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End-to-End Learnable Category Assignment Implementation Summary

Overview

Successfully implemented end-to-end learnable category assignments for VQ-VAE using Gumbel-Softmax, enabling gradient-based optimization of category assignments during training.

Status: ✅ All core modules implemented and tested

Implementation Summary

Phase 1: Core Modules ✅ (Completed)

Files Created:

  1. src/spinlock/encoding/learnable_assignment.py (~167 lines)

    • SoftAssignmentMatrix: Learnable [D, K] assignment probabilities
    • PerFamilyAssignmentMatrix: Per-family with block-diagonal constraints
    • initialize_from_clustering: Clustering → high-confidence logits

  2. src/spinlock/encoding/soft_routing.py (~55 lines)

    • SoftGroupedFeatureExtractor: Weighted feature routing

  3. src/spinlock/encoding/training/assignment_losses.py (~88 lines)

    • soft_orthogonality_loss: Inter-category correlation penalty
    • soft_balance_loss: Category size variance penalty
    • family_constraint_loss: Cross-family assignment penalty

  4. src/spinlock/encoding/training/annealing.py (~37 lines)

    • TemperatureScheduler: Linear/exponential/cosine annealing

Tests: 13/13 passing (100%)

Phase 2: Integrated Model ✅ (Completed)

Files Created: 5. src/spinlock/encoding/learnable_categorical_vqvae.py (~282 lines)

  • LearnableAssignmentConfig: Configuration dataclass
  • LearnableCategoricalVQVAE: Integrated model with soft assignments
  • Forward pass with temperature-controlled Gumbel-Softmax
  • Assignment loss computation
  • Freeze functionality (convert to hard GroupedFeatureExtractor)

Tests: 3/3 integration tests passing (100%)

Phase 3: Training Integration ✅ (Completed)

Files Created: 6. src/spinlock/encoding/training/learnable_trainer.py (~169 lines)

  • LearnableVQVAETrainer: Extends VQVAETrainer
  • Dual optimizer (model + assignments)
  • Temperature annealing per epoch
  • Assignment loss integration
  • Gradient clipping for stability

Files Modified: 7. src/spinlock/cli/train_vqvae.py (~150 lines modified)

  • Added --learnable CLI flag
  • Branching logic for learnable vs static mode
  • Assignment matrix initialization from clustering
  • Separate learnable config section

Phase 4: Configuration & Testing ✅ (Completed)

Files Created: 8. configs/vqvae/learnable_assignment.yaml (~150 lines)

  • Complete configuration example
  • Learnable assignment parameters
  • Temperature annealing config
  • Assignment loss weights
  • Clustering initialization params

  1. tests/test_learnable_assignment.py (~351 lines)

    • 13 unit tests for core modules
    • Tests for soft assignments, routing, losses, annealing
    • All passing

  2. tests/test_learnable_integration.py (~165 lines)

    • 3 integration tests
    • End-to-end forward/backward pass
    • Freeze functionality
    • All passing

Files Modified: 11. src/spinlock/encoding/__init__.py - Exported LearnableCategoricalVQVAE and LearnableAssignmentConfig

Key Design Decisions

1. Separate Model Class

  • Created LearnableCategoricalVQVAE instead of modifying existing CategoricalHierarchicalVQVAE
  • Benefit: Clean separation, backward compatible, easier to test

2. Dual Optimizer

  • Separate optimizer for assignment matrix with lower LR (0.001 vs 0.01)
  • Benefit: Assignments converge more slowly, prevents instability

3. Initialization from Clustering

  • Still run initial clustering, but use as initialization (high-confidence logits)
  • Benefit: Warm start accelerates convergence, provides reasonable initial categories

4. Temperature Annealing

  • Linear annealing from 1.0 → 0.1 over training epochs
  • Benefit: Smooth transition from soft to hard assignments

5. Block-Diagonal Enforcement (Per-Family)

  • Hard constraint via block-diagonal assignment matrix
  • Benefit: Zero probability for invalid cross-family assignments

Total Code Statistics

New Files: 10 files, ~1,477 lines

  • Core modules: ~347 lines
  • Integrated model: ~282 lines
  • Training integration: ~169 lines
  • Tests: ~516 lines
  • Config: ~150 lines
  • CLI integration: ~150 lines (modifications)

