vllm/tests/v1/e2e/test_spec_decode.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import random
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

import pytest
import torch

from tests.evals.gsm8k.gsm8k_eval import _build_gsm8k_prompts, evaluate_gsm8k_offline
from tests.utils import (
    get_attn_backend_list_based_on_platform,
    large_gpu_mark,
    multi_gpu_marks,
    multi_gpu_only,
    single_gpu_only,
)
from vllm import LLM, SamplingParams
from vllm.assets.base import VLLM_S3_BUCKET_URL
from vllm.assets.image import VLM_IMAGES_DIR
from vllm.benchmarks.datasets import InstructCoderDataset
from vllm.config import VllmConfig
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.engine.arg_utils import EngineArgs
from vllm.platforms import current_platform
from vllm.v1.metrics.reader import Metric
from vllm.v1.spec_decode.utils import create_vllm_config_for_draft_model

MTP_SIMILARITY_RATE = 0.8


def _skip_if_insufficient_gpus_for_tp(tp_size: int):
    """Skip test if available GPUs < tp_size on ROCm."""
    available_gpus = torch.cuda.device_count()
    if available_gpus < tp_size:
        pytest.skip(
            f"Test requires {tp_size} GPUs, but only {available_gpus} available"
        )


Messages = list[dict[str, Any]]


def get_test_prompts(mm_enabled: bool, num_prompts: int = 100) -> list[Messages]:
    prompt_types = ["repeat", "gsm8k"]
    if mm_enabled:
        prompt_types.append("mm")
    prompts: list[Messages] = []

    num_repeat_prompts = num_prompts // len(prompt_types)
    if mm_enabled:
        num_gsm8k_prompts = num_prompts // len(prompt_types)
        num_mm_prompts = num_prompts - num_repeat_prompts - num_gsm8k_prompts
    else:
        num_mm_prompts = 0
        num_gsm8k_prompts = num_prompts - num_repeat_prompts

    # Generate a mixed batch of prompts, some of which can be easily
    # predicted by n-gram matching and some which likely cannot.
    random.seed(0)
    for _ in range(num_repeat_prompts):
        word_choices = ["test", "temp", "hello", "where"]
        word = random.choice(word_choices)
        prompts.append(
            [
                {
                    "role": "user",
                    "content": f"""
        please repeat the word '{word}' 10 times.
        give no other output than the word at least ten times in a row,
        in lowercase with spaces between each word and without quotes.
        """,
                }
            ]
        )
    prompts.extend(
        [{"role": "user", "content": prompt}]
        for prompt in _build_gsm8k_prompts(
            num_questions=num_gsm8k_prompts, num_shots=5
        )[0]
    )
    for _ in range(num_mm_prompts):
        placeholders = [
            {
                "type": "image_url",
                "image_url": {
                    "url": f"{VLLM_S3_BUCKET_URL}/{VLM_IMAGES_DIR}/stop_sign.jpg"
                },
            }
        ]
        prompt = [
            *placeholders,
            {"type": "text", "text": "The meaning of the image is"},
        ]
        prompts.append([{"role": "user", "content": prompt}])

    return prompts


def get_instruct_coder_messages(n: int) -> list[Messages]:
    dataset = InstructCoderDataset(
        dataset_path="likaixin/InstructCoder", dataset_split="train"
    )
    prompts: Iterable[str] = dataset.sample_prompts(n=n)
    return [[{"role": "user", "content": prompt}] for prompt in prompts]


@pytest.fixture
def sampling_config():
    return greedy_sampling()


def greedy_sampling() -> SamplingParams:
    return SamplingParams(temperature=0, max_tokens=10, ignore_eos=False)


def stochastic_sampling() -> SamplingParams:
    return SamplingParams(temperature=1.0, max_tokens=10, ignore_eos=False)


@pytest.fixture
def model_name():
    return "meta-llama/Llama-3.1-8B-Instruct"


def evaluate_llm_for_gsm8k(llm: LLM, expected_accuracy_threshold: float = 0.70) -> None:
    """Evaluate the LLM on GSM8K and check that accuracy is above a sanity threshold.

