Test Writing Guide¶

This document describes how to author tests for each CI level (L1–L5). For the level matrix, CI triggers, and Buildkite upload behavior, see Test System Overview.

Test Directory Structure¶

Canonical placement by CI level is in the Test Dir column of the level matrix in Test System Overview.

Summary:

• Component / unit: tests/{component}/… mirroring vllm_omni/{component}/
• Model E2E: tests/e2e/online_serving/, tests/e2e/offline_inference/, tests/e2e/accuracy/
• Feature integration: tests/e2e/features/<feature>/
• Doc example tests: tests/examples/online_serving/, tests/examples/offline_inference/
• Performance: tests/dfx/perf/tests/ (JSON configs; runners under tests/dfx/perf/scripts/)
• Stability: tests/dfx/stability/tests/
• Reliability: tests/dfx/reliability/
• Do not add new top-level tests/ directories unrelated to a component (or to e2e / dfx / helpers / examples / buildkite)

Deploy YAMLs: vllm_omni/deploy/ via tests.helpers.stage_config.get_deploy_config_path (see stage configs).

Markers for Tests¶

By adding markers before test functions, tests can later be executed uniformly by simply declaring the corresponding marker type.

Current Markers¶

Defined in pyproject.toml:

* Means those markers are auto-added by @hardware_test (parametrization decorator) or hardware_marks (only returning the list of marks for flexibility).

Example usage for markers¶

from tests.helpers.mark import hardware_test

@pytest.mark.core_model
@pytest.mark.omni
@hardware_test(
   res={"cuda": "L4", "rocm": "MI325", "npu": "A2"},
   num_cards=2,
)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_video_to_audio()
    ...

Decorator: @hardware_test¶

This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The hardware_test decorator in vllm-omni/tests/helpers/mark.py performs the following actions:

1. Applies platform and resource markers
   Adds the appropriate pytest markers for each specified hardware platform (e.g., cuda, rocm, xpu, npu) and resource type (e.g., L4, H100, MI325, B60, A2, A3).

   @pytest.mark.cuda
   @pytest.mark.L4
   

2. Handles multi-card (distributed) scenarios
   For tests requiring multiple cards, it automatically adds distributed markers such as distributed_cuda, distributed_rocm, or distributed_npu.

   @pytest.mark.distributed_cuda(num_cards=num_cards)
   

3. Supports flexible card requirements
   Accepts num_cards as either a single integer for all platforms or as a dictionary with per-platform values. If not specified, defaults to 1 card per platform.

4. Integrates resource validation
   On CUDA, adds a skip marker (skipif_cuda) if the system does not have the required number of devices. Support for skipif_rocm and skipif_npu will be implemented later.

5. Works with pytest filtering
   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., -m "distributed_cuda and L4").

Example usage for decorator¶

• Single call for multiple platforms:

  @hardware_test(
      res={"cuda": "L4", "rocm": "MI325", "xpu": "B60", "npu": "A2"},
      num_cards={"cuda": 2, "rocm": 2, "xpu": 2, "npu": 2},
  )
  

  or

  @hardware_test(
      res={"cuda": "L4", "rocm": "MI325", "xpu": "B60", "npu": "A2"},
      num_cards=2,
  )
  

• res must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), XPU (B60), MUSA (S5000), NPU (A2/A3)
• num_cards can be int (all platforms) or dict (per platform); defaults to 1 when missing
• Distributed markers (distributed_cuda, distributed_rocm, distributed_npu) are auto-added for multi-card cases
• Filtering examples:
  • CUDA only: pytest -m "distributed_cuda and L4"
  • ROCm only: pytest -m "distributed_rocm and MI325"
  • NPU only: pytest -m "distributed_npu"

Function: hardware_marks¶

hardware_marks returns a list of pytest mark objects with the same signature as @hardware_test. Use it when you need more flexibility, such as attaching hardware marks to individual pytest.param entries rather than an entire test function.

from tests.helpers.mark import hardware_marks

MULTI_CARD_MARKS = hardware_marks(
    res={"cuda": "H100", "rocm": "MI325", "npu": "A2"}, num_cards=2
)

@pytest.mark.parametrize("omni_server", [
    pytest.param(OmniServerParams(...), id="case_001", marks=MULTI_CARD_MARKS),
], indirect=True)
def test_feature(omni_server):
    ...

JSON mark field (L4 perf configs)¶

Perf JSON under tests/dfx/perf/tests/ can attach pytest marks per case (test_name block). Parsed by tests.dfx.conftest.resolve_pytest_marks and applied to each parametrized run in run_benchmark.py / run_diffusion_benchmark.py.

