Read rope_theta from rope_parameters across Inference V2

[alanhuangyoo]

Follow-up to #8341, which covers the same drift in the v1 kernel-injection policy. Separate engine, separate change; #8341 noted this exposure but did not touch it.

What breaks

transformers 5.0 folded the rotary settings into config.rope_parameters and dropped the rope_theta attribute. Eight V2 models still read the attribute.

Seven of them read it directly and raise; exaone4 reads it through a getattr default and quietly uses that default instead:

V2 model	on master (transformers 5.8.0)	with this PR
llama_v2	AttributeError: 'LlamaConfig' object has no attribute 'rope_theta'	10000.0
mistral	AttributeError: 'MistralConfig' …	10000.0
mixtral	AttributeError: 'MixtralConfig' …	1000000.0
phi	AttributeError: 'PhiConfig' …	10000.0
phi3	AttributeError: 'Phi3Config' …	10000.0
qwen_v2	AttributeError: 'Qwen2Config' …	10000.0
qwen_v2_moe	AttributeError: 'Qwen2MoeConfig' …	10000.0
exaone4	silently 1000000.0	10000.0

exaone4 is the worse of the two. Exaone4Config carries rope_parameters['rope_theta'] == 10000.0, so the getattr default puts a 100x rotary base into RotateHalfConfig with nothing raised — wrong frequencies rather than a startup failure.

requirements-inf.txt asks for transformers>=4.32.1 with no upper bound, so 5.x is in range.

The fix

exaone4_5 already reads both spellings — added with the model in #8121:

theta = rope_parameters.get("rope_theta", getattr(config, "rope_theta", None))

Hoist the same lookup onto DSTransformerModelBase as a rope_theta property. DSMoETransformerModelBase extends it, so all eight models are covered by one place and each call site becomes theta_base=self.rope_theta.

The attribute is tried first, so pre-5.0 installs take exactly the path they take today. When neither spelling carries a base it raises instead of guessing one — that is the only behaviour change beyond the fix, and it replaces exaone4's silent 1e6.

exaone4_5 is left alone; its own helper also handles the nested per-layer sliding_attention dict, which is specific to that model.

Test

tests/unit/inference/v2/model_implementations/test_rope_theta.py — the three layouts, the precedence between them, the raise, plus a parametrization over the eight real configs from the installed transformers:

13 passed

tests/unit/inference/v2/model_implementations/   19 passed
yapf --diff / flake8                             clean

I exercised the property and the configs, not a full V2 engine run against downloaded checkpoints.