[Test] Add Omni Model Performance Benchmark Test (#1321)

Signed-off-by: yenuo26 <410167048@qq.com>
Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com>

.buildkite/test-nightly.yaml

@@ -55,3 +55,40 @@ steps:
             - "HF_HOME=/fsx/hf_cache"
           volumes:
             - "/fsx/hf_cache:/fsx/hf_cache"
+
+
+  - label: "Omni Model Perf Test"
+    timeout_in_minutes: 120
+    depends_on: image-build
+    if: build.env("NIGHTLY") == "1"
+    commands:
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      - pytest -s -v tests/perf/scripts/run_benchmark.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

tests/conftest.py

@@ -828,7 +828,7 @@ def modify_stage_config(
                         # Find stage by ID
                         target_stage = None
                         for stage in stage_args:
-                            if stage.get("stage_id") == stage_id:
+                            if stage.get("stage_id") == int(stage_id):
                                 target_stage = stage
                                 break
 
@@ -847,43 +847,42 @@ def modify_stage_config(
                 # Delete entire key
                 del config[key]
 
-    if updates:
-        # Apply updates
-        for key, value in updates.items():
-            if key == "stage_args":
-                if value and isinstance(value, dict):
-                    stage_args = config.get("stage_args", [])
-                    if not stage_args:
-                        raise ValueError("stage_args does not exist in config")
+    # Apply updates
+    for key, value in updates.items():
+        if key == "stage_args":
+            if value and isinstance(value, dict):
+                stage_args = config.get("stage_args", [])
+                if not stage_args:
+                    raise ValueError("stage_args does not exist in config")
 
-                    for stage_id, stage_updates in value.items():
-                        # Find stage by ID
-                        target_stage = None
-                        for stage in stage_args:
-                            if stage.get("stage_id") == stage_id:
-                                target_stage = stage
-                                break
+                for stage_id, stage_updates in value.items():
+                    # Find stage by ID
+                    target_stage = None
+                    for stage in stage_args:
+                        if stage.get("stage_id") == int(stage_id):
+                            target_stage = stage
+                            break
 
-                        if target_stage is None:
-                            available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
-                            raise KeyError(f"Stage ID {stage_id} not found, available: {available_ids}")
+                    if target_stage is None:
+                        available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
+                        raise KeyError(f"Stage ID {stage_id} not found, available: {available_ids}")
 
-                        # Apply updates to this stage
-                        for path, val in stage_updates.items():
-                            # Check if this is a simple key (not dot-separated)
-                            # Example: 'engine_input_source' vs 'engine_args.max_model_len'
-                            if "." not in path:
-                                # Direct key assignment (e.g., updating a list value)
-                                target_stage[path] = val
-                            else:
-                                # Dot-separated path (e.g., nested dict access)
-                                apply_update(target_stage, path, val)
-            elif "." in key:
-                # Apply using dot-separated path
-                apply_update(config, key, value)
-            else:
-                # Direct top-level key
-                config[key] = value
+                    # Apply updates to this stage
+                    for path, val in stage_updates.items():
+                        # Check if this is a simple key (not dot-separated)
+                        # Example: 'engine_input_source' vs 'engine_args.max_model_len'
+                        if "." not in path:
+                            # Direct key assignment (e.g., updating a list value)
+                            target_stage[path] = val
+                        else:
+                            # Dot-separated path (e.g., nested dict access)
+                            apply_update(target_stage, path, val)
+        elif "." in key:
+            # Apply using dot-separated path
+            apply_update(config, key, value)
+        else:
+            # Direct top-level key
+            config[key] = value
 
