xc-llm-ascend/tests/e2e/singlecard/test_offline_inference.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
#
"""Compare the short outputs of HF and vLLM when using greedy sampling.

Run `pytest tests/test_offline_inference.py`.
"""
import os
from unittest.mock import patch

import pytest
import vllm  # noqa: F401
from vllm import SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.assets.image import ImageAsset

import vllm_ascend  # noqa: F401
from tests.e2e.conftest import VllmRunner

MODELS = [
    "Qwen/Qwen2.5-0.5B-Instruct",
    "Qwen/Qwen3-0.6B-Base",
]
MULTIMODALITY_VL_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
MULTIMODALITY_AUDIO_MODELS = ["Qwen/Qwen2-Audio-7B-Instruct"]

os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
AUDIO_ASSETS = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
AUDIO_PROMPT_TEMPLATES = {
    1: "What is recited in the audio?",
    2: "What sport and what nursery rhyme are referenced?"
}


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half", "float16"])
@pytest.mark.parametrize("max_tokens", [5])
def test_models(model: str, dtype: str, max_tokens: int) -> None:
    # 5042 tokens for gemma2
    # gemma2 has alternating sliding window size of 4096
    # we need a prompt with more than 4096 tokens to test the sliding window
    prompt = "The following numbers of the sequence " + ", ".join(
        str(i) for i in range(1024)) + " are:"
    example_prompts = [prompt]

    with VllmRunner(model,
                    max_model_len=8192,
                    dtype=dtype,
                    enforce_eager=True,
                    gpu_memory_utilization=0.7) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@pytest.mark.parametrize("model", MULTIMODALITY_VL_MODELS)
def test_multimodal_vl(model, prompt_template, vllm_runner):
    image = ImageAsset("cherry_blossom") \
        .pil_image.convert("RGB")
    img_questions = [
        "What is the content of this image?",
        "Describe the content of this image in detail.",
        "What's in the image?",
        "Where is this image taken?",
    ]
    images = [image] * len(img_questions)
    prompts = prompt_template(img_questions)
    with vllm_runner(model,
                     max_model_len=4096,
                     mm_processor_kwargs={
                         "min_pixels": 28 * 28,
                         "max_pixels": 1280 * 28 * 28,
                         "fps": 1,
                     }) as vllm_model:
        vllm_model.generate_greedy(prompts=prompts,
                                   images=images,
                                   max_tokens=64)


def prepare_audio_inputs(audio_count: int):
    audio_prompt = "".join([
        f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
        for idx in range(audio_count)
    ])
    question = AUDIO_PROMPT_TEMPLATES[audio_count]
    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
              "<|im_start|>user\n"
              f"{audio_prompt}{question}<|im_end|>\n"
              "<|im_start|>assistant\n")
    mm_data = {
        "audio":
        [asset.audio_and_sample_rate for asset in AUDIO_ASSETS[:audio_count]]
    }
    inputs = {"prompt": prompt, "multi_modal_data": mm_data}
    return inputs


@pytest.mark.parametrize("model", MULTIMODALITY_AUDIO_MODELS)
@pytest.mark.parametrize("audio_count", [2])
@pytest.mark.parametrize("max_tokens", [10])
def test_multimodal_audio(model: str, audio_count: int,
                          max_tokens: int) -> None:
    inputs = prepare_audio_inputs(audio_count)

    sampling_params = SamplingParams(temperature=0.2,
                                     max_tokens=max_tokens,
                                     stop_token_ids=None)

    with VllmRunner(model,
                    max_model_len=4096,
                    max_num_seqs=5,
                    enforce_eager=False,
                    dtype="bfloat16",
                    limit_mm_per_prompt={"audio": audio_count},
                    gpu_memory_utilization=0.9) as vllm_model:
        vllm_model.generate(inputs, sampling_params=sampling_params)


@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION": "1"})
def test_models_topk() -> None:
    example_prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(max_tokens=5,
                                     temperature=0.0,
                                     top_k=50,
                                     top_p=0.9)

    with VllmRunner("Qwen/Qwen2.5-0.5B-Instruct",
                    max_model_len=8192,
                    dtype="float16",
                    enforce_eager=True,
                    gpu_memory_utilization=0.7) as vllm_model:
        vllm_model.generate(example_prompts, sampling_params)


def test_models_prompt_logprobs() -> None:

    example_prompts = [
        "Hello, my name is",
    ]

    with VllmRunner("Qwen/Qwen2.5-0.5B-Instruct",
                    max_model_len=8192,
                    dtype="float16",
                    enforce_eager=True,
                    gpu_memory_utilization=0.7) as vllm_model:
        vllm_model.generate_greedy_logprobs(example_prompts,
                                            max_tokens=5,
                                            num_logprobs=1)