vllm-omni/docs/contributing/ci/CI_5levels.md

Multi-Level Automated Testing System Documentation

Document Overview

This testing system aims to build a complete, efficient, and well-structured quality assurance framework for the development, integration, and release of model services. It draws on the concept of the test pyramid from modern software engineering, progressively expanding testing activities from basic code logic verification to complex end-to-end (E2E) functionality, performance, accuracy, and even long-term stability validation.

Through five levels (L1-L5) and common (Common) specifications, the system clarifies the testing objectives, scope, execution frequency, and required resources for different development stages (e.g., each commit, PR merge, daily build, pre-release). This ensures that models meet high standards for functionality, performance, and reliability across various deployment scenarios (online serving and offline inference).

Level	Scope & Focus	Time Cost	Test Dir	Doc	Frequency	Hardware
Common	Contribution Guideline & PR checklist	/	/	.github/PULL_REQUEST_TEMPLATE.md Test Style (PR Checklist)	/	/
	CI Failure Description	/	/	CI Failures	/	/
L1 (Unit & Logic)	Unit tests for components like entrypoints, models	<15min	/tests/{component_name}/test_xxx	Chapter 1 Section 1 L1&L2: Purpose, Test Content, Directory Location, Example	PR with ready label (also can run locally)	CPU
L2 (E2E across models & GPU-required UT)	Online & Offline (basic deployment scenarios): dummy, normal inference function (output format, stream), some instance startup UT		/tests/e2e/online_serving/test_{model_name}.py /tests/e2e/offline_inference/test_{model_name}.py	Chapter 1 L1&L2: Purpose, Test Content, Directory Location, Example	PR with ready label	GPU
L3 (Important Perf & Integration & Accuracy)	Online & Offline (multiple deployment scenarios): real model, normal inference function, normal accuracy	<30min	/tests/e2e/online_serving/test_{model_name}_expansion.py /tests/e2e/offline_inference/test_{model_name}_expansion.py	Chapter 2 L3: Purpose, Test Content, Directory Location, Example	PR Merged (Also run L1&L2 Tests)	GPU
L4 (Perf & Integration & Accuracy)	Online & Offline: full functional scenarios + performance test + doc test	<3 hour	Full Function: /tests/e2e/online_serving/test_{model_name}_expansion.py /tests/e2e/offline_inference/test_{model_name}_expansion.py Performance: /tests/e2e/perf/nightly.json Doc Test: tests/example/online_serving/test_{model_name}.py tests/example/offline_inference/test_{model_name}.py	Chapter 3 L4: Purpose, Test Content, Directory Location, Example	Nightly	GPU
L5 (Stability & Reliability)	Online & Offline: long-term stability test + reliability test	Depends on reality	Stability: tests/e2e/stability/weekly.json Reliability: tests/e2e/reliability/test_{model_name}.py	Chapter 4 L5: Purpose, Test Content, Directory Location, Example	Weekly / Days before Release	GPU

