Upload README.md

README.md

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+# 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.