LeapQuant

One repository containing the Gated DeltaNet (GDN) and Kimi Delta Attention (KDA) window-boundary state re-quantization kernels. Both use CuTe DSL and Blackwell tensor cores. The kernel algorithms and bytes are unchanged from their final accepted optimization submissions.

These are B300 deployment-kernel measurements against the request's TileLang baseline, from the 2026-10-24 paired evaluations. They do not measure a complete model or the allocation/packing overhead of the functional interface. Both implementations passed the original six workloads and deployment validation; the component directories retain the reports and exact source hashes.

Install and load

Tested on NVIDIA B300 (compute capability 10.3), Python 3.12, PyTorch 2.12.1+cu130 and nvidia-cutlass-dsl[cu13] 4.6.0. Other architectures are not advertised. See each component README for shapes and input contracts.

python -m pip install torch==2.12.1 --index-url https://download.pytorch.org/whl/cu130
python -m pip install "nvidia-cutlass-dsl[cu13]==4.6.0" cuda-bindings==13.3.1 kernels==0.17.2 huggingface-hub==2.2.0

from kernels import get_kernel

op = get_kernel("humanfia-lab/leapquant", version=1, trust_remote_code=True)
gdn = op.gdn  # run(...), Pool(...), flush_inplace(...)
kda = op.kda  # run(...), Pool(...), flush_inplace(...)

The main and v1 branches both contain the complete release. v1 is the version loaded above. The runtime sources are build/torch-cuda/_gdn.py and build/torch-cuda/_kda.py; metadata, the public import wrapper and the runtime license are alongside them. Each component directory contains its README, original workload, benchmark, baseline and reference definition, plus benchmark and precision results. Dependency pins and Apache-2.0 licensing are shared at the root.

Precision

See the standalone precision analysis for the original and current arithmetic paths, deliberate approximations, matched measurements, and validation limits. Input/output formats and original scoring thresholds are unchanged; both implementations pass all 18 original comparison cases.

The additional individual-state check found one current KDA outlier in the batch-128 random case (error ratio 1.01256057 against the individual limit 1.01). The original benchmark averages the batch dimension; the extra check treats individual states separately. Full records are linked in the analysis document. Reproduce with python compare_precision.py gdn and python compare_precision.py kda.

Reproduce

hf download humanfia-lab/leapquant --repo-type kernel --revision v1 --local-dir leapquant-kernels
cd leapquant-kernels
python -m pip install -r requirements.txt
cd gdn
python benchmark.py --impl ../build/torch-cuda/_gdn.py
cd ../kda
python benchmark.py --impl ../build/torch-cuda/_kda.py

Run python benchmark.py in either component directory for the original TileLang baseline (requires the pinned TileLang and nvcc on PATH). Construct and warm the caller-owned pool before CUDA graph capture. The flush operates only on heads whose counter equals 16; the counter remains at 16 for the decode step.