fix(qwen3_next/qwen3_5): repair gdn import after mamba.gdn package refactor

[RhizoNymph]

What

Repairs the import of GatedDeltaNetAttention in qwen3_next.py and qwen3_5.py. Both pointed at the flat module vllm.model_executor.layers.mamba.gdn_linear_attn, which the upstream mamba-attention refactor (vllm-project#41126) moved into the mamba.gdn package as mamba.gdn.qwen_gdn_linear_attn.QwenGatedDeltaNetAttention. The integration merge (#186) left these two files on the dead path, so Qwen3-Next and Qwen3.5 failed to import entirely.

Changes:

• Both files now import QwenGatedDeltaNetAttention from mamba.gdn.qwen_gdn_linear_attn and use it for self.linear_attn (matches upstream/main's own qwen3_next.py/qwen3_5.py).
• qwen3_5.py additionally dropped the create_in_proj_qkvz=... kwarg: the present QwenGatedDeltaNetAttention ctor (identical to upstream/main) does not accept it. Upstream's qwen3_5.py does not pass it either. The fork's steering wiring in this file is untouched.

Why this is not duplicating an existing PR

This is an internal feat/integration merge-skew repair; the corresponding files are already correct on upstream/main, so there is no upstream PR to duplicate.

Tests run

• tests/model_executor/test_qwen3_5_quantization.py — 2 failed → 2 passed with this change (it imports qwen3_5, so it was failing on feat/integration; verified red without the fix, green with it).
• Import smoke: import vllm.model_executor.models.qwen3_next, qwen3_5 now succeeds (previously ModuleNotFoundError).
• With this fix applied on top of the mlp_in/mlp_out branch (feat(capture): wire mlp_in/mlp_out hooks for transcoder training #206), the previously-skipped test_qwen3_next_mlp_hooks runs and passes (5/5 in test_mlp_capture_hooks.py).

Known limitation

Dropping create_in_proj_qkvz means LoRA-on-Qwen3.5 reverts to the always-fused in_proj_qkvz path (same as upstream/main). Restoring the LoRA-aware split requires porting the newer QwenGatedDeltaNetAttention that accepts the kwarg; out of scope for this import repair.

Not validated here

End-to-end GPU inference (Qwen3-Next / Qwen3.5 weights on a GPU, mamba/linear-attn forward) — needs hardware + weights, not runnable in this environment.

AI assistance was used for this change.