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Akahsizrr
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kernelstrain2

Tasks:
Text Generation
Tags:
cuda
triton
kernel-optimization
gpu
inference
llm-inference
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metadata 
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.