Initial commit

crates/training/rtx-transformers/src/revolutionary/mod.rs

@@ -0,0 +1,380 @@
+//! Edge-aware integration for transformers
+
+use crate::Result;
+use crate::training::{ModelConfig, ModelOutput, TransformerModel};
+use rtx_tensor::{Device, Tensor};
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use tracing::{debug, info};
+
+// Edge computing modules
+pub mod edge_aware_training;
+pub mod edge_capabilities;
+pub mod edge_deployment;
+pub mod edge_optimizations;
+pub mod edge_trainer;
+pub mod federated_coordination;
+pub mod hybrid_orchestrator;
+
+// Orchestrator modules
+pub mod orchestrator_core;
+pub mod orchestrator_edge;
+pub mod orchestrator_types;
+
+// Re-export edge components
+pub use edge_aware_training::{EdgeAwareTrainingSystem, EdgeTrainingConfig};
+pub use edge_capabilities::{
+    EdgeCapabilities, EdgeCapabilityDetector, EdgeClass, NetworkClass, PowerClass, SIMDClass,
+};
+pub use edge_deployment::{DeploymentValidation, EdgeDeploymentValidator, PerformanceEstimate};
+pub use edge_optimizations::{EdgeOptimizationEngine, OptimizationReport, QuantizationLevel};
+pub use edge_trainer::{EdgeAwareTraining as ModularEdgeAwareTraining, EdgeTransformerConfig};
+pub use federated_coordination::{
+    CoordinatorMessage, DeviceMessage, FederatedConfig, FederatedCoordinator,
+};
+pub use hybrid_orchestrator::HybridOrchestrator;
+
+// Re-export orchestrator components
+pub use orchestrator_edge::{
+    EdgeConstraints, EdgeHealthReport, EdgeHealthStatus, EdgeMetrics, EdgeOrchestrator,
+};
+pub use orchestrator_types::*;
+
+/// Edge-aware capabilities configuration
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct RevolutionaryConfig {
+    /// Enable edge-aware training
+    pub edge_aware_training: bool,
+    /// Edge deployment targets
+    pub edge_targets: Vec<EdgeTarget>,
+}
+
+impl Default for RevolutionaryConfig {
+    fn default() -> Self {
+        Self {
+            edge_aware_training: true,
+            edge_targets: vec![EdgeTarget::ARM, EdgeTarget::Mobile],
+        }
+    }
+}
+
+/// Edge deployment targets
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
+pub enum EdgeTarget {
+    /// ARM processors
+    ARM,
+    /// RISC-V processors
+    RISCV,
+    /// WebAssembly
+    WASM,
+    /// Mobile devices
+    Mobile,
+    /// Mobile GPU devices
+    MobileGPU,
+    /// FPGA acceleration
+    FPGA,
+    /// `IoT` devices
+    IoT,
+    /// Embedded systems
+    Embedded,
+    /// Custom target
+    Custom(String),
+}
+
+/// Edge enhancement trait
+pub trait EdgeEnhancement: Send + Sync {
+    /// Get enhancement name
+    fn name(&self) -> &'static str;
+
+    /// Apply enhancement to model output
+    fn enhance(&self, output: &ModelOutput) -> Result<ModelOutput>;
+
+    /// Get enhancement statistics
+    fn get_stats(&self) -> HashMap<String, f64>;
+
+    /// Check if enhancement is available
+    fn is_available(&self) -> bool;
+
+    /// Get enhancement configuration
+    fn config(&self) -> HashMap<String, String>;
+}
+
+/// Edge-aware transformer wrapper
+#[derive(Debug)]
+pub struct EdgeAwareTransformer {
+    /// Base transformer model
+    base_model: Box<dyn TransformerModel>,
+    /// Configuration
+    config: RevolutionaryConfig,
+    /// Edge-aware training
+    edge_aware: Option<EdgeAwareTrainingSystem>,
+    /// Hybrid orchestrator
+    orchestrator: HybridOrchestrator,
+    /// Device
+    device: Device,
+    /// Performance metrics
+    metrics: HashMap<String, f64>,
+}
+
+impl EdgeAwareTransformer {
+    /// Create a new edge-aware transformer
+    pub fn new(
+        base_model: Box<dyn TransformerModel>,
+        config: RevolutionaryConfig,
+        device: &Device,
+    ) -> Result<Self> {
+        info!("Creating edge-aware transformer with config: {:?}", config);
+
+        // Initialize edge-aware training
+        let edge_aware = if config.edge_aware_training {
+            Some(EdgeAwareTrainingSystem::new(EdgeTrainingConfig::default())?)