    The default threshold assumes the LLM uses the same target model as the "model_name"
    fixture, with max model len == 4096. Precomputed reference value is 75% to 80%
    on GSM8K with greedy decoding, so we check that it's above a sanity threshold of 70%
    to verify that the model is correct.
    """
    if expected_accuracy_threshold <= 0.0:
        print("Skipping GSM8K evaluation")
        return
    results = evaluate_gsm8k_offline(llm)
    accuracy = results["accuracy"]
    print(f"GSM8K accuracy: {accuracy:.3f}")
    assert accuracy >= expected_accuracy_threshold, (
        f"Expected GSM8K accuracy >= {expected_accuracy_threshold}, got {accuracy:.3f}"
    )


@pytest.fixture(autouse=True)
def reset_torch_dynamo():
    """Reset torch dynamo cache before each test"""
    yield
    # Cleanup after test
    torch._dynamo.reset()


@pytest.mark.parametrize(
    "speculative_config",
    [
        {
            "method": "ngram",
            "prompt_lookup_max": 5,
            "prompt_lookup_min": 3,
            "num_speculative_tokens": 3,
        },
        {
            "method": "suffix",
            "suffix_decoding_max_spec_factor": 2.0,
        },
    ],
)
@single_gpu_only
@large_gpu_mark(min_gb=20)
def test_ngram_and_suffix_correctness(
    speculative_config: dict,
    model_name: str,
):
    spec_llm = LLM(
        model=model_name,
        speculative_config=speculative_config,
        max_model_len=4096,
    )
    evaluate_llm_for_gsm8k(spec_llm)
    del spec_llm
    torch.cuda.empty_cache()
    cleanup_dist_env_and_memory()


@single_gpu_only
@large_gpu_mark(min_gb=20)
def test_suffix_decoding_acceptance(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_name: str,
):
    """
    Check that suffix decoding caching takes effect and improves acceptance
    lengths and acceptance rates over multiple runs of the same prompts.
    """
    test_prompts = get_test_prompts(mm_enabled=False)

    spec_llm = LLM(
        model=model_name,
        speculative_config={
            "method": "suffix",
            "suffix_decoding_max_spec_factor": 2.0,
            "suffix_decoding_max_cached_requests": 1000,
        },
        max_model_len=1024,
        disable_log_stats=False,
    )

    # Run several times and check that the accepted tokens increase.
    num_draft = []
    num_accept = []
    for i in range(10):  # Run multiple times to warm up the cache.
        spec_llm.chat(test_prompts, sampling_config)
        # Collect draft and acceptance stats.
        metrics = spec_llm.get_metrics()
        for metric in metrics:
            if metric.name == "vllm:spec_decode_num_draft_tokens":
                num_draft.append(metric.value)
            if metric.name == "vllm:spec_decode_num_accepted_tokens":
                num_accept.append(metric.value)

    # Calculate the acceptance rates for the first and last runs.
    first_accept_tokens = num_accept[0]
    first_draft_tokens = num_draft[0]
    first_accept_rate = first_accept_tokens / first_draft_tokens

    # Take the diff since the stats are cumulative.
    last_accept_tokens = num_accept[-1] - num_accept[-2]
    last_draft_tokens = num_draft[-1] - num_draft[-2]
    last_accept_rate = last_accept_tokens / last_draft_tokens

    # Expect the acceptance length to improve.
    assert first_accept_tokens < last_accept_tokens

    # Expect the acceptance rate to improve.
    assert first_accept_rate < last_accept_rate

    # Heuristic: expect at least 80.0% acceptance rate at the end.
    assert last_accept_rate > 0.80

    del spec_llm
    torch.cuda.empty_cache()
    cleanup_dist_env_and_memory()


@pytest.mark.parametrize(
    ["model_path", "expected_accuracy_threshold"],
    [
        ("RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3", 0.7),  # ref: 75%-80%
        ("RedHatAI/Qwen3-8B-speculator.eagle3", 0.8),  # ref: 87%-92%
    ],
    ids=["llama3_eagle3_speculator", "qwen3_eagle3_speculator"],
)
@single_gpu_only
@large_gpu_mark(min_gb=24)
def test_speculators_model_integration(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_path: str,
    expected_accuracy_threshold: float,
):
    """
    Test that speculators models work with the simplified integration.

