guychuk/kernel-opt-rl

Kernel-Opt RL

Real-world GPU kernel optimization data mined from production LLM inference engines (vLLM, SGLang, 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 RL tasks, with the benchmark gains reported in each PR description extracted as structured measurements.

Subsets

tuned_configs: kernel + deployment → best launch config

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
documented gains	148 tuning PRs traced, 66 with benchmark tables; speedup_vs_default on 399 rows from 46 config files (median 1.30×, p10 1.03×, p90 1.98×)

One row is one (config file, batch size M) entry. prompt 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). It asks for a JSON config. best_config is the autotuned answer.

Gain fields: config_pr_url is the newest PR that added or changed the config file. config_pr_gains holds all measurements extracted from that PR. speedup_vs_default is the documented speedup of the tuned config over vLLM/SGLang's default heuristic at this exact M. It is set only when the attribution is unambiguous: the GPU and shape match, and multiple matching rows agree within 5%.

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: optimize a kernel file the way the real perf PR did

rows	1,517 file-level tasks (1,365 train / 152 test) from 850 PRs
languages	CUDA (.cu/.cuh), C++, Triton, CuTe DSL
date range	2023-04 → 2026-10
documented gains	529 PRs with benchmark numbers → 913 rows; kernel_speedup on 596 rows (median 1.33×, p10 1.02×, p90 4.25×), e2e_speedup on 556 rows

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.

prompt gives the goal (the PR title, which plays the role of the issue in SWE-RL), the target hardware and models, and the full before_code. It asks for SEARCH/REPLACE edits. The PR body is withheld from the prompt because it usually describes the fix. The reference answer is after_code / diff. Prompt lengths: median 21K chars (6K tokens), p90 ~74K chars. Filter on len(prompt) for short-context RL.

Other fields: models_mentioned, hardware_mentioned, speedup_claims (regex over the commit message), pr_body, gains (structured measurements from the PR), kernel_speedup / e2e_speedup (median documented speedup at kernel and end-to-end scope), license.

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.

gains / config_pr_gains format

A JSON list of measurements extracted from the PR description: {scope: "kernel"|"e2e", hardware, model, shape, batch_size, metric, unit, higher_is_better, baseline, optimized, stated_speedup, speedup}. speedup is computed in code from baseline/optimized and normalized so that > 1 means the PR is better. When a PR states only a ratio, stated_speedup is used. Extraction was done by Qwen3-235B-A22B-Instruct-2507, with gpt-oss-120b as a fallback for very long tables, using an extract-only prompt (no computed numbers). Audit: in 98.8% of extracted baseline/optimized pairs, both values appear verbatim in the PR text. The remainder were mostly averages of repeated runs. Extraction failed for 5 PRs (malformed model output), which have empty gains.

Using it for RL

Rewards live in code/reward.py:

• tuned_configs: config_reward is deliberately a stub, because the partial-credit scheme is a design choice. Compare the parsed JSON config to best_config as an offline proxy (exact match is sparse, so use partial credit or log-distance on the power-of-two tile sizes). speedup_vs_default tells you how much a config actually matters for that row and can weight the reward. On hardware, launch kernel_source with the predicted config and reward t_best / t_pred. The tuned config is only the best within the tuner's search space, so it can be beaten.
• kernel_commits: patch_reward(completion, before_code, after_code) follows SWE-RL. It returns −1 if the edits are malformed or don't apply, and otherwise the difflib similarity between the changed lines and the real commit's. Validated on this data: the oracle edit scores 1.0 on 98.7% of rows and ≥ 0.95 on all but one. For an execution reward, run through an environment such as KernelGYM. Watch for reward hacking (timer exploits, cached outputs; see the CUDA-L1 notes).

from datasets import load_dataset
ds = load_dataset("guychuk/kernel-opt-rl", "kernel_commits", split="train")
ds = ds.filter(lambda r: len(r["prompt"]) < 32_000 and r["kernel_speedup"])  # short, with documented gains

Both subsets have a plain-string prompt column. TRL's GRPOTrainer forwards the other columns (best_config, before_code, after_code, …) to reward functions as keyword arguments.

Limitations

• Tuned configs record only the winner, not the full latency table, so offline config rewards are a proxy. speedup_vs_default covers only rows whose PR documented that exact M.
• 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.
• PR gains are self-reported by the authors, on their hardware and benchmark setup, and are not reproduced.
• Snapshots are from each repo's HEAD at build time (see commit). Rebuild for fresher data.

Licensing

All code comes from vLLM, SGLang and FlashInfer (all Apache-2.0). Some vendored files carry their own licenses. Each row has a license field taken from the file header (Apache-2.0: 1,481 commit rows and all config rows; BSD-3-Clause / BSD: 35; MIT: 1). Files under non-open-source licenses (the NVIDIA TensorRT Source Code License Agreement and NVIDIA proprietary headers) are excluded. License and copyright headers in the code are kept verbatim. The upstream LICENSE and NOTICE files are in licenses/, as Apache-2.0 §4 requires. The compilation itself is released under Apache-2.0. PR descriptions are quoted from public GitHub PRs and linked via pr_url.

Related datasets

KernelBench (eval; keep held out), KernelBook (PyTorch→Triton pairs), Nemotron-SFT-CUDA-v1 (CUDA SFT with tests), CUDA-L1 (RL-optimized KernelBench solutions per GPU).

Reproduce

The build code is in code/. uv run build.py --push <repo_id> clones the three repos as blobless clones, mines configs and commits, fetches PR descriptions with gh, extracts gains via HF Inference Providers (cached), and pushes.