| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - cuda |
| - triton |
| - kernel-optimization |
| - gpu |
| - inference |
| - llm-inference |
| - synthetic-data |
| size_categories: |
| - 1G<n<10G |
| --- |
| |
| # kernelstrain2 — kernelinfer |
|
|
| Synthetic long-horizon GPU kernel-optimization trajectories, **inference-only**. |
|
|
| Each row is a complete optimization *session* (~30–200 steps, simulating |
| 1–5 hours of work): the model receives an inference-kernel task, emits a |
| full CUDA or Triton candidate, and gets simulated compile / correctness / |
| benchmark feedback — iterating, self-correcting, and tuning launch |
| parameters until convergence. No reasoning traces; assistant turns are |
| kernel code only. |
|
|
| ## Scope |
|
|
| **Inference workloads only** — no training kernels. Operations cover: |
|
|
| - decode/prefill attention: flash_decode, flash_prefill, swa_decode, |
| mla_decode, tree_attn, chunked_prefill, cross_attn, fused_qkv_rope |
| - quantized GEMM/GEMV: fp16, int8, int4, fp4, fp8 (incl. blockwise DeepGEMM |
| style), split-K, wgmma/TMA persistent variants |
| - KV-cache ops: paged_kv_copy, kv_append, kv_int8_quant |
| - samplers: topk, nucleus (top-p), min-p, logits_softmax |
| - MoE: scatter, combine, grouped_gemm |
| - SSM: mamba_scan, mamba_step |
| - misc: rmsnorm, rope, swiglu_act, embedding_gather, conv1d_causal, |
| conv2d_nhwc, gemv_fp16 |
| |
| 41 ops, CUDA + Triton implementations. |
| |
| ## Hardware targets |
| |
| H100 (sm_90a), H200 (sm_90a), A100 (sm_80), A10 (sm_86), RTX A6000 (sm_86), |
| RTX 5090 (sm_120). Arch-gating is modeled correctly (wgmma/TMA only on |
| sm_90a; fp8 on sm_89+/sm_90/sm_120; fp4 on sm_120; per-block smem caps |
| 99/164/227KB). |
|
|
| ## Format |
|
|
| JSONL, one session per line: |
|
|
| - `messages` — training data: system + user (task, then `[t+H:MM:SS] RESULT` |
| feedback turns) + assistant (full kernel source, no prose, no CoT) |
| - `trace` — compact per-step metadata (tag/kind/params/status/time/kept) |
| - top-level: `op`, `impl` (cuda|triton), `gpu`, `arch`, `dtype`, `shape`, |
| `signature`, `desc`, `baseline_ms`, `best{step,time_ms,speedup}`, |
| `attempts`, `elapsed_s`, `elapsed_hms`, `num_steps` |
|
|
| `batches/` holds 100-session shards; `sessions.jsonl` is the merged view; |
| `tasks_holdout.jsonl` = 195 held-out task specs (no solutions). |
|
|
| ## Stats |
|
|
| - 1,899 sessions, all unique (op, gpu, shape, dtype) combos |
| - ~180K total steps; avg 94.6 steps/session |
| - avg 2.18h simulated (min 1.03h, max 4.93h); avg 13.4x speedup |
| - step statuses: 151.8K ok / 22.4K compile_error / 5.3K wrong_result |
| - ~1.5GB |
|
|
| ## Provenance |
|
|
| Fully synthetic: benchmark/correctness numbers come from an analytic |
| roofline-style model — **no kernels were ever compiled or executed**. |
| Intended for training small models on long-horizon iterative optimization |
| behavior. Per-batch QA reports in the generating repo |
| (`kernelinfer/qa_reports/`); all 19 batches scored 10/10 on mechanical |
| checks and ≥8.5/10 on qualitative review after fixes. |
|
|