    This verifies the `vllm serve <speculator-model>` use case where
    speculative config is automatically detected from the model config
    without requiring explicit --speculative-config argument.

    Tests:
    1. Speculator model is correctly detected
    2. Verifier model is extracted from speculator config
    3. Speculative decoding is automatically enabled
    4. Text generation works correctly
    5. GSM8k accuracy of the model passes a sanity check when speculative decoding on
    6. Output matches reference (non-speculative) generation
    """
    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")

    # Generate test prompts
    test_prompts = get_test_prompts(mm_enabled=False)

    # First run: Direct speculator model (simplified integration)
    spec_llm = LLM(model=model_path, max_model_len=4096)
    evaluate_llm_for_gsm8k(
        spec_llm, expected_accuracy_threshold=expected_accuracy_threshold
    )
    spec_outputs = spec_llm.chat(test_prompts, sampling_config)

    # Verify speculative config was auto-detected
    assert spec_llm.llm_engine.vllm_config.speculative_config is not None, (
        f"Speculative config should be auto-detected for {model_path}"
    )

    spec_config = spec_llm.llm_engine.vllm_config.speculative_config
    assert spec_config.num_speculative_tokens > 0, (
        f"Expected positive speculative tokens, "
        f"got {spec_config.num_speculative_tokens}"
    )

    # Verify draft model is set to the speculator model
    assert spec_config.model == model_path, (
        f"Draft model should be {model_path}, got {spec_config.model}"
    )

    # Extract verifier model for reference run
    verifier_model = spec_llm.llm_engine.vllm_config.model_config.model

    del spec_llm
    torch.cuda.empty_cache()
    cleanup_dist_env_and_memory()

    # Second run: Reference without speculative decoding
    ref_llm = LLM(model=verifier_model, max_model_len=4096)
    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
    del ref_llm
    torch.cuda.empty_cache()
    cleanup_dist_env_and_memory()

    # Compare outputs
    matches = sum(
        1
        for ref, spec in zip(ref_outputs, spec_outputs)
        if ref.outputs[0].text == spec.outputs[0].text
    )

    # Heuristic: expect at least 66% of prompts to match exactly
    assert matches >= int(0.66 * len(ref_outputs)), (
        f"Only {matches}/{len(ref_outputs)} outputs matched. "
        f"Expected at least {int(0.66 * len(ref_outputs))} matches."
    )


def _run_eagle_correctness(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_setup: tuple[str, str, str, int],
    mm_enabled: bool,
    expected_accuracy_threshold: float,
    enable_chunked_prefill: bool,
    model_impl: str,
    attn_backend: str,
):
    """
    Compare the outputs of an original LLM and a speculative LLM
    which should be the same when using eagle speculative decoding.
    """
    if attn_backend == "TREE_ATTN":
        pytest.skip(
            "TREE_ATTN is flaky in the test disable for now until it can be "
            "resolved (see https://github.com/vllm-project/vllm/issues/22922)"
        )
    if model_impl == "transformers":
        import transformers
        from packaging.version import Version

        installed = Version(transformers.__version__)
        required = Version("5.0.0")
        if installed < required:
            pytest.skip(
                "Eagle3 with the Transformers modeling backend requires "
                f"transformers>={required}, but got {installed}"
            )

    test_prompts = get_test_prompts(mm_enabled)

    if "Llama-4-Scout" in model_setup[1] and attn_backend == "FLASH_ATTN":
        if current_platform.is_rocm():
            print(
                "FLASH_ATTN for spec_decode not supported on "
                "ROCm currently. Changing to FLEX_ATTENTION backend."
            )
            attention_config = {"backend": "FLEX_ATTENTION"}
        else:
            attention_config = None
    else:
        attention_config = {"backend": attn_backend}

