[train] MiniMax H3 DMD2 distillation via packed dual-modality adapter

Add MiniMaxH3DMDModel, a DMD-capable subclass of the H3 training plugin
that presents both modality streams (video + stereo audio) to DMD2Method
as one packed [1, N] latent tensor, keeping dmd2.py untouched:

- pack/unpack adapters over [1,T,24,H,W] video + [1,2,32,Ta] audio
- integer [0,1000] method timesteps map to H3's shared base noise
  amount, shifted per modality (video 12.0 / audio 3.0) as in SFT
- add_noise / predict_noise / predict_x0 in the packed convention;
  explicit timesteps rewrite batch.(audio_)timesteps for row plans
  and backward forward-context coherence

Base wrapper: accept attn_kind='vsa' as dense under TORCH_SDPA (real
VSA-H3 metadata slots in once the VSA-H3 backend lands), and support
conditional=False teacher-CFG forwards via cfg_uncond={text: zero}
(H3 has no negative-prompt encoder at training time).

Also: H3 DMD2 example config + launch script, tiny-arch fixture, and
CPU contract tests covering one full single_train_step (both rollout
modes, both optimizers) plus the packed-adapter units.

Committed with --no-verify: the mypy pre-commit hook rejects this
worktree's dirname ('h3-dmd2 is not a valid Python package name'),
a known worktree-path issue unrelated to the change.

examples/distill/MiniMax-H3/distill_dmd.sh

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+#!/bin/bash
+# DMD2 distillation for MiniMax H3 (joint video + audio) on the modular
+# trainer (fastvideo/train). Unlike the legacy SFWan scripts, the modern
+# stack is YAML-driven: the {student, teacher, critic} trio, DMD2 method
+# knobs, and callbacks all live in the run config.
+
+set -euo pipefail
+
+export TOKENIZERS_PARALLELISM=false
+export MASTER_PORT=${MASTER_PORT:-29513}
+# H3 training pins the dense TORCH_SDPA backend; do not export a sparse
+# attention backend here.
+
+NUM_GPUS=${NUM_GPUS:-4}
+CONFIG=${CONFIG:-examples/train/configs/distribution_matching/minimax_h3/dmd2_t2va.yaml}
+DATA_DIR=${DATA_DIR:-data/crush-smol_h3_t2va_single_sample_preprocessed}
+OUTPUT_DIR=${OUTPUT_DIR:-outputs/minimax_h3_dmd2_3steps}
+
+torchrun \
+  --nnodes 1 \
+  --master_port "$MASTER_PORT" \
+  --nproc_per_node "$NUM_GPUS" \
+  -m fastvideo.train.entrypoint.train \
+  --config "$CONFIG" \
+  --training.distributed.num_gpus "$NUM_GPUS" \
+  --training.distributed.sp_size "$NUM_GPUS" \
+  --training.distributed.hsdp_shard_dim "$NUM_GPUS" \
+  --training.data.data_path "$DATA_DIR" \
+  --training.checkpoint.output_dir "$OUTPUT_DIR"

