hao-ai-lab-FastVideo/docs/contributing/testing.md

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

Type	Location	Purpose
Unit tests	fastvideo/tests/api, fastvideo/tests/dataset, fastvideo/tests/entrypoints, fastvideo/tests/workflow, CPU-safe fastvideo/tests/train subsets	Validate individual functions, APIs, contracts, and lightweight workflows.
Component tests	fastvideo/tests/encoders, fastvideo/tests/transformers, fastvideo/tests/vaes	Validate loading and basic behavior for model components.
Golden gates	fastvideo/tests/golden_gate	Compare small, deterministic component outputs exactly against device/runtime-matched reference tensors.
Train framework tests	fastvideo/tests/train/models, fastvideo/tests/train/methods	Exercise the new fastvideo/train/ framework on real checkpoints and tiny synthetic batches.
SSIM tests	fastvideo/tests/ssim	Compare generated videos against references to catch visual regressions.
Training tests	fastvideo/tests/training	Validate legacy training loops, LoRA, distillation, self-forcing, and VSA behavior.
Inference tests	fastvideo/tests/inference	Validate specialized inference paths such as LoRA inference and V-MoBA.
Performance tests	fastvideo/tests/performance	Gate latency, throughput, peak memory, and stage timings. See Performance Benchmarks.
Eval tests	fastvideo/tests/eval	Check eval metrics against pinned reference scores and assets.
DreamVerse app tests	apps/dreamverse	Validate the DreamVerse backend, frontend, and mock-backed browser flows.

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's existing golden checks transformer block 0; it does not cover the VAE. Until a Wan VAE golden exists, use its focused encode/decode parity test. Keep independent-reference parity and end-to-end coverage for changes outside a golden's tested boundary.

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.

This is the developer iteration order, not a change to required CI checks or their scheduling. See CI/CD Architecture for current policy.

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.

!!! note Add enough prompts, seeds, backends, and parameter combinations to cover the behavior you want to protect, but keep runtime reasonable for CI.

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:

File	Purpose
.buildkite/pipeline.yml	Static, validated 20-lane Slurm test graph.
.github/scripts/plan_merge_ci.py	Trusted path-to-lane and focused quality-test policy for /merge.
.buildkite/scripts/unit_test.sh, .buildkite/scripts/lanes/*.sh	Repository-owned test payloads executed inside Slurm containers.
fastvideo/tests/ssim/ci_runner.py	Four-GPU SSIM task discovery and scheduling.
.buildkite/scripts/pr_test.sh, fastvideo/tests/modal/*.py	Dormant manual rollback path; rejected in Buildkite.

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.