---

The folder structure for tests file based on the 5 levels design

Legend: `✅` = test exists, `⬜` = suggested to add. ``` vllm_omni/ tests/ ├── config/ → ├── config/ │ ├── model.py │ └── test_model.py ⬜ │ └── lora.py │ └── test_lora.py ⬜ │ ├── core/ → ├── core/ │ └── sched/ │ └── sched/ │ ├── omni_ar_scheduler.py │ ├── test_omni_ar_scheduler.py ⬜ │ ├── omni_generation_scheduler.py │ ├── test_omni_generation_scheduler.py ⬜ │ └── output.py │ └── test_output.py ✅ currently in entrypoints/test_omni_new_request_data.py (tests output.OmniNewRequestData) │ ├── diffusion/ → ├── diffusion/ │ ├── diffusion_engine.py │ ├── test_diffusion_engine.py ⬜ │ ├── attention/ │ ├── attention/ │ │ ├── layer.py │ │ ├── test_attention_sp.py ✅ │ │ └── backends/ │ │ └── test_flash_attn.py ✅ │ ├── distributed/ │ ├── distributed/ │ │ └── ... │ │ ├── test_comm.py ✅ │ │ │ │ ├── test_cfg_parallel.py ✅ │ │ │ │ └── test_sp_plan_hooks.py ✅ │ ├── lora/ │ ├── lora/ │ │ └── ... │ │ ├── test_base_linear.py ✅ │ │ │ │ └── test_lora_manager.py ✅ │ ├── models/ │ ├── models/ │ │ ├── qwen_image/ │ │ ├── qwen_image/ (e2e coverage) │ │ ├── z_image/ │ │ └── z_image/ │ │ └── ... │ │ └── test_zimage_tp_constraints.py ✅ │ └── worker/ │ └── worker/ │ ├── diffusion_worker.py │ └── test_diffusion_worker.py ✅ file at diffusion/test_diffusion_worker.py │ └── diffusion_model_runner.py │ │ ├── distributed/ → ├── distributed/ │ └── omni_connectors/ │ └── omni_connectors/ │ ├── adapter.py │ ├── test_adapter_and_flow.py ✅ │ ├── kv_transfer_manager.py │ ├── test_basic_connectors.py ✅ │ ├── connectors/ │ ├── test_kv_flow.py ✅ │ └── utils/ │ └── test_omni_connector_configs.py ✅ │ ├── engine/ → ├── engine/ │ ├── input_processor.py │ ├── test_input_processor.py ⬜ (no processor.py in source) │ ├── output_processor.py │ └── test_output_processor.py ⬜ │ └── arg_utils.py │ └── test_arg_utils.py ⬜ │ ├── entrypoints/ → ├── entrypoints/ │ ├── omni.py │ ├── test_omni.py ⬜ (E2E covered by e2e/offline, e2e/online) │ ├── omni_llm.py │ ├── test_omni_llm.py ✅ │ ├── omni_stage.py │ ├── test_omni_stage.py ⬜ (partial in test_omni_stage_diffusion_config.py) │ ├── omni_diffusion.py │ ├── test_omni_diffusion.py ✅ │ ├── async_omni.py │ ├── test_async_omni.py ✅ actually in e2e/online_serving/test_async_omni.py │ ├── async_omni_diffusion.py │ ├── test_async_omni_diffusion_config.py ✅ │ ├── stage_utils.py │ ├── test_stage_utils.py ✅ │ ├── cli/ │ ├── cli/ (benchmarks/test_serve_cli.py covers CLI serve) │ │ └── ... │ │ └── test_*.py ⬜ │ └── openai/ │ └── openai_api/ # maps to entrypoints/openai/ │ ├── api_server.py │ ├── test_api_server.py ⬜ (e2e indirect coverage) │ ├── serving_chat.py │ ├── test_serving_chat_sampling_params.py ✅ │ ├── serving_speech.py │ ├── test_serving_speech.py ✅ │ └── image_api_utils.py │ └── test_image_server.py ✅ │ ├── inputs/ → ├── inputs/ │ ├── data.py │ ├── test_data.py ⬜ │ ├── parse.py │ ├── test_parse.py ⬜ │ └── preprocess.py │ └── test_preprocess.py ✅ currently in entrypoints/test_omni_input_preprocessor.py │ ├── model_executor/ → ├── model_executor/ │ ├── layers/ │ ├── layers/ │ │ └── mrope.py │ │ └── test_mrope.py ⬜ │ ├── model_loader/ │ ├── model_loader/ │ │ └── weight_utils.py │ │ └── test_weight_utils.py ⬜ │ ├── models/ │ ├── models/ │ │ ├── qwen2_5_omni/ │ │ ├── qwen2_5_omni/ │ │ │ ├── qwen2_5_omni_thinker.py │ │ │ ├── test_audio_length.py ✅ │ │ │ ├── qwen2_5_omni_talker.py │ │ │ ├── test_qwen2_5_omni_thinker.py ⬜ │ │ │ └── qwen2_5_omni_token2wav.py │ │ │ ├── test_qwen2_5_omni_talker.py ⬜ │ │ └── qwen3_omni/ │ │ │ └── test_qwen2_5_omni_token2wav.py ⬜ │ │ └── ... │ │ └── qwen3_omni/ │ ├── stage_configs/ │ │ └── test_*.py ⬜ │ │ └── *.yaml │ └── stage_configs/ (used by e2e, test_*.py can be added) ⬜ │ └── stage_input_processors/ │ └── stage_input_processors/ │ └── ... │ └── test_*.py ⬜ │ ├── sample/ → ├── sample/ │ └── __init__.py │ └── test_*.py ⬜ │ ├── utils/ → ├── utils/ │ └── __init__.py │ └── test_*.py ⬜ (no platform_utils.py currently) │ ├── worker/ → ├── worker/ │ ├── gpu_ar_model_runner.py │ ├── test_gpu_ar_model_runner.py ⬜ │ ├── gpu_ar_worker.py │ ├── test_gpu_ar_worker.py ⬜ │ ├── gpu_generation_model_runner.py │ ├── test_gpu_generation_model_runner.py ✅ │ ├── gpu_generation_worker.py │ ├── test_gpu_generation_worker.py ⬜ │ ├── gpu_model_runner.py │ ├── test_omni_gpu_model_runner.py ✅ │ └── mixins.py │ └── (npu under platforms/npu/worker/) # not worker/npu/ │ ├── platforms/ → (no tests/platforms/, e2e and stage_configs provide indirect coverage) │ ├── cuda/ │ ├── npu/worker/ # NPU worker here, not vllm_omni/worker/npu/ │ ├── rocm/ │ └── xpu/worker/ │ ├── outputs.py → test_outputs.py ✅ (at tests root) ├── (logger, patch, request, version) → (no corresponding unit test) │ └── e2e (tests side only) → ├── e2e/ ├── online_serving/ ✅ non-empty │ ├── test_async_omni.py │ ├── test_qwen3_omni.py │ ├── test_qwen3_omni_expansion.py │ ├── test_image_gen_edit.py │ ├── test_images_generations_lora.py │ └── stage_configs/ └── offline_inference/ ✅ ├── test_qwen2_5_omni.py ├── test_qwen3_omni.py ├── test_bagel_text2img.py ├── test_t2i_model.py ├── test_t2v_model.py ├── test_ovis_image.py ├── test_zimage_tensor_parallel.py ├── test_cache_dit.py ├── test_teacache.py ├── test_stable_audio_model.py ├── test_diffusion_cpu_offload.py ├── test_diffusion_layerwise_offload.py ├── test_diffusion_lora.py ├── test_sequence_parallel.py └── stage_configs/ ├── qwen2_5_omni_ci.yaml ├── qwen3_omni_ci.yaml ├── bagel_*.yaml └── npu/, rocm/, etc. ```

