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docs: document MXFP numerics (mxfp8/mxfp4/nvfp4) and hardware dispatch#324

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docs: document MXFP numerics (mxfp8/mxfp4/nvfp4) and hardware dispatch#324
lokic233 wants to merge 1 commit into
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lokic233:docs/mxfp-numerics-hardware-dispatch

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@lokic233

@lokic233 lokic233 commented Jul 6, 2026

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Summary

Qwix supports mxfp8, mxfp4, and nvfp4 as user-facing weight_qtype/act_qtype (see numerics.py, qarray.py tile_size=32/16, and the PTQ/QT provider tests) but they are entirely absent from the user docs (docs/source/, README.md).

This adds a docs/source/mxfp.md page documenting:

  • Block/tile sizes (32 for mxfp8/mxfp4, 16 for nvfp4)
  • Scale format (float8_e8m0fnu vs float8_e4m3fn)
  • Hardware dispatch table: GPU uses fused jax.nn.scaled_matmul; TPU/CPU fall through to native tiled fp8 dot_general (_fast_dot_general), which is correct and MXU-accelerated
  • Performance note about activation-quant overhead vs weight caching

No code change — docs only.

Qwix supports mxfp8, mxfp4, and nvfp4 as user-facing weight/activation
qtypes, but they are absent from the user documentation. This adds a docs
page covering:

- Block/tile sizes (32 for mxfp8/mxfp4, 16 for nvfp4)
- Scale format (e8m0fnu vs e4m3fn)
- Hardware dispatch table (GPU fused via scaled_matmul; TPU/CPU native
  fp8 dot_general fallback)
- Performance note about activation-quant overhead

No code change — docs only.
@google-cla

google-cla Bot commented Jul 6, 2026

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Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).

View this failed invocation of the CLA check for more information.

For the most up to date status, view the checks section at the bottom of the pull request.

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