Upload README.md with huggingface_hub

README.md

@@ -1,104 +1,117 @@
 ---
-dataset_info:
-- config_name: kernel_commits
-  features:
-  - name: repo
-    dtype: string
-  - name: commit
-    dtype: string
-  - name: date
-    dtype: string
-  - name: pr_number
-    dtype: int64
-  - name: pr_url
-    dtype: string
-  - name: title
-    dtype: string
-  - name: message
-    dtype: string
-  - name: path
-    dtype: string
-  - name: language
-    dtype: string
-  - name: before_code
-    dtype: string
-  - name: after_code
-    dtype: string
-  - name: diff
-    dtype: string
-  - name: lines_added
-    dtype: int64
-  - name: lines_removed
-    dtype: int64
-  - name: files_in_commit
-    dtype: int64
-  - name: models_mentioned
-    list: string
-  - name: hardware_mentioned
-    list: string
-  - name: speedup_claims
-    list: string
-  splits:
-  - name: train
-    num_bytes: 89931604
-    num_examples: 1376
-  - name: test
-    num_bytes: 12592140
-    num_examples: 153
-  download_size: 31563045
-  dataset_size: 102523744
-- config_name: tuned_configs
-  features:
-  - name: repo
-    dtype: string
-  - name: commit
-    dtype: string
-  - name: config_path
-    dtype: string
-  - name: kernel_file
-    dtype: string
-  - name: kernel_name
-    dtype: string
-  - name: kernel_source
-    dtype: string
-  - name: device_name
-    dtype: string
-  - name: dtype
-    dtype: string
-  - name: block_shape
-    dtype: string
-  - name: triton_version
-    dtype: string
-  - name: shape
-    dtype: string
-  - name: M
-    dtype: int64
-  - name: candidate_models
-    list: string
-  - name: best_config
-    dtype: string
-  - name: prompt
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 386513390
-    num_examples: 17561
-  - name: test
-    num_bytes: 33347832
-    num_examples: 1726
-  download_size: 26750592
-  dataset_size: 419861222
+license: apache-2.0
+language: [en, code]
+pretty_name: Kernel-Opt RL (vLLM / SGLang / FlashInfer)
+size_categories: [10K<n<100K]
+task_categories: [text-generation]
+tags: [cuda, triton, gpu-kernels, kernel-optimization, reinforcement-learning, autotuning, vllm, sglang, flashinfer]
 configs:
-- config_name: kernel_commits
-  data_files:
-  - split: train
-    path: kernel_commits/train-*
-  - split: test
-    path: kernel_commits/test-*
-- config_name: tuned_configs
-  data_files:
-  - split: train
-    path: tuned_configs/train-*
-  - split: test
-    path: tuned_configs/test-*
+  - config_name: tuned_configs
+    data_files:
+      - {split: train, path: tuned_configs/train-*}
+      - {split: test, path: tuned_configs/test-*}
+  - config_name: kernel_commits
+    data_files:
+      - {split: train, path: kernel_commits/train-*}
+      - {split: test, path: kernel_commits/test-*}
 ---
+
+# Kernel-Opt RL
+
+Real-world GPU kernel optimization data mined from production LLM inference engines
+([vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang),
+[FlashInfer](https://github.com/flashinfer-ai/flashinfer)). It's built for RL: given a base kernel and a
+concrete deployment (model shape, GPU, dtype, batch size), choose the optimization.
+
+Inference engines ship one generic kernel and specialize it per model and per GPU. Most of that
+specialization is not new code. It is **autotuned launch parameters** checked in as JSON
+(`E=8,N=14336,device_name=NVIDIA_H100_80GB_HBM3.json`), plus a stream of **perf commits** that rewrite
+kernels for specific models and hardware. This dataset turns both into training signal.
+
+## Subsets
+
+### `tuned_configs`: kernel + deployment → best launch config (verifiable)
+
+| | |
+|---|---|
+| rows | 19,287 (17,561 train / 1,726 test) |
+| tuned config files | 1,138 |
+| GPUs | 48 (NVIDIA A100 → B300/GB300, AMD MI300X/MI325X/MI355X, Intel Arc, …) |
+| kernels | `fused_moe_kernel` (vLLM + SGLang), `fused_moe_kernel_gptq_awq`, `_w8a8_triton_block_scaled_mm`, `_w8a8_block_fp8_matmul`, `_w8a8_block_int8_matmul`, `_selective_scan_update_kernel` |
+| dtypes | bf16/fp16, fp8_w8a8 (per-tensor + 128×128 block), int8_w8a8, int8_w8a16, int4_w4a16 |
+| M (tokens) | 1 … 262,144 |
+
+One row is one (config file, batch size M) entry. `prompt` is ready to use: it contains the Triton kernel
+source, the hardware, the problem shape, and the MoE models whose shape matches under TP/EP
+(`candidate_models`, resolved against 32 MoE model configs on the Hub). `best_config` is the
+autotuned answer (`BLOCK_SIZE_M/N/K`, `GROUP_SIZE_M`, `num_warps`, `num_stages`, …).