    if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
        pytest.skip(
            "TRITON_ATTN does not support "
            "multi-token eagle spec decode on current platform"
        )

    with monkeypatch.context() as m:
        m.setenv("VLLM_MLA_DISABLE", "1")

        if attn_backend == "ROCM_AITER_FA" and current_platform.is_rocm():
            if "deepseek" in model_setup[1].lower():
                m.setenv("VLLM_ROCM_USE_AITER", "1")
                m.delenv("VLLM_MLA_DISABLE", raising=False)
                attention_config = {"backend": "TRITON_MLA"}
            else:
                m.setenv("VLLM_ROCM_USE_AITER", "1")

        method, model_name, spec_model_name, tp_size = model_setup
        _skip_if_insufficient_gpus_for_tp(tp_size)

        max_model_len = 2048
        max_num_batched_tokens = 128 if enable_chunked_prefill else max_model_len

        ref_llm = LLM(
            model=model_name,
            max_model_len=max_model_len,
            tensor_parallel_size=tp_size,
            attention_config=attention_config,
        )
        evaluate_llm_for_gsm8k(
            ref_llm, expected_accuracy_threshold=expected_accuracy_threshold
        )
        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
        del ref_llm
        torch.cuda.empty_cache()
        cleanup_dist_env_and_memory()

        spec_llm = LLM(
            model=model_name,
            trust_remote_code=True,
            tensor_parallel_size=tp_size,
            speculative_config={
                "method": method,
                "model": spec_model_name,
                "num_speculative_tokens": 3,
                "max_model_len": max_model_len,
            },
            max_model_len=max_model_len,
            max_num_batched_tokens=max_num_batched_tokens,
            enable_chunked_prefill=enable_chunked_prefill,
            model_impl=model_impl,
            attention_config=attention_config,
        )
        evaluate_llm_for_gsm8k(
            spec_llm, expected_accuracy_threshold=expected_accuracy_threshold
        )
        spec_outputs = spec_llm.chat(test_prompts, sampling_config)
        matches = 0
        misses = 0
        for ref_output, spec_output in zip(ref_outputs, spec_outputs):
            if ref_output.outputs[0].text == spec_output.outputs[0].text:
                matches += 1
            else:
                misses += 1
                print(f"ref_output: {ref_output.outputs[0].text}")
                print(f"spec_output: {spec_output.outputs[0].text}")

        assert matches > int(0.6 * len(ref_outputs))
        del spec_llm
        torch.cuda.empty_cache()
        cleanup_dist_env_and_memory()


@single_gpu_only
@pytest.mark.parametrize(
    [
        "model_setup",
        "mm_enabled",
        "enable_chunked_prefill",
        "model_impl",
        "expected_accuracy_threshold",
    ],
    [
        (
            (
                "eagle",
                "eagle618/deepseek-v3-random",
                "eagle618/eagle-deepseek-v3-random",
                1,
            ),
            False,
            False,
            "auto",
            0.0,
        ),
    ],
    ids=["deepseek_eagle"],
)
@pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
def test_eagle_correctness_light(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_setup: tuple[str, str, str, int],
    mm_enabled: bool,
    expected_accuracy_threshold: float,
    enable_chunked_prefill: bool,
    model_impl: str,
    attn_backend: str,
):
    _run_eagle_correctness(
        monkeypatch,
        sampling_config,
        model_setup,
        mm_enabled,
        expected_accuracy_threshold,
        enable_chunked_prefill,
        model_impl,
        attn_backend,
    )