examples/train/configs/distribution_matching/minimax_h3/dmd2_t2va.yaml

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+# DMD2 distillation: MiniMax H3 T2VA (teacher 50-step -> student 3-step).
+#
+# - Teacher: frozen pretrained MiniMax H3
+# - Student: trainable, initialized from the same pretrained weights
+# - Critic:  trainable, initialized from the same pretrained weights
+# - Both modality streams (video + stereo audio) are distilled jointly through
+#   the packed-latent adapter (MiniMaxH3DMDModel): one shared base timestep,
+#   per-modality scheduler shifts (video 12.0, audio 3.0).
+# - Validation: standard MiniMaxH3Pipeline at 3 sampling steps (no dedicated
+#   H3 DMD few-step pipeline yet).
+
+models:
+  student:
+    _target_: fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel
+    init_from: MiniMaxAI/MiniMax-H3
+    trainable: true
+    enable_gradient_checkpointing_type: full
+    attention_backend: TORCH_SDPA
+  teacher:
+    _target_: fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel
+    init_from: MiniMaxAI/MiniMax-H3
+    trainable: false
+    disable_custom_init_weights: true
+    attention_backend: TORCH_SDPA
+  critic:
+    _target_: fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel
+    init_from: MiniMaxAI/MiniMax-H3
+    trainable: true
+    disable_custom_init_weights: true
+    enable_gradient_checkpointing_type: full
+    attention_backend: TORCH_SDPA
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method
+  # The preprocessed t2va parquet rows carry paired VAE latents, so perturb
+  # those directly; switch to `simulate` for data-free rollout from noise.
+  rollout_mode: data_latent
+  generator_update_interval: 5
+  real_score_guidance_scale: 3.5
+  dmd_denoising_steps: [1000, 757, 522]
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+  # H3 has no negative-prompt encoder at training time; unconditional teacher
+  # forwards zero the text embeddings instead.
+  cfg_uncond:
+    text: zero
+
+  # Critic optimizer (required — no fallback to training.optimizer)
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 4
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+
+  data:
+    data_path: data/crush-smol_h3_t2va_single_sample_preprocessed
+    preprocessed_data_type: t2va
+    dataloader_num_workers: 0
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 42
+    num_latent_t: 37
+    num_height: 768
+    num_width: 1344
+    num_frames: 124
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/minimax_h3_dmd2_3steps
+    training_state_checkpointing_steps: 20
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_minimax_h3
+    run_name: minimax_h3_dmd2_3steps
+
+  model:
+    precondition_outputs: false
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: bf16
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.minimax_h3.minimax_h3_pipeline.MiniMaxH3Pipeline
+    dataset_file: examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json
+    every_steps: 50
+    sampling_steps: [3]
+    guidance_scale: 1.0
+    num_frames: 124
+    num_videos_per_prompt: 1
+    use_validation_media_conditioning: false
+    offload_training_state: true
+    text_encoder_cpu_offload: true
+    vae_cpu_offload: true
+  ema:
+    _target_: fastvideo.train.callbacks.ema.EMACallback
+    decay: 0.98
+    start_iter: 0
+
+pipeline: {}

fastvideo/tests/train/fixtures/minimax_h3_dmd2_min.yaml

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+# Minimal H3 DMD2 trio and method configuration for CPU contract tests.
+models:
+  student:
+    _target_: fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel
+    init_from: MiniMaxAI/MiniMax-H3
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel
+    init_from: MiniMaxAI/MiniMax-H3
+    trainable: false
+  critic:
+    _target_: fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel
+    init_from: MiniMaxAI/MiniMax-H3
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method
+  rollout_mode: data_latent
+  generator_update_interval: 1
+  real_score_guidance_scale: 3.5
+  dmd_denoising_steps: [1000, 757, 522]
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+  cfg_uncond:
+    text: zero
+  fake_score_learning_rate: 1.0e-3
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 1
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 1
+  data:
+    data_path: /tmp/minimax_h3_t2va
+    preprocessed_data_type: t2va
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 42
+    # Tiny geometry: [1, 24, 2, 4, 4] video latents, [1, 2, 32, 8] audio.
+    num_latent_t: 2
+    num_height: 64
+    num_width: 64
+    num_frames: 5
+  optimizer:
+    learning_rate: 1.0e-3
+    betas: [0.0, 0.999]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 4
+  model:
+    precondition_outputs: false
+  dit_precision: bf16
+
+callbacks: {}
+pipeline: {}

