af04ee9e7a
### What this PR does / why we need it?
Fix Qwen MoE accuracy bug in DP scenario.
Now the implentment of `FusedMoE` in vLLM use `All2AllManager` to
manager different all2all algorithm branch. And the default branch use
`Multicast` in `dispatch` phase and `all_reduce` in `combine` phase,
which are not implented in vLLM-Ascend. This leading to invoking into a
default implentment in `base_communicator`, with empty `dispatch` and
`combine` operations, thus causing the accuracy issue on it.
This pr is a temporary workaround, refacting all2all in vLLM-Ascend
could be a better way.
- vLLM version: v0.10.0
- vLLM main:
ad57f23f6a
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
74 lines
2.0 KiB
Python
74 lines
2.0 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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#
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"""
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Compare the outputs of vLLM with and without aclgraph.
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Run `pytest tests/multicard/test_data_parallel.py`.
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"""
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import os
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import subprocess
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import sys
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from unittest.mock import patch
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import pytest
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MODELS = ["Qwen/Qwen2.5-0.5B-Instruct", "Qwen/Qwen3-30B-A3B"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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@patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1"})
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def test_data_parallel_inference(model, max_tokens):
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script = "examples/offline_data_parallel.py"
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env = os.environ.copy()
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cmd = [
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sys.executable,
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script,
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"--model",
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model,
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"--dp-size",
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"2",
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"--tp-size",
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"1",
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"--node-size",
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"1",
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"--node-rank",
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"0",
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"--trust-remote-code",
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"--enforce-eager",
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]
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if model == "Qwen/Qwen3-30B-A3B":
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cmd.append("--enable-expert-parallel")
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print(f"Running subprocess: {' '.join(cmd)}")
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proc = subprocess.run(cmd,
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env=env,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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timeout=600)
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output = proc.stdout.decode()
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print(output)
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assert "DP rank 0 needs to process" in output
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assert "DP rank 1 needs to process" in output
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assert "Generated text:" in output
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assert proc.returncode == 0
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