     # Save to new file with timestamp
     timestamp = int(time.time())

tests/perf/scripts/run_benchmark.py

@@ -0,0 +1,255 @@
+import os
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
+
+import json
+import subprocess
+import threading
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import pytest
+
+from tests.conftest import OmniServer, modify_stage_config
+
+
+def load_configs(config_path: str) -> list[dict[str, Any]]:
+    try:
+        abs_path = Path(config_path).resolve()
+        with open(abs_path, encoding="utf-8") as f:
+            configs = json.load(f)
+
+        return configs
+
+    except json.JSONDecodeError as e:
+        raise ValueError(f"JSON parsing error: {str(e)}")
+    except FileNotFoundError:
+        raise ValueError(f"Configuration file not found: {config_path}")
+    except Exception as e:
+        raise RuntimeError(f"Failed to load configuration file: {str(e)}")
+
+
+def modify_stage(default_path, updates, deletes):
+    kwargs = {}
+    if updates is not None:
+        kwargs["updates"] = updates
+    if deletes is not None:
+        kwargs["deletes"] = deletes
+    if kwargs:
+        path = modify_stage_config(default_path, **kwargs)
+    else:
+        path = default_path
+
+    return path
+
+
+def create_unique_server_params(configs: list[dict[str, Any]]) -> list[tuple[str, str, str]]:
+    unique_params = set()
+    for config in configs:
+        test_name = config["test_name"]
+        model = config["server_params"]["model"]
+        stage_config_name = config["server_params"]["stage_config_name"]
+        stage_config_path = str(Path(__file__).parent.parent / "stage_configs" / stage_config_name)
+        delete = config["server_params"].get("delete", None)
+        update = config["server_params"].get("update", None)
+        stage_config_path = modify_stage(stage_config_path, update, delete)
+        unique_params.add((test_name, model, stage_config_path))
+
+    return list(unique_params)
+
+
+def create_test_parameter_mapping(configs: list[dict[str, Any]]) -> dict[str, dict]:
+    mapping = {}
+    for config in configs:
+        test_name = config["test_name"]
+        if test_name not in mapping:
+            mapping[test_name] = {
+                "test_name": test_name,
+                "benchmark_params": [],
+            }
+        mapping[test_name]["benchmark_params"].extend(config["benchmark_params"])
+    return mapping
+
+
+CONFIG_FILE_PATH = str(Path(__file__).parent.parent / "tests" / "test.json")
+BENCHMARK_CONFIGS = load_configs(CONFIG_FILE_PATH)
+
+
+test_params = create_unique_server_params(BENCHMARK_CONFIGS)
+server_to_benchmark_mapping = create_test_parameter_mapping(BENCHMARK_CONFIGS)
+
+_omni_server_lock = threading.Lock()
+
+
+@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.
+    """
+    with _omni_server_lock:
+        test_name, model, stage_config_path = request.param
+
+        print(f"Starting OmniServer with test: {test_name}, model: {model}")
+
+        with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "120"]) as server:
+            print("OmniServer started successfully")
+            yield server
+            print("OmniServer stopping...")
+
+        print("OmniServer stopped")
+
+
+def run_benchmark(args: list, test_name: str, flow, dataset_name: str, num_prompt) -> Any:
+    """Generate synthetic image with random values."""
+    current_dt = datetime.now().strftime("%Y%m%d-%H%M%S")
+    result_filename = f"result_{test_name}_{dataset_name}_{flow}_{num_prompt}_{current_dt}.json"
+    if "--result-filename" in args:
+        print(f"The result file will be overwritten by {result_filename}")
+    command = (
+        ["vllm", "bench", "serve", "--omni"]
+        + args
+        + [
+            "--backend",
+            "openai-chat-omni",
+            "--endpoint",
+            "/v1/chat/completions",
+            "--save-result",
+            "--result-filename",
+            result_filename,
+        ]
+    )
+    process = subprocess.Popen(
+        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1, universal_newlines=True
+    )
+
+    for line in iter(process.stdout.readline, ""):
+        print(line, end=" ")
+
+    for line in iter(process.stderr.readline, ""):
+        print(line, end=" ")
+
+    if "--result-dir" in args:
+        index = args.index("--result-dir")
+        result_dir = args[index + 1]
+    else:
+        result_dir = "./"
+
+    with open(os.path.join(result_dir, result_filename), encoding="utf-8") as f:
+        result = json.load(f)
+    return result
+
+
+def get_benchmark_params_for_server(test_name: str) -> list:
