[Model] Make the dummy-run recipe feature-neutral

Per review, the warmup recipe is a property of the model's request
geometry, not of distributed layerwise offload: rename
dlo_dummy_run_recipe to dummy_run_recipe and drop the DLO gate in
_make_dummy_request. A model whose geometry needs recipe-specific sampling
arguments warms up at startup whatever features are enabled — under DLO
the generation also primes the component cache-retention budget; without
it, the same warmup covers any other first-request cost. Validated both
ways on L20X: DLO boot unchanged (releases stay in the startup window) and
a non-DLO MiniMax-H3 boot now runs the warmup and serves normally.

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>

docs/design/feature/offloader/distributed_layerwise_offload.md

@@ -185,9 +185,10 @@ resolution. It starts at zero: a cold start pays one release per observation
 step while the peak climbs, after which boundaries retain steadily, and a
 workload change whose cached footprint grows by more than the margin pays one
 further release while the new peak lands. Models whose request geometry opts
-out of the generic dummy run can declare `dlo_dummy_run_recipe`; MiniMax-H3
-declares one minimal t2va generation, so under DLO the engine's startup
-warmup runs it and the cold-start flushes land before real traffic. The budget is capped at a quarter
+out of the generic dummy run declare a feature-neutral `dummy_run_recipe`
+instead; MiniMax-H3 declares a maximum-duration t2va generation, so the
+engine's startup warmup runs it and, under DLO, the cold-start flushes land
+before real traffic. The budget is capped at a quarter
 of device capacity so a spurious observation cannot monopolize a small
 device, and detaching the last attached stager resets the learned peak so one
 workload never permanently influences another. No user-chosen fraction, no