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/distributed/communicator.py

@@ -0,0 +1,76 @@
+"""
+NPUCommunicator — HCCL-based device communicator for Ascend NPU.
+
+Extends ``DeviceCommunicatorBase`` with NPU-specific collective
+operations using the HCCL backend.
+"""
+
+from typing import List, Optional
+
+import torch
+import torch.distributed as dist
+from vllm.distributed.device_communicators.base_device_communicator import (
+    DeviceCommunicatorBase,
+)
+
+
+class NPUCommunicator(DeviceCommunicatorBase):
+    """Device communicator for Ascend NPU using HCCL."""
+
+    def __init__(
+        self,
+        cpu_group: dist.ProcessGroup,
+        device: Optional[torch.device] = None,
+        device_group: Optional[dist.ProcessGroup] = None,
+        unique_name: str = "",
+    ):
+        super().__init__(cpu_group, device, device_group, unique_name)
+        import torch_npu  # noqa: F401
+        self.device = torch.npu.current_device()
+
+    def all_to_all(
+        self,
+        input_: torch.Tensor,
+        scatter_dim: int = 0,
+        gather_dim: int = -1,
+        scatter_sizes: Optional[List[int]] = None,
+        gather_sizes: Optional[List[int]] = None,
+    ) -> torch.Tensor:
+        """All-to-all communication for NPU tensors."""
+        if scatter_dim < 0:
+            scatter_dim += input_.dim()
+        if gather_dim < 0:
+            gather_dim += input_.dim()
+
+        if scatter_sizes is not None and gather_sizes is not None:
+            input_list = [
+                t.contiguous()
+                for t in torch.split(input_, scatter_sizes, scatter_dim)
+            ]
+            output_list = []
+            tensor_shape_base = input_list[self.rank].size()
+            for i in range(self.world_size):
+                tensor_shape = list(tensor_shape_base)
+                tensor_shape[gather_dim] = gather_sizes[i]
+                output_list.append(
+                    torch.empty(
+                        tensor_shape,
+                        dtype=input_.dtype,
+                        device=input_.device,
+                    )
+                )
+        else:
+            input_list = [
+                t.contiguous()
+                for t in torch.tensor_split(
+                    input_, self.world_size, scatter_dim
+                )
+            ]
+            output_list = [
+                torch.empty_like(input_list[i])
+                for i in range(self.world_size)
+            ]
+
+        dist.all_to_all(output_list, input_list, group=self.device_group)
+        output_tensor = torch.cat(output_list, dim=gather_dim).contiguous()
+        return output_tensor