@single_gpu_only
@large_gpu_mark(min_gb=24)
@pytest.mark.parametrize(
    [
        "model_setup",
        "mm_enabled",
        "enable_chunked_prefill",
        "model_impl",
        "expected_accuracy_threshold",
    ],
    [
        (
            ("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1),
            False,
            False,
            "auto",
            0.8,
        ),
        (
            ("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1),
            False,
            False,
            "transformers",
            0.8,
        ),
        pytest.param(
            (
                "eagle3",
                "Qwen/Qwen3-VL-8B-Instruct",
                "taobao-mnn/Qwen3-VL-8B-Instruct-Eagle3",
                1,
            ),
            False,
            False,
            "auto",
            0.8,
            marks=pytest.mark.skip(
                reason="architecture of its eagle3 is LlamaForCausalLMEagle3"
            ),
        ),
        pytest.param(
            (
                "eagle3",
                "Qwen/Qwen2.5-VL-7B-Instruct",
                "Rayzl/qwen2.5-vl-7b-eagle3-sgl",
                1,
            ),
            False,
            False,
            "auto",
            0.7,
            marks=pytest.mark.skip(
                reason="Skipping due to its head_dim not being a multiple of 32"
            ),
        ),
        (
            (
                "eagle3",
                "meta-llama/Llama-3.1-8B-Instruct",
                "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
                1,
            ),
            False,
            False,
            "auto",
            0.7,
        ),
    ],
    ids=[
        "qwen3_eagle3",
        "qwen3_eagle3-transformers",
        "qwen3_vl_eagle3",
        "qwen2_5_vl_eagle3",
        "llama3_eagle3",
    ],
)
@pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
def test_eagle_correctness_medium(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_setup: tuple[str, str, str, int],
    mm_enabled: bool,
    expected_accuracy_threshold: float,
    enable_chunked_prefill: bool,
    model_impl: str,
    attn_backend: str,
):
    _run_eagle_correctness(
        monkeypatch,
        sampling_config,
        model_setup,
        mm_enabled,
        expected_accuracy_threshold,
        enable_chunked_prefill,
        model_impl,
        attn_backend,
    )


@pytest.mark.parametrize(
    [
        "model_setup",
        "mm_enabled",
        "enable_chunked_prefill",
        "model_impl",
        "expected_accuracy_threshold",
    ],
    [
        pytest.param(
            (
                "eagle",
                "meta-llama/Llama-3.1-8B-Instruct",
                "yuhuili/EAGLE-LLaMA3.1-Instruct-8B",
                1,
            ),
            False,
            True,
            "auto",
            0.7,
            marks=large_gpu_mark(min_gb=40),
            id="llama3_eagle",
        ),
        pytest.param(
            (
                "eagle",
                "meta-llama/Llama-4-Scout-17B-16E-Instruct",
                "morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct",
                4,
            ),
            False,
            False,
            "auto",
            0.8,
            marks=[*multi_gpu_marks(num_gpus=4), large_gpu_mark(min_gb=40)],
            id="llama4_eagle",
        ),
        pytest.param(
            (
                "eagle",
                "meta-llama/Llama-4-Scout-17B-16E-Instruct",
                "morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct",
                4,
            ),
            True,
            True,
            "auto",
            0.8,
            marks=[*multi_gpu_marks(num_gpus=4), large_gpu_mark(min_gb=80)],
            id="llama4_eagle_mm",
        ),
    ],
)
@pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
def test_eagle_correctness_heavy(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_setup: tuple[str, str, str, int],
    mm_enabled: bool,
    expected_accuracy_threshold: float,
    enable_chunked_prefill: bool,
    model_impl: str,
    attn_backend: str,
):
    _run_eagle_correctness(
        monkeypatch,
        sampling_config,
        model_setup,
        mm_enabled,
        expected_accuracy_threshold,
        enable_chunked_prefill,
        model_impl,
        attn_backend,
    )


@pytest.mark.parametrize(
    ["model_setup", "mm_enabled", "expected_accuracy_threshold"],
    [
        (("mtp", "XiaomiMiMo/MiMo-7B-Base", 1), False, 0.5),  # ref: 65%-70%
        (("mtp", "ZixiQi/DeepSeek-V3-4layers-MTP-FP8", 1), False, 0.0),  # dummy model
    ],
    ids=["mimo", "deepseek"],
)
@single_gpu_only
@large_gpu_mark(min_gb=20)
def test_mtp_correctness(
    monkeypatch: pytest.MonkeyPatch,
    sampling_config: SamplingParams,
    model_setup: tuple[str, str, int],
    mm_enabled: bool,
    expected_accuracy_threshold: float,
):
    """
    Compare the outputs of a original LLM and a speculative LLM
    which should be the same when using MTP speculative decoding. Due to some variance
    in the engine, it is possible for some outputs to differ, so we expect that at least
    6/10 output tokens match exactly, and that the GSM8k accuracy is above a precomputed
    reference threshold for each model.
    """
    # Generate test prompts inside the function instead of using fixture
    test_prompts = get_test_prompts(mm_enabled)
    with monkeypatch.context() as m:
        m.setenv("VLLM_MLA_DISABLE", "1")

