From 12e60816c68bab14b802f6216189da475031d6a1 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 14:54:29 -0400 Subject: [PATCH 1/2] Remove testing_report.md from docs - No references to this file in documentation - Not linked in README or config files - Clean up changelog formatting --- docs/changelog.md | 1 - docs/testing_report.md | 85 ------------------------------------------ 2 files changed, 86 deletions(-) delete mode 100644 docs/testing_report.md diff --git a/docs/changelog.md b/docs/changelog.md index 600b64f1..689c8ca9 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -114,7 +114,6 @@ This release introduces **intelligent, redundancy-aware pruning** with a complet #### **v0.1.0 → v0.2.0** -**Good news:** All changes are **100% backward compatible!** #### **Existing code continues to work:** ```python diff --git a/docs/testing_report.md b/docs/testing_report.md deleted file mode 100644 index 0a93c7ff..00000000 --- a/docs/testing_report.md +++ /dev/null @@ -1,85 +0,0 @@ -# Framework Testing Report - -## Environment -- Node: holygpu8a10101 -- GPU: NVIDIA H200 (144GB) -- Python: 3.9.19 -- PyTorch: 2.4.0 -- Framework: alignment v0.2.0 - -## Tests Completed - -### 1. Scientific Validation Tests -**Status:** PASSED (13/13) - -All theoretical predictions verified: -- Orthogonal weights have low redundancy -- Colinear weights have high redundancy -- Class-separated data produces high ΔRQ -- Independent variables have low MI -- Numerical stability confirmed - -### 2. MLP Pruning (MNIST) -**Status:** PASSED - -Results: -- Baseline: 97.26% accuracy -- Pruning at 50% sparsity: - - Random: 97.34% (-0.08% drop) - - Magnitude: 97.41% (-0.15% drop) - - RQ: 97.62% (-0.36% drop) - - Composite: 96.93% (+0.33% drop) - -All strategies functional. - -### 3. Framework API Components -**Status:** PASSED - -Verified: -- ModelWrapper (layer detection) -- get_metric() (fixed to return instance) -- RayleighQuotient computation -- Redundancy (output-based mode) -- Composite scoring -- Services (capture, scoring) -- Quantization (INT8) - -### 4. CNN Considerations -**Note:** For CNNs with large spatial dimensions, use: -- Smaller batch sizes -- output-based metrics (not covariance-based) -- Or channel-variance mode (TODO: add to config) - -## Issues Found and Fixed - -1. **get_metric() API** - - Was returning class instead of instance - - Fixed: Now returns instantiated metric object - -2. **forward_with_activations() signature** - - Added **kwargs for compatibility with services - - Fixed in base.py - -## Conclusion - -**Framework Status:** PRODUCTION-READY - -**Verified Working:** -- Metrics computation (RQ, redundancy, synergy) -- Pruning strategies (magnitude, RQ, composite) -- Services (activation capture, scoring) -- Quantization support (INT8, INT4, mixed) -- Evaluation (classification, perplexity) - -**Ready For:** -- MLP experiments -- CNN experiments (with appropriate preprocessing) -- LLM experiments (LLaMA-3) -- Quantization studies -- Publication-quality research - -**Next Steps:** -- Run full experiments on datasets -- Compare pruning strategies -- Study quantization effects -- Publish results From b69e68091cf678ce86d6f5abc35d71f189d46bf8 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 14:57:01 -0400 Subject: [PATCH 2/2] Simplify changelog - remove release versions - No releases yet, just track recent updates - Reduce from 229 to 29 lines - Focus on features not versions --- docs/changelog.md | 240 ++++------------------------------------------ 1 file changed, 20 insertions(+), 220 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 689c8ca9..bcf5708c 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,228 +1,28 @@ # Changelog -All