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Enable native Kev inference with the Vulkan backend

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@TejasweeSulekh 已经在做这个了。

开始于 2026年9月29日。

  • #23272 来自 @TejasweeSulekh —— 已关闭，未合并
  • #23808 来自 @TejasweeSulekh —— 未关闭

评估

难度
5/5
预计耗时
一周以上
新手友好度
30/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
活跃
技术栈
c, cmake, cpp, python, pytorch
领域
ai-infra-agents, backend, desktop-dev, mobile-dev

调研方向

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.

由索引模型根据 Issue 内容生成。

描述

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

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环境准备

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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