methods

Modules

fastvideo.tests.train.methods.test_wan_causal_dfsft

Per-method GPU smoke test: WanCausalModel + DiffusionForcingSFTMethod.

Mirrors test_wan_finetune.py for the diffusion-forcing SFT (DFSFT) algorithm on the causal Wan transformer. The harness is intentionally identical so the two tests are easy to compare and so future per-method tests can copy this template verbatim.

DFSFT samples inhomogeneous timesteps per chunk (chunk_size=3 in the fixture) and is the natural training counterpart of the WanCausalModel plugin.

Classes

Functions

fastvideo.tests.train.methods.test_wan_finetune

Per-method GPU smoke test: WanModel + FineTuneMethod.

Establishes the per-method test pattern for fastvideo/train:

1. Instantiate the model + method via their public constructors (no Trainer setup, no FSDP wrapping).
2. Feed a synthetic raw_batch dict through method.single_train_step() + method.backward().
3. Assert that the loss is finite and that the first transformer block received a finite, non-zero gradient.

The first block's gradient is the last one computed during backprop, so a healthy grad there implies the full forward + chain-rule path is intact. Keeping the assertion to a single block keeps the reference surface tiny — a later PR layers a device-keyed grad-norm regression on top of this same harness.

Classes

Functions