[Model] ColQwen3.5: fix retrieval correctness (bias + bidirectional)

[athrael-soju]

Summary

Follow-up to #36887. The in-tree ColQwen3_5 model deviates from the native
colpali ColQwen3_5Processor inference pipeline in three silent ways, none caught
by the current sanity-level tests. Measured cost: ~2.5 ndcg@10 on Vidore3.
This affects every ColQwen3.5 checkpoint — e.g. athrael-soju/colqwen3.5-4.5B-v3
and athrael-soju/VultronRetrieverPrime-Qwen3.5-8B — both of which MTEB loads
through the one ColQwen3_5Wrapper. This fixes the shared class so both load
correctly on vanilla vLLM with their published architectures: ["ColQwen3_5"].

1. Dropped projection bias

colpali's custom_text_proj = nn.Linear(hidden, dim) is bias=True by default and
the trained checkpoints ship a custom_text_proj.bias, but the class built it
bias=False, so load_weights silently skipped the trained bias. Now built with a
bias and zero-initialized: a bias-less checkpoint is unchanged; a trained bias is
loaded instead of dropped.

2. Causal attention (should be bidirectional)

ColQwen3.5 retrieval encodes bidirectionally, but the Qwen3-Next full_attention
layers built Attention(...) with the default AttentionType.DECODER and never
read config.is_causal. This PR:

• adds ColQwen3_5Config (a VerifyAndUpdateConfig subclassing the Qwen3.5 one so
  mamba-cache handling is preserved) setting is_causal=False;
• has Qwen3NextAttention pass AttentionType.ENCODER_ONLY when is_causal is
  False (mirrors qwen3.py; generation arches leave is_causal unset → unchanged;
  the GatedDeltaNet linear_attention layers are untouched);
• makes Attention.get_kv_cache_spec return None for ENCODER_ONLY/ENCODER
  (encoder attention keeps no autoregressive KV cache; the hybrid runner iterates
  every attention module for its KV spec, so it reaches this for the now-encoder
  full_attention layers — previously it hit assert attn_type == DECODER).

3. Processor budget + prompt contract (docs)

The rerank example documents the visual-token budget
(max_num_visual_tokens=1792 → max_pixels=1792*32^2=1835008, min_pixels=65536,
via --mm-processor-kwargs) and the image instruction template + query
augmentation suffix that ColQwen3_5Processor applies, needed to match the native
pipeline.

Validation

B200, vLLM 0.22.1, vs a transformers+colpali reference on Vidore3HrRetrieval
(full corpus, 6 languages), canonical fp32 per-query MaxSim:

path	ndcg@10	Δ vs reference
transformers + ColQwen3_5Processor (reference)	0.6478	—
vLLM in-tree (before)	0.6231	−0.0247
vLLM + this PR	0.6558	+0.0080

The ~2.5pt deficit is eliminated; the residual +0.8pt is cross-stack numerical
noise (transformers+SDPA vs vLLM+FlashInfer, bf16).

Notes

• No new architecture: both checkpoints keep architectures: ["ColQwen3_5"] and
  load via the corrected class — consistent with how MTEB loads them.
• Adds a no-GPU test for the bidirectional config wiring; lists the 8B checkpoint
  in the token-embed docs and example.
• The edits were validated via the equivalent out-of-tree plugin on B200/0.22.1;
  please run pytest tests/models/multimodal/pooling/test_colqwen3_5.py and a
  Vidore3 sweep in CI before merge.
• Backbone hybrid-FLA correctness ([Bug]: Qwen3.5 (Qwen3_5ForConditionalGeneration) FLA linear attention tensor format mismatch causes gibberish output #38643) is orthogonal; not affected on
  B200/bf16 in this validation.

[mergify]

Documentation preview: https://vllm--46108.org.readthedocs.build/en/46108/

[athrael-soju]

@noooop are you able to add the ready label for the build to resume? Thanks

[noooop]

thanks