| ---
|
| license: mit
|
| pretty_name: KernelStrain
|
| task_categories:
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| - text-generation
|
| tags:
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| - code
|
| - cuda
|
| - triton
|
| - gpu-kernels
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| - kernel-optimization
|
| - synthetic-dataset
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| - sft
|
| - pytorch
|
| - llm-training
|
| size_categories:
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| - 100K<n<1M
|
| configs:
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| - config_name: episodes
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| data_files:
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| - split: train
|
| path: episodes.jsonl
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| - config_name: sft_multiturn
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| data_files:
|
| - split: train
|
| path: sft_multiturn.jsonl
|
| - config_name: sft_steps
|
| data_files:
|
| - split: train
|
| path: sft_steps.jsonl
|
| - config_name: holdout
|
| data_files:
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| - split: test
|
| path: tasks_holdout.jsonl
|
| ---
|
|
|
| # KernelStrain
|
|
|
| **Long-horizon GPU kernel optimization trajectories for training small models to iterate on CUDA and Triton kernels.**
|
|
|
| KernelStrain is a large synthetic dataset of kernel-optimization episodes: given a kernel task (shapes, dtype, GPU target, baseline code + timing), a model proposes successive *complete* kernel candidates, observes simulated benchmark / correctness / compile feedback, and keeps improving over many steps — structural rewrites, parameter sweeps, joint configs, rebenchmarks, and endgame fine-tuning.
|
|
|
| Built to teach a small model the *loop* of kernel work: propose → measure → regress → revert → recover → repeat, for tens of steps per task.
|
|
|
| > ⚠️ **All benchmark numbers are produced by an analytic roofline-style simulator — no kernel in this dataset was ever compiled or executed.** Timings are plausible, not measured. Kernel source is realistic-looking but unverified; use it for training dynamics, not as production code.
|
|
|
| ## At a glance
|
|
|
| | | |
|
| |---|---|
|
| | Episodes | 11,514 |
|
| | SFT step rows | ~282k |
|
| | Multiturn conversations | 11,514 |
|
| | Holdout tasks | 185 (descriptions only, no solutions) |
|
| | Op modules | 57 (45 distinct ops × CUDA/Triton impls) |
|
| | Avg steps / episode | ~24.5 |
|
| | Step statuses | ~94% ok, ~2.6% wrong-result, ~3.3% compile-error |
|
| | GPU targets | A100 (sm_80), H100 (sm_90), RTX 4090, MI300X (gfx942) |
|
|
|
| ## Coverage
|
|
|
| Kernels spanning AI **inference and training**:
|
|
|
| - **Attention**: flash prefill, paged decode, MLA decode (latent KV), sliding-window, cross-attention, tree attention (speculative decoding), flash attention backward
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| - **Norms**: RMSNorm, LayerNorm, fused add+RMSNorm, LayerNorm backward
|
| - **Activations/fusions**: SwiGLU fwd/bwd, bias+GELU, dropout (Philox)
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| - **Matmul**: fp16 GEMM/GEMV, int4/int8 GEMM, fp8 GEMM, blockwise-scaled fp8 GEMM (DeepGEMM-style), grouped GEMM
|
| - **Training**: AdamW, Adafactor, grad clipping, cross-entropy fwd/bwd, embedding fwd/bwd, reductions, prefix scan
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| - **Quant**: fp8 tensor quant, int8 KV-cache quant
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| - **Sampling**: top-k, nucleus (top-p) — sort, histogram-refine, and CDF-pick strategies
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| - **SSM/conv**: Mamba selective scan (CUDA + Triton), causal Conv1D
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| - **MoE**: scatter, grouped GEMM, combine
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| - **Misc**: RoPE, transpose, KV-cache append, embedding gather
|
|
|
| Optimization patterns exercised: vectorized loads (uint4/float4/half2), shared-memory staging, cp.async pipelines, TMA, warp shuffles, online softmax, split-K/split-KV with combine kernels, persistent kernels, tensor-core mma/wgmma, atomics, multi-kernel decompositions, occupancy/tile tuning — plus realistic failures: arch-gated compile errors (sm_90-only wgmma, no fp8 on A100, PTX on ROCm), shared-memory overflows, and wrong-result regressions that get reverted.
|
|
|
| ## Files
|
|
|
| | file | format | contents |
|
| |---|---|---|
|
| | `episodes.jsonl` | jsonl | Raw episodes — every step with full source, params, status, timing, smem, keep/revert decisions |
|
| | `sft_multiturn.jsonl` | chat | Multi-turn conversations; assistant turns contain **complete kernel code only** (no reasoning traces) |
|
| | `sft_steps.jsonl` | chat | One row per transition: task + history + current-best → next candidate |
|
| | `tasks_holdout.jsonl` | jsonl | Held-out task prompts without solutions (~8% of combos) |
|
| | `stats.json` | json | Generation statistics |
|
|
|
| Feedback messages report **best-so-far** only — no future information leaks into context.
|
|
|
| ## Usage
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| # step-level SFT rows
|
| ds = load_dataset("Akahsizrr/kernelstrain", "sft_steps", split="train")
|
|
|
| # full multiturn episodes
|
| mt = load_dataset("Akahsizrr/kernelstrain", "sft_multiturn", split="train")
|
| ```
|
|
|
| ## Generation
|
|
|
| Pure-stdlib simulator: `time = max(memory_time, compute_time) + launch_overhead`, with GPU-specific peaks, tunable efficiency scaling, arch/smem gates, and Gaussian rebench noise. Deterministic from seed. Source: the `kernelstrain` generator.
|
|
|
| ## Caveats
|
|
|
| - Perf numbers are analytic-model-plausible, **not measured** — the simulator can occasionally rank variants differently than real hardware would.
|
| - Kernel code is unverified; some templates may contain bugs that a real compiler would catch.
|
| - Assistant outputs are intentionally code-only; there are no reasoning/CoT traces by design.
|
|
|
| ## License
|
|
|
| MIT
|
|
|