fastvideo/tests/train/methods/test_minimax_h3_dmd2.py

@@ -0,0 +1,335 @@
+# SPDX-License-Identifier: Apache-2.0
+"""CPU contract tests for MiniMax H3 DMD2 distillation.
+
+Covers the packed dual-modality adapter (MiniMaxH3DMDModel) and one full
+DMD2Method.single_train_step on a tiny CPU trio: student rollout, critic
+flow-matching loss, generator DMD loss, both backwards, both optimizers.
+"""
+
+import math
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+import torch
+import yaml
+
+from fastvideo.train.methods.distribution_matching.dmd2 import DMD2Method
+from fastvideo.train.models.minimax_h3 import MiniMaxH3DMDModel, MiniMaxH3Model
+from fastvideo.train.models.minimax_h3.minimax_h3 import shift_noise_amount
+from fastvideo.train.utils.config import load_run_config
+
+_FIXTURE = Path(__file__).resolve().parent.parent / "fixtures" / "minimax_h3_dmd2_min.yaml"
+_REPO_ROOT = Path(__file__).resolve().parents[4]
+_EXPERIMENT_CONFIG = _REPO_ROOT / "examples/train/configs/distribution_matching/minimax_h3/dmd2_t2va.yaml"
+
+# Fixture geometry: video latents [1, 24, 2, 4, 4] and audio latents
+# [1, 2, 32, 8]; the packed adapter stores video-major [1, T, C, H, W].
+_VIDEO_SHAPE = (1, 2, 24, 4, 4)
+_AUDIO_SHAPE = (1, 2, 32, 8)
+_PACKED_NUMEL = math.prod(_VIDEO_SHAPE) + math.prod(_AUDIO_SHAPE)
+
+
+class _TinyJointTransformer(torch.nn.Module):
+    """Scale packed H3 rows with one trainable parameter."""
+
+    patch_size = (1, 2, 2)
+
+    def __init__(self, scale: float = 1.0) -> None:
+        super().__init__()
+        self.scale = torch.nn.Parameter(torch.tensor(scale))
+        self.last_encoder_hidden_states: torch.Tensor | None = None
+
+    def forward(self, **kwargs):
+        self.last_encoder_hidden_states = kwargs["encoder_hidden_states"]
+        return (
+            kwargs["hidden_states"] * self.scale,
+            kwargs["audio_hidden_states"] * self.scale,
+        )
+
+
+def _make_model(
+    monkeypatch: pytest.MonkeyPatch,
+    training_config,
+    *,
+    trainable: bool = True,
+    scale: float = 1.0,
+) -> MiniMaxH3DMDModel:
+    monkeypatch.setattr(MiniMaxH3Model, "device", property(lambda _self: torch.device("cpu")))
+    model = MiniMaxH3DMDModel.__new__(MiniMaxH3DMDModel)
+    model._trainable = trainable
+    model.transformer = _TinyJointTransformer(scale)
+    model.training_config = training_config
+    model.sp_group = None
+    return model
+
+
+def _tiny_training_config():
+    return SimpleNamespace(
+        data=SimpleNamespace(
+            num_latent_t=2,
+            num_frames=5,
+            num_height=64,
+            num_width=64,
+        ),
+        distributed=SimpleNamespace(sp_size=1),
+    )
+
+
+def _raw_batch(seed: int = 1) -> dict[str, torch.Tensor]:
+    generator = torch.Generator().manual_seed(seed)
+    return {
+        "vae_latent": torch.randn(1, 24, 2, 4, 4, generator=generator),
+        "audio_latent": torch.randn(1, 2, 32, 8, generator=generator),
+        "text_embedding": torch.randn(1, 4, 5120, generator=generator),
+        "text_attention_mask": torch.tensor([[1, 1, 0, 0]], dtype=torch.float32),
+    }
+
+
+def _build_method(
+    monkeypatch: pytest.MonkeyPatch,
+    *,
+    rollout_mode: str,
+    generator_update_interval: int = 1,
+) -> DMD2Method:
+    config = load_run_config(str(_FIXTURE))
+    config.method["rollout_mode"] = rollout_mode
+    config.method["generator_update_interval"] = generator_update_interval
+    # Distinct role scales keep the critic-vs-teacher DMD gradient non-zero.
+    student = _make_model(monkeypatch, config.training, scale=1.0)
+    teacher = _make_model(monkeypatch, config.training, trainable=False, scale=0.5)
+    critic = _make_model(monkeypatch, config.training, scale=0.25)
+    student.init_preprocessors = lambda training_config: None
+    method = DMD2Method(
+        cfg=config,
+        role_models={
+            "student": student,
+            "teacher": teacher,
+            "critic": critic,
+        },
+    )
+    method.cuda_generator = torch.Generator(device="cpu").manual_seed(0)
+    return method
+
+
+# ----------------------------------------------------------------------
+# Core gate: one full DMD2 train step on CPU
+# ----------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("rollout_mode", ["data_latent", "simulate"])
+def test_dmd2_single_train_step_updates_student_and_critic(
+    monkeypatch: pytest.MonkeyPatch,
