fix gemma2 checkpoint conversion validation check

[gagika]

Description

Fix Gemma 2 architecture validation during Hugging Face conversion

This updates the _validate_or_update_architecture logic in to_huggingface.py to correctly handle Gemma 2 specific discrepancies:

1. num_hidden_layers: MaxText bundles local and global attention layers into a single Gemma2DecoderLayer, halving the layer count relative to Hugging Face. The validation now multiplies the MaxText layer count by 2 for Gemma 2 before comparison.
2. vocab_size: MaxText pads vocabulary sizes up to multiples of 128 or 256 for optimal TPU efficiency (e.g., 256128 instead of 256000). The logic now gracefully accepts a padded vocab size up to a 256-token difference.

Tests

Tested locally

python3 -m maxtext.checkpoint_conversion.to_huggingface src/maxtext/configs//base.yml model_name=gemma2-2b ...

I0328 01:24:02.204004 140209930927680 to_huggingface.py:318] ✅ MaxText model successfully saved in HuggingFace format at ./tmp/hf/gemma2-2b/2026-03-27-12-47
I0328 01:24:02.204053 140209930927680 to_huggingface.py:319] Elapse for save: 0.18 min
I0328 01:24:02.204087 140209930927680 to_huggingface.py:320] Overall Elapse: 0.63 min
I0328 01:24:02.204361 140209930927680 utils.py:750] Peak Memory: 26.80 GB

Checklist

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• I have performed a self-review of my code. For an optional AI review, add the gemini-review label.
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• 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

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