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/layernorm.py

@@ -0,0 +1,41 @@
+"""
+NPU-optimized layer normalization for Ascend.
+
+Provides RMS norm operations using ``torch_npu.npu_rms_norm`` and
+``torch_npu.npu_add_rms_norm``.
+"""
+
+import torch
+
+
+def rms_norm_npu(
+    out: torch.Tensor,
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    epsilon: float,
+) -> None:
+    """RMS norm using Ascend NPU fused kernel.
+
+    Writes the result into ``out`` in-place.
+    """
+    import torch_npu  # noqa: F401
+    normed, _residual = torch_npu.npu_rms_norm(input, weight, epsilon)
+    out.copy_(normed)
+
+
+def fused_add_rms_norm_npu(
+    input: torch.Tensor,
+    residual: torch.Tensor,
+    weight: torch.Tensor,
+    epsilon: float,
+) -> None:
+    """Fused add + RMS norm using Ascend NPU kernel.
+
+    Modifies ``input`` and ``residual`` in-place.
+    """
+    import torch_npu  # noqa: F401
+    normed, residual_out = torch_npu.npu_add_rms_norm(
+        input, residual, weight, epsilon
+    )
+    input.copy_(normed)
+    residual.copy_(residual_out)