feat: initial vllm-npu-plugin for Ascend NPU adaptation

- NPUPlatform: device management, HCCL process group, config adaptation
- AscendAttentionBackend: npu_fusion_attention (prefill) + npu_incre_flash_attention (decode)
- NPUCommunicator: HCCL-based distributed communication
- NPUWorker: NPU device init, memory profiling
- Custom ops: SiluAndMul, RMS norm, rotary embedding
- Plugin registered via vllm.platform_plugins entry point

Based on vllm-ascend official pattern, targeting Ascend 910B

vllm_npu/ops/rotary_embedding.py

@@ -0,0 +1,31 @@
+"""
+NPU-optimized rotary embedding for Ascend.
+
+Wraps ``torch_npu._npu_rotary_embedding`` for fused RoPE application.
+"""
+
+import torch
+
+
+def rotary_embedding_npu(
+    positions: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    head_size: int,
+    cos_sin_cache: torch.Tensor,
+    is_neox: bool,
+) -> None:
+    """Apply rotary position embedding using Ascend NPU fused kernel.
+
+    Modifies ``query`` and ``key`` in-place.
+    """
+    import torch_npu  # noqa: F401
+
+    if not query.is_contiguous():
+        query = query.contiguous()
+    if not key.is_contiguous():
+        key = key.contiguous()
+
+    torch_npu._npu_rotary_embedding(
+        positions, query, key, head_size, cos_sin_cache, is_neox
+    )