use crate::FxGraph;
use std::collections::HashMap;
use torsh_core::error::Result;
use super::types::{
ActivationType, ArchitectureSearchSpace, ConnectionPattern, HardwareConstraints,
HardwareOptimization, HardwarePlatform, LayerType, MobileDeviceType, NeuralArchitectureSearch,
NormalizationType, ObjectiveWeights, PoolingType, SearchResults, SearchStrategy,
SelectionStrategy, SkipConnectionType,
};
pub fn create_default_search_space() -> ArchitectureSearchSpace {
ArchitectureSearchSpace {
layer_types: vec![
LayerType::Conv2d {
kernel_sizes: vec![3, 5, 7],
stride_options: vec![1, 2],
},
LayerType::DepthwiseConv2d {
kernel_sizes: vec![3, 5],
},
LayerType::Linear {
hidden_sizes: vec![64, 128, 256, 512],
},
LayerType::Attention {
head_options: vec![4, 8, 16],
dim_options: vec![64, 128, 256],
},
LayerType::Pooling {
pool_types: vec![PoolingType::Max, PoolingType::Average],
kernel_sizes: vec![2, 3],
},
],
depth_range: (3, 20),
width_constraints: vec![
(0, (32, 512)),
(1, (16, 256)),
(2, (64, 1024)),
(3, (64, 512)),
(4, (2, 8)),
],
connection_patterns: vec![
ConnectionPattern::Sequential,
ConnectionPattern::Skip { max_distance: 3 },
ConnectionPattern::Residual,
],
activation_functions: vec![
ActivationType::ReLU,
ActivationType::Swish,
ActivationType::GELU,
],
normalization_options: vec![
NormalizationType::BatchNorm,
NormalizationType::LayerNorm,
NormalizationType::None,
],
skip_connection_strategies: vec![
SkipConnectionType::Add,
SkipConnectionType::Concat,
SkipConnectionType::None,
],
}
}
pub fn create_mobile_constraints() -> HardwareConstraints {
HardwareConstraints {
target_platform: HardwarePlatform::Mobile {
device_type: MobileDeviceType::Smartphone,
},
max_latency_ms: Some(50.0),
max_memory_mb: Some(100.0),
max_energy_mj: Some(10.0),
max_model_size_mb: Some(10.0),
hardware_optimizations: vec![
HardwareOptimization::QuantizationFriendly,
HardwareOptimization::MemoryEfficient,
],
}
}
pub async fn start_neural_architecture_search(
_initial_graph: FxGraph,
target_platform: HardwarePlatform,
) -> Result<SearchResults> {
let search_space = create_default_search_space();
let search_strategy = SearchStrategy::Evolutionary {
population_size: 50,
mutation_rate: 0.1,
crossover_rate: 0.8,
selection_strategy: SelectionStrategy::Tournament { tournament_size: 3 },
};
let hardware_constraints = HardwareConstraints {
target_platform: target_platform.clone(),
max_latency_ms: Some(100.0),
max_memory_mb: Some(500.0),
max_energy_mj: Some(50.0),
max_model_size_mb: Some(50.0),
hardware_optimizations: vec![
HardwareOptimization::QuantizationFriendly,
HardwareOptimization::ParallelFriendly,
],
};
let objective_weights = ObjectiveWeights {
accuracy: 0.6,
latency: 0.2,
memory: 0.1,
energy: 0.05,
model_size: 0.05,
custom_objectives: HashMap::new(),
};
let nas = NeuralArchitectureSearch::new(
search_space,
search_strategy,
hardware_constraints,
objective_weights,
);
println!("🚀 Starting automated neural architecture search...");
println!("🎯 Target: Optimal architecture for {:?}", target_platform);
nas.search(100).await
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tracer::ModuleTracer;
#[test]
fn test_nas_creation() {
let search_space = create_default_search_space();
let search_strategy = SearchStrategy::Random { max_iterations: 10 };
let hardware_constraints = create_mobile_constraints();
let objective_weights = ObjectiveWeights {
accuracy: 0.8,
latency: 0.2,
memory: 0.0,
energy: 0.0,
model_size: 0.0,
custom_objectives: HashMap::new(),
};
let nas = NeuralArchitectureSearch::new(
search_space,
search_strategy,
hardware_constraints,
objective_weights,
);
assert!(nas.search_space.layer_types.len() > 0);
assert!(nas.search_space.depth_range.0 < nas.search_space.depth_range.1);
}
#[tokio::test]
async fn test_random_search() {
let search_space = create_default_search_space();
let search_strategy = SearchStrategy::Random { max_iterations: 5 };
let hardware_constraints = create_mobile_constraints();
let objective_weights = ObjectiveWeights {
accuracy: 1.0,
latency: 0.0,
memory: 0.0,
energy: 0.0,
model_size: 0.0,
custom_objectives: HashMap::new(),
};
let nas = NeuralArchitectureSearch::new(
search_space,
search_strategy,
hardware_constraints,
objective_weights,
);
let results = nas.search(5).await;
assert!(results.is_ok());
}
#[test]
fn test_architecture_generation() {
let search_space = create_default_search_space();
let search_strategy = SearchStrategy::Random { max_iterations: 10 };
let hardware_constraints = create_mobile_constraints();
let objective_weights = ObjectiveWeights {
accuracy: 1.0,
latency: 0.0,
memory: 0.0,
energy: 0.0,
model_size: 0.0,
custom_objectives: HashMap::new(),
};
let nas = NeuralArchitectureSearch::new(
search_space,
search_strategy,
hardware_constraints,
objective_weights,
);
let architecture = nas.generate_random_architecture();
assert!(architecture.is_ok());
let arch = architecture.expect("operation should succeed");
assert!(arch.graph.node_count() >= 2);
assert!(!arch.id.is_empty());
}
#[test]
fn test_architecture_encoding() {
let mut tracer = ModuleTracer::new();
tracer.add_input("x");
tracer.add_call("conv2d", vec!["x".to_string()]);
tracer.add_call("relu", vec!["node_0".to_string()]);
tracer.add_output("node_1");
let graph = tracer.finalize();
let search_space = create_default_search_space();
let search_strategy = SearchStrategy::Random { max_iterations: 10 };
let hardware_constraints = create_mobile_constraints();
let objective_weights = ObjectiveWeights {
accuracy: 1.0,
latency: 0.0,
memory: 0.0,
energy: 0.0,
model_size: 0.0,
custom_objectives: HashMap::new(),
};
let nas = NeuralArchitectureSearch::new(
search_space,
search_strategy,
hardware_constraints,
objective_weights,
);
let encoding = nas.encode_architecture(&graph);
assert!(encoding.is_ok());
let enc = encoding.expect("operation should succeed");
assert_eq!(enc.adjacency_matrix.len(), graph.node_count());
assert_eq!(enc.node_features.len(), graph.node_count());
assert_eq!(enc.global_features.len(), 4);
}
}