GPU kernels, tolerance and graders
Collection
Kernels measured on real hardware, and the graders that decide when two correct answers disagree. • 4 items • Updated
You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
These records were measured by Laela Zorana on a Kaggle TPU v5e worker. Use them for non-commercial work, keep the attribution with them, and tell me what you are building.
Log in or Sign Up to review the conditions and access this dataset content.
JAX/Pallas attention measured on one TPU v5e device
I measured a Pallas masked-softmax kernel against the JAX/XLA reference inside grouped-query attention. The Kaggle worker exposed eight TPU devices. The unsharded arrays ran on JAX's default single device, so these numbers make no multi-device scaling claim.
All seven correctness checks passed. The benchmark contains twenty cases, with five warmups and twenty synchronized timing samples in each case. XLA was faster in all ten paired comparisons. At sequence length 2048, full attention measured 0.8063 ms for XLA and 1.9225 ms for Pallas.
| Scope | Sequence | XLA reference | Pallas | Pallas slowdown |
|---|---|---|---|---|
| softmax | 128 | 0.1761 ms | 0.2166 ms | 1.23x |
| softmax | 256 | 0.1805 ms | 0.2526 ms | 1.40x |
| softmax | 512 | 0.2445 ms | 0.3516 ms | 1.44x |
| softmax | 1024 | 0.4080 ms | 0.5200 ms | 1.27x |
| softmax | 2048 | 1.0967 ms | 1.5886 ms | 1.45x |
| attention | 128 | 0.1811 ms | 0.2353 ms | 1.30x |
| attention | 256 | 0.1801 ms | 0.2462 ms | 1.37x |
| attention | 512 | 0.1840 ms | 0.3155 ms | 1.71x |
| attention | 1024 | 0.2354 ms | 0.4715 ms | 2.00x |
| attention | 2048 | 0.8063 ms | 1.9225 ms | 2.38x |
Inputs were bfloat16. Logits, normalization, and output accumulation used fp32. The Pallas result matched the reference in all seven checks, including causal and padding masks, an all-masked row, a non-128 key width, and gradients with respect to Q, K, and V.
Created and measured by Laela Zorana. The project is on Hugging Face and GitHub.
The two engineering layers
| Layer | What is tested |
|---|---|
| Kernel and compiler | 8x128 Pallas tiles, TPU padding, fp32 reductions, StableHLO, compile time, synchronized device time |
| Model and evaluation | Grouped-query head mapping, causal decoding, padding masks, all-masked rows, Q/K/V gradients, adversarial candidates |
The custom kernel owns masked softmax. XLA owns the QK and probability-times-V matrix multiplications. The StableHLO and raw timings record that boundary. Fusion and data movement are the next measurements.
Correctness and grader checks
| Risk | Check |
|---|---|
| Wrong grouped-query mapping | 4 query heads mapped to 2 distinct KV heads |
| Future-token leakage | Causal masks, including K longer than Q for cached decoding |
| Padding leakage | Non-power-of-two widths padded to TPU's 128-lane shape |
| Undefined all-masked rows | Exact zero output, no NaN or Inf |
| Precision loss | fp32 logits and reduction with bfloat16 inputs |
| Broken backward semantics | Q, K, and V gradient parity against pure JAX |
| Unsynchronized timing | Every timed result completes with block_until_ready() |
| Weak evaluation | Known-bad implementations must lose points |
Repository contents
src/tpu_kernel_lab/
attention.py pure-JAX oracle and Pallas-backed attention
pallas_softmax.py 8x128-tiled kernel with an explicit backward rule
adversarial.py missing-mask, low-precision, and wrong-GQA candidates
rubric.py correctness and gradient scorecard
benchmark.py synchronized JSON benchmark
tests/ parity, edge-case, gradient, and rubric tests
kaggle/ TPU measurement script
evidence/ hardware reports and StableHLO
dataset/ flat tables, raw JSON, data notes, and checksums
Reproduce the checks
python -m venv .venv
. .venv/bin/activate
python -m pip install -e ".[dev]"
pytest
tpu-kernel-rubric --candidate reference
tpu-kernel-rubric --candidate ignores-causal
On a CPU, Pallas runs in interpret mode for correctness. TPU_RUNBOOK.md carries the hardware
commands. The checked hardware evidence is under evidence/kaggle-v5e-8/, and the viewer tables are
under dataset/.
Rebuild the data release
After downloading a completed Kaggle run into kaggle-output/:
python scripts/build_publication.py
python scripts/verify_publication.py
The builder accepts only a run with eight TPU devices, seven passing correctness checks, and twenty successful benchmark cases. It keeps transfer, compilation, and synchronized steady-state time in separate fields and retains every timing sample.
The completed run first paired JAX 0.10.2 with the Kaggle image's June 2025 libtpu. Pallas rejected
that version gap. The measured run used JAX and JAXLIB 0.6.2 with the bundled TPU runtime, and the
bootstrap record stays in the data.
Licences
The source code is Apache 2.0. The records under dataset/ are CC BY-NC 4.0. Commercial licensing is
available through the Hugging Face profile. The notice file
carries the measurement attribution.
- Downloads last month
- 7