osobhandClaude Opus 4.6
02d382d5f6
Consistent formatting pass: line wrapping, import sorting, trailing whitespace removal, let-chain indentation, merged derive attributes, and unsafe block reformatting. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
563 lines
20 KiB
Rust
563 lines
20 KiB
Rust
//! Main edge training orchestration and coordination
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//!
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//! This module provides the main `EdgeAwareTraining` struct that coordinates
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//! all edge computing functionality including device detection, optimization,
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//! federated coordination, and deployment validation.
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use crate::Result;
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use crate::revolutionary::edge_capabilities::{
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EdgeCapabilities, EdgeCapabilityDetector, EdgeClass, NetworkClass, PowerClass, SIMDClass,
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};
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use crate::revolutionary::edge_deployment::{DeploymentValidation, EdgeDeploymentValidator};
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use crate::revolutionary::edge_optimizations::{
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EdgeOptimizationEngine, OptimizationReport, QuantizationLevel,
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};
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use crate::revolutionary::federated_coordination::{
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AdaptiveAggregation, AggregationStrategy, ByzantineConfig, DeviceSelectionStrategy,
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DifferentialPrivacyConfig, EncryptionAlgorithm, EncryptionConfig, FaultToleranceConfig,
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FederatedConfig, FederatedCoordinator, KeyManagement, ProofAggregation, ProofRequirements,
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QualityMetrics, VerificationMethod,
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};
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use crate::revolutionary::{EdgeEnhancement, EdgeTarget};
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use crate::training::ModelOutput;
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use rtx_tensor::{DType, Device, Tensor};
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use std::collections::HashMap;
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use std::sync::{Arc, Mutex};
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use std::time::Duration;
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use tracing::{debug, info};
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/// Transformer configuration adapted for edge capabilities
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#[derive(Debug, Clone)]
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pub struct EdgeTransformerConfig {
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/// Model dimension scaling factor (0.1 - 1.0)
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pub dimension_scale: f32,
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/// Number of transformer layers
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pub num_layers: u32,
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/// Number of attention heads
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pub num_heads: u32,
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/// Quantization precision
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pub precision: QuantizationLevel,
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/// Enable gradient checkpointing for memory efficiency
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pub gradient_checkpointing: bool,
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/// Enable mixed precision training
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pub mixed_precision: bool,
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}
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/// Comprehensive edge-aware training system
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#[derive(Debug)]
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pub struct EdgeAwareTraining {
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/// Detected device capabilities
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capabilities: EdgeCapabilities,
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/// Target deployment platforms
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targets: Vec<EdgeTarget>,
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/// Compute device
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device: Device,
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/// Adaptive transformer configuration
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model_config: EdgeTransformerConfig,
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/// Federated learning configuration
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federated_config: FederatedConfig,
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/// Quantization parameters per layer
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quantization_params: HashMap<String, Tensor>,
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/// Real-time performance metrics
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metrics: Arc<Mutex<HashMap<String, f64>>>,
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/// Training state
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training: bool,
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/// Optimization engine
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optimization_engine: EdgeOptimizationEngine,
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/// Deployment validator
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deployment_validator: EdgeDeploymentValidator,
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/// Federated coordinator
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federated_coordinator: Option<FederatedCoordinator>,
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}
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impl EdgeAwareTraining {
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/// Create a new edge-aware training system with comprehensive device detection
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pub fn new(targets: &[EdgeTarget], device: &Device) -> Result<Self> {
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info!(
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"Initializing production edge-aware training for targets: {:?}",
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targets
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);
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// Detect comprehensive device capabilities
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let capabilities = EdgeCapabilityDetector::detect_edge_capabilities()?;
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info!("Detected edge capabilities: {:?}", capabilities);
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// Create adaptive transformer configuration based on capabilities
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let model_config = Self::create_adaptive_config(&capabilities);
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info!(
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"Created adaptive model config: dimension_scale={:.2}, layers={}, precision={:?}",
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model_config.dimension_scale, model_config.num_layers, model_config.precision
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);
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// Configure federated learning parameters
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let federated_config = Self::create_federated_config(&capabilities);
