# Sparse Transformer: Experiment Suite + Triton Kernels

Comprehensive experiment infrastructure for the Chunked Sparse Backward Pass paper.

## Files

| File | Description |
|------|-------------|
| `triton_sparse.py` | Triton-fused sparse backward kernels (dW, dX, dBias) + Python-loop baseline + correctness tests + microbenchmark |
| `e2e_full.py` | End-to-end training benchmark: Dense vs PyLoop vs Triton at d_model ∈ {512, 1024, 2048} |
| `full_experiments.py` | 7-experiment ablation suite (baselines, predictor accuracy, chunk ablation, compute-matched, exploration, attention sparsification, sparsity sweep) |
| `analyze_results.py` | Publication figure generator (matplotlib) |

## Quick Start

```bash
pip install torch triton tiktoken matplotlib numpy

# Correctness test + microbenchmark
python triton_sparse.py

# End-to-end training (needs ≥24GB GPU for d=2048)
python e2e_full.py

# Full ablation suite (7 experiments, ~4-6 hours on A10G)
python full_experiments.py --experiment all --device cuda --steps 2000 --seeds "42,123,456"

# Single experiment
python full_experiments.py --experiment baselines --device cuda
```

## Results

See `RESULTS.md` for collected tables.