99 lines
4.2 KiB
Python
99 lines
4.2 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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import pytest
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import torch
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from pytest_mock import MockerFixture
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from tests.ut.base import PytestBase
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from vllm_ascend.ops.moe.comm_utils import (
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_gather_along_first_dim, async_all_to_all,
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gather_from_sequence_parallel_region)
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class TestDistributedCommunication(PytestBase):
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@pytest.fixture(autouse=True)
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def context(self, mocker: MockerFixture):
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mocker.patch("torch.npu.current_device", return_value="cpu")
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mocker.patch("torch.distributed.get_world_size", return_value=4)
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mocker.patch("torch.distributed.get_rank", return_value=0)
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@pytest.mark.parametrize(
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"input_tensor, output_split_sizes, input_split_sizes",
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[(torch.randn(8, 16), [2, 2, 2, 2], [2, 2, 2, 2]),
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(torch.randn(16, 32), None, None)])
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def test_async_all_to_all(self, input_tensor, output_split_sizes,
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input_split_sizes, mocker: MockerFixture):
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"""Test async_all_to_all"""
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mock_group = mocker.MagicMock()
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mocker.patch("torch.distributed.all_to_all_single",
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return_value=mocker.MagicMock())
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_, a2a_out, handle = async_all_to_all(input_tensor, output_split_sizes,
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input_split_sizes, mock_group)
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# Check if the output tensor is created properly
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if output_split_sizes is None:
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assert a2a_out.shape == input_tensor.shape
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else:
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total_output_size = sum(output_split_sizes)
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expected_shape = [total_output_size] + list(
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input_tensor.size())[1:]
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assert a2a_out.shape == torch.Size(expected_shape)
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# Ensure handle is returned from async operation
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assert handle is not None
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assert isinstance(handle, mocker.MagicMock)
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@pytest.mark.parametrize("world_size, test_tensor, expected",
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[(1, torch.randn(8, 16), (8, 16)),
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(4, torch.randn(8, 16), (32, 16))])
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def test_gather_along_first_dim(self, test_tensor, expected, world_size,
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mocker: MockerFixture):
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"""Test _gather_along_first_dim"""
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mocker.patch("torch.distributed.get_world_size",
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return_value=world_size)
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result = _gather_along_first_dim(test_tensor, mocker.MagicMock())
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assert result.shape == expected
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@pytest.mark.parametrize("input_tensor, output_split_sizes",
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[(torch.randn(8, 16), None),
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(torch.randn(8, 16), [2, 2, 2, 2])])
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def test_gather_from_sequence_parallel_region(self, input_tensor,
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output_split_sizes,
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mocker: MockerFixture):
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"""Test gather_from_sequence_parallel_region"""
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mock_group = mocker.MagicMock()
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result = gather_from_sequence_parallel_region(input_tensor, mock_group,
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output_split_sizes)
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# If output_split_sizes is not provided, result should have expanded first dimension by world size
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if output_split_sizes is None:
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expected_shape = [input_tensor.shape[0] * 4] + list(
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input_tensor.shape[1:])
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assert result.shape == torch.Size(expected_shape)
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else:
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# If output_split_sizes is provided, result shape is dictated by sum of output_split_sizes
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expected_shape = [sum(output_split_sizes)] + list(
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input_tensor.shape[1:])
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assert result.shape == torch.Size(expected_shape)
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