One-shot correctness checks for the flex_attention + paged KV path
against a vanilla HF `model(input_ids, use_cache=False)` forward on
the same prompt. Report max / mean abs diff on last-position logits
plus argmax match and top-10 overlap, so numerical drift and semantic
equivalence are both visible.
Shared approach: load the base model, deep-copy it for the flex path
so the attention patching does not mutate the vanilla comparison, run
both on the same tokenized prompt, report diffs.
`verify_gemma4_numerics.py` mirrors `gemma4_flex_inference.py`'s
text-only loader (Gemma4ForConditionalGeneration -> drop vision / audio
towers -> language_model into a Gemma4ForCausalLM shell) before
deep-copying. The softcap is NOT re-applied on the vanilla logits
since `Gemma4ForCausalLM.forward` already applies
`final_logit_softcapping`.
`verify_qwen3_numerics.py` runs `qwen3_flex_inference.FlexInference`
against either Qwen3 or Llama-3.2 via `--model_name`. `fa4_prefill` is
disabled because short prompts hit a CuteDSL sm_100 shape mismatch in
`handle_block_sparse_empty_tile_correction_sm100`.
Results on B200 bf16, 6 to 7 token prompt:
| Model | max abs | mean abs | argmax | top-10 |
|---------------------------------|---------|----------|--------|--------|
| unsloth/Qwen3-4B-Base | 0.313 | 0.088 | yes | 10/10 |
| unsloth/Llama-3.2-3B-Instruct | 0.125 | 0.021 | yes | 10/10 |
| unsloth/gemma-4-E2B-it | 0.375 | 0.080 | yes | 10/10 |
All three land in the same bf16 Triton-flex vs eager-matmul drift band;
Gemma-4's extra per-layer-input path and layer_scalar do not widen the
gap despite 20 of 35 layers going through the shared-KV link.