        method, model_name, tp_size = model_setup
        _skip_if_insufficient_gpus_for_tp(tp_size)

        ref_llm = LLM(
            model=model_name,
            max_model_len=2048,
            tensor_parallel_size=tp_size,
            trust_remote_code=True,
        )
        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
        evaluate_llm_for_gsm8k(
            ref_llm, expected_accuracy_threshold=expected_accuracy_threshold
        )
        del ref_llm
        torch.cuda.empty_cache()
        cleanup_dist_env_and_memory()

        spec_llm = LLM(
            model=model_name,
            trust_remote_code=True,
            tensor_parallel_size=tp_size,
            speculative_config={
                "method": method,
                "num_speculative_tokens": 1,
                "max_model_len": 2048,
            },
            max_model_len=2048,
        )
        evaluate_llm_for_gsm8k(
            spec_llm, expected_accuracy_threshold=expected_accuracy_threshold
        )
        spec_outputs = spec_llm.chat(test_prompts, sampling_config)
        matches = 0
        misses = 0
        for ref_output, spec_output in zip(ref_outputs, spec_outputs):
            if ref_output.outputs[0].text == spec_output.outputs[0].text:
                matches += 1
            else:
                misses += 1
                print(f"ref_output: {ref_output.outputs[0].text}")
                print(f"spec_output: {spec_output.outputs[0].text}")

        # Heuristic: expect at least 80% of the prompts to match exactly
        # Upon failure, inspect the outputs to check for inaccuracy.
        assert matches > int(MTP_SIMILARITY_RATE * len(ref_outputs))
        del spec_llm
        torch.cuda.empty_cache()
        cleanup_dist_env_and_memory()


@dataclass
class ArgsTest:
    target_model: str
    draft_model: str
    sampling_config: SamplingParams
    num_speculative_tokens: int
    expected_acceptance_rate: float
    expected_acceptance_len: float
    expected_gsm8k_accuracy: float = 0.0  # skip by default
    # Defaults
    enforce_eager: bool = True
    parallel_drafting: bool = False
    target_tensor_parallel_size: int = 1
    draft_tensor_parallel_size: int = 1
    max_model_len: int = 2048
    gpu_memory_utilization: float = 0.5
    dataset: str = "test_prompts"
    num_prompts: int = 100


cases = [
    # Same model for draft and target, greedy sampling.
    ArgsTest(
        target_model="Qwen/Qwen3-0.6B",
        draft_model="Qwen/Qwen3-0.6B",
        sampling_config=greedy_sampling(),
        num_speculative_tokens=3,  # K
        expected_acceptance_len=0.98 * (3 + 1),  # epsilon discount of K + 1
        expected_acceptance_rate=0.98,  # slight epsilon
        expected_gsm8k_accuracy=0.25,  # ref: 35-40%
    ),
    # Smaller draft model, stochastic sampling.
    ArgsTest(
        target_model="Qwen/Qwen3-1.7B",
        draft_model="Qwen/Qwen3-0.6B",
        sampling_config=stochastic_sampling(),
        num_speculative_tokens=3,
        expected_acceptance_len=3.4,  # ref: 3.7
        expected_acceptance_rate=0.80,  # ref: 0.90
        expected_gsm8k_accuracy=0.5,  # ref: 60%. Note gsm8k always runs greedy sampling
    ),
]