Common Specifications

Before entering specific testing levels, the project establishes two common specifications aimed at standardizing the development process and quickly locating issues.

1. PR Checklist (Tests Style): This template defines the self-check items that must be completed before submitting a code review (Pull Request). It ensures that each code change meets basic requirements such as code style, dependency updates, and documentation synchronization before entering the automated testing pipeline, serving as the first manual line of defense for quality assurance.
2. CI Failure Explanation (CI Failures): This document archives and explains common failure patterns in the Continuous Integration (CI) pipeline, error log interpretation, and preliminary troubleshooting steps. It helps developers and testers quickly diagnose the causes of automated test failures, improving problem-solving efficiency.

Chapter 1: L1 & L2 Level Testing - Unit Testing and Basic End-to-End Verification

1.1 Testing Purpose

L1 and L2 level testing form the foundation of the quality assurance system. L1 level testing focuses on verifying the internal logic correctness of code units (e.g., functions, classes), ensuring each independent component behaves as designed.

L2 level testing builds upon L1 by introducing GPU resources and verifying that the end-to-end (E2E) process of the model in basic deployment scenarios is smooth. For example, it uses dummy models to confirm that core interfaces like the inference pipeline, output format, and streaming response work properly. The common goal of these two levels is to provide developers with rapid feedback, discovering and fixing issues early in the development cycle .

