//! Comprehensive integration tests for edge-aware training system //! //! Tests cross-platform compatibility, federated coordination, //! and performance characteristics across target platforms. use rustytorch::revolutionary::*; use std::collections::HashMap; use std::time::{Duration, SystemTime}; use tokio::time::sleep; /// Test ARM NEON optimization path #[tokio::test] async fn test_arm_neon_optimization() { let mut optimizer = EdgeTargetOptimizer::new(); let arm_opts = ArmOptimizations { enable_neon: true, memory_prefetch: true, cache_optimization: true, big_little_scheduling: true, target_arch: ArmArchitecture::CortexA, }; optimizer.configure_arm(arm_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::ARM).unwrap(); // Verify NEON optimizations are applied assert!(metrics.performance_improvement > 2.0); // NEON should provide >2x speedup assert!(metrics.target_specific.contains_key("neon_enabled")); assert_eq!(metrics.target_specific["neon_enabled"], 1.0); assert_eq!(metrics.target_specific["vector_width"], 128.0); // Verify cache optimizations assert!(metrics.target_specific.contains_key("cache_optimized")); assert!(metrics.memory_reduction_ratio > 0.0); println!("ARM NEON optimization test passed: {:.2}x performance improvement", metrics.performance_improvement); } /// Test RISC-V vector extension optimization #[tokio::test] async fn test_riscv_vector_optimization() { let mut optimizer = EdgeTargetOptimizer::new(); let riscv_opts = RiscVOptimizations { enable_rvv: true, vector_length: RiscVVectorLength::VLEN512, custom_instructions: vec!["custom_matmul".to_string(), "custom_conv".to_string()], memory_model: RiscVMemoryModel::TSO, target_variant: RiscVVariant::Vector, }; optimizer.configure_riscv(riscv_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::RISCV).unwrap(); // Verify RVV optimizations provide significant speedup assert!(metrics.performance_improvement > 5.0); // RVV + custom instructions assert!(metrics.target_specific.contains_key("rvv_enabled")); assert_eq!(metrics.target_specific["vector_length"], 512.0); assert_eq!(metrics.target_specific["custom_instructions"], 2.0); // Verify power efficiency improvements assert!(metrics.power_efficiency_gain > 1.0); println!("RISC-V RVV optimization test passed: {:.2}x performance improvement", metrics.performance_improvement); } /// Test WebAssembly SIMD optimization #[tokio::test] async fn test_wasm_simd_optimization() { let mut optimizer = EdgeTargetOptimizer::new(); let wasm_opts = WasmOptimizations { enable_simd: true, enable_threads: true, memory_growth: WasmMemoryGrowth::Dynamic { max_pages: 4096 }, target_runtime: WasmRuntime::Browser, bulk_memory: true, }; optimizer.configure_wasm(wasm_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::WASM).unwrap(); // Verify WASM SIMD and threading optimizations assert!(metrics.performance_improvement > 1.5); // SIMD + threads assert!(metrics.target_specific.contains_key("wasm_simd_enabled")); assert!(metrics.target_specific.contains_key("wasm_threads_enabled")); assert_eq!(metrics.target_specific["max_pages"], 4096.0); println!("WASM SIMD optimization test passed: {:.2}x performance improvement", metrics.performance_improvement); } /// Test Mobile GPU optimization for different vendors #[tokio::test] async fn test_mobile_gpu_optimization() { let gpu_vendors = vec![ MobileGpuVendor::Mali, MobileGpuVendor::Adreno, MobileGpuVendor::PowerVR, MobileGpuVendor::Apple, ]; for vendor in gpu_vendors { let mut optimizer = EdgeTargetOptimizer::new(); let mobile_gpu_opts = MobileGpuOptimizations { gpu_vendor: vendor, compute_shaders: true, tile_based_rendering: true, bandwidth_optimization: true, power_efficiency: true, }; optimizer.configure_mobile_gpu(mobile_gpu_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::MobileGPU).unwrap(); // Verify GPU acceleration provides significant speedup assert!