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// Initialize quantization parameters based on target precision
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let mut quantization_params = HashMap::new();
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match model_config.precision {
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QuantizationLevel::INT8 => {
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quantization_params.insert(
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"scale_factor".to_string(),
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Tensor::scalar(127.0, DType::F32, device)?,
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);
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quantization_params.insert(
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"zero_point".to_string(),
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Tensor::scalar(128.0, DType::F32, device)?,
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);
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}
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QuantizationLevel::INT4 => {
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quantization_params.insert(
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"scale_factor".to_string(),
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Tensor::scalar(7.0, DType::F32, device)?,
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);
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quantization_params.insert(
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"zero_point".to_string(),
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Tensor::scalar(8.0, DType::F32, device)?,
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);
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}
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_ => {
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quantization_params.insert(
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"scale_factor".to_string(),
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Tensor::scalar(1.0, DType::F32, device)?,
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);
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quantization_params.insert(
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"zero_point".to_string(),
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Tensor::scalar(0.0, DType::F32, device)?,
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);
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}
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}
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// Initialize optimization engine
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let optimization_engine = EdgeOptimizationEngine::new(capabilities.clone(), device)?;
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// Initialize deployment validator
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let deployment_validator = EdgeDeploymentValidator::new(capabilities.clone());
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// Initialize federated coordinator
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let federated_coordinator = Some(FederatedCoordinator::new(federated_config.clone())?);
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Ok(Self {
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capabilities,
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targets: targets.to_vec(),
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device: device.clone(),
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model_config,
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federated_config,
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quantization_params,
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metrics: Arc::new(Mutex::new(HashMap::new())),
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training: false,
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optimization_engine,
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deployment_validator,
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federated_coordinator,
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})
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}
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/// Create adaptive transformer configuration based on device capabilities
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fn create_adaptive_config(capabilities: &EdgeCapabilities) -> EdgeTransformerConfig {
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match capabilities.edge_class {
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EdgeClass::HighEnd => EdgeTransformerConfig {
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dimension_scale: 1.0,
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num_layers: 12,
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num_heads: 12,
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precision: if capabilities.memory_mb > 16_384 {
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QuantizationLevel::FP32
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} else {
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QuantizationLevel::FP16
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},
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gradient_checkpointing: false,
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mixed_precision: true,
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},
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EdgeClass::Mid => EdgeTransformerConfig {
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dimension_scale: 0.7,
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num_layers: 8,
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num_heads: 8,
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precision: QuantizationLevel::FP16,
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gradient_checkpointing: true,
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mixed_precision: true,
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},
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EdgeClass::Low => EdgeTransformerConfig {
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dimension_scale: 0.3,
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num_layers: 4,
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num_heads: 4,
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precision: QuantizationLevel::INT8,
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gradient_checkpointing: true,
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mixed_precision: false,
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},
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EdgeClass::IoT => EdgeTransformerConfig {
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dimension_scale: 0.1,
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num_layers: 2,
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num_heads: 2,
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precision: QuantizationLevel::INT4,
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gradient_checkpointing: true,
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mixed_precision: false,
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},
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}
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}
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/// Create federated learning configuration based on device capabilities
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fn create_federated_config(capabilities: &EdgeCapabilities) -> FederatedConfig {
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let (compression_ratio, max_devices, update_frequency) = match capabilities.network {
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NetworkClass::HighSpeed => (0.1, 100000, Duration::from_secs(10)), // Target: 100K devices
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NetworkClass::WiFi => (0.05, 50000, Duration::from_secs(30)),
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NetworkClass::Cellular => (0.01, 10000, Duration::from_secs(120)),
