test_whisper.py

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

import os
from collections.abc import Sequence
from typing import Any

import librosa
import pytest
from transformers import AutoModelForSpeechSeq2Seq

from vllm.assets.audio import AudioAsset
from vllm.platforms import current_platform

from ....conftest import HfRunner, PromptAudioInput, VllmRunner
from ....utils import create_new_process_for_each_test, multi_gpu_test
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close, models_path_prefix

VLLM_PROMPT = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
HF_PROMPT = ""
# Whisper expects 16kHz audio
WHISPER_SAMPLE_RATE = 16000


@pytest.fixture(autouse=True)
def use_spawn_for_whisper(monkeypatch):
    """Whisper has issues with forked workers, use spawn instead."""
    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")

def run_test(
    hf_runner: type[HfRunner],
    vllm_runner: type[VllmRunner],
    inputs: Sequence[tuple[list[str], list[str], PromptAudioInput]],
    model: str,
    *,
    max_model_len: int,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    tensor_parallel_size: int,
    distributed_executor_backend: str | None = None,
    enforce_eager: bool = True,
) -> None:
    """Inference result should be the same between hf and vllm.

    All the audio fixtures for the test are from AudioAsset.
    For huggingface runner, we provide the audio as input.
    For vllm runner, we provide MultiModalDataDict objects
    and corresponding MultiModalConfig as input.
    """
    with vllm_runner(
        model,
        dtype=dtype,
        max_model_len=max_model_len,
        tensor_parallel_size=tensor_parallel_size,
        distributed_executor_backend=distributed_executor_backend,
        limit_mm_per_prompt={"audio": 2},
        enforce_eager=enforce_eager,
        disable_custom_all_reduce=True,
    ) as vllm_model:
        vllm_outputs_per_case = [
            vllm_model.generate_greedy_logprobs(
                vllm_prompts,
                max_tokens,
                num_logprobs=num_logprobs,
                audios=audios,
            )
            for vllm_prompts, _, audios in inputs
        ]

    with hf_runner(model, dtype=dtype, auto_cls=AutoModelForSpeechSeq2Seq) as hf_model:
        hf_outputs_per_case = [
            hf_model.generate_greedy_logprobs_limit(
                hf_prompts,
                max_tokens,
                num_logprobs=num_logprobs,
                audios=audios,
            )
            for _, hf_prompts, audios in inputs
        ]

    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
        check_logprobs_close(
            outputs_0_lst=hf_outputs,
            outputs_1_lst=vllm_outputs,
            name_0="hf",
            name_1="vllm",
        )


@pytest.fixture
def input_audios() -> list[tuple[list[str], list[str], list[tuple[Any, int]]]]:
    audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
    inputs = []
    for asset in audio_assets:
        audio, orig_sr = asset.audio_and_sample_rate
        # Resample to Whisper's expected sample rate (16kHz)
        if orig_sr != WHISPER_SAMPLE_RATE:
            audio = librosa.resample(
                audio, orig_sr=orig_sr, target_sr=WHISPER_SAMPLE_RATE
            )
        # vLLM prompts, HF prompts, audio inputs
        inputs.append(([VLLM_PROMPT], [HF_PROMPT], [(audio, WHISPER_SAMPLE_RATE)]))
    return inputs


def check_model_available(model: str) -> None:
    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")


@pytest.mark.core_model
@pytest.mark.cpu_model
@pytest.mark.parametrize("model", [os.path.join(models_path_prefix, "openai/whisper-large-v3-turbo")])
@pytest.mark.parametrize("dtype", ["half", "float"])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("enforce_eager", [True, False])
@create_new_process_for_each_test("spawn")
def test_models(
    hf_runner,
    vllm_runner,
    model: str,
    dtype: str,
    num_logprobs: int,
    input_audios,
    enforce_eager: bool,
) -> None:
    check_model_available(model)
    if current_platform.is_cpu() and not enforce_eager:
        pytest.skip("Skipping test for CPU with non-eager mode")
    run_test(
        hf_runner,
        vllm_runner,
        input_audios,
        model,
        dtype=dtype,
        max_model_len=448,
        max_tokens=200,
        num_logprobs=num_logprobs,
        tensor_parallel_size=1,
        enforce_eager=enforce_eager,
    )


@multi_gpu_test(num_gpus=2)
@pytest.mark.core_model
@pytest.mark.parametrize("model", [os.path.join(models_path_prefix, "openai/whisper-large-v3-turbo")])
@pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"])
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [200])
@pytest.mark.parametrize("num_logprobs", [5])
@create_new_process_for_each_test("spawn")
def test_models_distributed(
    hf_runner,
    vllm_runner,
    model: str,
    distributed_executor_backend: str,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    input_audios,
) -> None:
    check_model_available(model)
    run_test(
        hf_runner,
        vllm_runner,
        input_audios,
        model,
        dtype=dtype,
        max_model_len=448,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        tensor_parallel_size=2,
        distributed_executor_backend=distributed_executor_backend,
        enforce_eager=False,
    )