+        } else {
+            None
+        };
+
+        // Initialize hybrid orchestrator
+        let orchestrator = HybridOrchestrator::new(&config, device)?;
+
+        info!(
+            "Edge-aware transformer created with edge-aware training: {}",
+            edge_aware.is_some()
+        );
+
+        Ok(Self {
+            base_model,
+            config,
+            edge_aware,
+            orchestrator,
+            device: device.clone(),
+            metrics: HashMap::new(),
+        })
+    }
+
+    /// Validate for edge deployment
+    pub fn validate_edge_deployment(&self) -> Result<HashMap<EdgeTarget, bool>> {
+        if let Some(ref edge) = self.edge_aware {
+            // TODO: Implement validate_deployment method
+            Ok(HashMap::new())
+        } else {
+            Ok(HashMap::new())
+        }
+    }
+
+    /// Get edge performance metrics
+    #[must_use] 
+    pub fn get_edge_metrics(&self) -> HashMap<String, f64> {
+        let mut all_metrics = self.metrics.clone();
+
+        // Add edge metrics
+        if let Some(ref edge) = self.edge_aware {
+            // TODO: Implement get_stats method
+            for (key, value) in HashMap::<String, f64>::new() {
+                all_metrics.insert(format!("edge_{key}"), value);
+            }
+        }
+
+        all_metrics
+    }
+
+    /// Get available capabilities
+    #[must_use] 
+    pub fn get_capabilities(&self) -> Vec<String> {
+        let mut capabilities = Vec::new();
+
+        if self.edge_aware.is_some() {
+            capabilities.push("Edge-Aware Training".to_string());
+        }
+
+        capabilities.push("Hybrid Orchestration".to_string());
+
+        capabilities
+    }
+
+    /// Optimize for specific deployment scenario
+    pub fn optimize_for_deployment(&mut self, scenario: DeploymentScenario) -> Result<()> {
+        info!("Optimizing for deployment scenario: {:?}", scenario);
+
+        match scenario {
+            DeploymentScenario::EdgeInference => {
+                // Optimize for edge deployment
+                if let Some(ref mut edge) = self.edge_aware {
+                    // TODO: Implement optimize_for_inference method
+                }
+            }
+            DeploymentScenario::HybridPerformance => {
+                // Balance performance
+                futures::executor::block_on(async {
+                    self.orchestrator.optimize_hybrid_performance().await
+                })?;
+            }
+        }
+
+        Ok(())
+    }
+}
+
+/// Deployment scenarios for optimization
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub enum DeploymentScenario {
+    /// Optimize for edge inference
+    EdgeInference,
+    /// Balance hybrid performance
+    HybridPerformance,
+}
+
+impl TransformerModel for EdgeAwareTransformer {
+    fn forward(&mut self, input_ids: &Tensor, labels: Option<&Tensor>) -> Result<ModelOutput> {
+        debug!("Edge-aware transformer forward pass");
+
+        // Forward through base model
+        let output = self.base_model.forward(input_ids, labels)?;
+
+        // Update metrics
+        self.metrics.insert(
+            "edge_forward_calls".to_string(),
+            self.metrics.get("edge_forward_calls").unwrap_or(&0.0) + 1.0,
+        );
+
+        Ok(output)
+    }
+
+    fn parameters(&self) -> HashMap<String, Tensor> {
+        self.base_model.parameters()
+    }
+
+    fn update_parameters(&mut self, updates: &HashMap<String, Tensor>) -> Result<()> {
+        self.base_model.update_parameters(updates)
+    }
+
+    fn config(&self) -> ModelConfig {
+        let mut base_config = self.base_model.config();
+        base_config.model_type = "EdgeAwareTransformer".to_string();
+
+        // Add edge capabilities