+
+Fields: `repo, commit, config_path, kernel_file, kernel_name, kernel_source, device_name, dtype,
+block_shape, triton_version, shape (JSON), M, candidate_models, best_config (JSON), prompt`.
+
+**Split:** grouped by config filename (hash), so a given (shape, GPU, dtype) never appears in both
+train and test, whatever the repo, Triton version or M.
+
+### `kernel_commits`: before → after kernel rewrites from perf commits
+
+| | |
+|---|---|
+| rows | 1,529 file-level pairs (1,376 train / 153 test) from 873 commits |
+| languages | CUDA (`.cu/.cuh`), C++, Triton, CuTe DSL |
+| date range | 2023-04 → 2026-10 |
+
+Selected from non-merge commits that touch kernel directories and carry a performance signal in the
+title (`[Perf]`, optimize, speedup, faster, tune, throughput, latency, …), excluding build, CI, docs,
+tests and reverts. Only modified kernel files are kept: ≤10 per commit, ≤120 KB per file, ≤800 changed
+lines. Each row is tagged with the `models_mentioned` (DeepSeek, Qwen, Kimi, Mamba, GLM, …), the
+`hardware_mentioned` (SM90/SM100/SM120, B200, H100, MI355X, CPU, …), and the `speedup_claims` parsed
+from the message.
+
+Fields: `repo, commit, date, pr_number, pr_url, title, message, path, language, before_code,
+after_code, diff, lines_added, lines_removed, files_in_commit, models_mentioned,
+hardware_mentioned, speedup_claims`.
+
+**Split:** temporal. The newest ~10% of rows are test (2026-10-18 onward), the code least likely to be in
+a model's pretraining data.
+
+## Using it for RL
+
+**`tuned_configs`** gives a verifiable reward without any execution harness:
+- *offline proxy*: score the predicted config against `best_config`. Exact match is sparse, so partial
+  credit per parameter (or log-distance on the power-of-two tile sizes) works better.
+- *on hardware*: launch `kernel_source` with the predicted config at the given shape and reward
+  `t_best / t_pred`. Note that the tuned config is only the best among the tuner's search space, so a
+  model can beat it.
+
+**`kernel_commits`** suits:
+- *edit-style RL / SFT warm start*: `before_code` + `title` (+ model/hardware context) → `after_code`;
+- *preference pairs*: (`before_code`, `after_code`) as (rejected, chosen) for the stated workload;
+- *execution reward*: compile, check correctness and benchmark through an execution environment such as
+  [KernelGYM](https://github.com/hkust-nlp/KernelGYM). Watch for reward hacking (timer exploits, cached
+  outputs; see the [CUDA-L1](https://github.com/ornith-ai/CUDA-L1) notes).
+
+```python
+from datasets import load_dataset
+ds = load_dataset("guychuk/kernel-opt-rl", "tuned_configs", split="train")
+print(ds[0]["prompt"]); print(ds[0]["best_config"])
+```
+
+## Limitations
+
+- Tuned configs record only the *winner*, not the full latency table, so offline rewards are a proxy.
+- `candidate_models` comes from shape matching (`E == experts`, `N == intermediate / TP`, or
+  `E == experts / EP`). Shapes are shared across models, e.g. DeepSeek-V3, Kimi-K2 and MiMo-V2-Flash.
+- Commit messages from squash merges often omit the PR body, so `speedup_claims` is sparse. The
+  `pr_url` points to the full benchmark discussion.
+- Snapshots are from each repo's HEAD at build time (see `commit`). Rebuild with `build.py` for fresher data.
+
+## Related datasets
+
+[KernelBench](https://huggingface.co/datasets/ScalingIntelligence/KernelBench) (eval; keep held out),
+[KernelBook](https://huggingface.co/datasets/GPUMODE/KernelBook) (PyTorch→Triton pairs),
+[Nemotron-SFT-CUDA-v1](https://huggingface.co/datasets/nvidia/Nemotron-SFT-CUDA-v1) (CUDA SFT with tests),
+[CUDA-L1](https://github.com/ornith-ai/CUDA-L1) (RL-optimized KernelBench solutions per GPU).
+
+## License
+
+Source code is from vLLM, SGLang and FlashInfer, all Apache-2.0. This compilation is Apache-2.0.