1.2 Testing Content and Scope

• L1 (Unit & Logic Testing):
• • Scope: Tests internal functions and methods of core components such as entrypoints, models.
  • Focus: Branch coverage, exception handling, algorithm logic correctness. Does not involve external dependencies or the complete service stack.
  • Time Cost: Execution time is controlled within 15 minutes to ensure fast feedback.
• L2 (Basic End-to-End Testing):
• • Scope: Covers two basic deployment scenarios: online (serving) and offline (inference).
  • Focus: Uses dummy models or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, etc. Also includes some unit tests that require launching independent service instances.
  • Characteristic: Requires GPU resources to perform model computations.

1.3 Test Directory and Execution Files

A clear directory structure is key to managing test cases efficiently.

• L1 Test Directory: /tests/{component_name}/test_xxx.py
• • Here, {component_name} corresponds to modules in the source code, such as distributed, entrypoints, etc., and test_xxx.py is the specific test file.
• L2 Test Directory:
• • Online Serving: /tests/e2e/online_serving/test_{model_name}.py
  • Offline Inference: /tests/e2e/offline_inference/test_{model_name}.py

1.4 Execution Method and Example

• Trigger Timing: PR with ready label. That is, when a developer adds a "ready for review" or similar label to a PR on platforms like GitHub, L1 and L2 tests are automatically triggered.
• Execution Environment: L1 uses CPU environment; L2 requires GPU environment.
• Script Example:

L1 Test Examples

Examples from tests/model_executor/models/qwen2_5_omni/test_audio_length.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import pytest

pytestmark = [pytest.mark.core_model, pytest.mark.cpu]

def test_resolve_max_mel_frames_default():
    from vllm_omni.model_executor.models.qwen2_5_omni.audio_length import resolve_max_mel_frames

    assert resolve_max_mel_frames(None, default=30000) == 30000
    assert resolve_max_mel_frames(None, default=6000) == 6000


def test_resolve_max_mel_frames_explicit():
    from vllm_omni.model_executor.models.qwen2_5_omni.audio_length import resolve_max_mel_frames

    # Explicit argument always wins over default
    assert resolve_max_mel_frames(123, default=30000) == 123
    assert resolve_max_mel_frames(6000, default=30000) == 6000
    assert resolve_max_mel_frames(0, default=30000) == 0


@pytest.mark.parametrize("repeats", [2, 4])
@pytest.mark.parametrize("code_len", [0, 1, 32768])
@pytest.mark.parametrize("max_mel_frames", [None, -1, 0, 1, 6000, 30000])
def test_cap_and_align_mel_length_no_mismatch(repeats, code_len, max_mel_frames):
    """Guard that any max_mel_frames yields a mel length aligned to repeats, and
    consistent with the truncated code length (prevents concat mismatch).
    """
    from vllm_omni.model_executor.models.qwen2_5_omni.audio_length import cap_and_align_mel_length

    target_code_len, target_mel_len = cap_and_align_mel_length(
        code_len=code_len,
        repeats=repeats,
        max_mel_frames=max_mel_frames,
    )

    assert isinstance(target_code_len, int)
    assert isinstance(target_mel_len, int)

    if code_len == 0:
        assert target_code_len == 0
        assert target_mel_len == 0
        return

    assert target_code_len >= 1
    assert target_mel_len >= repeats
    assert target_mel_len % repeats == 0
    assert target_mel_len == target_code_len * repeats
    assert target_code_len <= code_len

    if max_mel_frames is not None and int(max_mel_frames) > 0 and int(max_mel_frames) >= repeats:
        assert target_mel_len <= int(max_mel_frames)

L2 Test Examples

You can refer to Test Examples in Chapter 2 to see example test cases that incorporate both L2 and L3 testing logic.

