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