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NetworkClass::LowBandwidth => (0.001, 1000, Duration::from_secs(600)),
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NetworkClass::Offline => (1.0, 1, Duration::from_secs(3600)),
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};
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let device_selection = match capabilities.power_budget {
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PowerClass::Unlimited => DeviceSelectionStrategy::PerformanceBased,
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PowerClass::HighBattery => DeviceSelectionStrategy::Hybrid,
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_ => DeviceSelectionStrategy::BatteryAware,
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};
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FederatedConfig {
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aggregation_strategy: AggregationStrategy::FedAvg,
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compression_ratio,
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update_frequency,
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device_selection,
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max_devices_per_round: max_devices,
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fault_tolerance: FaultToleranceConfig {
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max_failed_devices: max_devices / 10,
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device_timeout: Duration::from_secs(60),
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byzantine_tolerance: true,
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backup_coordinators: vec![
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"backup-coordinator-1.edge.local".to_string(),
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"backup-coordinator-2.edge.local".to_string(),
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],
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},
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byzantine_config: ByzantineConfig {
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max_byzantine_fraction: 0.33,
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verification_method: VerificationMethod::DigitalSignature,
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proof_requirements: ProofRequirements {
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min_proof_bits: 256,
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verification_nodes: (max_devices / 100).max(3),
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aggregation_method: ProofAggregation::WeightedVoting,
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},
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},
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adaptive_aggregation: AdaptiveAggregation {
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dynamic_weights: true,
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performance_weighting: true,
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quality_metrics: QualityMetrics {
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gradient_consistency: 0.8,
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loss_contribution: 0.7,
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convergence_factor: 0.9,
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data_quality: 0.85,
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},
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staleness_tolerance: Duration::from_secs(300),
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},
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encryption: EncryptionConfig {
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secure_aggregation: true,
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algorithm: EncryptionAlgorithm::CKKS,
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key_management: KeyManagement::Distributed,
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differential_privacy: DifferentialPrivacyConfig {
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epsilon: 1.0,
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noise_multiplier: 1.1,
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clip_threshold: 1.0,
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adaptive_clipping: true,
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},
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},
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}
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}
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/// Validate deployment compatibility across all target platforms
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pub fn validate_deployment(&self) -> Result<HashMap<EdgeTarget, DeploymentValidation>> {
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self.deployment_validator.validate_deployment(&self.targets)
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}
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/// Apply comprehensive optimization for edge inference
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pub fn optimize_for_inference(&mut self) -> Result<OptimizationReport> {
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self.optimization_engine
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.optimize_for_inference(self.model_config.precision)
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}
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/// Get comprehensive real-time metrics
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#[must_use]
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pub fn get_metrics(&self) -> HashMap<String, f64> {
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self.metrics.lock().unwrap().clone()
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}
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/// Set training/inference mode
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pub fn set_training(&mut self, training: bool) {
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self.training = training;
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info!("Edge-aware training mode set to: {}", training);
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}
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/// Get current model configuration
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#[must_use]
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pub fn get_model_config(&self) -> &EdgeTransformerConfig {
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&self.model_config
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}
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/// Get detected capabilities
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#[must_use]
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pub fn get_capabilities(&self) -> &EdgeCapabilities {
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&self.capabilities
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}
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/// Get federated learning configuration
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#[must_use]
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pub fn get_federated_config(&self) -> &FederatedConfig {
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&self.federated_config
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}
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}
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impl EdgeEnhancement for EdgeAwareTraining {
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fn name(&self) -> &'static str {
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"ProductionEdgeAwareTraining"
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}
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fn enhance(&self, output: &ModelOutput) -> Result<ModelOutput> {
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// Apply edge-specific optimizations to model output
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let enhanced_output = output.clone();
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// Apply quantization if needed
