f8e76a49fa
Upgrade transformers to >=4.56.4
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/entrypoints/llm/test_guided_generate.py
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import pytest
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from vllm import SamplingParams
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from vllm.assets.audio import AudioAsset
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from tests.e2e.conftest import VllmRunner
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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# Note: MiniCPM-2B is a MHA model, MiniCPM4-0.5B is a GQA model
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MINICPM_MODELS = [
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"openbmb/MiniCPM-2B-sft-bf16",
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"OpenBMB/MiniCPM4-0.5B",
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]
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WHISPER_MODELS = [
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"openai-mirror/whisper-large-v3-turbo",
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]
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@pytest.mark.parametrize("model", MINICPM_MODELS)
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def test_minicpm(model) -> None:
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(model,
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max_model_len=512,
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gpu_memory_utilization=0.7) as runner:
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runner.generate_greedy(example_prompts, max_tokens)
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@pytest.mark.parametrize("model", WHISPER_MODELS)
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def test_whisper(model) -> None:
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prompts = ["<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"]
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audios = [AudioAsset("mary_had_lamb").audio_and_sample_rate]
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sampling_params = SamplingParams(temperature=0.2,
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max_tokens=10,
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stop_token_ids=None)
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with VllmRunner(model,
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max_model_len=448,
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max_num_seqs=5,
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dtype="bfloat16",
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block_size=128,
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gpu_memory_utilization=0.9) as runner:
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outputs = runner.generate(prompts=prompts,
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audios=audios,
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sampling_params=sampling_params)
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assert outputs is not None, "Generated outputs should not be None."
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assert len(outputs) > 0, "Generated outputs should not be empty."
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