidden_dim as f32).sqrt();
if score.abs() < self.config.cross_modal_attention_threshold {
score = 0.0;
}
attention_weights[b * vision_seq_len * text_seq_len + v * text_seq_len + t] =
score;
}
}
}
self.apply_softmax_normalization(&mut attention_weights, vision_seq_len, text_seq_len)?;
Tensor::from_vec(
attention_weights,
&[batch_size, vision_seq_len, text_seq_len],
)
}
fn apply_softmax_normalization(
&self,
weights: &mut [f32],
vision_seq_len: usize,
text_seq_len: usize,
) -> Result<()> {
let batch_size = weights.len() / (vision_seq_len * text_seq_len);
for b in 0..batch_size {
for v in 0..vision_seq_len {
let start_idx = b * vision_seq_len * text_seq_len + v * text_seq_len;
let end_idx = start_idx + text_seq_len;
let max_val =
weights[start_idx..end_idx].iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
let mut sum = 0.0f32;
for i in start_idx..end_idx {
weights[i] = (weights[i] - max_val).exp();
sum += weights[i];
}
if sum > 0.0 {
for i in start_idx..end_idx {
weights[i] /= sum;
}
}
}
}
Ok(())
}
pub fn update_cache(&self, key: String, tensor: Tensor) -> Result<()> {
if let Ok(mut cache) = self.attention_cache.lock() {
let tensor_size = tensor.size() * std::mem::size_of::<f32>();
cache.insert(key, tensor);
if let Ok(mut usage) = self.memory_usage.lock() {
*usage += tensor_size;
}
}
Ok(())
}
pub fn clear_cache(&self) -> Result<()> {
if let Ok(mut cache) = self.attention_cache.lock() {
cache.clear();
}
if let Ok(mut usage) = self.memory_usage.lock() {
*usage = 0;
}
Ok(())
}
}
#[derive(Debug)]
pub struct StreamingMultiModalInference {
config: MultiModalConfig,
frame_buffer: Arc<Mutex<Vec<Tensor>>>,
audio_buffer: Arc<Mutex<Vec<f32>>>,
text_buffer: Arc<Mutex<String>>,
processing_queue: Arc<Mutex<Vec<MultiModalFrame>>>,
}
#[derive(Debug, Clone)]
pub struct MultiModalFrame {
pub timestamp: u64,
pub video_frame: Option<Tensor>,
pub audio_chunk: Option<Vec<f32>>,
pub text_segment: Option<String>,
pub frame_type: FrameType,
}
#[derive(Debug, Clone)]
pub enum FrameType {
Video,
Audio,
Text,
Combined,
}
impl StreamingMultiModalInference {
pub fn new(config: MultiModalConfig) -> Self {
Self {
config,
frame_buffer: Arc::new(Mutex::new(Vec::new())),
audio_buffer: Arc::new(Mutex::new(Vec::new())),
text_buffer: Arc::new(Mutex::new(String::new())),
processing_queue: Arc::new(Mutex::new(Vec::new())),
}
}
pub fn add_video_frame(&self, frame: Tensor, timestamp: u64) -> Result<()> {
if let Ok(mut buffer) = self.frame_buffer.lock() {
buffer.push(frame.clone());
if buffer.len() > self.config.max_video_frames {
buffer.remove(0);
}
}
let multimodal_frame = MultiModalFrame {
timestamp,
video_frame: Some(frame),
audio_chunk: None,
text_segment: None,
frame_type: FrameType::Video,
};
self.enqueue_frame(multimodal_frame)?;
Ok(())
}
pub fn add_audio_chunk(&self, audio_data: Vec<f32>, timestamp: u64) -> Result<()> {
if let Ok(mut buffer) = self.audio_buffer.lock() {
buffer.extend(audio_data.clone());
while buffer.len() > self.config.streaming_buffer_size {
buffer.remove(0);
}
}
let multimodal_frame = MultiModalFrame {
timestamp,
video_frame: None,
audio_chunk: Some(audio_data),
text_segment: None,
frame_type: FrameType::Audio,
};
self.enqueue_frame(multimodal_frame)?;
Ok(())
}
pub fn add_text_segment(&self, text: String, timestamp: u64) -> Result<()> {
if let Ok(mut buffer) = self.text_buffer.lock() {
buffer.push_str(&text);
buffer.push(' ');
if buffer.len() > self.config.streaming_buffer_size {
let half_size = self.config.streaming_buffer_size / 2;
*buffer = buffer.chars().skip(half_size).collect();
