67 lines
3.2 KiB
Python
67 lines
3.2 KiB
Python
import unittest
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from unittest.mock import MagicMock, patch
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import torch
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import torch.distributed as dist
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from vllm_ascend.distributed.device_communicators.npu_communicator import NPUCommunicator
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class TestNPUCommunicator(unittest.TestCase):
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@patch("vllm.config.get_current_vllm_config", return_value=None)
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@patch("torch.npu.current_device", return_value=MagicMock())
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@patch("torch.npu.set_device", return_value=MagicMock())
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@patch("torch.distributed.get_process_group_ranks", return_value={0: 0, 1: 1})
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@patch("torch.distributed.get_group_rank", return_value={0: 0, 1: 1})
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@patch("torch.distributed.is_initialized", return_value=True)
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@patch("torch.distributed.get_rank", return_value=1)
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@patch("torch.distributed.is_initialized", return_value=True)
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@patch("torch.distributed.get_backend", return_value="hccl")
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@patch("torch.distributed.get_rank", return_value=1)
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@patch("torch.distributed.get_world_size", return_value=2)
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@patch("torch.distributed.get_process_group_ranks", return_value=[0, 1])
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@patch("torch.npu.device")
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def test_all_to_all_with_sizes(self, *_):
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def patched_all_to_all(output_tensor_list, input_tensor_list, group=None, async_op=False):
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output_tensor_list[:] = [torch.tensor([10, 20]), torch.tensor([50, 60])]
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torch.distributed.all_to_all = patched_all_to_all
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scatter_sizes = [2, 2]
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gather_sizes = [2, 2]
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input_ = torch.tensor([10, 20, 30, 40])
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with patch.dict(dist.distributed_c10d._world.pg_map, {dist.group.WORLD: MagicMock()}, clear=False):
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comm = NPUCommunicator(cpu_group=dist.group.WORLD)
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output = comm.all_to_all(input_, scatter_sizes=scatter_sizes, gather_sizes=gather_sizes)
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assert output.tolist() == [10, 20, 50, 60]
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@patch("vllm.config.get_current_vllm_config", return_value=None)
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@patch("torch.npu.current_device", return_value=MagicMock())
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@patch("torch.npu.set_device", return_value=MagicMock())
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@patch("torch.distributed.get_process_group_ranks", return_value={0: 0, 1: 1})
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@patch("torch.distributed.get_group_rank", return_value={0: 0, 1: 1})
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@patch("torch.distributed.is_initialized", return_value=True)
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@patch("torch.distributed.get_rank", return_value=1)
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@patch("torch.distributed.is_initialized", return_value=True)
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@patch("torch.distributed.get_backend", return_value="hccl")
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@patch("torch.distributed.get_rank", return_value=1)
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@patch("torch.distributed.get_world_size", return_value=2)
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@patch("torch.distributed.get_process_group_ranks", return_value=[0, 1])
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@patch("torch.npu.device")
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def test_all_to_all_without_sizes(self, *_):
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def patched_all_to_all(output_tensor_list, input_tensor_list, group=None, async_op=False):
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output_tensor_list[:] = [torch.tensor([[10, 20]]), torch.tensor([[50, 60]])]
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torch.distributed.all_to_all = patched_all_to_all
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input_ = torch.tensor([[10, 20], [30, 40]])
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with patch.dict(dist.distributed_c10d._world.pg_map, {dist.group.WORLD: MagicMock()}, clear=False):
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comm = NPUCommunicator(cpu_group=dist.group.WORLD)
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output = comm.all_to_all(input_, scatter_dim=0, gather_dim=0)
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assert output.tolist() == [[10, 20], [50, 60]]
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