When mark is present, it must be an array with exactly one hardware_marks object (same semantics as hardware_marks() above), followed by registered pytest marker name strings such as full_model, omni, tts, or diffusion.

{
  "test_name": "test_cosmos3_t2i_official_demo_2gpu",
  "mark": [
    {"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}},
    "full_model",
    "diffusion"
  ],
  "server_type": "vllm-omni",
  "server_params": { "...": "..." },
  "benchmark_params": [{ "name": "1024x1024_steps4", "...": "..." }]
}

• Local bulk load: pytest -sv tests/dfx/perf/scripts/run_diffusion_benchmark.py -m "full_model and diffusion and H100"
• Nightly CI perf steps: --test-config-file tests/dfx/perf/tests/test_<model>_vllm_omni.json (file selects cases; no -m)
• Result filenames use runtime GPU detection (get_runtime_resource_label); H100 is omitted on the default CI pool

See L4 performance test examples for the full schema.

Add Support for a New Platform¶

If you want to add support for a new platform (e.g., "tpu" for a new accelerator), follow these steps:

1. Extend the marker list in your pytest config so that platform/resource markers are defined:

   # In pyproject.toml or pytest.ini
   [tool.pytest.ini_options]
   markers = [
       # ... existing markers ...
       "tpu: Tests that require TPU device",
       "TPU_V3: Tests that require TPU v3 hardware",
       "distributed_tpu: Tests that require multiple TPU devices",
   ]
   

2. Implement a marker construction function for your platform in vllm-omni/tests/helpers/mark.py:

   # In vllm-omni/tests/helpers/mark.py
   
   def tpu_marks(*, res: str, num_cards: int):
       test_platform = pytest.mark.tpu
       if res == "TPU_V3":
           test_resource = pytest.mark.TPU_V3
       else:
           raise ValueError(
               f"Invalid TPU resource type: {res}. Supported: TPU_V3")
   
       if num_cards == 1:
           return [test_platform, test_resource]
       else:
           test_distributed = pytest.mark.distributed_tpu(num_cards=num_cards)
           # Optionally: add skipif_tpu when implemented
           return [test_platform, test_resource, test_distributed]
   

3. Update hardware_marks to recognize your new platform: In the relevant place (see the hardware_marks implementation), add:

   if platform == "tpu":
       marks = tpu_marks(res=resource, num_cards=cards)
   

   (hardware_test calls hardware_marks internally, so both will pick up the change.)
4. (Recommended) Add a test using your new markers:

   @hardware_test(
       res={"tpu": "TPU_V3"},
       num_cards=2,
   )
   def test_my_tpu_feature():
       ...
   

Summary:
- Add pytest markers for your new platform/resources
- Implement a marker function (xxx_marks)
- Plug into hardware_marks
- You're done: tests using @hardware_test or hardware_marks with your platform now automatically get the correct markers, distribution, and isolation!

See code in vllm-omni/tests/helpers/mark.py for existing examples (cuda_marks, rocm_marks, npu_marks).

Test case style¶

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 weights (via deploy YAML patching at core_model) 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, and cheap payload checks at --run-level core_model (see below). Also includes some unit tests that require launching independent service instances.
  • L2 response validation (core_model): Implemented in tests/helpers/assertions.py and invoked by OpenAIClientHandler based on --run-level. At L2 we require request success plus minimal output sanity—not full accuracy:
    • Speech / TTS (assert_audio_speech_response): decoded audio must be present and non-empty (or exceed min_audio_bytes when set in request_config); response_format must match the returned content-type (e.g. wav, pcm). Whisper transcript similarity, PCM HNR, and preset-voice gender checks run only at L3+.
    • Diffusion (assert_diffusion_response): at least one non-empty image, video, or audio artifact. Resolution/frame-count parity and other deep checks run only at L3+.
    • Omni multimodal (assert_omni_response): L2 asserts successful completion; keyword, transcript, and cross-modal similarity checks run only at L3+.
  • 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. See the Test Dir column in Test System Overview for the canonical placement rules.

• L1 (component / unit): tests/{component_name}/… mirroring vllm_omni/{component_name}/ (e.g. tests/diffusion/, tests/entrypoints/). Do not invent new top-level tests/ folders unrelated to a component.
• L2 (basic model E2E):
  • Online serving: tests/e2e/online_serving/test_{model_name}.py
  • Offline inference: tests/e2e/offline_inference/test_{model_name}.py
• Feature-level integration (cross-cutting, not a single-model smoke): tests/e2e/features/<feature>/ (e.g. fullduplex/, custom_pipeline/, rlhf_test/).

