wehub-resource-sync
156 lines
4.7 KiB
Python
156 lines
4.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import pytest
|
|
|
|
from vllm.assets.audio import AudioAsset
|
|
from vllm.model_executor.models.moss_audio import MOSS_AUDIO_PLACEHOLDER
|
|
from vllm.platforms import current_platform
|
|
|
|
from ...registry import HF_EXAMPLE_MODELS
|
|
from ...utils import check_logprobs_close
|
|
|
|
CORE_MODEL = pytest.param(
|
|
"OpenMOSS-Team/MOSS-Audio-4B-Instruct",
|
|
marks=pytest.mark.core_model,
|
|
id="4b-instruct",
|
|
)
|
|
|
|
EXTENDED_MODELS = [
|
|
"OpenMOSS-Team/MOSS-Audio-4B-Thinking",
|
|
"OpenMOSS-Team/MOSS-Audio-8B-Instruct",
|
|
"OpenMOSS-Team/MOSS-Audio-8B-Thinking",
|
|
]
|
|
|
|
ACCURACY_MODELS = [CORE_MODEL, *EXTENDED_MODELS]
|
|
|
|
PARALLEL_SMOKE_CASES = [
|
|
pytest.param({"tensor_parallel_size": 2}, id="tp2"),
|
|
pytest.param({"pipeline_parallel_size": 2}, id="pp2"),
|
|
pytest.param(
|
|
{"tensor_parallel_size": 2, "pipeline_parallel_size": 2},
|
|
id="tp2_pp2",
|
|
),
|
|
]
|
|
|
|
HF_ACCURACY_SKIP_REASON = (
|
|
"HF AutoModelForCausalLM cannot load remote MOSS-Audio configs; "
|
|
"vLLM generation coverage is provided by the smoke tests below."
|
|
)
|
|
|
|
|
|
@pytest.mark.core_model
|
|
def test_moss_audio_generation_smoke(vllm_runner) -> None:
|
|
model = "OpenMOSS-Team/MOSS-Audio-4B-Instruct"
|
|
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")
|
|
|
|
prompts = [f"{MOSS_AUDIO_PLACEHOLDER}\nBriefly describe this audio."]
|
|
audios = [[AudioAsset("mary_had_lamb").audio_and_sample_rate[0]]]
|
|
|
|
with vllm_runner(
|
|
model,
|
|
dtype="half",
|
|
enforce_eager=True,
|
|
max_model_len=1024,
|
|
limit_mm_per_prompt={"audio": 1},
|
|
trust_remote_code=True,
|
|
) as vllm_model:
|
|
outputs = vllm_model.generate_greedy(
|
|
prompts,
|
|
max_tokens=4,
|
|
audios=audios,
|
|
)
|
|
|
|
assert len(outputs) == 1
|
|
assert len(outputs[0][1]) > 0
|
|
|
|
|
|
@pytest.mark.skip(reason=HF_ACCURACY_SKIP_REASON)
|
|
@pytest.mark.parametrize("model", ACCURACY_MODELS)
|
|
@pytest.mark.parametrize("dtype", ["half"])
|
|
@pytest.mark.parametrize("max_tokens", [8])
|
|
@pytest.mark.parametrize("num_logprobs", [5])
|
|
def test_moss_audio_hf_vllm_accuracy(
|
|
hf_runner,
|
|
vllm_runner,
|
|
model: str,
|
|
dtype: str,
|
|
max_tokens: int,
|
|
num_logprobs: int,
|
|
) -> 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")
|
|
|
|
prompts = [f"{MOSS_AUDIO_PLACEHOLDER}\nTranscribe this audio."]
|
|
audios = [[AudioAsset("mary_had_lamb").audio_and_sample_rate[0]]]
|
|
|
|
with vllm_runner(
|
|
model,
|
|
dtype=dtype,
|
|
enforce_eager=True,
|
|
max_model_len=1024,
|
|
limit_mm_per_prompt={"audio": 1},
|
|
trust_remote_code=True,
|
|
) as vllm_model:
|
|
vllm_outputs = vllm_model.generate_greedy_logprobs(
|
|
prompts,
|
|
max_tokens,
|
|
num_logprobs=num_logprobs,
|
|
audios=audios,
|
|
)
|
|
|
|
with hf_runner(model, dtype=dtype, trust_remote_code=True) as hf_model:
|
|
hf_outputs = hf_model.generate_greedy_logprobs_limit(
|
|
prompts,
|
|
max_tokens,
|
|
num_logprobs=num_logprobs,
|
|
audios=audios,
|
|
)
|
|
|
|
check_logprobs_close(
|
|
outputs_0_lst=hf_outputs,
|
|
outputs_1_lst=vllm_outputs,
|
|
name_0="hf",
|
|
name_1="vllm",
|
|
)
|
|
|
|
|
|
@pytest.mark.core_model
|
|
@pytest.mark.parametrize("parallel_kwargs", PARALLEL_SMOKE_CASES)
|
|
def test_moss_audio_parallel_smoke(vllm_runner, parallel_kwargs) -> None:
|
|
model = "OpenMOSS-Team/MOSS-Audio-4B-Instruct"
|
|
required_gpus = parallel_kwargs.get(
|
|
"tensor_parallel_size", 1
|
|
) * parallel_kwargs.get("pipeline_parallel_size", 1)
|
|
if current_platform.device_count() < required_gpus:
|
|
# TP/PP integration smoke runs on local or multi-GPU CI only.
|
|
pytest.skip(f"Requires at least {required_gpus} GPUs")
|
|
|
|
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")
|
|
|
|
prompts = [f"{MOSS_AUDIO_PLACEHOLDER}\nBriefly describe this audio."]
|
|
audios = [[AudioAsset("mary_had_lamb").audio_and_sample_rate[0]]]
|
|
|
|
with vllm_runner(
|
|
model,
|
|
dtype="half",
|
|
enforce_eager=True,
|
|
max_model_len=1024,
|
|
limit_mm_per_prompt={"audio": 1},
|
|
trust_remote_code=True,
|
|
**parallel_kwargs,
|
|
) as vllm_model:
|
|
outputs = vllm_model.generate_greedy(
|
|
prompts,
|
|
max_tokens=4,
|
|
audios=audios,
|
|
)
|
|
|
|
assert len(outputs) == 1
|
|
assert len(outputs[0][1]) > 0
|