+    if test_name not in server_to_benchmark_mapping:
+        return []
+    return server_to_benchmark_mapping[test_name]["benchmark_params"]
+
+
+def create_benchmark_indices():
+    indices = []
+    for test_name, config_data in server_to_benchmark_mapping.items():
+        params_list = config_data["benchmark_params"]
+        for idx in range(len(params_list)):
+            indices.append((test_name, idx))
+    return indices
+
+
+benchmark_indices = create_benchmark_indices()
+
+
+@pytest.fixture(params=benchmark_indices)
+def benchmark_params(request, omni_server):
+    """Benchmark parameters fixture with proper parametrization"""
+    test_name, param_index = request.param
+    all_params = get_benchmark_params_for_server(test_name)
+
+    if not all_params:
+        raise ValueError(f"No benchmark parameters found for test: {test_name}")
+
+    if param_index >= len(all_params):
+        raise ValueError(f"No benchmark parameters found for index {param_index} in test: {test_name}")
+
+    return {"test_name": test_name, "params": all_params[param_index]}
+
+
+def assert_result(result, params, num_prompt):
+    assert result["completed"] == num_prompt, "Request failures exist"
+    baseline_data = params.get("baseline", {})
+    for metric_name, baseline_value in baseline_data.items():
+        current_value = result[metric_name]
+        if "throughput" in metric_name:
+            assert current_value >= baseline_value, f"{metric_name}: {current_value} < {baseline_value}"
+        else:
+            assert current_value <= baseline_value, f"{metric_name}: {current_value} > {baseline_value}"
+
+
+@pytest.mark.parametrize("omni_server", test_params, indirect=True)
+@pytest.mark.parametrize("benchmark_params", benchmark_indices, indirect=True)
+def test_performance_benchmark(omni_server, benchmark_params):
+    test_name = benchmark_params["test_name"]
+    params = benchmark_params["params"]
+    dataset_name = params.get("dataset_name", "")
+
+    host = omni_server.host
+    port = omni_server.port
+    model = omni_server.model
+
+    print(f"Running benchmark for model: {model}")
+    print(f"Benchmark parameters: {benchmark_params}")
+
+    def to_list(value, default=None):
+        if value is None:
+            return [] if default is None else [default]
+        return [value] if not isinstance(value, (list, tuple)) else list(value)
+
+    qps_list = to_list(params.get("request_rate"))
+    num_prompt_list = to_list(params.get("num_prompts"))
+    max_concurrency_list = to_list(params.get("max_concurrency"))
+
+    max_len = max(len(qps_list), len(max_concurrency_list))
+    if len(num_prompt_list) == 1 and max_len > 1:
+        num_prompt_list = num_prompt_list * max_len
+    elif max_len == 1 and len(num_prompt_list) > 1:
+        if len(qps_list) == 1:
+            qps_list = qps_list * len(num_prompt_list)
+        if len(max_concurrency_list) == 1:
+            max_concurrency_list = max_concurrency_list * len(num_prompt_list)
+        max_len = max(len(qps_list), len(max_concurrency_list))
+    elif len(num_prompt_list) != max_len and max_len > 0:
+        raise ValueError("The number of prompts does not match the QPS or max_concurrency")
+
+    args = ["--host", host, "--port", str(port)]
+    exclude_keys = {"request_rate", "baseline", "num_prompts", "max_concurrency"}
+
+    for key, value in params.items():
+        if key in exclude_keys or value is None:
+            continue
+
+        arg_name = f"--{key.replace('_', '-')}"
+
+        if isinstance(value, bool) and value:
+            args.append(arg_name)
+        elif isinstance(value, dict):
+            json_str = json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+            args.extend([arg_name, json_str])
+        elif not isinstance(value, bool):
+            args.extend([arg_name, str(value)])
+
+    # QPS test
+    for qps, num_prompt in zip(qps_list, num_prompt_list):
+        args = args + ["--request-rate", str(qps), "--num-prompts", str(num_prompt)]
+        result = run_benchmark(
+            args=args, test_name=test_name, flow=qps, dataset_name=dataset_name, num_prompt=num_prompt
+        )
+        assert_result(result, params, num_prompt=num_prompt)
+
+    # concurrency test
+    for concurrency, num_prompt in zip(max_concurrency_list, num_prompt_list):
+        args = args + ["--max-concurrency", str(concurrency), "--num-prompts", str(num_prompt), "--request-rate", "inf"]
+        result = run_benchmark(
+            args=args, test_name=test_name, flow=concurrency, dataset_name=dataset_name, num_prompt=num_prompt
+        )
+        assert_result(result, params, num_prompt=num_prompt)