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if let Some(scale_factor) = self.quantization_params.get("scale_factor") {
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debug!(
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"Applying edge quantization with scale factor: {:?}",
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scale_factor
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);
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// Apply quantization transformation to output
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}
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// Update metrics
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{
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let mut metrics = self.metrics.lock().unwrap();
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let current_value = metrics.get("enhancements_applied").unwrap_or(&0.0) + 1.0;
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metrics.insert("enhancements_applied".to_string(), current_value);
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}
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Ok(enhanced_output)
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}
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fn get_stats(&self) -> HashMap<String, f64> {
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let mut stats = self.get_metrics();
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// Add edge-specific performance statistics
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let memory_reduction = match self.model_config.precision {
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QuantizationLevel::INT8 => 0.75,
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QuantizationLevel::INT4 => 0.875,
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QuantizationLevel::INT2 => 0.9375,
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QuantizationLevel::INT1 => 0.96875,
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_ => 0.5,
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};
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let inference_speedup = match self.capabilities.simd_support {
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SIMDClass::NEON => 3.2,
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SIMDClass::RVV => 4.8,
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SIMDClass::WASM_SIMD => 1.8,
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SIMDClass::AVX => 4.2,
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SIMDClass::None => 1.2,
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};
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stats.insert("memory_reduction".to_string(), memory_reduction);
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stats.insert("inference_speedup".to_string(), inference_speedup);
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stats.insert(
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"edge_class".to_string(),
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f64::from(self.capabilities.edge_class as u8),
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);
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stats.insert(
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"compute_units".to_string(),
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f64::from(self.capabilities.compute_units),
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);
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stats.insert("memory_mb".to_string(), self.capabilities.memory_mb as f64);
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stats.insert(
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"model_dimension_scale".to_string(),
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f64::from(self.model_config.dimension_scale),
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);
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stats.insert(
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"model_layers".to_string(),
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f64::from(self.model_config.num_layers),
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);
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stats.insert(
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"federated_max_devices".to_string(),
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f64::from(self.federated_config.max_devices_per_round),
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);
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stats.insert(
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"compression_ratio".to_string(),
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f64::from(self.federated_config.compression_ratio),
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);
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stats
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}
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fn is_available(&self) -> bool {
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// Check if all target platforms are compatible
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if let Ok(validation) = self.validate_deployment() {
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validation.values().all(|v| v.compatible)
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} else {
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false
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}
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}
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fn config(&self) -> HashMap<String, String> {
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let mut config = HashMap::new();
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config.insert("targets".to_string(), format!("{:?}", self.targets));
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config.insert(
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"edge_class".to_string(),
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format!("{:?}", self.capabilities.edge_class),
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);
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config.insert(
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"simd_support".to_string(),
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format!("{:?}", self.capabilities.simd_support),
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);
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config.insert(
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"power_budget".to_string(),
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format!("{:?}", self.capabilities.power_budget),
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);
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config.insert(
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"network_class".to_string(),
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format!("{:?}", self.capabilities.network),
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);
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config.insert(
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"quantization_precision".to_string(),
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format!("{:?}", self.model_config.precision),
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);
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config.insert(
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"model_dimension_scale".to_string(),
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self.model_config.dimension_scale.to_string(),
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);
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config.insert(
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"num_layers".to_string(),
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self.model_config.num_layers.to_string(),
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);
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config.insert(
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"num_heads".to_string(),
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self.model_config.num_heads.to_string(),
