Testing In FastVideo¶

This guide explains how to add and run tests in FastVideo. CI routing, slash-command mappings, and workflow ownership live in CI/CD Architecture.

Test Types¶

Running Tests Locally¶

Start with the cheapest checks that cover the changed behavior, and stop on a failure before starting heavier dependent checks:

1. Run pre-commit on changed files and focused import, config, and contract tests.
2. Run the smallest matching component golden gate. Prefer one GPU, tiny fixed inputs, cached component weights, and direct tensor comparisons over renders.
3. Run focused component parity or default SSIM when the golden does not cover the changed behavior, such as VAE normalization or pipeline wiring.
4. Run full-quality renders or broad suites when required by the change, an explicit request, or CI policy, rather than on every edit.

A golden must cover the component being changed. Wan has four small gates:

These use an immutable Wan checkpoint revision and only download the required component. The trajectory gate needs no tokenizer, text encoder, VAE, or video reference. Weight-free stage tests also exercise every step of a 50-step UniPC loop, CFG caching, expert switching, conditioning layouts, and DMD RNG order. These checks do not replace independent Diffusers parity or end-to-end SSIM.

Use the golden's matching GPU, dtype, backend, and runtime. A missing reference or environment mismatch is not a pass. Do not replace a reference with the candidate output just to clear a failure. For a relocation, the unchanged parent is the baseline; two imports of the same class are not numerical proof.

The VAE and transformer CI lanes run their Wan component goldens first. Selected integration lanes wait for the golden lane in merge/full builds. See CI/CD Architecture for direct-rerun and skip semantics.

For Wan, one command enforces the local ordering and stops on failure:

The initial contracts include tests/api/test_wan_definitions.py: all registered Wan aliases, local-manifest detector precedence, sampling/precision defaults, config isolation, and legacy serialized-config compatibility. These require no weights or Hub access; the current package import still needs its prepared runtime environment.

bash scripts/validate_wan.sh vae           # contracts, then the VAE golden
bash scripts/validate_wan.sh dense parity  # contracts, goldens, Diffusers parity
bash scripts/validate_wan.sh all default   # then focused T2V/I2V/causal SSIM

The second argument is an upper validation tier, not a reference override. Supply the GPU/backend/runtime and SSIM model/tier settings that match the reference. The script never updates references. Causal component coverage is a block-cache fingerprint, not independent full causal-pipeline parity.

New named-tensor gates write a missing output to *.candidate.pt and fail; running candidate code again cannot turn it into an approved reference. Seed from unchanged, pushed source, verify two independent processes bit-for-bit, then review and publish only the new reference files. Preserve the baseline source SHA, test recipe SHA, checkpoint revision, runtime, and comparison receipt with the run artifacts. Never overwrite an existing video reference as a side effect of adding a tensor gate.

Examples of focused checks:

pytest fastvideo/tests/loader/test_wan_family_imports.py -q
pytest fastvideo/tests/golden_gate/test_wan_t2v.py -q
pytest fastvideo/tests/vaes/test_wan_vae.py -q

GPU-heavy suites need the right hardware, credentials, local caches, and sometimes custom kernels. Document those assumptions in new tests.

SSIM Tests¶

SSIM tests generate videos using specific models and parameters, then compare the output against reference videos with Structural Similarity Index Measure. Use them for pipeline-level visual regression coverage when output quality or generation behavior must be preserved.

Directory Structure¶

fastvideo/tests/ssim/
├── reference_videos/
│   ├── default/
│   │   └── <GPU>_reference_videos/
│   │       └── <Model_Name>/
│   │           └── <Backend>/
│   │               └── <Video_File>
│   └── full_quality/
│       └── <GPU>_reference_videos/
├── generated_videos/
├── reference_videos_cli.py
├── test_wan_t2v_similarity.py
├── test_wan_i2v_similarity.py
└── ...

Adding An SSIM Test¶

1. Create or update a model-specific file, for example test_wan_t2v_similarity.py.
2. Define model parameters such as model path, dimensions, frame count, inference steps, guidance, seed, and GPU count.
3. Parametrize prompts, attention backends, and model variants where useful.
4. Generate the video with VideoGenerator.
5. Compare the generated video with the reference using the SSIM helpers.
6. Seed or update reference videos only after inspecting output quality.