(metrics.performance_improvement > 3.0); // GPU should provide >3x speedup assert!(metrics.memory_reduction_ratio > 0.2); // TBR memory savings assert!(metrics.power_efficiency_gain > 1.0); // Verify vendor-specific optimizations match vendor { MobileGpuVendor::Mali => assert!(metrics.target_specific.contains_key("mali_optimized")), MobileGpuVendor::Adreno => assert!(metrics.target_specific.contains_key("adreno_optimized")), MobileGpuVendor::PowerVR => assert!(metrics.target_specific.contains_key("powervr_optimized")), MobileGpuVendor::Apple => assert!(metrics.target_specific.contains_key("apple_gpu_optimized")), _ => {} } println!("Mobile GPU {:?} optimization test passed: {:.2}x performance improvement", vendor, metrics.performance_improvement); } } /// Test IoT ultra-low power optimization #[tokio::test] async fn test_iot_ultra_low_power_optimization() { let mut optimizer = EdgeTargetOptimizer::new(); let iot_opts = IoTOptimizations { ultra_low_power: true, minimal_memory: true, wake_on_inference: true, mesh_networking: true, target_platform: IoTPlatform::ESP32, }; optimizer.configure_iot(iot_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::IoT).unwrap(); // Verify ultra-low power optimizations assert!(metrics.power_efficiency_gain > 10.0); // 10x power efficiency target assert!(metrics.memory_reduction_ratio > 0.8); // 80% memory reduction assert!(metrics.target_specific.contains_key("ultra_low_power")); assert!(metrics.target_specific.contains_key("esp32_optimized")); assert_eq!(metrics.target_specific["memory_footprint_kb"], 64.0); println!("IoT ultra-low power optimization test passed: {:.2}x power efficiency", metrics.power_efficiency_gain); } /// Test federated coordination for massive scale #[tokio::test] async fn test_federated_coordination_massive_scale() { let (coordinator, sender) = FederatedCoordinator::new( "test-coordinator".to_string(), SelectionStrategy::Intelligent, GradientCompression { algorithm: CompressionAlgorithm::TopK, compression_ratio: 0.01, // 99% compression for 100K devices error_correction: true, adaptive_compression: true, }, AggregationStrategy { algorithm: AggregationAlgorithm::FedAvg, weighting: WeightingScheme::Adaptive, byzantine_tolerance: ByzantineTolerance { enabled: true, max_byzantine_fraction: 0.1, detection_algorithm: ByzantineDetection::Krum, }, differential_privacy: Some(DifferentialPrivacy { epsilon: 1.0, delta: 1e-5, noise_mechanism: NoiseMechanism::Gaussian, clipping_threshold: 1.0, }), }, ); // Register 10,000 devices (scaled down from 100K for test performance) let device_count = 10_000; for i in 0..device_count { let device_type = match i % 8 { 0 => EdgeDeviceType::HighEndMobile, 1 => EdgeDeviceType::StandardMobile, 2 => EdgeDeviceType::LowEndMobile, 3 => EdgeDeviceType::IoTSensor, 4 => EdgeDeviceType::EdgeServer, 5 => EdgeDeviceType::Embedded, 6 => EdgeDeviceType::Automotive, 7 => EdgeDeviceType::Industrial, _ => EdgeDeviceType::StandardMobile, }; let device = create_test_federated_device(&format!("device-{:06}", i), device_type); coordinator.register_device(device).await.unwrap(); } let metrics = coordinator.get_metrics(); assert_eq!(metrics.total_devices, device_count); // Test device selection at massive scale let selection_criteria = SelectionCriteria { min_battery_level: 0.3, min_bandwidth_mbps: 5.0, max_latency_ms: 200, required_availability_minutes: 30, min_data_quality: 0.7, }; let target_devices = 1000; // Select 1000 devices from 10K let selection_result = coordinator.select_devices(target_devices, selection_criteria).await.unwrap(); // Verify selection performance and results assert_eq!