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Enable native Kev inference with the WebGPU backend

Aperta
#23,099 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
35/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
c, cmake, cpp, python, wasm

Direzione di ricerca

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.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

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

Lingua principale
Python
Stelle
5k
Fork
1.2k
Merge medio
2g 13h
PR unite (30g)
595

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