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