Qwen3 / other-model application contributions & decode-attention operator roadmap #156

Is your feature request related to a problem? Please describe.

We adapted Qwen3-0.6B to run end-to-end on NPU2 (Strix Point) with IRON. Two questions before we contribute:

1. Does upstream accept model applications beyond Llama (e.g. Qwen3)? What are the acceptance criteria (app structure, test/benchmark requirements, weight handling)?
2. Are there plans for first-class decode-oriented attention (fused GQA with runtime seq_pos, KV cache streamed from DRAM)? Today a model port must assemble decode attention from GEMV + Softmax + Transpose per layer.

Describe the solution you'd like

• A place (or documented criteria) for model applications beyond Llama
• A decode-attention operator (fused scores-GEMV + online-softmax + context-GEMV) as a standard operator

For reference, we have a working pure-IRON Qwen3-0.6B decode (28 layers in a single OperatorSequence, ~65 ms/token, logits cosine 0.9985 vs HuggingFace):
https://github.com/ZhongYic00/IRON/tree/feat/qwen3-decode

• app: iron/applications/qwen3_0.6b/ (dialogue runner, HF cosine/e2e checks, TPOT bench, README)
• new operators: decode_attn (fused decode attention, runtime seq_pos, S_KV up to 4096), qk_norm, gemv_argmax(_bf16), etc.

Describe alternatives you've considered

Hand-rolling per-model decode attention from GEMV + Softmax + Transpose (what the Llama app does). Works, but it is ~10 ops per layer with per-op array reconfiguration; a fused decode_attn operator collapses that to one.