Adding support for fused_moe_gmm

[NicoGrande]

Description

This PR adds support for the tpu-inferece fused_moe_gmm kernel in the MaxText MoE inference codepath. Initial results using this kernel show up to ~4x generation throughput increase when testing with qwen3-30b-a3b.

Additionally, this PR introduces a second optimization to MaxText which pre-fuses the MoE weight kernels such that they can be efficiently passed into the fused_moe_gmm kernel. We show the impact of these optimizations below with autoregressive generation step times:

Baseline (MaxText sparse_matmul MoE): 28.353 ms

Fused MoE (prefuse_moe_weights=False): 20.432 ms

Fused MoE (prefuse_moe_weights=True): 6.114 ms

Tests

This PR adds new tests to tests/unit/moe_test.py. Additionally this PR was tested e2e with both qwen3-30b-a3b and qwen3-235b-a22b.

Checklist

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• I have performed a self-review of my code. For an optional AI review, add the gemini-review label.
• I have necessary comments in my code, particularly in hard-to-understand areas.
• I have run end-to-end tests tests and provided workload links above if applicable.
• I have made or will make corresponding changes to the doc if needed, including adding new documentation pages to the relevant Table of Contents (toctree directive) as explained in our documentation.

[codecov]

Codecov Report

❌ Patch coverage is 45.07042% with 39 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/maxtext/layers/moe.py	23.33%	20 Missing and 3 partials ⚠️
src/maxtext/utils/model_creation_utils.py	60.97%	13 Missing and 3 partials ⚠️

📢 Thoughts on this report? Let us know!

[RissyRan]

Overall LGTM. Could you add your tests to showcase the functionality? Thanks!

[gobbleturk]

Awesome!

[NuojCheng]

LGTM