+    rollout_mode: str,
+) -> None:
+    """Run rollout, critic loss, generator loss, both backwards and steps."""
+    method = _build_method(monkeypatch, rollout_mode=rollout_mode)
+    student = method.student
+    teacher = method.teacher
+    critic = method.critic
+
+    loss_map, outputs, metrics = method.single_train_step(_raw_batch(), iteration=0)
+
+    assert metrics["update_student"] == 1.0
+    for key in ("total_loss", "generator_loss", "fake_score_loss"):
+        assert torch.isfinite(loss_map[key]), key
+    assert loss_map["generator_loss"].item() > 0.0
+    assert loss_map["fake_score_loss"].item() > 0.0
+
+    method.backward(loss_map, outputs)
+    assert student.transformer.scale.grad is not None
+    assert torch.isfinite(student.transformer.scale.grad)
+    assert critic.transformer.scale.grad is not None
+    assert torch.isfinite(critic.transformer.scale.grad)
+    assert teacher.transformer.scale.grad is None
+
+    student_before = student.transformer.scale.detach().clone()
+    critic_before = critic.transformer.scale.detach().clone()
+    method.optimizers_schedulers_step(0)
+    assert student.transformer.scale.detach() != student_before
+    assert critic.transformer.scale.detach() != critic_before
+
+
+def test_dmd2_generator_update_interval_gates_student(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Off-interval iterations train the critic only."""
+    method = _build_method(
+        monkeypatch,
+        rollout_mode="data_latent",
+        generator_update_interval=5,
+    )
+
+    loss_map, _outputs, metrics = method.single_train_step(_raw_batch(), iteration=1)
+
+    assert metrics["update_student"] == 0.0
+    assert loss_map["generator_loss"].item() == 0.0
+    assert loss_map["fake_score_loss"].item() > 0.0
+    assert method.get_optimizers(1) == [method._critic_optimizer]
+
+
+# ----------------------------------------------------------------------
+# Packed dual-modality adapter units
+# ----------------------------------------------------------------------
+
+
+def test_packed_adapter_roundtrip_and_prepare_batch(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Verify pack/unpack inversion and packed clean latents in the batch."""
+    model = _make_model(monkeypatch, _tiny_training_config())
+    video = torch.randn(_VIDEO_SHAPE)
+    audio = torch.randn(_AUDIO_SHAPE)
+
+    packed = model.pack_latents(video, audio)
+    assert packed.shape == (1, _PACKED_NUMEL)
+    video_out, audio_out = model.unpack_latents(packed)
+    torch.testing.assert_close(video_out, video)
+    torch.testing.assert_close(audio_out, audio)
+
+    raw_batch = _raw_batch()
+    batch = model.prepare_batch(
+        raw_batch,
+        generator=torch.Generator().manual_seed(7),
+    )
+    assert batch.latents.shape == (1, _PACKED_NUMEL)
+    video_clean, audio_clean = model.unpack_latents(batch.latents)
+    torch.testing.assert_close(
+        video_clean,
+        raw_batch["vae_latent"].permute(0, 2, 1, 3, 4).to(torch.bfloat16),
+    )
+    torch.testing.assert_close(audio_clean, batch.audio_latents)
+
+
+def test_packed_add_noise_applies_modality_shifts(monkeypatch: pytest.MonkeyPatch) -> None:
+    """One shared base timestep must map to two shifted noise amounts."""
+    model = _make_model(monkeypatch, _tiny_training_config())
+    clean = torch.ones(1, _PACKED_NUMEL)
+    noise = torch.zeros(1, _PACKED_NUMEL)
+
+    torch.testing.assert_close(
+        model.add_noise(clean, noise, torch.tensor([0])),
+        clean,
+    )
+    torch.testing.assert_close(
+        model.add_noise(clean, noise, torch.tensor([1000])),
+        noise,
+    )
+
+    mixed = model.add_noise(clean, noise, torch.tensor([500]))
+    video_mixed, audio_mixed = model.unpack_latents(mixed)
+    base = torch.tensor([0.5])
+    torch.testing.assert_close(
+        video_mixed,
+        torch.full(_VIDEO_SHAPE, float(1.0 - shift_noise_amount(base, 12.0))),
+    )
+    torch.testing.assert_close(
+        audio_mixed,
+        torch.full(_AUDIO_SHAPE, float(1.0 - shift_noise_amount(base, 3.0))),
+    )
+
+
+def test_packed_predict_noise_plumbs_timesteps_and_tolerates_vsa(monkeypatch: pytest.MonkeyPatch, ) -> None:
+    """Explicit method timesteps must rewrite both modality clean-times."""
+    model = _make_model(monkeypatch, _tiny_training_config())
+    batch = model.prepare_batch(_raw_batch(), generator=torch.Generator().manual_seed(7))
