test_hybrid.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import pytest

from tests.models.registry import HF_EXAMPLE_MODELS
from tests.utils import multi_gpu_test
from vllm.engine.arg_utils import EngineArgs
from vllm.sampling_params import SamplingParams

from ...utils import check_logprobs_close, check_outputs_equal

# Mark all tests as hybrid
pytestmark = pytest.mark.hybrid_model

# NOTE: The first model in each list is taken as the primary model,
# meaning that it will be used in all tests in this file
# The rest of the models will only be tested by test_models

SSM_MODELS = [
    "state-spaces/mamba-130m-hf",
    "tiiuae/falcon-mamba-tiny-dev",
    "mistralai/Mamba-Codestral-7B-v0.1",
]

HYBRID_MODELS = [
    "ai21labs/Jamba-tiny-dev",
    # skipping until vLLM implementation issues are resolved
    # "pfnet/plamo-2-1b",
    "Zyphra/Zamba2-1.2B-instruct",
    "hmellor/tiny-random-BambaForCausalLM",
    "ibm-ai-platform/Bamba-9B-v1",
    "nvidia/Nemotron-H-8B-Base-8K",
    "ibm-granite/granite-4.0-tiny-preview",
    "tiiuae/Falcon-H1-0.5B-Base",
]

HF_UNSUPPORTED_MODELS = [
    # The HF transformers implementation of
    # Mamba2 is buggy for Codestral as it doesn't handle n_groups, so the test
    # doesn't compare vLLM output with HF output.
    # See https://github.com/huggingface/transformers/pull/35943
    "mistralai/Mamba-Codestral-7B-v0.1",
    # Note: I'm not seeing the same output from vLLM V0 vs. HF transformers
    # for Nemotron-H-8B; currently only compare vLLM V0 vs. vLLM V1
    "nvidia/Nemotron-H-8B-Base-8K",
    # NOTE: Currently the test fails due to HF transformers issue fixed in:
    # https://github.com/huggingface/transformers/pull/39033
    # We will enable vLLM test for Granite after next HF transformers release.
    "ibm-granite/granite-4.0-tiny-preview",
]

V1_SUPPORTED_MODELS = [
    "state-spaces/mamba-130m-hf",
    "ai21labs/Jamba-tiny-dev",
    "mistralai/Mamba-Codestral-7B-v0.1",
    "ibm-ai-platform/Bamba-9B-v1",
    "Zyphra/Zamba2-1.2B-instruct",
    "nvidia/Nemotron-H-8B-Base-8K",
    "ibm-granite/granite-4.0-tiny-preview",
    "tiiuae/Falcon-H1-0.5B-Base",
]

# Avoid OOM
MAX_NUM_SEQS = 4


@pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
    hf_runner,
    vllm_runner,
    example_prompts,
    monkeypatch,
    model: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:

    try:
        model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
        model_info.check_available_online(on_fail="skip")
        hf_version_check = model_info.check_transformers_version(
            on_fail="return")
    except ValueError:
        hf_version_check = None

    if hf_version_check is not None:
        print(f"Skipping transformers comparison because: {hf_version_check}")

    with hf_runner(model) as hf_model:
        if model not in HF_UNSUPPORTED_MODELS and hf_version_check is None:
            hf_outputs = hf_model.generate_greedy_logprobs_limit(
                example_prompts, max_tokens, num_logprobs)
        else:
            hf_outputs = None

    with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
        vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)

    if model in V1_SUPPORTED_MODELS:
        with monkeypatch.context() as m:
            m.setenv("VLLM_USE_V1", "1")
            if model in HYBRID_MODELS:
                # required due to reorder_batch behaviour
                m.setenv("VLLM_ATTENTION_BACKEND", "FLASHINFER")
            with vllm_runner(model,
                             max_num_seqs=MAX_NUM_SEQS,
                             enable_prefix_caching=False) as vllm_model:
                vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
                    example_prompts, max_tokens, num_logprobs)
    else:
        vllm_v1_outputs = None

    if hf_outputs is not None:
        check_logprobs_close(
            outputs_0_lst=hf_outputs,
            outputs_1_lst=vllm_v0_outputs,
            name_0="hf",
            name_1="vllm-v0",
        )

    if model in V1_SUPPORTED_MODELS:
        ref_outputs = hf_outputs if hf_outputs is not None else vllm_v0_outputs
        check_logprobs_close(
            outputs_0_lst=ref_outputs,
            outputs_1_lst=vllm_v1_outputs,
            name_0="hf" if hf_outputs is not None else "vllm-v0",
            name_1="vllm-v1",
        )


@pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_batching(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:

    try:
        model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
        model_info.check_available_online(on_fail="skip")
        model_info.check_transformers_version(on_fail="skip")
    except ValueError:
        pass

    for_loop_outputs = []
    with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
        for prompt in example_prompts:
            single_output, = vllm_model.generate_greedy_logprobs([prompt],
                                                                 max_tokens,
                                                                 num_logprobs)
            for_loop_outputs.append(single_output)

        batched_outputs = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)

    check_logprobs_close(
        outputs_0_lst=for_loop_outputs,
        outputs_1_lst=batched_outputs,
        name_0="for_loop_vllm",
        name_1="batched_vllm",
    )


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16])
def test_chunked_prefill(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
    num_logprobs: int,
    chunked_prefill_token_size: int,
) -> None:
    max_num_seqs = chunked_prefill_token_size
    max_num_batched_tokens = chunked_prefill_token_size

    with vllm_runner(model,
                     enable_chunked_prefill=True,
                     max_num_batched_tokens=max_num_batched_tokens,
                     max_num_seqs=max_num_seqs) as vllm_model:
        chunked = vllm_model.generate_greedy_logprobs(example_prompts,
                                                      max_tokens, num_logprobs)

    with vllm_runner(model,
                     enable_chunked_prefill=False,
                     max_num_seqs=max_num_seqs) as vllm_model:
        non_chunked = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)

    check_logprobs_close(
        outputs_0_lst=chunked,
        outputs_1_lst=non_chunked,
        name_0="chunked",
        name_1="non_chunked",
    )


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [10])
def test_chunked_prefill_with_parallel_sampling(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
) -> None:
    """
    Tests chunked prefill in conjunction with n > 1. 
    