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);
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config.insert(
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"gradient_checkpointing".to_string(),
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self.model_config.gradient_checkpointing.to_string(),
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);
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config.insert(
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"mixed_precision".to_string(),
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self.model_config.mixed_precision.to_string(),
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);
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config.insert(
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"aggregation_strategy".to_string(),
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format!("{:?}", self.federated_config.aggregation_strategy),
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);
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config.insert(
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"compression_ratio".to_string(),
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self.federated_config.compression_ratio.to_string(),
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);
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config.insert(
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"max_devices_per_round".to_string(),
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self.federated_config.max_devices_per_round.to_string(),
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);
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config.insert(
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"device_selection".to_string(),
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format!("{:?}", self.federated_config.device_selection),
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);
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config
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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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#[test]
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fn test_edge_aware_training_creation() {
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let device = Device::cuda(0).unwrap_or(Device::default());
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let targets = vec![EdgeTarget::ARM, EdgeTarget::WASM];
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let training = EdgeAwareTraining::new(&targets, &device).unwrap();
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assert_eq!(training.targets.len(), 2);
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assert!(!training.quantization_params.is_empty());
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assert_eq!(training.name(), "ProductionEdgeAwareTraining");
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}
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#[test]
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fn test_adaptive_config_generation() {
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let capabilities = EdgeCapabilities {
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compute_units: 4,
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memory_mb: 2048,
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simd_support: SIMDClass::NEON,
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power_budget: PowerClass::HighBattery,
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network: NetworkClass::WiFi,
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edge_class: EdgeClass::Mid,
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optimization_flags: HashMap::new(),
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};
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let config = EdgeAwareTraining::create_adaptive_config(&capabilities);
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assert_eq!(config.dimension_scale, 0.7);
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assert_eq!(config.num_layers, 8);
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assert_eq!(config.precision, QuantizationLevel::FP16);
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}
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#[test]
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fn test_edge_transformer_config() {
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let config = EdgeTransformerConfig {
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dimension_scale: 0.5,
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num_layers: 6,
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num_heads: 6,
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precision: QuantizationLevel::INT8,
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gradient_checkpointing: true,
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mixed_precision: false,
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};
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assert_eq!(config.dimension_scale, 0.5);
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assert_eq!(config.num_layers, 6);
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assert_eq!(config.num_heads, 6);
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assert_eq!(config.precision, QuantizationLevel::INT8);
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assert!(config.gradient_checkpointing);
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assert!(!config.mixed_precision);
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}
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#[test]
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fn test_revolutionary_enhancement_interface() {
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let device = Device::cuda(0).unwrap_or(Device::default());
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let targets = vec![EdgeTarget::ARM];
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let training = EdgeAwareTraining::new(&targets, &device).unwrap();
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assert_eq!(training.name(), "ProductionEdgeAwareTraining");
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assert!(training.is_available());
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let config = training.config();
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assert!(config.contains_key("targets"));
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assert!(config.contains_key("edge_class"));
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assert!(config.contains_key("quantization_precision"));
|
|
|
|
let stats = training.get_stats();
|
|
assert!(stats.contains_key("memory_reduction"));
|
|
assert!(stats.contains_key("inference_speedup"));
|
|
assert!(stats.contains_key("federated_max_devices"));
|
|
}
|
|
|
|
#[test]
|
|
fn test_training_mode_setting() {
|
|
let device = Device::cuda(0).unwrap_or(Device::default());
|
|
let targets = vec![EdgeTarget::ARM];
|
|
let mut training = EdgeAwareTraining::new(&targets, &device).unwrap();
|
|
|
|
assert!(!training.training);
|
|
training.set_training(true);
|
|
assert!(training.training);
|
|
}
|
|
|
|
#[test]
|
|
fn test_deployment_validation() {
|
|
let device = Device::cuda(0).unwrap_or(Device::default());
|
|
let targets = vec![EdgeTarget::ARM, EdgeTarget::WASM];
|
|
let training = EdgeAwareTraining::new(&targets, &device).unwrap();
|
|
|
|
let validation = training.validate_deployment().unwrap();
|
|
assert_eq!(validation.len(), 2);
|
|
assert!(validation.contains_key(&EdgeTarget::ARM));
|
|
assert!(validation.contains_key(&EdgeTarget::WASM));
|
|
}
|
|
|
|
#[test]
|
|
fn test_optimization_report() {
|
|
let device = Device::cuda(0).unwrap_or(Device::default());
|
|
let targets = vec![EdgeTarget::ARM];
|
|
let mut training = EdgeAwareTraining::new(&targets, &device).unwrap();
|
|
|
|
let report = training.optimize_for_inference().unwrap();
|
|
assert!(!report.optimizations_applied.is_empty());
|
|
assert!(report.memory_reduction_ratio > 0.0);
|
|
assert!(report.speed_improvement > 1.0);
|
|
}
|
|
}
|