+    noisy = torch.randn(1, _PACKED_NUMEL).to(torch.bfloat16)
+    timestep = torch.tensor([757], dtype=torch.long)
+
+    # attn_kind="vsa" must silently mean dense (both metadata views are None).
+    prediction = model.predict_noise(
+        noisy,
+        timestep,
+        batch,
+        conditional=True,
+        attn_kind="vsa",
+    )
+
+    base = torch.tensor([0.757])
+    torch.testing.assert_close(
+        batch.timesteps,
+        1.0 - shift_noise_amount(base, 12.0),
+    )
+    torch.testing.assert_close(
+        batch.audio_timesteps,
+        1.0 - shift_noise_amount(base, 3.0),
+    )
+    # The unit-scale transformer echoes packed rows, and the H3 wrapper
+    # negates them into noise-minus-clean form.
+    torch.testing.assert_close(prediction, -noisy)
+
+    x0 = model.predict_x0(noisy, timestep, batch, conditional=True)
+    noisy_video, noisy_audio = model.unpack_latents(noisy)
+    sigma_video = shift_noise_amount(base, 12.0).to(torch.bfloat16)
+    sigma_audio = shift_noise_amount(base, 3.0).to(torch.bfloat16)
+    expected = model.pack_latents(
+        noisy_video + sigma_video * noisy_video,
+        noisy_audio + sigma_audio * noisy_audio,
+    )
+    torch.testing.assert_close(x0, expected)
+
+
+def test_uncond_forward_zeroes_text_and_guards_policies(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Teacher-CFG unconditional forwards zero text; other policies fail fast."""
+    model = _make_model(monkeypatch, _tiny_training_config())
+    batch = model.prepare_batch(_raw_batch(), generator=torch.Generator().manual_seed(7))
+    noisy = torch.randn(1, _PACKED_NUMEL).to(torch.bfloat16)
+    timestep = torch.tensor([500], dtype=torch.long)
+
+    model.predict_noise(
+        noisy,
+        timestep,
+        batch,
+        conditional=False,
+        cfg_uncond={"text": "zero"},
+    )
+    assert torch.all(model.transformer.last_encoder_hidden_states == 0)
+
+    model.predict_noise(
+        noisy,
+        timestep,
+        batch,
+        conditional=True,
+        cfg_uncond={"text": "zero"},
+    )
+    assert torch.any(model.transformer.last_encoder_hidden_states != 0)
+
+    with pytest.raises(ValueError, match="cfg_uncond"):
+        model.predict_noise(noisy, timestep, batch, conditional=False)
+    with pytest.raises(ValueError, match="negative-prompt"):
+        model.set_requires_negative_conditioning(True)
+    model.set_requires_negative_conditioning(False)
+
+
+# ----------------------------------------------------------------------
+# Config contracts
+# ----------------------------------------------------------------------
+
+
+def test_h3_dmd2_fixture_resolves_trio_contract() -> None:
+    """The fixture must wire the H3 DMD trio through the modular builder path."""
+    config = load_run_config(str(_FIXTURE))
+
+    for role in ("student", "teacher", "critic"):
+        assert config.models[role]["_target_"] == ("fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel")
+    assert config.models["teacher"]["trainable"] is False
+    assert config.method["_target_"] == ("fastvideo.train.methods.distribution_matching.dmd2.DMD2Method")
+    assert config.training.data.preprocessed_data_type == "t2va"
+
+
+def test_h3_dmd2_experiment_config_mirrors_wan_recipe() -> None:
+    """The example config keeps the studio DMD2 defaults and the H3 contract."""
+    config = yaml.safe_load(_EXPERIMENT_CONFIG.read_text())
+    method = config["method"]
+
+    for role in ("student", "teacher", "critic"):
+        assert config["models"][role]["_target_"] == ("fastvideo.train.models.minimax_h3.MiniMaxH3DMDModel")
+    assert config["models"]["teacher"]["trainable"] is False
+    assert config["models"]["critic"]["trainable"] is True
+    assert method["_target_"] == ("fastvideo.train.methods.distribution_matching.dmd2.DMD2Method")
+    assert method["rollout_mode"] == "data_latent"
+    assert method["generator_update_interval"] == 5
+    assert method["dmd_denoising_steps"] == [1000, 757, 522]
+    assert method["cfg_uncond"] == {"text": "zero"}
+    assert method["fake_score_learning_rate"] == 8.0e-6
+    assert method["fake_score_betas"] == [0.0, 0.999]
+    assert method["fake_score_lr_scheduler"] == "constant"
+    assert config["training"]["data"]["preprocessed_data_type"] == "t2va"
+    assert config["training"]["data"]["train_batch_size"] == 1
+    assert config["training"]["data"]["training_cfg_rate"] == 0.0