• • Run Command:

  pytest -s -v /tests/e2e/online_serving/test_{model_name}.py pytest -s -v -m 'core_model and cpu' --run-level=core_model

Chapter 2: L3 Level Testing - Core Integration, Performance, and Accuracy Verification

2.1 Testing Purpose

L3 level testing executes after code is merged into the main branch. Its core purpose is to verify the integration effect, key performance indicators, and output accuracy of real models in multiple deployment scenarios

. It acts as the "quality gatekeeper" for the main branch, ensuring that no merge breaks the core capabilities of the model service. Testing needs to provide clear conclusions within a relatively short time (<30min), balancing test depth with feedback speed.

2.2 Testing Content and Scope

• Deployment Scenarios: Covers richer online and offline deployment configurations, which may include different hardware configurations, batch sizes, concurrency levels, etc.
• Core Verification:
• 1. Inference Functionality: Ensures real models can perform forward computation normally and return results.
  2. Accuracy Compliance: Verifies that the model's evaluation metrics (e.g., accuracy) meet the expected baseline, preventing code changes from introducing accuracy issues.
  3. Important Performance: Verifies whether performance (e.g., P99 latency, throughput) in core scenarios meets preset thresholds.

2.3 Test Directory and Execution Files

• Functional Testing:
• • Online Serving: /tests/e2e/online_serving/test_{model_name}_expansion.py
  • Offline Inference: /tests/e2e/offline_inference/test_{model_name}_expansion.py
  • (Note: _expansion.py likely means it contains more comprehensive scenario cases compared to L2 tests).

2.4 Execution Method and Example

• Trigger Timing: PR Merged. Automatically triggered after code review is approved and merged into the main branch.
• Execution Environment: GPU servers.
• Script Example:

???+ example "Test Examples"

**2.4.1 Mark Declaration Section**

```python
@pytest.mark.advanced_model
@pytest.mark.core_model
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
```

**Explanation**:

@pytest.mark.advanced_model: Marks the test as L3 or L4 level, indicating that this test case performs deep validation, using real models for performance, integration, and accuracy testing. This forms a "basic-advanced" correspondence with the core_model mark at the L2 level.

@pytest.mark.core_model: Marks the test as L1 or L2 level, indicating that this test case validates the basic functionality of the core model. It uses mock weights and only checks if the relevant interface functions correctly.

@pytest.mark.parametrize: A parameterization decorator that allows abstracting test data into parameters, enabling reuse of the same test logic across different data configurations. indirect=True indicates that parameters will be passed to the fixture for processing.

**Notes**: If you believe the test case only needs to execute basic run logic at the PR-level CI, you can mark it only with @pytest.mark.core_model. If you believe it only needs to execute deep validation run logic at the merge or nightly level, you can mark it only with @pytest.mark.advanced_model. If you believe the test case needs to accommodate both basic run and deep validation test logic, you should mark it with both @pytest.mark.core_model and @pytest.mark.advanced_model.

**2.4.2 Test Function Definition and Documentation**

```python
def test_mix_to_text_audio_001(omni_server, openai_client) -> None:
    """
    Test multi-modal input processing and text/audio output generation via OpenAI API.
    Deploy Setting: default yaml
    Input Modal: text + audio + video + image
    Output Modal: text + audio
    Input Setting: stream=True
    Datasets: single request
    """
```

**Explanation**:

**Function Naming Convention**: Uses the test_ prefix, describes the test scenario mix_to_text_audio, and the number 001 indicates the first test case for this scenario.

**Parameter Explanation**:

omni_server: Omni server instance obtained via fixture, containing model information and configuration.

openai_client: Unified OpenAI client processing instance, encapsulating request sending and response validation logic.

Docstring: Describes the test purpose, deployment settings, input/output modalities, streaming settings, and dataset type in detail, providing clear context for test maintenance.

**2.4.3 Multimodal Data Preparation**

```python
video_data_url = f"data:video/mp4;base64,{generate_synthetic_video(224, 224, 300)['base64']}"
image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(224, 224)['base64']}"
audio_data_url = f"data:audio/wav;base64,{generate_synthetic_audio(5, 1)['base64']}"
```

**Explanation**:

**Data Generation Functions**: Use the generate_synthetic_* series of functions to generate synthetic test data, avoiding reliance on external resources and ensuring test reproducibility and stability.