}
}
let multimodal_frame = MultiModalFrame {
timestamp,
video_frame: None,
audio_chunk: None,
text_segment: Some(text),
frame_type: FrameType::Text,
};
self.enqueue_frame(multimodal_frame)?;
Ok(())
}
fn enqueue_frame(&self, frame: MultiModalFrame) -> Result<()> {
if let Ok(mut queue) = self.processing_queue.lock() {
queue.push(frame);
queue.sort_by_key(|f| f.timestamp);
while queue.len() > self.config.max_video_frames * 2 {
queue.remove(0);
}
}
Ok(())
}
pub fn process_temporal_window(&self) -> Result<Vec<MultiModalFrame>> {
if let Ok(mut queue) = self.processing_queue.lock() {
if queue.len() < self.config.temporal_window_size {
return Ok(Vec::new());
}
let window_frames: Vec<MultiModalFrame> =
queue.drain(0..self.config.temporal_window_size).collect();
Ok(window_frames)
} else {
Ok(Vec::new())
}
}
pub fn get_combined_features(&self, frames: &[MultiModalFrame]) -> Result<Tensor> {
let mut combined_features = Vec::new();
for frame in frames {
match &frame.frame_type {
FrameType::Video => {
if let Some(ref video_frame) = frame.video_frame {
let flattened = self.flatten_video_features(video_frame)?;
combined_features.extend(flattened);
}
},
FrameType::Audio => {
if let Some(ref audio_chunk) = frame.audio_chunk {
let features = self.extract_audio_features(audio_chunk)?;
combined_features.extend(features);
}
},
FrameType::Text => {
if let Some(ref text_segment) = frame.text_segment {
let features = self.extract_text_features(text_segment)?;
combined_features.extend(features);
}
},
FrameType::Combined => {},
}
}
if combined_features.is_empty() {
combined_features = vec![0.0; 512];
}
let features_len = combined_features.len();
Tensor::from_vec(combined_features, &[1, features_len])
}
fn flatten_video_features(&self, video_frame: &Tensor) -> Result<Vec<f32>> {
let data = video_frame.data()?;
Ok(data.to_vec())
}
fn extract_audio_features(&self, audio_chunk: &[f32]) -> Result<Vec<f32>> {
let mut features = Vec::new();
let mean = audio_chunk.iter().sum::<f32>() / audio_chunk.len() as f32;
features.push(mean);
let variance =
audio_chunk.iter().map(|&x| (x - mean).powi(2)).sum::<f32>() / audio_chunk.len() as f32;
features.push(variance.sqrt());
let max_val = audio_chunk.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
let min_val = audio_chunk.iter().fold(f32::INFINITY, |a, &b| a.min(b));
features.push(max_val);
features.push(min_val);
let mut spectral_features = vec![0.0f32; 128];
for (i, &sample) in audio_chunk.iter().take(128).enumerate() {
spectral_features[i] = sample;
}
features.extend(spectral_features);
Ok(features)
}
fn extract_text_features(&self, text_segment: &str) -> Result<Vec<f32>> {
let mut features = Vec::new();
features.push(text_segment.len() as f32);
features.push(text_segment.split_whitespace().count() as f32);
features.push(text_segment.chars().filter(|c| c.is_alphabetic()).count() as f32);
features.push(text_segment.chars().filter(|c| c.is_numeric()).count() as f32);
let word_hash = text_segment
.split_whitespace()
.map(|word| {
word.chars()
.map(|c| c as u32)
.fold(0u32, |acc, c| acc.wrapping_mul(31).wrapping_add(c))
})
.fold(0u32, |acc, hash| acc.wrapping_add(hash));
features.push((word_hash % 1000) as f32 / 1000.0);
let mut embedding = vec![0.0f32; 128];
for (i, byte) in text_segment.bytes().take(128).enumerate() {
embedding[i] = (byte as f32) / 255.0;
}
features.extend(embedding);
Ok(features)
}
}
#[derive(Debug)]
pub struct CrossModalKnowledgeDistillation {
teacher_models: HashMap<String, TeacherModel>,
