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

import os

import pytest
from vllm import SamplingParams

from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal

MODELS = [
    "deepseek-ai/DeepSeek-V2-Lite",
]


@pytest.mark.parametrize("model", MODELS)
def test_deepseek_v2_lite_enable_shared_expert_dp_tp2(model: str) -> None:
    if "HCCL_OP_EXPANSION_MODE" in os.environ:
        del os.environ["HCCL_OP_EXPANSION_MODE"]

    prompts = [
        "Hello, my name is",
        "The capital of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)

    with VllmRunner(
        model,
        max_model_len=1024,
        enforce_eager=True,
        tensor_parallel_size=2,
        enable_expert_parallel=True,
    ) as runner:
        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)

    os.environ["VLLM_ASCEND_ENABLE_FLASHCOMM1"] = "1"
    with VllmRunner(
        model,
        max_model_len=1024,
        enforce_eager=True,
        tensor_parallel_size=2,
        enable_expert_parallel=True,
        additional_config={
            "enable_shared_expert_dp": True,
        },
    ) as runner:
        shared_expert_dp_eager_outputs = runner.model.generate(prompts, sampling_params)

    with VllmRunner(
        model,
        max_model_len=1024,
        tensor_parallel_size=2,
        enable_expert_parallel=True,
        compilation_config={
            "cudagraph_capture_sizes": [1, 4, 8, 16],
            "cudagraph_mode": "FULL_DECODE_ONLY",
        },
        additional_config={
            "enable_shared_expert_dp": True,
        },
    ) as runner:
        shared_expert_dp_aclgraph_outputs = runner.model.generate(prompts, sampling_params)

    vllm_eager_outputs_list = []
    for output in vllm_eager_outputs:
        vllm_eager_outputs_list.append((output.outputs[0].index, output.outputs[0].text))

    shared_expert_dp_eager_outputs_list = []
    for output in shared_expert_dp_eager_outputs:
        shared_expert_dp_eager_outputs_list.append((output.outputs[0].index, output.outputs[0].text))

    shared_expert_dp_aclgraph_outputs_list = []
    for output in shared_expert_dp_aclgraph_outputs:
        shared_expert_dp_aclgraph_outputs_list.append((output.outputs[0].index, output.outputs[0].text))

    check_outputs_equal(
        outputs_0_lst=vllm_eager_outputs_list,
        outputs_1_lst=shared_expert_dp_eager_outputs_list,
        name_0="vllm_eager_outputs",
        name_1="shared_expert_dp_eager_outputs",
    )

    check_outputs_equal(
        outputs_0_lst=vllm_eager_outputs_list,
        outputs_1_lst=shared_expert_dp_aclgraph_outputs_list,
        name_0="vllm_eager_outputs",
        name_1="shared_expert_dp_aclgraph_outputs",
    )