Add ut for test_communicator.py (#2293)

### What this PR does / why we need it?

Add ut for test_communicator.py 

- vLLM version: v0.10.0
- vLLM main:
vllm-project/vllm@e5ebeeb

Signed-off-by: yangqinghao-cmss <[email protected]>

Author: yangqinghao-cmss

tests/ut/distributed/test_communicator.py

@@ -0,0 +1,155 @@
+import unittest
+from unittest.mock import MagicMock, Mock, patch
+
+import torch
+import torch.distributed as dist
+
+from vllm_ascend.distributed.communicator import NPUCommunicator
+
+
+class TestNPUCommunicator(unittest.TestCase):
+
+    @patch("vllm.config.get_current_vllm_config", return_value=None)
+    @patch("torch.npu.current_device", return_value=MagicMock())
+    @patch("torch.npu.set_device", return_value=MagicMock())
+    @patch("torch.distributed.get_process_group_ranks",
+           return_value={
+               0: 0,
+               1: 1
+           })
+    @patch("torch.distributed.get_group_rank", return_value={0: 0, 1: 1})
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_backend", return_value="hccl")
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.get_world_size", return_value=2)
+    @patch("torch.distributed.get_process_group_ranks", return_value=[0, 1])
+    @patch("torch.npu.device")
+    def test_all_to_all_with_sizes(self, *_):
+
+        def patched_all_to_all(output_tensor_list,
+                               input_tensor_list,
+                               group=None,
+                               async_op=False):
+            output_tensor_list[:] = ([
+                torch.tensor([10, 20]),
+                torch.tensor([50, 60])
+            ])
+
+        torch.distributed.all_to_all = patched_all_to_all
+
+        scatter_sizes = [2, 2]
+        gather_sizes = [2, 2]
+        input_ = torch.tensor([10, 20, 30, 40])
+
+        comm = NPUCommunicator(cpu_group=dist.group.WORLD)
+
+        output = comm.all_to_all(input_,
+                                 scatter_sizes=scatter_sizes,
+                                 gather_sizes=gather_sizes)
+
+        assert output.tolist() == [10, 20, 50, 60]
+
+    @patch("vllm.config.get_current_vllm_config", return_value=None)
+    @patch("torch.npu.current_device", return_value=MagicMock())
+    @patch("torch.npu.set_device", return_value=MagicMock())
+    @patch("torch.distributed.get_process_group_ranks",
+           return_value={
+               0: 0,
+               1: 1
+           })
+    @patch("torch.distributed.get_group_rank", return_value={0: 0, 1: 1})
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_backend", return_value="hccl")
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.get_world_size", return_value=2)
+    @patch("torch.distributed.get_process_group_ranks", return_value=[0, 1])
+    @patch("torch.npu.device")
+    def test_all_to_all_without_sizes(self, *_):
+
+        def patched_all_to_all(output_tensor_list,
+                               input_tensor_list,
+                               group=None,
+                               async_op=False):
+            output_tensor_list[:] = ([
+                torch.tensor([[10, 20]]),
+                torch.tensor([[50, 60]])
+            ])
+
+        torch.distributed.all_to_all = patched_all_to_all
+
+        input_ = torch.tensor([[10, 20], [30, 40]])
+
+        comm = NPUCommunicator(cpu_group=dist.group.WORLD)
+        output = comm.all_to_all(input_, scatter_dim=0, gather_dim=0)
+
+        assert output.tolist() == [[10, 20], [50, 60]]
+
+    @patch("vllm.config.get_current_vllm_config", return_value=None)
+    @patch("torch.npu.current_device", return_value=MagicMock())
+    @patch("torch.npu.set_device", return_value=MagicMock())
+    @patch("torch.distributed.get_process_group_ranks",
+           return_value={
+               0: 0,
+               1: 1
+           })
+    @patch("torch.distributed.get_group_rank", return_value={0: 0, 1: 1})
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_backend", return_value="hccl")
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.get_world_size", return_value=2)
+    @patch("torch.distributed.get_process_group_ranks", return_value=[0, 1])
+    @patch("torch.npu.device")
+    def test_dispatch(self, *_):
+        comm = NPUCommunicator(cpu_group=dist.group.WORLD)
+        comm.all2all_manager = Mock()
+        hidden_states = torch.randn(2, 4, 8)
+        router_logits = torch.randn(2, 4, 2)
+
+        mock_dispatch_result = (torch.randn(2, 4, 8), torch.randn(2, 4, 2))
+        comm.all2all_manager.dispatch.return_value = mock_dispatch_result
+
+        result_hidden, result_logits = comm.dispatch(hidden_states,
+                                                     router_logits)
+
+        assert torch.allclose(result_hidden, mock_dispatch_result[0])
+        assert torch.allclose(result_logits, mock_dispatch_result[1])
+
+        comm.all2all_manager.dispatch.assert_called_once_with(
+            hidden_states, router_logits)
+
+    @patch("vllm.config.get_current_vllm_config", return_value=None)
+    @patch("torch.npu.current_device", return_value=MagicMock())
+    @patch("torch.npu.set_device", return_value=MagicMock())
+    @patch("torch.distributed.get_process_group_ranks",
+           return_value={
+               0: 0,
+               1: 1
+           })
+    @patch("torch.distributed.get_group_rank", return_value={0: 0, 1: 1})
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.is_initialized", return_value=True)
+    @patch("torch.distributed.get_backend", return_value="hccl")
+    @patch("torch.distributed.get_rank", return_value=1)
+    @patch("torch.distributed.get_world_size", return_value=2)
+    @patch("torch.distributed.get_process_group_ranks", return_value=[0, 1])
+    @patch("torch.npu.device")
+    def test_combine(self, *_):
+        comm = NPUCommunicator(cpu_group=dist.group.WORLD)
+        comm.all2all_manager = Mock()
+        hidden_states = torch.randn(2, 4, 8)
+
+        mock_combine_result = torch.randn(2, 4, 8)
+        comm.all2all_manager.combine.return_value = mock_combine_result
+
+        result = comm.combine(hidden_states)
+
+        assert torch.allclose(result, mock_combine_result)
+
+        comm.all2all_manager.combine.assert_called_once_with(hidden_states)