distillation_config: DistillationConfig,
}
#[derive(Debug)]
pub struct TeacherModel {
pub model_type: String,
pub modality: Modality,
pub layer_outputs: HashMap<String, Tensor>,
}
#[derive(Debug, Clone)]
pub enum Modality {
Vision,
Text,
Audio,
CrossModal,
}
#[derive(Debug, Clone)]
pub struct DistillationConfig {
pub temperature: f32,
pub alpha: f32,
pub feature_matching_weight: f32,
pub attention_transfer_weight: f32,
pub cross_modal_alignment_weight: f32,
}
impl Default for DistillationConfig {
fn default() -> Self {
Self {
temperature: 4.0,
alpha: 0.7,
feature_matching_weight: 0.3,
attention_transfer_weight: 0.2,
cross_modal_alignment_weight: 0.5,
}
}
}
impl CrossModalKnowledgeDistillation {
pub fn new(distillation_config: DistillationConfig) -> Self {
Self {
teacher_models: HashMap::new(),
distillation_config,
}
}
pub fn add_teacher_model(&mut self, name: String, model: TeacherModel) {
self.teacher_models.insert(name, model);
}
pub fn compute_distillation_loss(
&self,
student_outputs: &HashMap<String, Tensor>,
teacher_outputs: &HashMap<String, Tensor>,
) -> Result<f32> {
let mut total_loss = 0.0f32;
let mut loss_components = 0;
for (layer_name, student_output) in student_outputs {
if let Some(teacher_output) = teacher_outputs.get(layer_name) {
let feature_loss =
self.compute_feature_matching_loss(student_output, teacher_output)?;
total_loss += feature_loss * self.distillation_config.feature_matching_weight;
loss_components += 1;
if layer_name.contains("attention") {
let attention_loss =
self.compute_attention_transfer_loss(student_output, teacher_output)?;
total_loss +=
attention_loss * self.distillation_config.attention_transfer_weight;
}
}
}
if loss_components > 0 {
total_loss /= loss_components as f32;
}
Ok(total_loss)
}
fn compute_feature_matching_loss(&self, student: &Tensor, teacher: &Tensor) -> Result<f32> {
if student.shape() != teacher.shape() {
return Ok(0.0);
}
let student_data = student.data()?;
let teacher_data = teacher.data()?;
if student_data.len() != teacher_data.len() {
return Ok(0.0);
}
let mut mse_loss = 0.0f32;
for (student_val, teacher_val) in student_data.iter().zip(teacher_data.iter()) {
let diff = student_val - teacher_val;
mse_loss += diff * diff;
}
Ok(mse_loss / student_data.len() as f32)
}
fn compute_attention_transfer_loss(&self, student: &Tensor, teacher: &Tensor) -> Result<f32> {
if student.shape() != teacher.shape() {
return Ok(0.0);
}
let student_data = student.data()?;
let teacher_data = teacher.data()?;
if student_data.len() != teacher_data.len() {
return Ok(0.0);
}
let batch_size = student.shape()[0];
let seq_len = student.shape()[1];
let mut kl_loss = 0.0f32;
for b in 0..batch_size {
for i in 0..seq_len {
#[allow(dead_code)]
let mut student_entropy = 0.0f32;
let mut teacher_entropy = 0.0f32;
let mut cross_entropy = 0.0f32;
for j in 0..seq_len {
let idx = b * seq_len * seq_len + i * seq_len + j;
if idx < student_data.len() && idx < teacher_data.len() {
let s_prob = student_data[idx].max(1e-8);
let t_prob = teacher_data[idx].max(1e-8);
student_entropy -= s_prob * s_prob.ln();
teacher_entropy -= t_prob * t_prob.ln();
cross_entropy -= t_prob * s_prob.ln();
}
}
kl_loss += cross_entropy - teacher_entropy;
}
}
Ok(kl_loss / (batch_size * seq_len) as f32)
}
pub fn align_cross_modal_representations(
&self,
vision_features: &Tensor,
text_features: &Tensor,
) -> Result<f32> {
if vision_features.shape()[0] != text_features.shape()[0] {
return Ok(0.0);
}
let batch_size = vision_features.shape()[0];
let vision_dim = vision_features.shape().iter().skip(1).product::<usize>();