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.
• Diff-aware step skipping: On L2, E2E Test jobs may be omitted at pipeline upload when the PR diff does not touch their source_file_dependencies prefixes; non-E2E groups still always upload. See CI Settings — Diff-aware CI.
• Execution Environment: L1 uses CPU environment; L2 requires GPU environment.
• Script Example:

# 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)

• • 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

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.
• L3 response validation (advanced_model): At --run-level advanced_model, tests/helpers/assertions.py adds semantic checks on top of L2 payload gates (see L1 & L2 above):
  • Speech / TTS (assert_audio_speech_response): Whisper transcript of returned audio vs request_config["input"] (cosine similarity > 0.9 when input text is set); optional min_audio_bytes floor; PCM harmonic-to-noise ratio when response_format is pcm; preset voice gender via _assert_preset_voice_gender_from_audio when voice matches a known preset in _PRESET_VOICE_GENDER_MAP (pitch/F0-based classifier; skipped for unknown voices or pcm output).
  • Omni multimodal (assert_omni_response): non-empty text/audio outputs; key_words in transcript or text; text–audio similarity / containment; preset speaker gender check (same helper as TTS).
  • Diffusion (assert_*_diffusion_response): image/video dimension and frame-count parity with request parameters where configured.
  • Accuracy suites: pixel/video similarity and metric baselines under /tests/e2e/accuracy/ (separate from inline helper assertions).

2.3 Test Directory and Execution Files¶

• Functional / model E2E:
  • Online serving: tests/e2e/online_serving/test_{model_name}_expansion.py
  • Offline inference: tests/e2e/offline_inference/test_{model_name}_expansion.py
  • Accuracy / similarity: tests/e2e/accuracy/
  • (_expansion.py holds broader scenario coverage than the L2 smoke file.)
• Feature-level integration: tests/e2e/features/<feature>/ (not under a new top-level tests/ directory).

2.4 Execution Method and Example¶

• Trigger Timing: PR Merged. Automatically triggered after code review is approved and merged into the main branch (typically via merge-test label on the PR before merge).
• Diff-aware step skipping: Same source_file_dependencies mechanism as L2—all E2E Test leaf steps are diff-gated; other L3 groups always upload. See CI Settings — Diff-aware CI.
• Execution Environment: GPU servers.
• Script Example:

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

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
    """

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']}"

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"]
    },
}

openai_client.send_omni_request(request_config, request_num=1)  # for omni-understanding models
# or
openai_client.send_diffusion_request(request_config, request_num=1)  # for diffusion models

• Run Command (L3 merge): pytest -s -v /tests/e2e/online_serving/test_{model_name}.py -m advanced_model --run-level=advanced_model

• Run Command (L4 nightly expansion): pytest -s -v /tests/e2e/online_serving/test_{model_name}_expansion.py -m full_model --run-level=full_model

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 tests/dfx/perf/tests/test_qwen3_omni_*.json (Omni), test_tts.json / test_voxcpm2.json / test_higgs_audio_v3.json (TTS), and diffusion configs tests/dfx/perf/tests/test_*_vllm_omni.json (passed to run_benchmark.py or run_diffusion_benchmark.py via --test-config-file in nightly Perf Test steps) to drive throughput, latency, and memory benchmarks. Each JSON case may declare an optional top-level mark array: exactly one hardware_marks object plus pytest marker name strings (full_model, omni / tts / diffusion, …). Runners attach those marks to each parametrized (server, benchmark index) pair so local bulk runs can filter with -m (for example -m "full_model and H100 and diffusion"). Nightly CI perf jobs select workloads by --test-config-file, not -m. Details are in the Performance Tests example below.
• 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/dfx/perf/tests/test_qwen3_omni_*.json, test_tts.json, test_voxcpm2.json, test_higgs_audio_v3.json, and diffusion configs tests/dfx/perf/tests/test_*_vllm_omni.json (e.g. test_qwen_image_vllm_omni.json, test_cosmos3_vllm_omni.json). Optional per-case mark for local -m filtering is documented in Section 3.4 Performance Tests.
• Documentation Example Tests:
  • tests/examples/online_serving/test_{model_name}.py
  • tests/examples/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_name": "test_qwen3_omni_async_chunk",
    "mark": [
        {"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}},
        "full_model",
        "omni"
    ],
    "server_params": {
        "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
        "stage_config_name": "qwen3_omni.yaml"
    },
    "benchmark_params": [
        {
            "name": "random_10p",
            "dataset_name": "random",
            "num_prompts": [10, 20],
            "max_concurrency": [1, 4],
            "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": [500, 800],
                "mean_audio_ttfp_ms": [2000, 3500],
                "mean_audio_rtf": [0.25, 0.35]
            }
        }
    ]
}