(selection_result.selected_devices.len(), target_devices as usize); assert!(selection_result.selection_time < Duration::from_secs(5)); // Should complete within 5 seconds // Test training round with selected devices let training_config = TrainingConfig { local_epochs: 3, local_batch_size: 16, // Smaller batch for edge devices learning_rate: 0.001, gradient_clipping: Some(1.0), early_stopping_patience: Some(5), }; let round_id = coordinator.start_training_round( selection_result.selected_devices.clone(), training_config, Duration::from_secs(600), // 10 minute deadline ).await.unwrap(); // Simulate gradient aggregation with compressed data let mut device_gradients = HashMap::new(); for device_id in &selection_result.selected_devices { // Simulate compressed gradients (very small due to high compression) device_gradients.insert(device_id.clone(), vec![0u8; 100]); // 100 bytes per device } let aggregated_gradients = coordinator.aggregate_gradients(round_id, device_gradients).await.unwrap(); // Verify aggregation completed successfully assert!(!aggregated_gradients.is_empty()); println!("Federated coordination massive scale test passed:"); println!("- Registered {} devices", device_count); println!("- Selected {} devices in {:?}", selection_result.selected_devices.len(), selection_result.selection_time); println!("- Completed training round and aggregation"); } /// Test cross-platform deployment validation #[tokio::test] async fn test_cross_platform_deployment_validation() { // Test all target platforms with comprehensive validation let target_platforms = vec![ (EdgeTarget::ARM, ArmOptimizations::default()), (EdgeTarget::RISCV, RiscVOptimizations::default()), (EdgeTarget::WASM, WasmOptimizations::default()), (EdgeTarget::MobileGPU, MobileGpuOptimizations::default()), (EdgeTarget::IoT, IoTOptimizations::default()), ]; let mut all_metrics = HashMap::new(); for (target, _) in &target_platforms { let mut optimizer = EdgeTargetOptimizer::new(); // Configure optimizations based on target match target { EdgeTarget::ARM => { optimizer.configure_arm(ArmOptimizations::default()); } EdgeTarget::RISCV => { optimizer.configure_riscv(RiscVOptimizations::default()); } EdgeTarget::WASM => { optimizer.configure_wasm(WasmOptimizations::default()); } EdgeTarget::MobileGPU => { optimizer.configure_mobile_gpu(MobileGpuOptimizations::default()); } EdgeTarget::IoT => { optimizer.configure_iot(IoTOptimizations::default()); } EdgeTarget::Embedded => { // Use default embedded optimizations } } let metrics = optimizer.optimize_for_target(*target).unwrap(); all_metrics.insert(*target, metrics); // Verify all platforms show performance improvements let target_metrics = all_metrics.get(target).unwrap(); assert!(target_metrics.performance_improvement > 1.0); assert!(target_metrics.optimization_time < Duration::from_secs(1)); } // Verify platform-specific performance characteristics let arm_metrics = all_metrics.get(&EdgeTarget::ARM).unwrap(); let riscv_metrics = all_metrics.get(&EdgeTarget::RISCV).unwrap(); let wasm_metrics = all_metrics.get(&EdgeTarget::WASM).unwrap(); let mobile_gpu_metrics = all_metrics.get(&EdgeTarget::MobileGPU).unwrap(); let iot_metrics = all_metrics.get(&EdgeTarget::IoT).unwrap(); // ARM should have good performance with NEON assert!(arm_metrics.performance_improvement > 2.0); // RISC-V should have the best future potential with RVV assert!(riscv_metrics.performance_improvement > arm_metrics.performance_improvement); // Mobile GPU should have highest raw performance assert!(mobile_gpu_metrics.performance_improvement > arm_metrics.performance_improvement); // IoT should have highest power efficiency assert!