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Enable native Kev inference with the Vulkan backend

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#23,098 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
30/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
c, cmake, cpp, python, pytorch
Área
ai-infra-agents, backend, desktop-dev, mobile-dev

Línea de trabajo

Start with the Kev example in examples/kev/ and the Vulkan export example in examples/vulkan/. Examine the Vulkan backend in backends/vulkan/ to identify missing operator support for GatedDeltaNet and dynamic shapes. Write a small GDN parity test as a first step, then extend the export and CMake linkage to allow kev_runner to use the Vulkan backend. Validate against the PyTorch reference implementation.

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

Descripción

good first issue module: examples module: vulkan

Enable the native Kev example on the Vulkan 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. The goal is to support this workflow through the existing C++ API on Vulkan-capable GPUs, without Python at inference.

Start with Kev's model/export code, the Vulkan export example, and the Vulkan backend. Qwen3.5 MoE's GatedDeltaNet implementation provides a reference for GDN math and state layouts; Vulkan will need its own lowering or shader support where coverage is missing.

Work to cover:

  • Identify operator and dynamic-shape gaps in Kev's prefill and score graphs. Add the required Vulkan support, including GDN, with focused backend regression tests. A small GDN parity test is a useful first step.
  • Add a Vulkan export option and CMake linkage so the existing kev_runner and kev_benchmark run the exported model through Module.
  • 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 path, retaining the fitted temperature and checkpoint metadata. Check 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 Vulkan and report any CPU fallback. Document a tested GPU, precision requirements, export/build/run commands, and measurements using kev_benchmark.

Keep the integration in the existing example and reuse the runner and benchmark. A Vulkan-capable GPU and the Vulkan SDK are needed for validation.

cc @SS-JIA @manuelcandales @digantdesai @cbilgin @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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