af2a886814
### What this PR does / why we need it?
The current linear.py has the following issues:
- There is redundant conditional logic in the `comm_group` and `forward`
selection for classes such as `AscendMergedColumnParallelLinear`.
- Inconsistent comm_group selection logic exists among
`AscendMergedColumnParallelLinear`, `AscendColumnParallelLinear`, and
`AscendQKVParallelLinear`.
To address these two issues, this PR encapsulates `comm_group` and
`forward` into classes and extracts the classes selection logic into
common functions. For future additions of custom communication groups or
forward methods, it will only be necessary to extend
`CustomColumnParallelOp` or `CustomRowParallelOp` and add new selection
logic.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
dd39baf717
---------
Signed-off-by: realliujiaxu <realliujiaxu@163.com>
Co-authored-by: weijinqian0 <weijinqian@huawei.com>
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import os
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import unittest
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from unittest import mock
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from unittest.mock import MagicMock, patch
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import torch
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from vllm_ascend import ascend_config
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from vllm_ascend.distributed import parallel_state
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from vllm_ascend.ops.linear import (AscendMergedColumnParallelLinear,
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AscendRowParallelLinear)
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class BaseLinearTest(unittest.TestCase):
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def setUp(self):
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self.mock_group = mock.MagicMock()
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self.mock_group.world_size = 2
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self.mock_group.rank_in_group = 0
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parallel_state._MLP_TP = self.mock_group
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parallel_state._OTP = self.mock_group
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self.mock_ascend_config = MagicMock()
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self.mock_ascend_config.oproj_tensor_parallel_size = 2
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self.patches = [
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patch("vllm_ascend.ascend_config.get_ascend_config",
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return_value=self.mock_ascend_config),
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patch("vllm_ascend.distributed.parallel_state.get_otp_group",
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return_value=self.mock_group),
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patch("vllm_ascend.distributed.parallel_state.get_mlp_tp_group",
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return_value=self.mock_group),
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patch("vllm_ascend.ops.linear_op.get_tp_group",
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return_value=self.mock_group),
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patch("vllm_ascend.utils.mlp_tp_enable", return_value=True),
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patch("vllm_ascend.utils.oproj_tp_enable", return_value=True)
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]
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for p in self.patches:
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p.start()
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def tearDown(self):
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for p in self.patches:
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p.stop()
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class TestAscendRowParallelLinear(BaseLinearTest):
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def test_mlp_optimize(self):
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os.environ["VLLM_ASCEND_ENABLE_MLP_OPTIMIZE"] = "1"
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linear = AscendRowParallelLinear(
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input_size=16,
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output_size=8,
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prefix="down_proj",
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)
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self.assertEqual(linear.custom_op.comm_group, parallel_state._MLP_TP)
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input_tensor = torch.randn(16, 8)
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linear(input_tensor)
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def test_oproj_tp(self):
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ascend_config._ASCEND_CONFIG = MagicMock()
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ascend_config._ASCEND_CONFIG.oproj_tensor_parallel_size = 2
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linear = AscendRowParallelLinear(
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input_size=16,
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output_size=8,
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prefix="o_proj",
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)
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self.assertEqual(linear.custom_op.comm_group, parallel_state._OTP)
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input_tensor = torch.randn(16, 8)
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linear(input_tensor)
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class TestAscendMergedColumnParallelLinear(BaseLinearTest):
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def test_merged_mlp_tp_init(self):
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os.environ["VLLM_ASCEND_ENABLE_MLP_OPTIMIZE"] = "1"
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linear = AscendMergedColumnParallelLinear(
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input_size=16,
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output_sizes=[8, 8],
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prefix="gate_up_proj",
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)
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self.assertEqual(linear.custom_op.comm_group, parallel_state._MLP_TP)
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if __name__ == '__main__':
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unittest.main()
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