let text_dim = text_features.shape().iter().skip(1).product::<usize>();
let min_dim = vision_dim.min(text_dim);
let mut alignment_loss = 0.0f32;
for b in 0..batch_size {
let mut vision_norm = 0.0f32;
let mut text_norm = 0.0f32;
let mut dot_product = 0.0f32;
let vision_data = vision_features.data()?;
let text_data = text_features.data()?;
for d in 0..min_dim {
let v_idx = b * vision_dim + d;
let t_idx = b * text_dim + d;
if v_idx < vision_data.len() && t_idx < text_data.len() {
let v_val = vision_data[v_idx];
let t_val = text_data[t_idx];
vision_norm += v_val * v_val;
text_norm += t_val * t_val;
dot_product += v_val * t_val;
}
}
let cosine_similarity = if vision_norm > 0.0 && text_norm > 0.0 {
dot_product / (vision_norm.sqrt() * text_norm.sqrt())
} else {
0.0
};
alignment_loss += 1.0 - cosine_similarity;
}
Ok(alignment_loss / batch_size as f32)
}
}
#[derive(Debug)]
pub struct RealTimeVideoProcessor {
config: MultiModalConfig,
frame_queue: Arc<Mutex<Vec<VideoFrame>>>,
processing_stats: Arc<Mutex<ProcessingStats>>,
}
#[derive(Debug, Clone)]
pub struct VideoFrame {
pub data: Tensor,
pub timestamp: u64,
pub frame_number: u64,
pub metadata: VideoMetadata,
}
#[derive(Debug, Clone)]
pub struct VideoMetadata {
pub width: usize,
pub height: usize,
pub channels: usize,
pub fps: f32,
pub codec: String,
}
#[derive(Debug, Default, Clone)]
pub struct ProcessingStats {
pub frames_processed: u64,
pub average_processing_time: f32,
pub memory_usage: usize,
pub dropped_frames: u64,
}
impl RealTimeVideoProcessor {
pub fn new(config: MultiModalConfig) -> Self {
Self {
config,
frame_queue: Arc::new(Mutex::new(Vec::new())),
processing_stats: Arc::new(Mutex::new(ProcessingStats::default())),
}
}
pub fn add_frame(&self, frame: VideoFrame) -> Result<()> {
if let Ok(mut queue) = self.frame_queue.lock() {
queue.push(frame);
if queue.len() > self.config.max_video_frames {
queue.remove(0);
if let Ok(mut stats) = self.processing_stats.lock() {
stats.dropped_frames += 1;
}
}
}
Ok(())
}
pub fn process_temporal_sequence(&self, sequence_length: usize) -> Result<Vec<Tensor>> {
let frames = if let Ok(mut queue) = self.frame_queue.lock() {
if queue.len() < sequence_length {
return Ok(Vec::new());
}
queue.drain(0..sequence_length).collect::<Vec<_>>()
} else {
return Ok(Vec::new());
};
let mut processed_features = Vec::new();
for (i, frame) in frames.iter().enumerate() {
let temporal_features =
self.extract_temporal_features(&frame.data, i, sequence_length)?;
processed_features.push(temporal_features);
}
self.update_processing_stats(sequence_length)?;
Ok(processed_features)
}
fn extract_temporal_features(
&self,
frame_data: &Tensor,
position: usize,
total_frames: usize,
) -> Result<Tensor> {
let frame_data_vec = frame_data.data()?;
let mut features = Vec::with_capacity(frame_data_vec.len() + 4);
features.extend_from_slice(&frame_data_vec);
let temporal_position = position as f32 / total_frames as f32;
features.push(temporal_position);
features.push((temporal_position * std::f32::consts::PI).sin());
features.push((temporal_position * std::f32::consts::PI).cos());
features.push(if position == 0 { 1.0 } else { 0.0 });
let features_len = features.len();
Tensor::from_vec(features, &[1, features_len])
}
fn update_processing_stats(&self, frames_processed: usize) -> Result<()> {
if let Ok(mut stats) = self.processing_stats.lock() {
stats.frames_processed += frames_processed as u64;
let current_time = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_millis() as f32;
