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/__init__.py

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+"""Ascend NPU custom op registrations."""

vllm_npu/ops/activation.py

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+"""
+NPU-optimized activation functions for Ascend.
+
+Provides ``AscendSiluAndMul`` that uses ``torch_npu.npu_swiglu`` for
+fused SiLU+Mul on NPU devices.
+"""
+
+import torch
+from vllm.model_executor.layers.activation import SiluAndMul
+
+
+class AscendSiluAndMul(SiluAndMul):
+    """SiluAndMul using torch_npu.npu_swiglu on Ascend NPU."""
+
+    def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
+        import torch_npu  # noqa: F401
+        return torch_npu.npu_swiglu(x)

vllm_npu/ops/layernorm.py

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+"""
+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)

vllm_npu/ops/rotary_embedding.py

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+"""
+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
+    )