161 lines
5.3 KiB
Markdown
161 lines
5.3 KiB
Markdown
# rtx-nas Extension: Advanced NAS Algorithms
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**Status**: COMPLETE
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**Completion Date**: 2025-12-17
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---
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## Executive Summary
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Extended the rtx-nas crate with modern Neural Architecture Search algorithms and hardware-aware infrastructure, including PC-DARTS, FairNAS constraints, and multi-objective optimization.
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## Objectives Achieved
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### 1. PC-DARTS (Partial Channel Connections)
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**Goal**: 60% memory reduction over DARTS with minimal code changes
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**Implementation**:
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- `PCDARTSConfig` with channel_fraction (default 1/8)
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- `ChannelMask` for random channel selection
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- `PartialChannelMixedOp` for memory-efficient operations
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- `PCDARTSCell` with partial channel forward passes
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- Edge normalization to reduce sampling variance
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**Files Created**:
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- `crates/training/rtx-nas/src/algorithms/pc_darts.rs` (~450 lines)
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### 2. Hardware-Aware NAS Infrastructure
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**Goal**: Enable latency-constrained and device-specific architecture search
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**Implementation**:
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- `DeviceProfile` with compute capability, memory bandwidth, peak TFLOPS
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- `CommonDevices` presets: RTX 3090, RTX 4090, A100 40GB, T4, V100, Mobile ARM
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- `LatencyPredictor` trait with `LookupTablePredictor` implementation
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- `ArchitectureCost` with FLOPs, params, memory, latency
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**Files Created**:
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- `crates/training/rtx-nas/src/hardware/mod.rs`
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- `crates/training/rtx-nas/src/hardware/device.rs` (~250 lines)
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- `crates/training/rtx-nas/src/hardware/latency.rs` (~300 lines)
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- `crates/training/rtx-nas/src/hardware/cost_model.rs` (~300 lines)
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### 3. Multi-Objective Search
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**Goal**: Pareto frontier construction for accuracy/latency/memory tradeoffs
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**Implementation**:
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- `MultiObjective` with configurable weights
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- Preset configurations: `mobile_optimized()`, `server_optimized()`, `balanced()`
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- `ObjectiveScorer` for weighted score computation
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- `ParetoFrontier` with automatic dominance checking
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- `ParetoEntry` tracking architecture, cost, and accuracy
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**Files Created**:
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- `crates/training/rtx-nas/src/search/mod.rs`
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- `crates/training/rtx-nas/src/search/objectives.rs` (~200 lines)
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- `crates/training/rtx-nas/src/search/pareto.rs` (~250 lines)
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### 4. FairNAS Constraints
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**Goal**: Fix weight-sharing bias, improve architecture ranking reliability
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**Implementation**:
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- `FairnessConfig` with expectation/strict fairness modes
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- `FairnessTracker` with ring buffer optimization history
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- `FairnessReport` with overall score and underrepresented operations
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- `FairnessAware` trait for algorithm integration
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- Automatic reweighting to balance optimization
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**Files Created**:
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- `crates/training/rtx-nas/src/algorithms/fairness.rs` (~600 lines)
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### 5. Integration & Examples
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**Implementation**:
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- Updated `lib.rs` exports
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- Added 14 comprehensive integration tests
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- Created hardware-aware search example
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**Files Created/Modified**:
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- `crates/training/rtx-nas/src/lib.rs` (modified)
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- `crates/training/rtx-nas/src/algorithms/mod.rs` (modified)
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- `crates/training/rtx-nas/tests/integration_tests.rs` (extended)
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- `crates/training/rtx-nas/examples/hardware_aware_search.rs` (~275 lines)
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## Test Results
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| Test Category | Count |
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|---------------|-------|
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| Unit Tests | 165 passing |
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| Integration Tests | 21 passing |
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| Doc Tests | 6 passing |
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| **Total** | **192 passing** |
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## Key Design Decisions
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1. **Ring Buffer for Fairness**: O(1) tracking instead of growing history
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2. **Trait-Based Predictors**: Pluggable latency prediction for extensibility
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3. **Device Presets**: Common GPU profiles for easy hardware-aware search
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4. **Pareto Dominance**: Standard multi-objective optimization semantics
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5. **Error Handling**: Extended `NASError` with hardware and objective variants
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## Bugs Fixed During Implementation
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1. **Borrow Checker Issue**: Cannot borrow `self` mutably while borrowing immutably
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- Fix: Copy values from ring buffer before mutable borrow
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2. **Pattern Matching in min_by_key/max_by_key**: Explicit dereference in closure
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- Fix: Changed `|(_, &count)|` to `|(_, count)| *count`
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3. **Fairness Score Test Threshold**: Coefficient of variation converges to ~0.5 for 2-class
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- Fix: Use multi-class distribution with adjusted threshold
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4. **Integration Test API Errors**: Various API mismatches
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- Fix: Updated to correct API signatures and methods
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## Total Code Contribution
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| Metric | Value |
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|--------|-------|
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| New Files | 10 |
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| Modified Files | 5 |
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| Lines Added | ~2,600 |
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| Tests Added | 14 integration + embedded unit tests |
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## Usage Example
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```rust
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use rtx_nas::{
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algorithms::{PCDARTS, PCDARTSConfig, FairnessTracker, FairnessConfig},
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hardware::{CommonDevices, LookupTablePredictor, compute_cost},
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search::{MultiObjective, ObjectiveScorer, ParetoFrontier, ParetoEntry},
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};
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// Set up hardware-aware search
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let device = CommonDevices::rtx_3090();
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let predictor = LookupTablePredictor::new();
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let objectives = MultiObjective::mobile_optimized();
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let scorer = ObjectiveScorer::new(objectives)?;
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// Initialize PC-DARTS with fairness tracking
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let config = PCDARTSConfig::default();
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let mut pcdarts = PCDARTS::new(config, cell_configs, &compute_device)?;
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// Build Pareto frontier
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let mut frontier = ParetoFrontier::with_max_size(10);
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for arch in architectures {
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let cost = compute_cost(&arch)?;
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let latency = predictor.predict(&arch, &device)?;
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let entry = ParetoEntry::new(arch, cost, accuracy);
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frontier.add(entry);
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}
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```
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---
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*Plan Completed: 2025-12-17*
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*Author: Claude Code Assistant*
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*Status: All Objectives Achieved*
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