{
    "test_name": "test_bagel_single_device_single_stage_t2i",
    "mark": [
        {"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 1}},
        "full_model",
        "diffusion"
    ],
    "server_type": "vllm-omni",
    "server_params": { "...": "..." },
    "benchmark_params": [ { "name": "1024x1024_steps20", "...": "..." } ]
}

{
    "test_name": "test_cosmos3_t2i_official_demo_2gpu",
    "mark": [
        {"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}},
        "full_model",
        "diffusion"
    ],
    "server_type": "vllm-omni",
    "benchmark_endpoint": "/v1/images/generations",
    "server_params": { "...": "..." },
    "benchmark_params": [ { "name": "1024x1024_steps4", "...": "..." } ]
}

# Bulk load + filter by JSON mark
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m "full_model and H100 and diffusion"
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "full_model and omni and H100"

# Single file (same as nightly CI Perf steps)
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py \
  --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
  --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json

# Optional baseline assertion (default off)
pytest -sv tests/dfx/perf/scripts/run_diffusion_benchmark.py --assert-baseline \
  --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json

"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
    }
}

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

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 JSON under tests/dfx/stability/tests/ (for example test_qwen3_omni.json and test_wan22.json) 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 pytest suites under tests/dfx/reliability/ to inject controlled faults against a live vllm_omni serve instance (same omni_server / omni_server_function fixture style as E2E). Current suites emphasize GPU memory pressure (CUDA sidecar “memory hog”), worker / runtime process kill (SIGKILL on VLLM::Worker for Qwen3-Omni, multiprocessing.spawn for Wan2.2 video workers, or vLLM-Omni:: for HunyuanImage DiT workers), large multimodal chat, /v1/videos, /v1/images/generations, or /v1/audio/speech jobs under OOM, /health → 503 and fast-fail / non-hanging concurrent requests after kill, and OpenAI-style 5xx error contracts (e.g. text vs text+audio under OOM). Post-fault recovery checks exist where enabled (some cases may be skip while issues are tracked). See the Reliability <details> block in Section 4.4 for file-level responsibilities and CI markers (slow, hardware_test, POSIX-only kill).

4.3 Test Directory and Execution Files¶

• Stability Test Configuration: tests/dfx/stability/tests/test_qwen3_omni.json, tests/dfx/stability/tests/test_wan22.json (one JSON per model / runner family)
• Reliability Test Suite (tests/dfx/reliability/):
  • test_reliability_qwen3_omni.py — Qwen3-Omni chat / multimodal reliability (GPU OOM, process kill, recovery, error contract under --async-chunk vs default).
  • test_reliability_wan22.py — Wan2.2 I2V video API reliability (/v1/videos under OOM and process kill, recovery).
  • test_reliability_hunyuan_image.py — HunyuanImage-3.0-Instruct DiT-only reliability (/v1/images/generations under OOM and process kill; deploy hunyuan_image3_dit.yaml, H100 × 4).
  • test_reliability_voxcpm2.py — VoxCPM2 TTS reliability (/v1/audio/speech under OOM and process kill; deploy voxcpm2.yaml, L4 × 1).
  • helpers.py — Shared primitives used by current suites: raw HTTP probes for /v1/chat/completions and /health, OpenAI-style error parsing, GPU OOM sidecar (inject_gpu_oom / stop_gpu_oom_hogs), and pgrep-based process-kill injector construction (make_process_kill_fault_injector).
  • conftest.py — fault_injector and omni_server_after_fault / omni_server_after_fault_function fixtures to run a callable after the server is ready.
  • README.md — Short local run commands for this directory.

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_name": "test_qwen3_omni_stability",
    "server_params": {
        "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
        "stage_config_name": "qwen3_omni.yaml"
    },
    "benchmark_params": [
        {
            "dataset_name": "random",
            "backend": "openai-chat-omni",
            "endpoint": "/v1/chat/completions",
            "duration_sec": 43200,
            "request_rate": 0.5,
            "num_prompts_per_batch": 20,
            "random_input_len": 2500,
            "random_output_len": 900,
            "ignore_eos": true,
            "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration"
        }
    ]
}

pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"

pytest --collect-only tests/dfx/reliability
pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m slow
pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m slow
pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m slow
pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m slow

• • Stability: pytest -s -v tests/dfx/stability/scripts/test_stability_qwen3_omni.py or pytest -s -v tests/dfx/stability/scripts/test_stability_wan22.py (or add test_stability_<model>.py alongside a matching JSON config)
  • Reliability: pytest -s -v tests/dfx/reliability/test_reliability_<model>.py -m slow (current suites: qwen3_omni, wan22, hunyuan_image, voxcpm2; add test_reliability_<suite>.py and a matching step in .buildkite/cuda/test-weekly.yml for new models)