Modified Files: 2 files, ~150 lines changed

  • train_vqvae.py: Branching logic + initialization
  • __init__.py: Exports

Total Implementation: ~1,627 lines

Testing Summary

✅ All tests passing (16/16)

Unit Tests (13 tests)

  • test_learnable_assignment.py: 13/13 passing
    • SoftAssignmentMatrix: forward, temperature, hard assignments, gradients
    • PerFamilyAssignmentMatrix: block-diagonal, no leakage
    • SoftRouting: forward shape, gradients
    • Assignment losses: balance, family constraints
    • TemperatureScheduler: linear, exponential, cosine

Integration Tests (3 tests)

  • test_learnable_integration.py: 3/3 passing
    • Full forward pass with soft assignments
    • Backward pass with gradient flow
    • Freeze assignments functionality

Usage

Command Line

# Train with learnable assignments
poetry run spinlock train-vqvae \
  --config configs/vqvae/learnable_assignment.yaml \
  --learnable \
  --verbose

# Or set in config
poetry run spinlock train-vqvae \
  --config configs/vqvae/learnable_assignment.yaml \
  --verbose

Configuration

# Enable learnable mode
training:
  category_assignment: learnable

# Learnable assignment parameters
learnable_assignment:
  temperature_start: 1.0
  temperature_end: 0.1
  temperature_schedule: "linear"
  orthogonality_weight: 0.1
  balance_weight: 0.05
  family_constraint_weight: 1.0
  assignment_lr: 0.001
  freeze_after_epochs: null  # Optional: freeze after N epochs

Expected Benefits

  1. Task-Optimal Categories

    • Learned to minimize reconstruction loss, not just correlation
    • Categories adapt to the specific modeling task

  2. Automatic Balancing

    • Balance loss prevents category collapse
    • No manual dead code reset needed

  3. Simpler Configuration

    • No max_category_size, max_split_recursion_depth, etc.
    • Fewer hyperparameters to tune

  4. End-to-End Differentiable

    • Gradients flow through entire pipeline
    • Unified optimization objective

Future Work

Not Yet Implemented

  1. Variable-Length Temporal Support

    • Currently disabled for variable-length mode
    • Would require runtime temporal encoding before soft routing

  2. Hybrid INITIAL Encoding Support

    • Currently falls back to static mode
    • Would need integration with VQVAEWithInitial wrapper

  3. Advanced Initialization Methods

    • Currently only supports clustering initialization
    • Could add: random, pretrained, user-specified

  4. Curriculum Learning

    • Gradual increase of assignment learning difficulty
    • Could start with more categories, merge during training

Potential Enhancements

  1. Adaptive Temperature Scheduling

    • Adjust temperature based on assignment convergence
    • Stop annealing when assignments stabilize

  2. Category Pruning

    • Automatically remove unused categories during training
    • Dynamic category count adjustment

  3. Multi-Stage Training

    • Train model with frozen assignments first
    • Fine-tune assignments in second stage
    • Alternate between model and assignment optimization

References

Design Principles:

  • Single Responsibility: Each module <200 lines, does ONE thing
  • Composition over Inheritance: Built from small, reusable pieces
  • Explicit Data Flow: No hidden state, clear inputs/outputs
  • Backward Compatible: Static assignment (default) + learnable (opt-in)

Related Work:

  • Gumbel-Softmax: Jang et al. (2016) - Categorical Reparameterization with Gumbel-Softmax
  • VQ-VAE: van den Oord et al. (2017) - Neural Discrete Representation Learning
  • Temperature Annealing: Typical in discrete latent variable models

Conclusion

Successfully implemented a complete, well-tested learnable category assignment system for VQ-VAE. The implementation:

  • ✅ Is backward compatible (static mode still works)
  • ✅ Follows clean code principles (small, focused modules)
  • ✅ Has comprehensive tests (100% passing)
  • ✅ Includes example configuration
  • ✅ Integrates smoothly with existing training pipeline
  • ✅ Provides clear path for future enhancements

Total implementation time: ~4 hours (as estimated in plan)