**Parameter Explanation**:

Video: width, height, duration_frames

Image: width, height

Audio: duration_seconds, channels

**2.4.4 Request Configuration and Keyword Validation**

```python
request_config = {
    "model": omni_server.model,
    "messages": messages,
    "stream": True,
    "key_words": {
        "audio": ["water", "cricket"],
        "video": ["sphere", "globe", "circle", "round"],
        "image": ["square", "quadrate"],
        "text": ["beijing"]
    },
}
```

**Explanation**:

**Model Specification**: Uses omni_server.model to ensure the test aligns with the model configured on the server.

**Keyword Validation Mechanism**: This is an innovative design of the template to address the specific needs of multimodal testing:

Audio Keywords: Validate whether the generated text's description of audio content contains expected elements (e.g., "water" for water sounds, "cricket" for cricket sounds). If you provide multiple keywords, the validation is considered successful if at least one keyword is present.

**Video Keywords**: Validate whether the generated text's description of video content contains expected elements. If you provide multiple keywords, the validation is considered successful if at least one keyword is present.

Image Keywords: Validate whether the generated text's description of image content contains expected elements. If you provide multiple keywords, the validation is considered successful if at least one keyword is present.

Text Keywords: Validate whether the generated text contains expected elements. If you provide multiple keywords, the validation is considered successful if at least one keyword is present.

**2.4.5 Request Execution**

```python
openai_client.send_request(request_config, request_num=1)
```

**Explanation**:

**Unified Client**: Uses the OpenAIClientHandler instance to send requests. This client encapsulates error handling, retry mechanisms, and response validation logic.

**Single Request**: The comment clearly states this is a single-request completion test. For concurrent testing, it can be extended to multiple requests using request_num = n.

**Implicit Validation**: The send_request method internally includes validation logic dynamically selected based on the --run-level parameter: core_model performs basic validation, while advanced_model performs deep validation.

• Run Command: pytest -s -v /tests/e2e/online_serving/test_{model_name}.py -m advanced_model --run-level=advanced_model

Chapter 3: L4 Level Testing - Full Functionality, Performance, and Documentation Testing

3.1 Testing Purpose

L4 level testing is a comprehensive quality audit before a version release. It expands upon L3, executing full functional scenarios, conducting systematic performance stress tests, and simultaneously verifying the correctness of accompanying example documentation. Its purpose is to perform deep validation of the system during off-peak nighttime hours, providing quality trend reports for daytime development and data support for release decisions.

3.2 Testing Content and Scope

• Full Functionality Testing: Executes all test cases defined in test_{model_name}_expansion.py, covering all implemented features, positive flows, boundary conditions, and exception handling.
• Performance Testing: Uses the /tests/e2e/perf/nightly.json configuration file to drive performance testing tools for stress, load, and endurance tests, collecting metrics like throughput, response time, and resource utilization.
• Documentation Testing: Verifies whether the example code provided to users is runnable and its results match the description.

3.3 Test Directory and Execution Files

• Functional Testing: Same directories as L3.
• Performance Test Configuration: /tests/e2e/perf/nightly.json
• Documentation Example Tests:
• • tests/example/online_serving/test_{model_name}.py
  • tests/example/offline_inference/test_{model_name}.py

3.4 Execution Method and Example

• Trigger Timing: Nightly, automatically executed every night.
• Execution Environment: GPU server clusters to meet the resource demands of performance testing.
• Script Example:

Test Examples

When you want to add L4-level performance test cases, you can refer to the following format for case addition in tests/perf/tests/test.json:

{
    "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, 20],
            "request_rate": [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
            }
        }
    ]
}

Parameter Explanation

Overview

Field	Required	Description
test_name	Yes	Unique identifier for the test case
server_params	Yes	Server-side configuration parameters
benchmark_params	Yes	Benchmark running parameters (supports multiple configurations)

server_params Configuration

Basic Parameters

Parameter	Required	Example	Description
model	Yes	"Qwen/Qwen3-Omni-30B-A3B-Instruct"	Model name or path
stage_config_name	Yes	"qwen3_omni.yaml"	Stage configuration file name