notable changes to the Alignment Framework. - -## [0.2.0] - 2025-10-03 - -### Major Release: Information-Theoretic Metrics & Service Layer - -This release introduces **intelligent, redundancy-aware pruning** with a complete service layer architecture and three new information-theoretic metrics. - ---- +## Recent Updates ### Added - -#### New Service Layer -- `alignment.services.ActivationCaptureService` - Centralized activation and weight collection -- `alignment.services.NodeScoringService` - Composite importance scoring engine -- `alignment.services.MaskOperations` - Unified pruning mask utilities -- `alignment.services.ActivationData` - Dataclass for captured data -- `alignment.services.CompositeScores` - Dataclass for multi-metric scores - -**Impact:** Reduces code duplication by ~40%, cleaner APIs - -#### New Information-Theoretic Metrics -- `alignment.metrics.PairwiseRedundancyGaussian` - Per-neuron redundancy measurement - - Computes `R(Y_i, Y_j) = -0.5 * log(1 - ρ²)` for sampled neuron pairs - - Identifies redundant neurons for pruning - - Supports random, nearest, and all-pairs sampling - -- `alignment.metrics.SynergyGaussianMMI` - Target-conditional synergy measurement - - Computes `S_MMI(Z; Y_i, Y_j)` using MMI redundancy axiom - - Identifies complementary neuron pairs - - Preserves synergistic features during pruning - -**Impact:** Enables redundancy-aware pruning with expected +3-5% accuracy retention - -#### Enhanced Existing Metrics -- `alignment.metrics.RayleighQuotient.compute_class_conditioned()` - New method - - Class-conditioned RQ: `RQ(w; Σ_{X|y})` - - Discriminative alignment: `ΔRQ = RQ - E[RQ|class]` - - Identifies task-relevant neurons - -**Impact:** Task-aware pruning decisions - -#### **Hook Management** 🔒 -- `alignment.models.HookManager` - Context-managed hook lifecycle -- `alignment.models.PersistentHookManager` - Long-lived hooks with auto-cleanup -- `BaseModelWrapper.forward_with_activations()` - Safe activation capture -- `BaseModelWrapper.capture_activations_safe()` - Convenience method - -**Impact:** Prevents memory leaks in long experiments - -#### **Examples** 📖 -- `examples/06_redundancy_aware_pruning.py` - Complete workflow demonstration - - Shows all new features in action - - Compares redundancy-aware vs baseline pruning - - Demonstrates ΔRQ analysis - ---- +- Service layer architecture: `ActivationCaptureService`, `NodeScoringService`, `MaskOperations` +- Information-theoretic metrics: `PairwiseRedundancyGaussian`, `SynergyGaussianMMI` +- Class-conditioned RQ: `RayleighQuotient.compute_class_conditioned()` +- Hook management: `HookManager`, `PersistentHookManager` +- `forward_with_activations()` for safe activation capture ### Changed - -#### **RayleighQuotient Improvements** -- Added `regularization` parameter (default: 1e-6) for numerical stability -- Automatic diagonal regularization prevents singular covariance matrices -- Better handling of small batches and edge cases - -#### **BaseModelWrapper Enhancements** -- Integrated `HookManager` for automatic cleanup -- Improved activation capture with guaranteed hook removal -- Added `__del__` to ensure cleanup on destruction - -#### **Package Version** -- Version bumped: 0.1.0 → 0.2.0 - ---- +- RayleighQuotient: Added regularization parameter for numerical stability +- BaseModelWrapper: Integrated HookManager for automatic cleanup ### Fixed - -- **Memory leaks:** HookManager ensures all hooks are properly removed -- **Numerical stability:** Covariance regularization prevents crashes on small batches -- **Hook accumulation:** Context managers prevent hook buildup over multiple captures - ---- - -### Performance - -- **Code duplication:** Reduced by ~40% via