train

Modules

fastvideo.tests.train.callbacks

Modules

fastvideo.tests.train.callbacks.test_callback

CPU-only unit tests for :mod:fastvideo.train.callbacks.callback.

Covers the Callback base class no-op contract and the CallbackDict instantiation / dispatch / state-dict logic.

The concrete callback subclasses (GradNormClipCallback, EMACallback, ValidationCallback) have their own test files.

Classes

fastvideo.tests.train.callbacks.test_ema

CPU-only unit tests for :mod:fastvideo.train.callbacks.ema.

Exercises the EMA lifecycle (lazy init, start_iter gating, decay math, ema_context swap, state-dict round-trip) on a tiny CPU nn.Linear. EMA_FSDP works without dist.init_process_group because dist.is_initialized() returns False and _to_local_tensor falls through to raw tensors for non-DTensor inputs.

Classes

fastvideo.tests.train.callbacks.test_grad_clip

CPU-only unit tests for :mod:fastvideo.train.callbacks.grad_clip.

Exercises GradNormClipCallback.on_before_optimizer_step against synthetic nn.Module targets with manually populated gradients.

Classes

fastvideo.tests.train.callbacks.test_validation

CPU-only unit tests for :mod:fastvideo.train.callbacks.validation.

Covers the parts of ValidationCallback that don't need a real pipeline or distributed init:

• constructor type coercions and defaults,
• on_validation_begin gating logic (every_steps + modulo),
• _find_ema_callback lookup via _callback_dict,
• state_dict / load_state_dict rng round-trip.

The heavy _run_validation path needs a real diffusion pipeline plus distributed init and is exercised by Phase ⅔ tests.

Classes

fastvideo.tests.train.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

fastvideo.tests.train.models

Modules

fastvideo.tests.train.models.test_load_hunyuan

GPU loading + forward smoke test for HunyuanModel.

Loads the real HunyuanVideo checkpoint (~13B at bf16) via HunyuanModel.__init__ and runs one transformer forward pass on synthetic inputs. Hunyuan's transformer takes a slightly different forward signature than Wan (no encoder_attention_mask, no return_dict); this test mirrors the kwargs in HunyuanModel._build_distill_input_kwargs.

Classes

Functions

fastvideo.tests.train.models.test_load_wan

GPU loading + forward smoke test for WanModel.

Loads the real Wan2.1 1.3B checkpoint via WanModel.__init__ and runs one transformer forward pass on synthetic inputs. Catches loader or forward-signature regressions in fastvideo.train.models.wan.WanModel and the underlying WanTransformer3DModel.

Classes

Functions

fastvideo.tests.train.models.test_load_wan_causal

GPU loading smoke test for WanCausalModel.

Verifies that WanCausalModel.__init__ resolves the CausalWanTransformer3DModel class override and successfully loads weights from the regular Wan2.1 1.3B checkpoint.

A real forward pass is intentionally omitted here: the causal transformer requires per-frame timesteps, a block-causal attention mask, and KV cache state that WanCausalModel.predict_noise_streaming manages for production callers. PR 5 (per-method tests) exercises that streaming forward path end-to-end.

Classes

Functions

fastvideo.tests.train.utils

Modules

fastvideo.tests.train.utils.test_checkpoint

CPU-only unit tests for :mod:fastvideo.train.utils.checkpoint.

Covers the pure-Python portions of the checkpoint manager: name parsing, resume-path resolution, metadata round-trip, rolling-delete cleanup, the _is_stateful predicate, and the maybe_save gating logic. Code paths that touch DCP (dcp.save / dcp.load) and CUDA RNG snapshots are intentionally not covered here — those need a GPU runner and will be tested in later phases.

Classes

Functions

fastvideo.tests.train.utils.test_checkpoint.test_resolve_unknown_dir_raises

test_resolve_unknown_dir_raises(tmp_path: Path) -> None

A dir that is neither a checkpoint nor an output_dir-with-checkpoints.

Source code in fastvideo/tests/train/utils/test_checkpoint.py

def test_resolve_unknown_dir_raises(tmp_path: Path) -> None:
    """A dir that is neither a checkpoint nor an output_dir-with-checkpoints."""
    bogus = tmp_path / "bogus"
    bogus.mkdir()
    with pytest.raises(ValueError, match="Could not resolve"):
        _resolve_resume_checkpoint(str(bogus), output_dir=str(tmp_path))

fastvideo.tests.train.utils.test_config

CPU-only unit tests for :func:load_run_config.

Classes

Functions

fastvideo.tests.train.utils.test_config.test_hsdp_shard_dim_defaults_to_num_gpus

test_hsdp_shard_dim_defaults_to_num_gpus(tmp_path: Path) -> None

When unset, hsdp_shard_dim and sp_size fall back to num_gpus.

Source code in fastvideo/tests/train/utils/test_config.py

def test_hsdp_shard_dim_defaults_to_num_gpus(tmp_path: Path) -> None:
    """When unset, hsdp_shard_dim and sp_size fall back to num_gpus."""
    data = _minimal_yaml()
    data["training"] = {"distributed": {"num_gpus": 4}}
    cfg = load_run_config(_write_yaml(tmp_path, data))
    assert cfg.training.distributed.hsdp_shard_dim == 4
    assert cfg.training.distributed.sp_size == 4

fastvideo.tests.train.utils.test_config.test_overrides_create_intermediate_keys

test_overrides_create_intermediate_keys(tmp_path: Path) -> None

Overrides into a nested key absent from YAML should still apply.

Source code in fastvideo/tests/train/utils/test_config.py

def test_overrides_create_intermediate_keys(tmp_path: Path) -> None:
    """Overrides into a nested key absent from YAML should still apply."""
    data = _minimal_yaml()
    # No `training.checkpoint` block in the minimal YAML.
    path = _write_yaml(tmp_path, data)
    cfg = load_run_config(
        path,
        overrides=["--training.checkpoint.checkpoints_total_limit=5"],
    )
    assert cfg.training.checkpoint.checkpoints_total_limit == 5