if stats.frames_processed > 0 {
stats.average_processing_time =
(stats.average_processing_time + current_time) / 2.0;
} else {
stats.average_processing_time = current_time;
}
}
Ok(())
}
pub fn get_processing_stats(&self) -> ProcessingStats {
if let Ok(stats) = self.processing_stats.lock() {
(*stats).clone()
} else {
ProcessingStats::default()
}
}
pub fn optimize_for_realtime(&self) -> Result<()> {
if let Ok(mut queue) = self.frame_queue.lock() {
if queue.len() > self.config.max_video_frames / 2 {
let target_size = self.config.max_video_frames / 4;
let skip_ratio = queue.len() / target_size;
let mut optimized_queue = Vec::new();
for (i, frame) in queue.iter().enumerate() {
if i % skip_ratio == 0 {
optimized_queue.push(frame.clone());
}
}
*queue = optimized_queue;
}
}
Ok(())
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_cross_modal_attention_optimizer() {
let config = MultiModalConfig::default();
let optimizer = CrossModalAttentionOptimizer::new(config);
let vision_features = Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], &[1, 2, 3])
.expect("Failed to create vision tensor");
let text_features = Tensor::from_vec(vec![0.5, 1.5, 2.5, 3.5, 4.5, 5.5], &[1, 2, 3])
.expect("Failed to create text tensor");
let strategy = AdaptiveStrategy::Progressive;
let result =
optimizer.optimize_cross_modal_attention(&vision_features, &text_features, &strategy);
assert!(result.is_ok());
let attention_tensor = result.expect("tensor operation failed");
assert_eq!(attention_tensor.shape(), &[1, 2, 2]);
}
#[test]
fn test_streaming_multimodal_inference() {
let config = MultiModalConfig::default();
let inference = StreamingMultiModalInference::new(config);
let video_frame =
Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0], &[2, 2]).expect("Failed to create tensor");
assert!(inference.add_video_frame(video_frame, 1000).is_ok());
let audio_chunk = vec![0.1, 0.2, 0.3, 0.4];
assert!(inference.add_audio_chunk(audio_chunk, 1001).is_ok());
let text_segment = "Hello world".to_string();
assert!(inference.add_text_segment(text_segment, 1002).is_ok());
let frames = inference.process_temporal_window().expect("temp file creation failed");
assert!(frames.len() <= 3);
}
#[test]
fn test_cross_modal_knowledge_distillation() {
let config = DistillationConfig::default();
let distillation = CrossModalKnowledgeDistillation::new(config);
let mut student_outputs = HashMap::new();
let mut teacher_outputs = HashMap::new();
let student_tensor =
Tensor::from_vec(vec![0.1, 0.2, 0.3, 0.4], &[2, 2]).expect("Failed to create tensor");
let teacher_tensor = Tensor::from_vec(vec![0.15, 0.25, 0.35, 0.45], &[2, 2])
.expect("Failed to create tensor");
student_outputs.insert("layer1".to_string(), student_tensor);
teacher_outputs.insert("layer1".to_string(), teacher_tensor);
let loss = distillation.compute_distillation_loss(&student_outputs, &teacher_outputs);
assert!(loss.is_ok());
assert!(loss.expect("operation failed in test") >= 0.0);
}
#[test]
fn test_real_time_video_processor() {
let config = MultiModalConfig::default();
let processor = RealTimeVideoProcessor::new(config);
let frame_data =
Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0], &[2, 2]).expect("Failed to create tensor");
let metadata = VideoMetadata {
width: 640,
height: 480,
channels: 3,
fps: 30.0,
codec: "H264".to_string(),
};
let frame = VideoFrame {
data: frame_data,
timestamp: 1000,
frame_number: 1,
metadata,
};
assert!(processor.add_frame(frame).is_ok());
let result = processor.process_temporal_sequence(1);
assert!(result.is_ok());
}
}