(iot_metrics.power_efficiency_gain > mobile_gpu_metrics.power_efficiency_gain); // WASM should have decent performance despite interpreter overhead assert!(wasm_metrics.performance_improvement > 1.2); println!("Cross-platform deployment validation test passed:"); for (target, metrics) in &all_metrics { println!("- {:?}: {:.2}x performance, {:.2}x power efficiency", target, metrics.performance_improvement, metrics.power_efficiency_gain); } } /// Test adaptive model configuration based on device capabilities #[tokio::test] async fn test_adaptive_model_configuration() { // Test different device capability scenarios let capability_scenarios = vec![ ("high_end", EdgeCapabilities { compute_units: 8, memory_mb: 16_384, simd_support: SIMDClass::NEON, power_budget: PowerClass::Unlimited, network: NetworkClass::HighSpeed, edge_class: EdgeClass::HighEnd, optimization_flags: HashMap::new(), }), ("mid_range", EdgeCapabilities { compute_units: 4, memory_mb: 4096, simd_support: SIMDClass::NEON, power_budget: PowerClass::HighBattery, network: NetworkClass::WiFi, edge_class: EdgeClass::Mid, optimization_flags: HashMap::new(), }), ("low_end", EdgeCapabilities { compute_units: 2, memory_mb: 1024, simd_support: SIMDClass::None, power_budget: PowerClass::StandardBattery, network: NetworkClass::Cellular4G, edge_class: EdgeClass::Low, optimization_flags: HashMap::new(), }), ("iot", EdgeCapabilities { compute_units: 1, memory_mb: 64, simd_support: SIMDClass::None, power_budget: PowerClass::UltraLowPower, network: NetworkClass::LPWAN, edge_class: EdgeClass::IoT, optimization_flags: HashMap::new(), }), ]; for (scenario_name, capabilities) in capability_scenarios { // Test model configuration adaptation let config = create_adaptive_transformer_config(&capabilities); match capabilities.edge_class { EdgeClass::HighEnd => { assert_eq!(config.dimension_scale, 1.0); assert_eq!(config.num_layers, 12); assert!(matches!(config.precision, QuantizationLevel::FP32 | QuantizationLevel::FP16)); } EdgeClass::Mid => { assert_eq!(config.dimension_scale, 0.7); assert_eq!(config.num_layers, 8); assert_eq!(config.precision, QuantizationLevel::FP16); assert!(config.gradient_checkpointing); } EdgeClass::Low => { assert_eq!(config.dimension_scale, 0.3); assert_eq!(config.num_layers, 4); assert_eq!(config.precision, QuantizationLevel::INT8); assert!(config.gradient_checkpointing); } EdgeClass::IoT => { assert_eq!(config.dimension_scale, 0.1); assert_eq!(config.num_layers, 2); assert_eq!(config.precision, QuantizationLevel::INT4); assert!(config.gradient_checkpointing); assert!(!config.mixed_precision); } } // Test federated configuration adaptation let fed_config = create_federated_config(&capabilities); match capabilities.network { NetworkClass::HighSpeed => { assert_eq!(fed_config.max_devices_per_round, 100000); // Target: 100K devices assert_eq!(fed_config.compression_ratio, 0.1); } NetworkClass::WiFi => { assert_eq!(fed_config.max_devices_per_round, 50000); assert_eq!(fed_config.compression_ratio, 0.05); } NetworkClass::Cellular4G => { assert_eq!(fed_config.max_devices_per_round, 10000); assert_eq!(fed_config.compression_ratio, 0.01); } NetworkClass::LPWAN => { assert_eq!(fed_config.max_devices_per_round, 1000); assert_eq!(fed_config.compression_ratio, 0.001); } _ => {} } println!("Adaptive configuration test passed for {}: scale={:.1}, layers={}, precision={:?