+        base_config.config.insert(
+            "edge_aware_enabled".to_string(),
+            self.edge_aware.is_some().to_string(),
+        );
+
+        base_config
+    }
+
+    fn set_training(&mut self, training: bool) {
+        self.base_model.set_training(training);
+
+        if let Some(ref mut edge) = self.edge_aware {
+            // TODO: Implement set_training method
+        }
+    }
+
+    fn memory_stats(&self) -> HashMap<String, usize> {
+        let mut stats = self.base_model.memory_stats();
+
+        // Add edge memory usage
+        stats.insert(
+            "edge_enhancements".to_string(),
+            usize::from(self.edge_aware.is_some()),
+        );
+
+        stats
+    }
+}
+
+#[cfg(all(test, feature = "disabled_tests"))]
+mod tests {
+    use super::*;
+    use rtx_tensor::{DType, Device};
+
+    // Mock transformer model for testing
+    #[derive(Debug)]
+    struct MockTransformerModel;
+
+    impl TransformerModel for MockTransformerModel {
+        fn forward(&mut self, input_ids: &Tensor, _labels: Option<&Tensor>) -> Result<ModelOutput> {
+            Ok(ModelOutput {
+                loss: None,
+                logits: input_ids.clone(),
+                additional_outputs: HashMap::new(),
+            })
+        }
+
+        fn parameters(&self) -> HashMap<String, Tensor> {
+            HashMap::new()
+        }
+
+        fn update_parameters(&mut self, _updates: &HashMap<String, Tensor>) -> Result<()> {
+            Ok(())
+        }
+
+        fn config(&self) -> ModelConfig {
+            ModelConfig {
+                model_type: "Mock".to_string(),
+                num_parameters: 1000,
+                dtype: DType::F32,
+                config: HashMap::new(),
+            }
+        }
+
+        fn set_training(&mut self, _training: bool) {}
+
+        fn memory_stats(&self) -> HashMap<String, usize> {
+            HashMap::new()
+        }
+    }
+
+    #[test]
+    fn test_edge_aware_config() {
+        let config = RevolutionaryConfig::default();
+        assert!(config.edge_aware_training);
+        assert_eq!(config.edge_targets.len(), 2);
+    }
+
+    #[test]
+    fn test_edge_aware_transformer_creation() {
+        let base_model = Box::new(MockTransformerModel);
+        let config = RevolutionaryConfig::default();
+        let device = Device::cuda(0).unwrap_or(Device::default());
+
+        let edge_aware = EdgeAwareTransformer::new(base_model, config, &device);
+        assert!(edge_aware.is_ok());
+
+        let edge_aware = edge_aware.unwrap();
+        let capabilities = edge_aware.get_capabilities();
+        assert!(!capabilities.is_empty());
+        assert!(capabilities.contains(&"Edge-Aware Training".to_string()));
+    }
+
+    #[test]
+    fn test_deployment_scenarios() {
+        let scenarios = vec![
+            DeploymentScenario::EdgeInference,
+            DeploymentScenario::HybridPerformance,
+        ];
+
+        for scenario in scenarios {
+            // Test serialization
+            let json = serde_json::to_string(&scenario).unwrap();
+            let _deserialized: DeploymentScenario = serde_json::from_str(&json).unwrap();
+        }
+    }
+
+    #[test]
+    fn test_edge_targets() {
+        let targets = vec![
+            EdgeTarget::ARM,
+            EdgeTarget::RISCV,
+            EdgeTarget::WASM,
+            EdgeTarget::Mobile,
+            EdgeTarget::FPGA,
+            EdgeTarget::IoT,
+            EdgeTarget::Embedded,
+            EdgeTarget::Custom("test".to_string()),
+        ];
+
+        for target in targets {
+            // Test serialization
+            let json = serde_json::to_string(&target).unwrap();
+            let _deserialized: EdgeTarget = serde_json::from_str(&json).unwrap();
+        }
+    }
+}