tests/perf/stage_configs/qwen3_omni.yaml

@@ -0,0 +1,101 @@
+# Stage config for running Qwen3-Omni-MoE with 3-stage architecture
+# Stage 0: Thinker (multimodal understanding + text generation)
+# Stage 1: Talker (text embeddings → 8-layer RVQ codec codes)
+# Stage 2: Code2Wav (8-layer RVQ codes → audio waveform)
+
+# The following config has been verified on 2x H100-80G GPUs.
+async_chunk: false
+stage_args:
+  - stage_id: 0
+    stage_type: llm  # Use llm stage type to launch OmniLLM
+    runtime:
+      devices: "0"
+      max_batch_size: 64
+    engine_args:
+      model_stage: thinker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.9
+      enforce_eager: false
+      trust_remote_code: true
+      engine_output_type: latent  # Output hidden states for talker
+      distributed_executor_backend: "mp"
+      enable_prefix_caching: false
+      max_num_batched_tokens: 32768
+      hf_config_name: thinker_config
+      tensor_parallel_size: 1
+    final_output: true
+    final_output_type: text
+    is_comprehension: true
+    default_sampling_params:
+      temperature: 0.4
+      top_p: 0.9
+      top_k: 1
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+
+  - stage_id: 1
+    stage_type: llm  # Use llm stage type to launch OmniLLM
+    runtime:
+      devices: "1"
+      max_batch_size: 64
+    engine_args:
+      model_stage: talker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.6
+      enforce_eager: false
+      trust_remote_code: true
+      engine_output_type: latent  # Output codec codes for code2wav
+      enable_prefix_caching: false
+      max_num_batched_tokens: 32768
+      distributed_executor_backend: "mp"
+      hf_config_name: talker_config
+    engine_input_source: [0]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
+    # final_output: true
+    # final_output_type: text
+    default_sampling_params:
+      temperature: 0.9
+      top_k: 50
+      max_tokens: 4096
+      seed: 42
+      detokenize: False
+      repetition_penalty: 1.05
+      stop_token_ids: [2150]
+
+  - stage_id: 2
+    stage_type: llm  # Use llm stage type to launch OmniLLM
+    runtime:
+      devices: "1"
+      max_batch_size: 64
+    engine_args:
+      model_stage: code2wav
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_type: generation
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      enforce_eager: true
+      trust_remote_code: true
+      async_scheduling: false
+      enable_prefix_caching: false
+      engine_output_type: audio  # Final output: audio waveform
+      gpu_memory_utilization: 0.1
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 1000000
+      hf_config_name: thinker_config
+    engine_input_source: [1]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
+    final_output: true
+    final_output_type: audio
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 65536
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1