Example shape:

import pytest


MY_MODEL_PARAMS = {
    "num_gpus": 1,
    "model_path": "organization/model-name",
    "height": 480,
    "width": 832,
    "num_frames": 45,
    "num_inference_steps": 20,
}


@pytest.mark.parametrize("prompt", TEST_PROMPTS)
@pytest.mark.parametrize("attention_backend", ["FLASH_ATTN"])
def test_my_model_similarity(prompt, attention_backend):
    # Set backend, generate video, and compare against reference.
    ssim_values = compute_video_ssim_torchvision(
        reference_path,
        generated_path,
        use_ms_ssim=True,
    )
    assert ssim_values[0] >= 0.98

Reference Videos¶

When a reference is missing, the test writes generated output under:

fastvideo/tests/ssim/generated_videos/<quality-tier>/<GPU>_reference_videos

After inspecting the generated video, copy it into the matching reference tree:

fastvideo/tests/ssim/reference_videos/<quality-tier>/<GPU>_reference_videos/<Model>/<Backend>/

The helper CLI can copy, upload, and download references:

python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
  --quality-tier default \
  --reference-dir fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos

python fastvideo/tests/ssim/reference_videos_cli.py upload --quality-tier all

python fastvideo/tests/ssim/reference_videos_cli.py download \
  --quality-tier full_quality \
  --device-folder H200_reference_videos

Running SSIM Locally¶

pytest fastvideo/tests/ssim/ -vs

Use a machine whose GPU and backend match the reference folder you are testing.

Slurm CI Runs For SSIM¶

Comment /test ssim on a pull request to run the canonical four-GPU SSIM lane on the Slinky Slurm cluster. fastvideo/tests/ssim/ci_runner.py discovers the suite without importing test modules, packs independent pytest processes across the four assigned GPUs, and stops the lane on the first failure.

The change-aware /merge planner may run only the SSIM test basenames owned by the changed model family. Shared SSIM harness changes still select the complete lane. Independently, main runs the full SSIM matrix every Sunday at 05:00 UTC so infrequently touched model families retain periodic coverage.

For a focused developer run, invoke pytest directly and optionally select one model from a parameterized test through FASTVIDEO_SSIM_MODEL_ID:

pytest fastvideo/tests/ssim/test_wan_t2v_similarity.py -vs

FASTVIDEO_SSIM_MODEL_ID=Wan2.1-T2V-1.3B-Diffusers \
pytest fastvideo/tests/ssim/test_wan_t2v_similarity.py -vs

The files under fastvideo/tests/modal/ are retained only as a disabled manual rollback implementation. No active CI trigger invokes them.

SSIM Bootstrap Mode¶

Normal SSIM runs are strict: if a reference video or latent is missing, the test fails. For new-model PRs, CI can run SSIM in bootstrap mode so missing references are uploaded as draft artifacts for review instead of immediately blocking on a missing canonical reference.

Buildkite enables SSIM bootstrap mode when either condition is true:

• the PR title or Buildkite message contains [new-model];
• FASTVIDEO_SSIM_BOOTSTRAP_MODE=1 is set for the Buildkite job.

Bootstrap mode passes --ssim-bootstrap-mode to pytest. When a generated artifact is available, the test uploads it under the drafts/... namespace in the SSIM reference repo and marks that case as expected-failed. After reviewing the draft, promote it into the canonical reference layout:

python fastvideo/tests/ssim/reference_videos_cli.py promote-draft \
  --quality-tier default \
  --device-folder L40S_reference_videos \
  --model-id <model_id>

CI Integration¶

FastVideo GPU CI is orchestrated by Buildkite and runs only on isolated Slinky Slurm workers. The main files are:

For exact tier membership, slash commands, runner isolation, and aggregate statuses, see CI/CD Architecture.

Adding A New CI Test Category¶

If a new test does not fit an existing lane:

1. Put the test payload in an executable .buildkite/scripts/lanes/<lane>.sh.
2. Add its static step to .buildkite/pipeline.yml, its changed-path ownership to .github/scripts/plan_merge_ci.py, and extend the CI contract tests.
3. Add the /test mapping in .github/workflows/ci-slash-commands.yml.
4. Coordinate the matching GPU, timeout, dependency, secret, and artifact policy in the private Slurm runner allowlist.
5. Document the new category in CI/CD Architecture and add authoring notes here if contributors need them.

When a test only extends an existing category, update that category's tests instead of adding a new CI lane.