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Module textenc

Module textenc 

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Gemma-2 text ENCODER forward (the Lumina-Image 2.0 prompt encoder): token ids → per-token hidden states of every layer.

Second increment of the image-generation runtime (docs/GENERATIVE.ru.md). A deliberately standalone, f32, full-sequence forward — no KV cache, no sampling — loaded from a diffusers/HF text_encoder/ directory (config.json + sharded safetensors). Gemma-2 specifics carried exactly: embedding scale √hidden, RMSNorm with (1 + w), sandwich norms around attention AND the GeGLU MLP, GQA with query_pre_attn_scalar scaling, attention logit softcapping tanh(x/50)·50, RoPE θ=10000. Sliding-window layers are exact for prompts shorter than the 4096 window (image prompts are), enforced by an assert.

Parity: python/gemma_ref.py + tests/textenc_parity.rs on the real Lumina text-encoder weights.

Structs§

GemmaEncoder
Gemma-2 encoder: exact f32 from a diffusers directory, or CMF-quantized (mmap-resident, per-token embed dequant) from a packaged file.