test_config.py

import pytest

from vllm.config import ModelConfig
from vllm.model_executor.layers.pooler import PoolingType
from vllm.platforms import current_platform


@pytest.mark.parametrize(("model_id", "expected_task"), [
    ("facebook/opt-125m", "generate"),
    ("intfloat/e5-mistral-7b-instruct", "embedding"),
])
def test_auto_task(model_id, expected_task):
    config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
    )

    assert config.task == expected_task


@pytest.mark.parametrize(("model_id", "bad_task"), [
    ("facebook/opt-125m", "embedding"),
    ("intfloat/e5-mistral-7b-instruct", "generate"),
])
def test_incorrect_task(model_id, bad_task):
    with pytest.raises(ValueError, match=r"does not support the .* task"):
        ModelConfig(
            model_id,
            task=bad_task,
            tokenizer=model_id,
            tokenizer_mode="auto",
            trust_remote_code=False,
            seed=0,
            dtype="float16",
        )


MODEL_IDS_EXPECTED = [
    ("Qwen/Qwen1.5-7B", 32768),
    ("mistralai/Mistral-7B-v0.1", 4096),
    ("mistralai/Mistral-7B-Instruct-v0.2", 32768),
]


@pytest.mark.parametrize("model_id_expected", MODEL_IDS_EXPECTED)
def test_disable_sliding_window(model_id_expected):
    model_id, expected = model_id_expected
    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
        disable_sliding_window=True,
    )
    assert model_config.max_model_len == expected


def test_get_sliding_window():
    TEST_SLIDING_WINDOW = 4096
    # Test that the sliding window is correctly computed.
    # For Qwen1.5/Qwen2, get_sliding_window() should be None
    # when use_sliding_window is False.
    qwen2_model_config = ModelConfig(
        "Qwen/Qwen1.5-7B",
        task="auto",
        tokenizer="Qwen/Qwen1.5-7B",
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    qwen2_model_config.hf_config.use_sliding_window = False
    qwen2_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
    assert qwen2_model_config.get_sliding_window() is None

    qwen2_model_config.hf_config.use_sliding_window = True
    assert qwen2_model_config.get_sliding_window() == TEST_SLIDING_WINDOW

    mistral_model_config = ModelConfig(
        "mistralai/Mistral-7B-v0.1",
        task="auto",
        tokenizer="mistralai/Mistral-7B-v0.1",
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )
    mistral_model_config.hf_config.sliding_window = None
    assert mistral_model_config.get_sliding_window() is None

    mistral_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
    assert mistral_model_config.get_sliding_window() == TEST_SLIDING_WINDOW


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_pooling_config():
    model_id = "sentence-transformers/all-MiniLM-L12-v2"
    minilm_model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    minilm_pooling_config = minilm_model_config._init_pooler_config(
        pooling_type=None,
        pooling_norm=None,
        pooling_returned_token_ids=None,
        pooling_softmax=None,
        pooling_step_tag_id=None)

    assert minilm_pooling_config.pooling_norm
    assert minilm_pooling_config.pooling_type == PoolingType.MEAN.name


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_pooling_config_from_args():
    model_id = "sentence-transformers/all-MiniLM-L12-v2"
    minilm_model_config = ModelConfig(model_id,
                                      task="auto",
                                      tokenizer=model_id,
                                      tokenizer_mode="auto",
                                      trust_remote_code=False,
                                      seed=0,
                                      dtype="float16",
                                      revision=None)

    minilm_pooling_config = minilm_model_config._init_pooler_config(
        pooling_type='CLS',
        pooling_norm=True,
        pooling_returned_token_ids=None,
        pooling_softmax=None,
        pooling_step_tag_id=None)

    assert minilm_pooling_config.pooling_norm
    assert minilm_pooling_config.pooling_type == PoolingType.CLS.name


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_bert_tokenization_sentence_transformer_config():
    bge_model_config = ModelConfig(
        model="BAAI/bge-base-en-v1.5",
        task="auto",
        tokenizer="BAAI/bge-base-en-v1.5",
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    bert_bge_model_config = bge_model_config._get_encoder_config()

    assert bert_bge_model_config["max_seq_length"] == 512
    assert bert_bge_model_config["do_lower_case"]


def test_rope_customization():
    TEST_ROPE_SCALING = {"rope_type": "dynamic", "factor": 2.0}
    TEST_ROPE_THETA = 16_000_000.0
    LONGCHAT_ROPE_SCALING = {"rope_type": "linear", "factor": 8.0}

    llama_model_config = ModelConfig(
        "meta-llama/Meta-Llama-3-8B-Instruct",
        task="auto",
        tokenizer="meta-llama/Meta-Llama-3-8B-Instruct",
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )
    assert getattr(llama_model_config.hf_config, "rope_scaling", None) is None
    assert getattr(llama_model_config.hf_config, "rope_theta", None) == 500_000
    assert llama_model_config.max_model_len == 8192

    llama_model_config = ModelConfig(
        "meta-llama/Meta-Llama-3-8B-Instruct",
        task="auto",
        tokenizer="meta-llama/Meta-Llama-3-8B-Instruct",
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
        rope_scaling=TEST_ROPE_SCALING,
        rope_theta=TEST_ROPE_THETA,
    )
    assert getattr(llama_model_config.hf_config, "rope_scaling",
                   None) == TEST_ROPE_SCALING
    assert getattr(llama_model_config.hf_config, "rope_theta",
                   None) == TEST_ROPE_THETA
    assert llama_model_config.max_model_len == 16384

    longchat_model_config = ModelConfig(
        "lmsys/longchat-13b-16k",
        task="auto",
        tokenizer="lmsys/longchat-13b-16k",
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )
    # Check if LONGCHAT_ROPE_SCALING entries are in longchat_model_config
    assert all(
        longchat_model_config.hf_config.rope_scaling.get(key) == value
        for key, value in LONGCHAT_ROPE_SCALING.items())
    assert longchat_model_config.max_model_len == 16384

    longchat_model_config = ModelConfig(
        "lmsys/longchat-13b-16k",
        task="auto",
        tokenizer="lmsys/longchat-13b-16k",
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
        rope_scaling=TEST_ROPE_SCALING,
    )
    assert getattr(longchat_model_config.hf_config, "rope_scaling",
                   None) == TEST_ROPE_SCALING
    assert longchat_model_config.max_model_len == 4096


@pytest.mark.parametrize(("model_id", "is_encoder_decoder"), [
    ("facebook/opt-125m", False),
    ("facebook/bart-base", True),
    ("meta-llama/Llama-3.2-1B", False),
    ("meta-llama/Llama-3.2-11B-Vision", True),
])
def test_is_encoder_decoder(model_id, is_encoder_decoder):
    config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )

    assert config.is_encoder_decoder == is_encoder_decoder


@pytest.mark.parametrize(("model_id", "uses_mrope"), [
    ("facebook/opt-125m", False),
    ("Qwen/Qwen2-VL-2B-Instruct", True),
])
def test_uses_mrope(model_id, uses_mrope):
    config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )

    assert config.uses_mrope == uses_mrope