@pytest.mark.parametrize("args", cases)
@pytest.mark.parametrize("enforce_eager", [True, False])
@single_gpu_only
def test_draft_model_correctness(args: ArgsTest, enforce_eager: bool):
    args.enforce_eager = enforce_eager
    assert_draft_model_correctness(args)


@single_gpu_only
def test_draft_model_realistic_example():
    args = ArgsTest(
        target_model="Qwen/Qwen3-1.7B",
        draft_model="Qwen/Qwen3-0.6B",
        dataset="likaixin/InstructCoder",
        num_speculative_tokens=3,
        sampling_config=greedy_sampling(),
        enforce_eager=False,
        expected_acceptance_len=2.6,  # ref: 2.86
        expected_acceptance_rate=0.5,  # ref: 0.62
    )
    assert_draft_model_correctness(args)


@single_gpu_only
def test_draft_model_parallel_drafting():
    args = ArgsTest(
        target_model="Qwen/Qwen3-1.7B",
        draft_model="amd/PARD-Qwen3-0.6B",
        dataset="likaixin/InstructCoder",
        num_speculative_tokens=3,
        sampling_config=greedy_sampling(),
        parallel_drafting=True,
        enforce_eager=False,
        expected_acceptance_len=2.3,  # ref: 2.52
        expected_acceptance_rate=0.4,  # ref: 0.51
    )
    assert_draft_model_correctness(args)


@pytest.mark.parametrize(
    "models",
    [
        # target_model,         draft_model
        ("Qwen/Qwen3-1.7B-FP8", "Qwen/Qwen3-0.6B"),  # target quantized
        ("Qwen/Qwen3-1.7B", "Qwen/Qwen3-0.6B-FP8"),  # draft quantized
    ],
    ids=["target_quantized", "draft_quantized"],
)
@pytest.mark.parametrize("enforce_eager", [True, False])
@single_gpu_only
def test_draft_model_quantization(models: tuple[str, str], enforce_eager: bool):
    tgt_model, draft_model = models
    sd_case = ArgsTest(
        target_model=tgt_model,
        draft_model=draft_model,
        **some_high_acceptance_metrics(),
        enforce_eager=enforce_eager,
    )
    assert_draft_model_correctness(sd_case)


@multi_gpu_only(num_gpus=2)
def test_draft_model_tensor_parallelism():
    """Ensure spec decode works when running with TP > 1."""
    _skip_if_insufficient_gpus_for_tp(2)
    sd_case = ArgsTest(
        target_model="Qwen/Qwen3-1.7B",
        target_tensor_parallel_size=2,
        draft_model="Qwen/Qwen3-0.6B",
        draft_tensor_parallel_size=2,
        **some_high_acceptance_metrics(),
        enforce_eager=False,
        expected_gsm8k_accuracy=0.5,
    )
    assert_draft_model_correctness(sd_case)


@multi_gpu_only(num_gpus=2)
def test_draft_model_engine_args_tensor_parallelism():
    """Ensure the vllm_config for the draft model is created correctly,
    and independently of the target model (quantization, TP, etc.)"""
    _skip_if_insufficient_gpus_for_tp(2)

    engine_args = EngineArgs(
        model="Qwen/Qwen3-1.7B-FP8",  # <<< tgt quantized
        tensor_parallel_size=2,
        speculative_config={
            "model": "Qwen/Qwen3-0.6B",  # <<< draft not quantized
            "method": "draft_model",
            "num_speculative_tokens": 3,
            "draft_tensor_parallel_size": 1,  # <<< valid arg name
        },
    )
    tgt_vllm_config: VllmConfig = engine_args.create_engine_config()
    assert tgt_vllm_config.parallel_config.tensor_parallel_size == 2
    assert tgt_vllm_config.quant_config.get_name() == "fp8"

    draft_vllm_config: VllmConfig = create_vllm_config_for_draft_model(tgt_vllm_config)
    assert draft_vllm_config.parallel_config.tensor_parallel_size == 1
    assert draft_vllm_config.quant_config is None


def test_draft_model_engine_args_rejects_invalid_tp_argname():
    """The user should pass "draft_tensor_parallel_size" rather than
    "tensor_parallel_size". We enforce this with validation."""