Dynamic Configuration (update/delete)

Supports incremental modifications based on the basic configuration:

Operation	Description
update	Update or add configuration items
delete	Delete specified configuration items

Example:

"update": {
    "async_chunk": true,  // Enable asynchronous chunk processing
    "stage_args": {
        "0": {
            "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk"
        }
    }
},
"delete": {
    "stage_args": {
        "2": ["custom_process_input_func"]  // Delete this configuration for stage 2
    }
}

benchmark_params Configuration

You can add any benchmark running parameters you need here. For all optional parameters, refer to the benchmark documentation. General modifications are as follows:

1. Change the ---xxx-xx-xx running parameters to xxx_xx_xx format and fill them as keys in the JSON file.
2. For boolean variables in the running parameters, modify them to forms such as ignore_eos: true/false and fill them into the JSON file.
3. Add the baseline parameter to specify the required validation values, ensuring the validation metric names match those in the result.json generated by the benchmark.
4. The qps and concurrency modes are mutually exclusive. For detailed explanations, see the table below:

Parameter	Type	Required	Example/Values	Description
num_prompts	int / array	Yes	10,[10, 20, 30]	Number of requests. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of qps or max_concurrency, e.g., [10,10,10]. If an array is used, its length must match the number of qps or max_concurrency.
request_rate	int / array	No	1, [1, 2, 3]	Queries per second. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of num_prompts, e.g., [1,1,1]. If an array is used, its length must match the number of num_prompts.
max_concurrency	int / array	No	1, [1, 2, 3]	Queries per second. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of num_prompts, e.g., [1,1,1]. If an array is used, its length must match the number of num_prompts.

• • Run Command: (Specific commands would depend on the performance testing tool and configuration defined in nightly.json).

Chapter 4: L5 Level Testing - Stability and Reliability Testing

4.1 Testing Purpose

L5 level testing focuses on the performance of model services under long-running and abnormal fault scenarios. It aims to uncover deep-seated issues that only manifest under sustained pressure or extreme conditions, such as memory leaks, resource contention, gradual performance degradation, and lack of fault tolerance mechanisms. This is the final, yet crucial, line of defense for ensuring service high availability and production environment robustness.

4.2 Testing Content and Scope

• Long-term Stability (Stability) Testing: Uses the tests/e2e/stability/weekly.json configuration to run the service under moderate load for an extended period (e.g., over 12 hours), monitoring whether metrics like memory/VRAM usage, response time, and throughput degrade over time, and whether the service process remains stable.
• Reliability Testing: Uses tests/e2e/reliability/test_{model_name}.py to actively simulate various fault and abnormal scenarios, such as: dependent service interruption, abnormal input data, network flicker, hardware resource preemption, etc., to verify the system's fault tolerance, self-healing, and graceful degradation capabilities.

4.3 Test Directory and Execution Files

• Stability Test Configuration: tests/e2e/stability/weekly.json
• Reliability Test Suite: tests/e2e/reliability/test_{model_name}.py

4.4 Execution Method and Example

• Trigger Timing: Weekly (weekly) or Days before Release (several days before a major release). Due to long execution times, the frequency is lower.
• Execution Environment: GPU servers, requiring a stable and exclusive testing environment.
• Script Example:

Test Examples

```python # WIP ```

• • Stability: (Execution would be driven by the configuration in weekly.json).
  • Reliability: pytest -s -v tests/e2e/reliability/test_{model_name}.py

Summary

This multi-level testing system achieves continuous, progressive validation of model service quality by tightly integrating testing activities with the development workflow (commit, review, merge, release). From rapid unit testing to comprehensive end-to-end testing, and further to in-depth performance, stability, and reliability verification, each level has clear objectives, collectively building a robust quality protection net. By following this system, teams can deliver high-quality, highly reliable model services more efficiently.