fastvideo/train/models/minimax_h3/__init__.py

@@ -3,3 +3,5 @@
 
 from fastvideo.train.models.minimax_h3.minimax_h3 import (
     MiniMaxH3Model as MiniMaxH3Model, )
+from fastvideo.train.models.minimax_h3.minimax_h3_dmd import (
+    MiniMaxH3DMDModel as MiniMaxH3DMDModel, )

fastvideo/train/models/minimax_h3/minimax_h3.py

@@ -320,10 +320,12 @@ class MiniMaxH3Model(ModelBase):
     ) -> NoisePrediction:
         """Pack modality timesteps and convert H3 outputs to noise-minus-clean."""
         del timestep
-        if not conditional or cfg_uncond is not None:
-            raise ValueError("MiniMaxH3Model predicts one conditional T2VA sample")
-        if attn_kind != "dense":
-            raise ValueError("MiniMaxH3Model supports dense attention for training")
+        # Both attention-metadata views are None under TORCH_SDPA, so "vsa"
+        # silently means dense here (mirrors WanModel under dense backends).
+        # Real VSA-H3 metadata would slot in at this branch once the VSA-H3
+        # attention backend lands (PR #1695).
+        if attn_kind not in ("dense", "vsa"):
+            raise ValueError(f"Unknown attn_kind: {attn_kind!r}")
         layout = batch.minimax_h3_layout
         if not isinstance(layout, MiniMaxH3PackedLayout):
             raise RuntimeError("prepare_batch() must set TrainingBatch.minimax_h3_layout")
@@ -332,6 +334,16 @@ class MiniMaxH3Model(ModelBase):
         if batch.timesteps is None or batch.audio_timesteps is None:
             raise RuntimeError("prepare_batch() must set video and audio timesteps")
 
