rustytorch/.ai/plans/rtx-nas-extension.md

rtx-nas Extension: Advanced NAS Algorithms

Status: COMPLETE Completion Date: 2025-12-17

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Executive Summary

Extended the rtx-nas crate with modern Neural Architecture Search algorithms and hardware-aware infrastructure, including PC-DARTS, FairNAS constraints, and multi-objective optimization.

Objectives Achieved

1. PC-DARTS (Partial Channel Connections)

Goal: 60% memory reduction over DARTS with minimal code changes

Implementation:

• PCDARTSConfig with channel_fraction (default 1/8)
• ChannelMask for random channel selection
• PartialChannelMixedOp for memory-efficient operations
• PCDARTSCell with partial channel forward passes
• Edge normalization to reduce sampling variance

Files Created:

• crates/training/rtx-nas/src/algorithms/pc_darts.rs (~450 lines)

2. Hardware-Aware NAS Infrastructure

Goal: Enable latency-constrained and device-specific architecture search

Implementation:

• DeviceProfile with compute capability, memory bandwidth, peak TFLOPS
• CommonDevices presets: RTX 3090, RTX 4090, A100 40GB, T4, V100, Mobile ARM
• LatencyPredictor trait with LookupTablePredictor implementation
• ArchitectureCost with FLOPs, params, memory, latency

Files Created:

• crates/training/rtx-nas/src/hardware/mod.rs
• crates/training/rtx-nas/src/hardware/device.rs (~250 lines)
• crates/training/rtx-nas/src/hardware/latency.rs (~300 lines)
• crates/training/rtx-nas/src/hardware/cost_model.rs (~300 lines)

3. Multi-Objective Search

Goal: Pareto frontier construction for accuracy/latency/memory tradeoffs

Implementation:

• MultiObjective with configurable weights
• Preset configurations: mobile_optimized(), server_optimized(), balanced()
• ObjectiveScorer for weighted score computation
• ParetoFrontier with automatic dominance checking
• ParetoEntry tracking architecture, cost, and accuracy

Files Created:

• crates/training/rtx-nas/src/search/mod.rs
• crates/training/rtx-nas/src/search/objectives.rs (~200 lines)
• crates/training/rtx-nas/src/search/pareto.rs (~250 lines)

4. FairNAS Constraints

Goal: Fix weight-sharing bias, improve architecture ranking reliability

Implementation:

• FairnessConfig with expectation/strict fairness modes
• FairnessTracker with ring buffer optimization history
• FairnessReport with overall score and underrepresented operations
• FairnessAware trait for algorithm integration
• Automatic reweighting to balance optimization

Files Created:

• crates/training/rtx-nas/src/algorithms/fairness.rs (~600 lines)

5. Integration & Examples

Implementation:

• Updated lib.rs exports
• Added 14 comprehensive integration tests
• Created hardware-aware search example

Files Created/Modified:

• crates/training/rtx-nas/src/lib.rs (modified)
• crates/training/rtx-nas/src/algorithms/mod.rs (modified)
• crates/training/rtx-nas/tests/integration_tests.rs (extended)
• crates/training/rtx-nas/examples/hardware_aware_search.rs (~275 lines)

Test Results

Test Category	Count
Unit Tests	165 passing
Integration Tests	21 passing
Doc Tests	6 passing
Total	192 passing

Key Design Decisions

1. Ring Buffer for Fairness: O(1) tracking instead of growing history
2. Trait-Based Predictors: Pluggable latency prediction for extensibility
3. Device Presets: Common GPU profiles for easy hardware-aware search
4. Pareto Dominance: Standard multi-objective optimization semantics
5. Error Handling: Extended NASError with hardware and objective variants

Bugs Fixed During Implementation

1. Borrow Checker Issue: Cannot borrow self mutably while borrowing immutably

   • Fix: Copy values from ring buffer before mutable borrow

2. Pattern Matching in min_by_key/max_by_key: Explicit dereference in closure

   • Fix: Changed |(_, &count)| to |(_, count)| *count

3. Fairness Score Test Threshold: Coefficient of variation converges to ~0.5 for 2-class

   • Fix: Use multi-class distribution with adjusted threshold

4. Integration Test API Errors: Various API mismatches

   • Fix: Updated to correct API signatures and methods

Total Code Contribution

Metric	Value
New Files	10
Modified Files	5
Lines Added	~2,600
Tests Added	14 integration + embedded unit tests

Usage Example

use rtx_nas::{
    algorithms::{PCDARTS, PCDARTSConfig, FairnessTracker, FairnessConfig},
    hardware::{CommonDevices, LookupTablePredictor, compute_cost},
    search::{MultiObjective, ObjectiveScorer, ParetoFrontier, ParetoEntry},
};

// Set up hardware-aware search
let device = CommonDevices::rtx_3090();
let predictor = LookupTablePredictor::new();
let objectives = MultiObjective::mobile_optimized();
let scorer = ObjectiveScorer::new(objectives)?;

// Initialize PC-DARTS with fairness tracking
let config = PCDARTSConfig::default();
let mut pcdarts = PCDARTS::new(config, cell_configs, &compute_device)?;

// Build Pareto frontier
let mut frontier = ParetoFrontier::with_max_size(10);
for arch in architectures {
    let cost = compute_cost(&arch)?;
    let latency = predictor.predict(&arch, &device)?;
    let entry = ParetoEntry::new(arch, cost, accuracy);
    frontier.add(entry);
}

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Plan Completed: 2025-12-17 Author: Claude Code Assistant Status: All Objectives Achieved