    engine_args = EngineArgs(
        model="Qwen/Qwen3-1.7B",
        tensor_parallel_size=1,
        speculative_config={
            "model": "Qwen/Qwen3-0.6B",
            "method": "draft_model",
            "num_speculative_tokens": 3,
            "tensor_parallel_size": 1,  # <<< invalid arg name
        },
    )
    with pytest.raises(ValueError):
        engine_args.create_engine_config()


def assert_draft_model_correctness(args: ArgsTest):
    """Compare the outputs using and not using speculative decoding.
    In the greedy decoding case, the outputs must match EXACTLY."""
    test_prompts: list[Messages] = get_messages(
        dataset=args.dataset, n=args.num_prompts
    )

    spec_llm = LLM(
        model=args.target_model,
        speculative_config={
            "model": args.draft_model,
            "method": "draft_model",
            "num_speculative_tokens": args.num_speculative_tokens,
            "max_model_len": args.max_model_len,
            "enforce_eager": args.enforce_eager,
            "draft_tensor_parallel_size": args.draft_tensor_parallel_size,
            "parallel_drafting": args.parallel_drafting,
        },
        max_num_seqs=100,  # limit cudagraph capture runtime
        max_model_len=args.max_model_len,
        gpu_memory_utilization=args.gpu_memory_utilization,
        tensor_parallel_size=args.target_tensor_parallel_size,
        enforce_eager=args.enforce_eager,
        disable_log_stats=False,  # enables get_metrics()
    )
    # we don't check the outputs, only check the metrics
    spec_llm.chat(test_prompts, args.sampling_config)
    metrics = spec_llm.get_metrics()
    acceptance_rate: float = compute_acceptance_rate(metrics)
    acceptance_len: float = compute_acceptance_len(metrics)

    # Need to evaluate after getting metrics to avoid polluting the AR
    evaluate_llm_for_gsm8k(
        spec_llm, expected_accuracy_threshold=args.expected_gsm8k_accuracy
    )

    del spec_llm  # CLEANUP
    torch.cuda.empty_cache()
    cleanup_dist_env_and_memory()

    print(
        f"spec-decode: target={args.target_model}, draft={args.draft_model}, "
        f"temperature={args.sampling_config.temperature:.2f}, "
        f"acceptance_rate={acceptance_rate:.2f}, "
        f"acceptance_len={acceptance_len:.2f}, "
    )

    assert acceptance_rate >= args.expected_acceptance_rate
    assert acceptance_len >= args.expected_acceptance_len


def get_messages(dataset: str, n: int) -> list[Messages]:
    if dataset == "test_prompts":
        return get_test_prompts(mm_enabled=False, num_prompts=n)
    elif dataset == "likaixin/InstructCoder":
        return get_instruct_coder_messages(n=n)
    else:
        raise NotImplementedError(f"Dataset '{dataset}' not implemented")


def some_high_acceptance_metrics() -> dict:
    return {
        "sampling_config": greedy_sampling(),
        "num_speculative_tokens": 3,
        "expected_acceptance_len": 3.4,  # ref: 3.75
        "expected_acceptance_rate": 0.8,  # ref: 0.9
    }


def compute_acceptance_rate(metrics: list[Metric]) -> float:
    name2metric = {metric.name: metric for metric in metrics}
    n_draft_toks = name2metric["vllm:spec_decode_num_draft_tokens"].value  # type: ignore
    if n_draft_toks == 0:
        return float("nan")
    n_accepted_toks = name2metric["vllm:spec_decode_num_accepted_tokens"].value  # type: ignore
    return n_accepted_toks / n_draft_toks


def compute_acceptance_len(metrics: list[Metric]) -> float:
    name2metric = {metric.name: metric for metric in metrics}
    n_drafts = name2metric["vllm:spec_decode_num_drafts"].value  # type: ignore
    n_accepted_toks = name2metric["vllm:spec_decode_num_accepted_tokens"].value  # type: ignore
    if n_drafts == 0:
        return 1
    return 1 + (n_accepted_toks / n_drafts)