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Enable native Kev inference with the WebGPU backend

Abierto
  • ¿Algo no está bien?
#23,099 2 comentarios 0 reacciones 1 asignado Ver en GitHub

Los mantenedores suelen responder en 1 día

@akdybala ya está trabajando en esto.

Desde el 25/9/2026.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
c, cmake, cpp, python, wasm
Área
ai-infra-agents, backend, compilers, desktop-dev, mobile-dev

Línea de trabajo

Start with the WebGPU backend guide and operator tests in the repository. Examine the existing C++ Module API and native Dawn build. Review the Kev example's prefill and score graphs, and the Qwen3.5 MoE model reference for GDN math. The work involves auditing operator support, adding WGSL kernels, and integrating with the kev_runner and kev_benchmark. Success is measured by matching logits and probabilities against the PyTorch implementation.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

good first issue module: examples module: webgpu

Enable the native Kev example on the WebGPU backend, extending the XNNPACK and MLX support added in #23023.

Kev runs prefill followed by pointer-head scoring, with an immutable prefix snapshot reused across question batches. Start with the existing C++ Module API and a native Dawn build, without Python at inference.

Use the WebGPU backend guide and operator tests as starting points. Qwen3.5 MoE's GatedDeltaNet implementation provides a reference for GDN math and state layouts. Coordinate shared export/lowering work with the Vulkan issue, #23098.

Work to cover:

  • Audit Kev's prefill and score graphs against WebGPU runtime support. Add missing WGSL kernels and lowering support, including GDN, with focused backend regression tests. Export success alone is insufficient: WebGPUPartitioner shares Vulkan's frontend, whose operator coverage is broader.
  • Add a WebGPU export option and CMake linkage for the existing kev_runner and kev_benchmark. Respect the current mutually exclusive Vulkan/WebGPU runtime registration when linking.
  • Preserve system_one, explicit prefill/evaluate, configurable token limits, variable question/option counts, and batching beyond eight questions. Repeated evaluations must leave the prefix unchanged.
  • Start with an unquantized FP32 baseline, retaining the fitted temperature and checkpoint metadata. Compare logits, probabilities, and reused state against upstream Kev's PyTorch implementation and the existing FP32 path, with documented tolerances.
  • Verify the backbone, including GDN, executes on WebGPU and report any CPU fallback. Document a tested adapter, device/buffer limits, export/build/run commands, and measurements using kev_benchmark.

Keep the integration in the existing example and reuse the runner and benchmark. Document browser/WebAssembly constraints separately from the initial native Dawn support.

cc @iseeyuan @lucylq @helunwencser @tarun292 @kimishpatel @jackzhxng

Lenguaje dominante
Python
Estrellas
5k
Forks
1.2k
Merge medio
2 d 9 h
PR fusionados (30 d)
555

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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