test_oot_registration.py

# SPDX-License-Identifier: Apache-2.0

import pytest

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset

from ..utils import fork_new_process_for_each_test


@fork_new_process_for_each_test
def test_plugin(
    monkeypatch: pytest.MonkeyPatch,
    dummy_opt_path: str,
):
    # V1 shuts down rather than raising an error here.
    with monkeypatch.context() as m:
        m.setenv("VLLM_USE_V1", "0")
        m.setenv("VLLM_PLUGINS", "")

        with pytest.raises(Exception) as excinfo:
            LLM(model=dummy_opt_path, load_format="dummy")
        error_msg = "has no vLLM implementation and the Transformers implementation is not compatible with vLLM"  # noqa: E501
        assert (error_msg in str(excinfo.value))


@fork_new_process_for_each_test
def test_oot_registration_text_generation(
    monkeypatch: pytest.MonkeyPatch,
    dummy_opt_path: str,
):
    with monkeypatch.context() as m:
        m.setenv("VLLM_PLUGINS", "register_dummy_model")
        prompts = ["Hello, my name is", "The text does not matter"]
        sampling_params = SamplingParams(temperature=0)
        llm = LLM(model=dummy_opt_path, load_format="dummy")
        first_token = llm.get_tokenizer().decode(0)
        outputs = llm.generate(prompts, sampling_params)

        for output in outputs:
            generated_text = output.outputs[0].text
            # make sure only the first token is generated
            rest = generated_text.replace(first_token, "")
            assert rest == ""


@fork_new_process_for_each_test
def test_oot_registration_embedding(
    monkeypatch: pytest.MonkeyPatch,
    dummy_gemma2_embedding_path: str,
):
    with monkeypatch.context() as m:
        m.setenv("VLLM_PLUGINS", "register_dummy_model")
        prompts = ["Hello, my name is", "The text does not matter"]
        llm = LLM(model=dummy_gemma2_embedding_path, load_format="dummy")
        outputs = llm.embed(prompts)

        for output in outputs:
            assert all(v == 0 for v in output.outputs.embedding)


image = ImageAsset("cherry_blossom").pil_image.convert("RGB")


@fork_new_process_for_each_test
def test_oot_registration_multimodal(
    monkeypatch: pytest.MonkeyPatch,
    dummy_llava_path: str,
):
    with monkeypatch.context() as m:
        m.setenv("VLLM_PLUGINS", "register_dummy_model")
        prompts = [{
            "prompt": "What's in the image?<image>",
            "multi_modal_data": {
                "image": image
            },
        }, {
            "prompt": "Describe the image<image>",
            "multi_modal_data": {
                "image": image
            },
        }]

        sampling_params = SamplingParams(temperature=0)
        llm = LLM(model=dummy_llava_path,
                  load_format="dummy",
                  max_num_seqs=1,
                  trust_remote_code=True,
                  gpu_memory_utilization=0.98,
                  max_model_len=4096,
                  enforce_eager=True,
                  limit_mm_per_prompt={"image": 1})
        first_token = llm.get_tokenizer().decode(0)
        outputs = llm.generate(prompts, sampling_params)

        for output in outputs:
            generated_text = output.outputs[0].text
            # make sure only the first token is generated
            rest = generated_text.replace(first_token, "")
            assert rest == ""