295018ec0f
### What this PR does / why we need it?
Based on the RFC:https://github.com/vllm-project/vllm-ascend/issues/5604
This PR is a refactoring of vllm_ascend/distributed, moving all
kv_transfer realtaed codes into a dedicated folder, which has already
been done in vLLM
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: lty <linhebiwen@gmail.com>
91 lines
3.4 KiB
Python
91 lines
3.4 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 \
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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",
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return_value={
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0: 0,
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1: 1
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})
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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,
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input_tensor_list,
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group=None,
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async_op=False):
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output_tensor_list[:] = ([
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torch.tensor([10, 20]),
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torch.tensor([50, 60])
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])
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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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comm = NPUCommunicator(cpu_group=dist.group.WORLD)
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output = comm.all_to_all(input_,
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scatter_sizes=scatter_sizes,
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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",
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return_value={
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0: 0,
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1: 1
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})
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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,
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input_tensor_list,
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group=None,
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async_op=False):
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output_tensor_list[:] = ([
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torch.tensor([[10, 20]]),
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torch.tensor([[50, 60]])
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])
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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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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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