[Diffusion] Register runtime cache for direct DLO H2D

Avoid recurrent mmap-to-pinned packing in no-AllGather DLO by registering transform-complete runtime-cache mappings under an explicit per-worker budget. Copy mapped tensors directly into the rotating device buffers while preserving bounded-staging fallback and ordered shutdown cleanup.

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

tests/diffusion/model_loader/test_diffusers_loader.py

@@ -492,6 +492,59 @@ def test_dlo_runtime_cache_is_built_after_final_post_load_mutation(monkeypatch,
     assert loader.take_host_weight_plan() is runtime_plan
 
 
+def test_dlo_registration_prefers_final_runtime_cache_over_checkpoint_plan(monkeypatch, tmp_path):
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+    import vllm_omni.diffusion.model_loader.runtime_weight_cache as cache_mod
+
+    od_config = SimpleNamespace(
+        dtype=torch.float32,
+        parallel_config=SimpleNamespace(
+            use_hsdp=False,
+            tensor_parallel_size=1,
+            sequence_parallel_size=1,
+            data_parallel_size=1,
+            cfg_parallel_size=1,
+            pipeline_parallel_size=1,
+        ),
+        quantization_config=None,
+        enable_distributed_layerwise_offload=True,
+        dlo_use_allgather=False,
+        dlo_enable_runtime_cache=True,
+        dlo_runtime_cache_dir=str(tmp_path),
+        dlo_runtime_cache_pin_limit_gib=1.0,
+        model="unused",
+    )
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+    model = nn.Module()
+    model.transformer = nn.Linear(2, 2, bias=False)
+    events: list[str] = []
+    runtime_plan = HostWeightPlan(
+        backing_kind="runtime_cache",
+        bindings={},
+        runtime_layout_key="layout-key",
+        post_load_complete=True,
+    )
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    loader.load_weights = lambda _model: events.append("load")  # type: ignore[method-assign]
+    loader._process_weights_after_loading = lambda *_args: events.append("process")  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+
+    def unexpected_checkpoint_plan(*_args, **_kwargs):
+        pytest.fail("registered runtime-cache mode must not select the raw checkpoint mmap plan")
+
+    def build_cache(_pipeline, **_kwargs):
+        events.append("cache")
+        return HostWeightPlanResult(runtime_plan)
+
+    monkeypatch.setattr(loader_mod, "build_checkpoint_mmap_plan", unexpected_checkpoint_plan)
+    monkeypatch.setattr(cache_mod, "build_runtime_weight_cache_plan", build_cache)
+
+    assert loader.load_model(load_device="cpu") is model
+    assert events == ["load", "process", "cache"]
+    assert loader.take_host_weight_plan() is runtime_plan
+
+
 def test_dlo_runtime_cache_failure_retains_ordinary_weights(monkeypatch, tmp_path):
     import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
     import vllm_omni.diffusion.model_loader.runtime_weight_cache as cache_mod