xc-llm-ascend/tests/e2e/multicard/2-cards/test_qwen3_moe.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
#
import json
import os
from unittest.mock import patch

import openai
import pytest
from vllm.utils.network_utils import get_open_port

from tests.e2e.conftest import RemoteOpenAIServer, VllmRunner


@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
def test_qwen3_moe_distributed_mp_tp2_ep():
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5
    with VllmRunner(
        "Qwen/Qwen3-30B-A3B",
        tensor_parallel_size=2,
        enable_expert_parallel=True,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        distributed_executor_backend="mp",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


def test_qwen3_moe_w8a8_distributed_tp2():
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5
    with VllmRunner(
        "vllm-ascend/Qwen3-30B-A3B-W8A8",
        max_model_len=8192,
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        quantization="ascend",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


def test_qwen3_moe_distributed_aiv_tp2():
    os.environ["HCCL_OP_EXPANSION_MODE"] = "AIV"
    example_prompts = [
        "Hello, my name is",
    ]
    dtype = "auto"
    max_tokens = 5
    with VllmRunner(
        "Qwen/Qwen3-30B-A3B",
        dtype=dtype,
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@pytest.mark.asyncio
async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
    port = get_open_port()
    compilation_config = json.dumps({"cudagraph_capture_sizes": [8]})
    server_args = [
        "--max_model_len",
        "8192",
        "--tensor_parallel_size",
        "2",
        "--enable_expert_parallel",
        "--quantization",
        "ascend",
        "--port",
        str(port),
        "--compilation-config",
        compilation_config,
    ]
    env_dict = {"HCCL_BUFFSIZE": "1024"}
    with RemoteOpenAIServer(model, server_args, server_port=port, auto_port=False, env_dict=env_dict) as server:
        client = server.get_async_client()
        batch = await client.completions.create(
            model=model, prompt="What is deeplearning?", max_tokens=400, temperature=0, top_p=1.0, n=1
        )
        gt_choices: list[openai.types.CompletionChoice] = batch.choices

    # dynamic eplb test
    # Since pytest runs as a daemon, it conflicts with the dynamic eplb manager
    # during initialization in offline mode, so the online mode is used instead.
    env_dict.update({"DYNAMIC_EPLB": "true"})
    additional_config = {
        "eplb_config": {
            "dynamic_eplb": True,
            "expert_heat_collection_interval": 100,
            "algorithm_execution_interval": 20,
            "num_redundant_experts": 2,
        }
    }
    server_args.extend(["--additional-config", json.dumps(additional_config)])
    with RemoteOpenAIServer(model, server_args, server_port=port, auto_port=False, env_dict=env_dict) as server:
        client = server.get_async_client()
        batch = await client.completions.create(
            model=model, prompt="What is deeplearning?", max_tokens=400, temperature=0, top_p=1.0, n=1
        )
        eplb_choices: list[openai.types.CompletionChoice] = batch.choices
    assert gt_choices[0].text == eplb_choices[0].text, f"{gt_choices[0].text=} \n {eplb_choices[0].text=}"