service layer -- **Memory safety:** 100% - all hooks auto-managed -- **Computation cost:** Redundancy/synergy scale as O(n·K), not O(n²) - ---- - -### Documentation - -#### **New Documentation** -- `CODEBASE_IMPROVEMENTS.md` - 40+ pages of architectural recommendations -- `IMPLEMENTATION_ROADMAP.md` - Week-by-week implementation plan -- `unified_manuscript.tex` - Publication-ready research paper -- `PROGRESS_REPORT.md` - Phase 1 progress tracking -- `PHASE2_COMPLETE.md` - Phase 2 achievements -- `SESSION_SUMMARY.md` - Overall session summary -- `IMPLEMENTATION_COMPLETE.md` - Comprehensive overview -- This CHANGELOG - -#### **Updated Documentation** -- All new classes have comprehensive docstrings -- Type hints added throughout -- Examples demonstrate new features - ---- - -### Migration Guide - -#### **v0.1.0 → v0.2.0** - - -#### **Existing code continues to work:** -```python -# This still works exactly as before -from alignment.metrics import get_metric -rq = get_metric('rayleigh_quotient') -scores = rq.compute(inputs, weights) -``` - -#### **Recommended updates (optional):** - -**1. Use HookManager for safety:** -```python -# Before -wrapper = BaseModelWrapper(model) -outputs = model(inputs) -activations = wrapper._activation_cache - -# After (safer) -wrapper = BaseModelWrapper(model) -outputs, activations = wrapper.forward_with_activations(inputs) -# Hooks auto-cleaned! -``` - -**2. Use services for cleaner code:** -```python -# Before -# Manual activation capture and metric computation -# (50+ lines of boilerplate) - -# After -from alignment.services import ActivationCaptureService, NodeScoringService - -capture = ActivationCaptureService(wrapper) -data = capture.capture(input_batch) - -scorer = NodeScoringService(metrics={...}) -scores = scorer.compute_layerwise_scores(data, targets) -# (5 lines!) -``` - -**3. Enable redundancy-aware pruning:** -```python -# Before -rq_scores = rq_metric.compute(inputs, weights) -mask = create_mask(rq_scores, amount=0.5) - -# After -scores = scorer.compute_composite_scores( - inputs, weights, targets, - include_redundancy=True, - include_synergy=True -) -mask = MaskOperations.create_structured_mask(scores.composite, amount=0.5) -``` - ---- - -### Deprecations - -**None.** All existing APIs maintained. - ---- - -### Breaking Changes - -**None.** Full backward compatibility. - ---- - -## Future Plans - -### **v0.3.0 - Planned Features** -- [ ] Backend abstraction (eager/JIT/CUDA) -- [ ] Experiment component extraction -- [ ] Comprehensive test suite (>85% coverage) -- [ ] Recipe gallery with 5-7 examples -- [ ] Performance optimizations - -### **v0.4.0 - Planned Features** -- [ ] Transformer-specific metrics -- [ ] LLM support (attention head analysis) -- [ ] Custom CUDA kernels -- [ ] Distributed covariance estimation - ---- - -## Contributors - -- Houman Safaai (Architecture, Implementation) -- Andrew Landau (Original RQ implementation) - ---- - -## See Also - -- **Documentation:** `docs/` directory -- **Examples:** `examples/` directory -- **Tests:** `tests/` directory -- **Roadmap:** `IMPLEMENTATION_ROADMAP.md` -- **Paper:** `drafts/unified_manuscript.tex` - ---- - -## License - -See LICENSE file for details. - ---- - -Version 0.2.0 - +- Memory leaks from accumulated hooks +- Numerical stability in small batch scenarios + +## Core Features + +- Rayleigh Quotient and class-conditioned variants +- Mutual Information (Gaussian and binning methods) +- Information-theoretic metrics (redundancy, synergy, PID) +- Similarity metrics (CKA, activation correlation) +- Model wrappers for PyTorch models +- Pruning strategies (magnitude, gradient, alignment-based) +- Service layer for activation capture and scoring