dynquant-kernels

Compiled CUDA kernels for DynQuant.

You almost certainly want pip install dynquant, which resolves this wheel for your platform. Install it directly only to force a specific variant.

What is in here

One shared object, exposed through the PyTorch dispatcher under the dynquant namespace (not pybind, so torch.compile can trace through it):

• n-bit tile dequantization
• decode GEMV, templated on bit-width — the memory-bound case that dominates serving
• tensor-core prefill GEMM (cuBLASLt, then fused CUTLASS)
• MoE grouped GEMM — one launch for all experts, not one per expert

Built with Thrust, CUB, cuBLASLt and CUTLASS. -use_fast_math is off by design: it implies denormals-to-zero, and a dequantized weight near the bottom of a 2-bit group's range is exactly the denormal case, so accuracy measured with it on is not reproducible.

Variants

The wheel version carries a PEP 440 local segment identifying what it was built against, the same scheme torch uses:

dynquant-kernels==0.5.3+cu128torch28

Compatibility with dynquant-core is enforced by an ABI number baked into the binary, checked at import. A mismatch refuses to load with an actionable message rather than returning wrong numbers — see csrc/include/dynquant/abi.h.

Diagnosing an install

This package never raises on import, so a broken binary degrades to the reference backend rather than taking down import dynquant. To find out what happened:

import dynquant_kernels

print(dynquant_kernels.report())

That works with dynquant-core absent, which is the point — it makes this wheel diagnosable on its own. dynquant doctor gives the same information in context.

Building from source

pip install --no-build-isolation -e packages/dynquant-kernels

Needs CMake ≥ 3.26, nvcc, and a C++17 compiler. --no-build-isolation is required: an isolated build resolves the newest torch, and an extension linked against a different libtorch than the one you run fails to import.

With no CUDA toolkit present the build still succeeds, producing a CPU-only extension — deliberately, so a cheap CI runner catches CMake and registration errors before a GPU runner is needed. Release builds set DYNQUANT_REQUIRE_CUDA=ON so that a toolkit which failed detection is a build failure, not a wheel advertising kernels it does not contain.

License

Apache-2.0.