}", scenario_name, config.dimension_scale, config.num_layers, config.precision); } } /// Test performance targets across all platforms #[tokio::test] async fn test_performance_targets() { let mut all_results = HashMap::new(); // Test ARM performance targets { let mut optimizer = EdgeTargetOptimizer::new(); let arm_opts = ArmOptimizations { enable_neon: true, target_arch: ArmArchitecture::AppleSilicon, ..Default::default() }; optimizer.configure_arm(arm_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::ARM).unwrap(); all_results.insert("ARM_Apple_Silicon", metrics); } // Test RISC-V performance targets { let mut optimizer = EdgeTargetOptimizer::new(); let riscv_opts = RiscVOptimizations { enable_rvv: true, vector_length: RiscVVectorLength::VLEN512, target_variant: RiscVVariant::Vector, ..Default::default() }; optimizer.configure_riscv(riscv_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::RISCV).unwrap(); all_results.insert("RISCV_Vector_512", metrics); } // Test WASM performance targets { let mut optimizer = EdgeTargetOptimizer::new(); let wasm_opts = WasmOptimizations { enable_simd: true, enable_threads: true, target_runtime: WasmRuntime::Wasmtime, ..Default::default() }; optimizer.configure_wasm(wasm_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::WASM).unwrap(); all_results.insert("WASM_SIMD_Threads", metrics); } // Test Mobile GPU performance targets { let mut optimizer = EdgeTargetOptimizer::new(); let gpu_opts = MobileGpuOptimizations { gpu_vendor: MobileGpuVendor::Apple, compute_shaders: true, tile_based_rendering: true, bandwidth_optimization: true, power_efficiency: true, }; optimizer.configure_mobile_gpu(gpu_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::MobileGPU).unwrap(); all_results.insert("Apple_GPU", metrics); } // Test IoT performance targets { let mut optimizer = EdgeTargetOptimizer::new(); let iot_opts = IoTOptimizations { ultra_low_power: true, minimal_memory: true, wake_on_inference: true, target_platform: IoTPlatform::ESP32, ..Default::default() }; optimizer.configure_iot(iot_opts); let metrics = optimizer.optimize_for_target(EdgeTarget::IoT).unwrap(); all_results.insert("ESP32_ULP", metrics); } // Validate performance targets are met println!("Performance Target Validation Results:"); println!("====================================="); for (platform, metrics) in &all_results { println!("{}: {:.2}x perf, {:.2}x power efficiency, {:.1}% memory reduction", platform, metrics.performance_improvement, metrics.power_efficiency_gain, metrics.memory_reduction_ratio * 100.0); // Performance target assertions match platform { platform if platform.contains("ARM") => { assert!(metrics.performance_improvement >= 2.5, "ARM performance target not met"); } platform if platform.contains("RISCV") => { assert!(metrics.performance_improvement >= 3.0, "RISC-V performance target not met"); } platform if platform.contains("WASM") => { assert!(metrics.performance_improvement >= 1.8, "WASM performance target not met"); } platform if platform.contains("GPU") => { assert!(metrics.performance_improvement >= 4.0, "Mobile GPU performance target not met"); } platform if platform.contains("ESP32") => { assert!(metrics.power_efficiency_gain >= 10.0, "IoT power efficiency target not met"); assert!(metrics.memory_reduction_ratio >= 0.8, "IoT memory reduction target not met"); } _ => {} } } println!("\nAll performance targets validated successfully!"); } // Helper functions for test setup fn create_test_federated_device(device_id: &str, device_type: EdgeDeviceType) -> FederatedDevice { use std::collections::HashSet; FederatedDevice { device_id: device_id.to_string(), device_type, status: DeviceStatus::Available, network_info: NetworkInfo { connection_type: ConnectionType::WiFi, bandwidth_mbps: 50.0, latency_ms: 20, reliability: 0.95, data_plan: DataPlan { unlimited: true, monthly_allowance_gb: None, current_usage_gb: 0.0, cost_per_gb: None, }, }, power_status: PowerStatus { battery_level: 0.8, is_charging: false, power_source: PowerSource::Battery, estimated_battery_life_minutes: Some(240), }, compute_capabilities: ComputeCapabilities { cpu_cores: 4, ram_mb: 4096, has_gpu: matches!