    In this case, prefill is populated with decoding tokens and
    we test that it doesn't fail.

    This test might fail if cache is not allocated correctly for n > 1
    decoding steps inside a chunked prefill forward pass
    (where we have both prefill and decode together)
    """
    sampling_params = SamplingParams(n=3,
                                     temperature=1,
                                     seed=0,
                                     max_tokens=max_tokens)
    with vllm_runner(
            model,
            enable_chunked_prefill=True,
            # forces prefill chunks with decoding
            max_num_batched_tokens=MAX_NUM_SEQS * 3,
            max_num_seqs=MAX_NUM_SEQS,
    ) as vllm_model:
        vllm_model.generate(example_prompts, sampling_params)


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [20])
def test_mamba_cache_cg_padding(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
) -> None:
    """
    This test is for verifying that mamba cache is padded to CG captured
    batch size. If it's not, a torch RuntimeError will be raised because
    tensor dimensions aren't compatible.
    """
    vllm_config = EngineArgs(model=model,
                             trust_remote_code=True).create_engine_config()
    while len(example_prompts) == vllm_config.pad_for_cudagraph(
            len(example_prompts)):
        example_prompts.append(example_prompts[0])

    try:
        with vllm_runner(model) as vllm_model:
            vllm_model.generate_greedy(example_prompts, max_tokens)
    except RuntimeError:
        pytest.fail(
            "Couldn't run batch size which is not equal to a Cuda Graph "
            "captured batch size. "
            "Could be related to mamba cache not padded correctly")


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [20])
def test_models_preemption_recompute(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
) -> None:
    """
    Tests that outputs are identical with and w/o preemptions (recompute).
    """
    with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
        scheduler = vllm_model.llm.llm_engine.scheduler[0]
        scheduler.ENABLE_ARTIFICIAL_PREEMPT = True
        preempt_vllm_outputs = vllm_model.generate_greedy(
            example_prompts, max_tokens)

        scheduler.ENABLE_ARTIFICIAL_PREEMPT = False
        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)

    check_outputs_equal(
        outputs_0_lst=preempt_vllm_outputs,
        outputs_1_lst=vllm_outputs,
        name_0="vllm_preepmtions",
        name_1="vllm",
    )


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
def test_fail_upon_inc_requests_and_finished_requests_lt_available_blocks(
    vllm_runner,
    example_prompts,
    model: str,
) -> None:
    """
    This test is for verifying that the hybrid inner state management doesn't
    collapse in case where the number of incoming requests and
    finished_requests_ids is larger than the maximum mamba block capacity.

    This could generally happen due to the fact that hybrid does support
    statelessness mechanism where it can cleanup new incoming requests in
    a single step.
    """
    try:
        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
            vllm_model.generate_greedy([example_prompts[0]] * 100, 10)
    except ValueError:
        pytest.fail("Hybrid inner state wasn't cleaned up properly between"
                    "steps finished requests registered unnecessarily ")


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
def test_state_cleanup(
    vllm_runner,
    example_prompts,
    model: str,
) -> None:
    """ 
    This test is for verifying that the Hybrid state is cleaned up between
    steps.
    
    If its not cleaned, an error would be expected.
    """
    try:
        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
            for _ in range(10):
                vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
    except ValueError:
        pytest.fail("Hybrid inner state wasn't cleaned up between states, "
                    "could be related to finished_requests_ids")


@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [64])
def test_multistep_correctness(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
) -> None:
    with vllm_runner(model, num_scheduler_steps=8,
                     max_num_seqs=2) as vllm_model:
        vllm_outputs_multistep = vllm_model.generate_greedy(
            example_prompts, max_tokens)

    with vllm_runner(model, num_scheduler_steps=1,
                     max_num_seqs=2) as vllm_model:
        vllm_outputs_single_step = vllm_model.generate_greedy(
            example_prompts, max_tokens)

    check_outputs_equal(
        outputs_0_lst=vllm_outputs_multistep,
        outputs_1_lst=vllm_outputs_single_step,
        name_0="vllm_outputs_multistep",
        name_1="vllm_outputs_single_step",
    )


@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_distributed_correctness(
    vllm_runner,
    example_prompts,
    model: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:
    with vllm_runner(model, tensor_parallel_size=1,
                     max_num_seqs=2) as vllm_model:
        vllm_outputs_tp_1 = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)

    with vllm_runner(model, tensor_parallel_size=2,
                     max_num_seqs=2) as vllm_model:
        vllm_outputs_tp_2 = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)

    check_logprobs_close(
        outputs_0_lst=vllm_outputs_tp_1,
        outputs_1_lst=vllm_outputs_tp_2,
        name_0="vllm_tp_1",
        name_1="vllm_tp_2",
    )