[Main2Main] Upgrade vLLM to 0303 (#6944)

### What this PR does / why we need it?
break:
- https://github.com/vllm-project/vllm/pull/34102 
Disable_full param replaced with valid_modes/invalid_modes API
- https://github.com/vllm-project/vllm/pull/35503
Now must return float compilation_time
- https://github.com/vllm-project/vllm/pull/35564
New sequence_lengths param added
- https://github.com/vllm-project/vllm/pull/33807
A check was performed (if runner_backend != "auto")
- https://github.com/vllm-project/vllm/pull/34861
`BaseDeviceCommunicator` now accesses PyTorch's internal `pg_map` to
check process group state
- https://github.com/vllm-project/vllm/pull/35274

**Important change:**
- https://github.com/vllm-project/vllm/pull/28672

`matcher_utils` directly accesses `torch.ops._C.*` during the import
phase. In the Ascend environment, some unregistered ops trigger
`AttributeError`, causing e2e initialization failure.

https://github.com/vllm-project/vllm-ascend/actions/runs/22607260487/job/65502047131#step:10:2323

https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/matcher_utils.py#L29

This PR adds temporary compatibility placeholders (rms_norm,
fused_add_rms_norm, rotate_embedding, static/dynamic fp8 quant,
silu_and_mul) to
`vllm_ascend/patch/platform/patch_fusion_matcher_compat_ops.py` to
ensure no crashes during the import phase. Upstream repairs will be
considered later.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.16.0
- vLLM main:
15d76f74e2

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Co-authored-by: Meihan-chen <jcccx.cmh@gmail.com>
Co-authored-by: Claude Code <noreply@anthropic.com>
Co-authored-by: gcanlin <canlinguosdu@gmail.com>

vllm_ascend/worker/worker.py

@@ -430,7 +430,7 @@ class NPUWorker(WorkerBase):
         with context, set_current_vllm_config(self.vllm_config):
             self.model_runner.load_model()
 
-    def compile_or_warm_up_model(self) -> None:
+    def compile_or_warm_up_model(self) -> float:
         # Note: need to adapt for graph mode.
         warmup_sizes = (self.vllm_config.compilation_config.compile_sizes or []).copy()
         if not self.model_config.enforce_eager:
@@ -462,6 +462,7 @@ class NPUWorker(WorkerBase):
         # Reset the seed to ensure that the random state is not affected by
         # the model initialization and profiling.
         set_random_seed(self.model_config.seed)
+        return self.vllm_config.compilation_config.compilation_time
 
     def _warm_up_atb(self):
         x = torch.rand((2, 4), dtype=torch.float16).npu()