tests/perf/tests/test.json

@@ -0,0 +1,266 @@
+[
+    {
+        "test_name": "test_qwen3_omni",
+        "server_params": {
+            "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+            "stage_config_name": "qwen3_omni.yaml"
+        },
+        "benchmark_params": [
+            {
+                "dataset_name": "random",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "request_rate": [
+                    0.1,
+                    0.5,
+                    1
+                ],
+                "random_input_len": 2500,
+                "random_output_len": 900,
+                "ignore_eos": true,
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            },
+            {
+                "dataset_name": "random",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "max_concurrency": [
+                    1,
+                    4,
+                    10
+                ],
+                "random_input_len": 2500,
+                "random_output_len": 900,
+                "ignore_eos": true,
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            },
+            {
+                "dataset_name": "random-mm",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "max_concurrency": [
+                    1,
+                    4,
+                    10
+                ],
+                "random_input_len": 100,
+                "random_output_len": 100,
+                "random_range_ratio": 0.0,
+                "ignore_eos": true,
+                "random_mm_base_items_per_request": 3,
+                "random_mm_num_mm_items_range_ratio": 0,
+                "random_mm_limit_mm_per_prompt": {
+                    "image": 1,
+                    "video": 1,
+                    "audio": 1
+                },
+                "random_mm_bucket_config": {
+                    "(32, 32, 1)": 0.5,
+                    "(0, 1, 1)": 0.1,
+                    "(32, 32, 2)": 0.4
+                },
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            },
+            {
+                "dataset_name": "random-mm",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "request_rate": [
+                    0.1,
+                    0.5,
+                    1
+                ],
+                "random_input_len": 100,
+                "random_output_len": 100,
+                "random_range_ratio": 0.0,
+                "ignore_eos": true,
+                "random_mm_base_items_per_request": 3,
+                "random_mm_num_mm_items_range_ratio": 0,
+                "random_mm_limit_mm_per_prompt": {
+                    "image": 1,
+                    "video": 1,
+                    "audio": 1
+                },
+                "random_mm_bucket_config": {
+                    "(32, 32, 1)": 0.5,
+                    "(0, 1, 1)": 0.1,
+                    "(32, 32, 2)": 0.4
+                },
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            }
+        ]
+    },
+    {
+        "test_name": "test_qwen3_omni_chunk",
+        "server_params": {
+            "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+            "stage_config_name": "qwen3_omni.yaml",
+            "update": {
+                "async_chunk": true,
+                "stage_args": {
+                    "0": {
+                        "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk"
+                    },
+                    "1": {
+                        "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_async_chunk"
+                    }
+                }
+            },
+            "delete": {
+                "stage_args": {
+                    "2": [
+                        "custom_process_input_func"
+                    ]
+                }
+            }
+        },
+        "benchmark_params": [
+            {
+                "dataset_name": "random",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "request_rate": [
+                    0.1,
+                    0.5,
+                    1
+                ],
+                "random_input_len": 2500,
+                "random_output_len": 900,
+                "ignore_eos": true,
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            },
+            {
+                "dataset_name": "random",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "max_concurrency": [
+                    1,
+                    4,
+                    10
+                ],
+                "random_input_len": 2500,
+                "random_output_len": 900,
+                "ignore_eos": true,
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            },
+            {
+                "dataset_name": "random-mm",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "max_concurrency": [
+                    1,
+                    4,
+                    10
+                ],
+                "random_input_len": 100,
+                "random_output_len": 100,
+                "random_range_ratio": 0.0,
+                "ignore_eos": true,
+                "random_mm_base_items_per_request": 3,
+                "random_mm_num_mm_items_range_ratio": 0,
+                "random_mm_limit_mm_per_prompt": {
+                    "image": 1,
+                    "video": 1,
+                    "audio": 1
+                },
+                "random_mm_bucket_config": {
+                    "(32, 32, 1)": 0.5,
+                    "(0, 1, 1)": 0.1,
+                    "(32, 32, 2)": 0.4
+                },
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            },
+            {
+                "dataset_name": "random-mm",
+                "num_prompts": [
+                    10,
+                    40,
+                    100
+                ],
+                "request_rate": [
+                    0.1,
+                    0.5,
+                    1
+                ],
+                "random_input_len": 100,
+                "random_output_len": 100,
+                "random_range_ratio": 0.0,
+                "ignore_eos": true,
+                "random_mm_base_items_per_request": 3,
+                "random_mm_num_mm_items_range_ratio": 0,
+                "random_mm_limit_mm_per_prompt": {
+                    "image": 1,
+                    "video": 1,
+                    "audio": 1
+                },
+                "random_mm_bucket_config": {
+                    "(32, 32, 1)": 0.5,
+                    "(0, 1, 1)": 0.1,
+                    "(32, 32, 2)": 0.4
+                },
+                "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
+                "baseline": {
+                    "mean_ttft_ms": 100000,
+                    "mean_audio_ttfp_ms": 100000,
+                    "mean_audio_rtf": 100000
+                }
+            }
+        ]
+    }
+]