(device_type, EdgeDeviceType::HighEndMobile | EdgeDeviceType::EdgeServer), simd_support: true, estimated_flops: 1e9, memory_bandwidth_gbps: 10.0, }, data_info: DataInfo { sample_count: 1000, quality_score: 0.9, privacy_level: PrivacyLevel::Personal, distribution: DataDistribution { distribution_type: "normal".to_string(), parameters: HashMap::new(), }, }, availability: AvailabilitySchedule { timezone_offset_hours: 0, available_hours: (0..24).collect(), preferred_duration_minutes: 30, blackout_periods: vec![], }, performance_metrics: PerformanceMetrics { avg_training_time_seconds: 300.0, avg_upload_time_seconds: 10.0, accuracy_contribution: 0.85, reliability_score: 0.9, communication_efficiency: 0.8, }, last_seen: SystemTime::now(), } } fn create_adaptive_transformer_config(capabilities: &EdgeCapabilities) -> EdgeTransformerConfig { match capabilities.edge_class { EdgeClass::HighEnd => EdgeTransformerConfig { dimension_scale: 1.0, num_layers: 12, num_heads: 12, precision: if capabilities.memory_mb > 16_384 { QuantizationLevel::FP32 } else { QuantizationLevel::FP16 }, gradient_checkpointing: false, mixed_precision: true, }, EdgeClass::Mid => EdgeTransformerConfig { dimension_scale: 0.7, num_layers: 8, num_heads: 8, precision: QuantizationLevel::FP16, gradient_checkpointing: true, mixed_precision: true, }, EdgeClass::Low => EdgeTransformerConfig { dimension_scale: 0.3, num_layers: 4, num_heads: 4, precision: QuantizationLevel::INT8, gradient_checkpointing: true, mixed_precision: false, }, EdgeClass::IoT => EdgeTransformerConfig { dimension_scale: 0.1, num_layers: 2, num_heads: 2, precision: QuantizationLevel::INT4, gradient_checkpointing: true, mixed_precision: false, }, } } fn create_federated_config(capabilities: &EdgeCapabilities) -> FederatedConfig { let (compression_ratio, max_devices, update_frequency) = match capabilities.network { NetworkClass::HighSpeed => (0.1, 100000, Duration::from_secs(10)), // Target: 100K devices NetworkClass::WiFi => (0.05, 50000, Duration::from_secs(30)), NetworkClass::Cellular4G => (0.01, 10000, Duration::from_secs(120)), NetworkClass::LPWAN => (0.001, 1000, Duration::from_secs(600)), _ => (0.01, 1000, Duration::from_secs(300)), }; let device_selection = match capabilities.power_budget { PowerClass::Unlimited => DeviceSelectionStrategy::PerformanceBased, PowerClass::HighBattery => DeviceSelectionStrategy::Hybrid, _ => DeviceSelectionStrategy::BatteryAware, }; FederatedConfig { aggregation_strategy: AggregationStrategy::FedAvg, compression_ratio, update_frequency, device_selection, max_devices_per_round: max_devices, fault_tolerance: FaultToleranceConfig { max_failed_devices: max_devices / 10, device_timeout: Duration::from_secs(60), byzantine_tolerance: true, backup_coordinators: vec![ "backup-coordinator-1.edge.local".to_string(), "backup-coordinator-2.edge.local".to_string(), ], }, } } // Additional type definitions needed for the tests #[derive(Debug, Clone)] struct EdgeTransformerConfig { dimension_scale: f32, num_layers: u32, num_heads: u32, precision: QuantizationLevel, gradient_checkpointing: bool, mixed_precision: bool, } #[derive(Debug, Clone)] struct EdgeCapabilities { compute_units: u32, memory_mb: u64, simd_support: SIMDClass, power_budget: PowerClass, network: NetworkClass, edge_class: EdgeClass, optimization_flags: HashMap, } #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] enum EdgeClass { HighEnd, Mid, Low, IoT, } #[derive(Debug, Clone, Copy, PartialEq)] enum SIMDClass { NEON, AVX, RVV, WASM_SIMD, None, } #[derive(Debug, Clone, Copy, PartialEq)] enum PowerClass { Unlimited, HighBattery, StandardBattery, LowPower, UltraLowPower, } #[derive(Debug, Clone, Copy, PartialEq)] enum NetworkClass { HighSpeed, WiFi, Cellular4G, LPWAN, }