test_models.py

# SPDX-License-Identifier: Apache-2.0
"""Compare the outputs of HF and vLLM when using greedy sampling.

Run `pytest tests/models/test_models.py`.
"""
import pytest

from ...utils import check_logprobs_close


@pytest.mark.parametrize(
    "model",
    [
        pytest.param(
            "bigscience/bloom-560m",  # bloom - testing alibi slopes
            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
        ),
        pytest.param(
            "openai-community/gpt2",  # gpt2
            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
        ),
        pytest.param("Milos/slovak-gpt-j-405M"),  # gptj
        pytest.param("bigcode/tiny_starcoder_py"),  # gpt_bigcode
        pytest.param("EleutherAI/pythia-70m"),  # gpt_neox
        pytest.param(
            "google/gemma-1.1-2b-it",  # gemma
            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
        ),
        pytest.param(
            "meta-llama/Llama-3.2-1B-Instruct",  # llama
            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
        ),
        pytest.param(
            "openbmb/MiniCPM3-4B",
            # fused_moe not supported on CPU
            marks=[pytest.mark.core_model],
        ),
        pytest.param(
            "facebook/opt-125m",  # opt
            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
        ),
        pytest.param(
            "microsoft/phi-2",  # phi
            marks=[pytest.mark.core_model],
        ),
        pytest.param(
            "Qwen/Qwen2.5-0.5B-Instruct",  # qwen2
            marks=[pytest.mark.core_model],
        ),
        pytest.param("stabilityai/stablelm-3b-4e1t"),  # stablelm
        pytest.param("bigcode/starcoder2-3b"),  # starcoder2
        pytest.param(
            "ehristoforu/Falcon3-MoE-2x7B-Insruct",  # mixtral
            marks=[pytest.mark.cpu_model],
        )
    ])
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:

    with hf_runner(model, dtype=dtype) as hf_model:
        hf_outputs = hf_model.generate_greedy_logprobs_limit(
            example_prompts, max_tokens, num_logprobs)

    with vllm_runner(model, dtype=dtype) as vllm_model:
        vllm_outputs = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)

        # This test is for verifying whether the model's extra_repr
        # can be printed correctly.
        def print_model(model):
            print(model)

        vllm_model.apply_model(print_model)

    check_logprobs_close(
        outputs_0_lst=hf_outputs,
        outputs_1_lst=vllm_outputs,
        name_0="hf",
        name_1="vllm",
    )