[Bugfix] fix benchmark audio timing error and add benchmark test (#1109)

Signed-off-by: wangyu31577 <wangyu31577@hundsun.com>
Co-authored-by: wangyu31577 <wangyu31577@hundsun.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
This commit is contained in:
authored and GitHub committed 2026-01-31 10:15:26 +08:00
1 parent e503a41941
commit 07f4c3fdd6
4 files changed
+110 -14

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+47 -11
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@@ -178,24 +178,60 @@ steps:
volumes:
- "/fsx/hf_cache:/fsx/hf_cache"
- label: "Benchmark Test"
timeout_in_minutes: 15
depends_on: image-build
commands:
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -s -v tests/benchmarks/test_serve_cli.py
agents:
queue: "mithril-h100-pool"
plugins:
- kubernetes:
podSpec:
containers:
- image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
resources:
limits:
nvidia.com/gpu: 2
volumeMounts:
- name: devshm
mountPath: /dev/shm
- name: hf-cache
mountPath: /root/.cache/huggingface
env:
- name: HF_HOME
value: /root/.cache/huggingface
nodeSelector:
node.kubernetes.io/instance-type: gpu-h100-sxm
volumes:
- name: devshm
emptyDir:
medium: Memory
- name: hf-cache
hostPath:
path: /mnt/hf-cache
type: DirectoryOrCreate
- label: "Omni Model Test"
timeout_in_minutes: 15
depends_on: image-build
commands:
- export VLLM_LOGGING_LEVEL=DEBUG
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
- export VLLM_LOGGING_LEVEL=DEBUG
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
agents:
queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
plugins:
- docker#v5.2.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
always-pull: true
propagate-environment: true
environment:
- "HF_HOME=/fsx/hf_cache"
volumes:
- "/fsx/hf_cache:/fsx/hf_cache"
- docker#v5.2.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
always-pull: true
propagate-environment: true
environment:
- "HF_HOME=/fsx/hf_cache"
volumes:
- "/fsx/hf_cache:/fsx/hf_cache"
- label: "Omni Model Test with H100"
timeout_in_minutes: 30
+1 -1
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@@ -128,7 +128,7 @@ Specify to save benchmark results to a json file
Second item: videos with resolution 720x1280 and 16 frames
Third item: audios with 1s duration and 5 channels w.p. 0.1
OBS.: If the probabilities do not sum to 1, they are normalized.
Only the random-mm mode supports this parameter.
Only the random-mm mode supports this parameter
## Usage Examples
+60
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@@ -0,0 +1,60 @@
import subprocess
from pathlib import Path
import pytest
from tests.conftest import OmniServer
models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
stage_configs = [str(Path(__file__).parent.parent / "e2e" / "stage_configs" / "qwen3_omni_ci.yaml")]
# Create parameter combinations for model and stage config
test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
@pytest.fixture(scope="module")
def omni_server(request):
"""Start vLLM-Omni server as a subprocess with actual model weights.
Uses session scope so the server starts only once for the entire test session.
Multi-stage initialization can take 10-20+ minutes.
"""
model, stage_config_path = request.param
print(f"Starting OmniServer with model: {model}")
print("This may take 10-20+ minutes for initialization...")
with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "120"]) as server:
print("OmniServer started successfully")
yield server
print("OmniServer stopped")
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_bench_serve_chat(omni_server):
command = [
"vllm",
"bench",
"serve",
"--omni",
"--model",
omni_server.model,
"--port",
str(omni_server.port),
"--dataset-name",
"random",
"--random-input-len",
"32",
"--random-output-len",
"4",
"--num-prompts",
"5",
"--endpoint",
"/v1/chat/completions",
"--backend",
"openai-chat-omni",
]
result = subprocess.run(command, capture_output=True, text=True)
print(result.stdout)
print(result.stderr)
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
+2 -2
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@@ -108,7 +108,7 @@ async def async_request_openai_chat_omni_completions(
output.prompt_len = request_func_input.prompt_len
generated_text = ""
generated_audio = ""
generated_audio = None
ttft = 0.0
st = time.perf_counter()
output.start_time = st
@@ -160,7 +160,7 @@ async def async_request_openai_chat_omni_completions(
if generated_audio is None:
generated_audio = seg
else:
generated_audio = seg + generated_audio
generated_audio = generated_audio + seg
elif usage := data.get("usage"):
output.output_tokens = usage.get("completion_tokens")