test_spec_decode.py

# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import random
from typing import Any

import pytest

from vllm import LLM, SamplingParams


@pytest.fixture
def test_prompts():
    prompt_types = ["repeat", "sentence"]
    num_prompts = 100
    prompts = []

    random.seed(0)
    random_prompt_type_choices = random.choices(prompt_types, k=num_prompts)

    # Generate a mixed batch of prompts, some of which can be easily
    # predicted by n-gram matching and some which likely cannot.
    for kind in random_prompt_type_choices:
        word_choices = ["test", "temp", "hello", "where"]
        word = random.choice(word_choices)
        if kind == "repeat":
            prompt = 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.
            """
        elif kind == "sentence":
            prompt = f"""
            please give a ten-word sentence that
            uses the word {word} at least once.
            give no other output than that simple sentence without quotes.
            """
        else:
            raise ValueError(f"Unknown prompt type: {kind}")
        prompts.append([{"role": "user", "content": prompt}])

    return prompts


@pytest.fixture
def sampling_config():
    # Only support greedy for now
    return SamplingParams(temperature=0, max_tokens=10, ignore_eos=False)


@pytest.fixture
def model_name():
    return "meta-llama/Meta-Llama-3-8B-Instruct"


@pytest.fixture
def eagle_model_name():
    return "yuhuili/EAGLE-LLaMA3-Instruct-8B"


def test_ngram_correctness(
    monkeypatch: pytest.MonkeyPatch,
    test_prompts: list[list[dict[str, Any]]],
    sampling_config: SamplingParams,
    model_name: str,
):
    '''
    Compare the outputs of a original LLM and a speculative LLM
    should be the same when using ngram speculative decoding.
    '''
    with monkeypatch.context() as m:
        m.setenv("VLLM_USE_V1", "1")

        ref_llm = LLM(model=model_name, max_model_len=1024)
        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
        del ref_llm

        spec_llm = LLM(
            model=model_name,
            speculative_config={
                "method": "ngram",
                "prompt_lookup_max": 5,
                "prompt_lookup_min": 3,
                "num_speculative_tokens": 3,
            },
            max_model_len=1024,
        )
        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 70% of the prompts to match exactly
        # Upon failure, inspect the outputs to check for inaccuracy.
        assert matches > int(0.7 * len(ref_outputs))
        del spec_llm


def test_eagle_correctness(
    monkeypatch: pytest.MonkeyPatch,
    test_prompts: list[list[dict[str, Any]]],
    sampling_config: SamplingParams,
    model_name: str,
    eagle_model_name: str,
):
    '''
    Compare the outputs of a original LLM and a speculative LLM
    should be the same when using eagle speculative decoding.
    '''
    with monkeypatch.context() as m:
        m.setenv("VLLM_USE_V1", "1")

        ref_llm = LLM(model=model_name, max_model_len=1024)
        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
        del ref_llm

        spec_llm = LLM(
            model=model_name,
            speculative_config={
                "method": "eagle",
                "model": eagle_model_name,
                "num_speculative_tokens": 3,
            },
            max_model_len=1024,
        )
        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 70% of the prompts to match exactly
        # Upon failure, inspect the outputs to check for inaccuracy.
        assert matches > int(0.7 * len(ref_outputs))
        del spec_llm