2e010e12dd
### What this PR does / why we need it?
Add dynamic EPLB CI for qwen3-moe-30B-W8A8
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
129 lines
4.7 KiB
Python
129 lines
4.7 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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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/e2e/multicard/test_qwen3_moe.py`.
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"""
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import json
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import os
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from unittest.mock import patch
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import openai
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import pytest
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from modelscope import snapshot_download # type: ignore
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from vllm.utils import get_open_port
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from tests.e2e.conftest import RemoteOpenAIServer, VllmRunner
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@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
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def test_qwen3_moe_distributed_mp_tp2_ep():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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tensor_parallel_size=2,
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enable_expert_parallel=True,
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distributed_executor_backend="mp",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_qwen3_moe_w8a8_distributed_tp2():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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snapshot_download("vllm-ascend/Qwen3-30B-A3B-W8A8"),
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max_model_len=8192,
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tensor_parallel_size=2,
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quantization="ascend",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_qwen3_moe_distributed_aiv_tp2():
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os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
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example_prompts = [
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"Hello, my name is",
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]
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dtype = "auto"
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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dtype=dtype,
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tensor_parallel_size=2,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@pytest.mark.asyncio
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async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
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model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
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port = get_open_port()
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server_args = [
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"--max_model_len", "8192", "--tensor_parallel_size", "2",
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"--enable_expert_parallel", "--quantization", "ascend", "--port",
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str(port), "--enforce_eager"
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]
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env_dict = {"HCCL_BUFFSIZE": "1024"}
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with RemoteOpenAIServer(model,
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server_args,
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server_port=port,
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auto_port=False,
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env_dict=env_dict) as server:
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client = server.get_async_client()
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batch = await client.completions.create(model=model,
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prompt="What is deeplearning?",
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max_tokens=300,
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temperature=0,
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top_p=1.0,
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n=1)
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gt_choices: list[openai.types.CompletionChoice] = batch.choices
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# dynamic eplb test
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# Since pytest runs as a daemon, it conflicts with the dynamic eplb manager
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# during initialization in offline mode, so the online mode is used instead.
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env_dict.update({"DYNAMIC_EPLB": "true"})
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additional_config = {
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"dynamic_eplb": True,
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"num_iterations_eplb_update": 100,
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"num_wait_worker_iterations": 20
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}
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server_args.extend(["--additional-config", json.dumps(additional_config)])
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with RemoteOpenAIServer(model,
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server_args,
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server_port=port,
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auto_port=False,
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env_dict=env_dict) as server:
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client = server.get_async_client()
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batch = await client.completions.create(model=model,
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prompt="What is deeplearning?",
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max_tokens=300,
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temperature=0,
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top_p=1.0,
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n=1)
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eplb_choices: list[openai.types.CompletionChoice] = batch.choices
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assert gt_choices[0].text == eplb_choices[
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0].text, f"{gt_choices[0].text=} \n {eplb_choices[0].text=}"
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