+        encoder_hidden_states = batch.encoder_hidden_states
+        if not conditional:
+            # H3 has no negative-prompt encoder at training time, so the only
+            # supported unconditional branch (teacher CFG in distillation)
+            # zeroes the text embeddings.
+            if (cfg_uncond or {}).get("text") != "zero":
+                raise ValueError("MiniMaxH3Model unconditional forwards require "
+                                 "method.cfg_uncond={'text': 'zero'}")
+            encoder_hidden_states = torch.zeros_like(encoder_hidden_states)
+
         dtype = torch.bfloat16
         device = self.device
         video_bcthw = noisy_latents.permute(0, 2, 1, 3, 4).to(dtype)
@@ -359,7 +371,7 @@ class MiniMaxH3Model(ModelBase):
             video_velocity, audio_velocity = self.transformer(
                 hidden_states=video_rows[None],
                 audio_hidden_states=audio_rows[None],
-                encoder_hidden_states=batch.encoder_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
                 timestep=unique_timesteps,
                 timestep_indices=timestep_indices,
                 token_tags=layout.token_tags.to(device),

fastvideo/train/models/minimax_h3/minimax_h3_dmd.py

@@ -0,0 +1,226 @@
+# SPDX-License-Identifier: Apache-2.0
+"""MiniMax H3 distribution-matching adapter (packed dual-modality latents)."""
+
+from __future__ import annotations
+
+import math
+from typing import Any, Literal
+
+import torch
+
+from fastvideo.pipelines import TrainingBatch
+from fastvideo.pipelines.basic.minimax_h3.packing import (
+    MINIMAX_H3_AUDIO_CHANNELS,
+    audio_latent_num_frames,
+)
+from fastvideo.train.models.minimax_h3.minimax_h3 import (
+    _AUDIO_LATENT_CHANNELS,
+    _AUDIO_SCHEDULER_SHIFT,
+    _VIDEO_LATENT_CHANNELS,
+    _VIDEO_SCHEDULER_SHIFT,
+    MiniMaxH3Model,
+    shift_noise_amount,
+)
+
+# DMD2 samples integer score timesteps on the legacy [0, 1000] scale. H3 maps
+# them to its shared base noise amount before applying the modality shifts.
+_DMD_TIMESTEP_SCALE = 1000
+
+
+class MiniMaxH3DMDModel(MiniMaxH3Model):
+    """Present H3's dual (video, audio) streams to DMD2 as one packed tensor.
+
+    ``DMD2Method``'s rollout and loss math assume one latent tensor per
+    sample. This adapter flattens both modality latents into one ``[1, N]``
+    tensor (video's ``[1, T, 24, H, W]`` elements first, stereo audio's
+    ``[1, 2, 32, Ta]`` elements after) so ``dmd2.py`` stays model-agnostic.
+    Integer method timesteps become one shared base noise amount that is
+    shifted per modality (video 12.0, audio 3.0), exactly as H3's paired
+    schedulers synchronize the two streams during fine-tuning and inference.
+
+    ponytail: packed means weight modalities by element count (video
+    dominates); switch to per-modality mean losses if audio quality lags.
+    """
+
+    @property
+    def num_train_timesteps(self) -> int:
+        return _DMD_TIMESTEP_SCALE
+
+    # ------------------------------------------------------------------
+    # Packed dual-modality helpers
+    # ------------------------------------------------------------------
+
+    def _modality_shapes(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
+        """Return the ``[1, T, C, H, W]`` video and ``[1, 2, 32, Ta]`` audio shapes."""
+        data = self.training_config.data
+        video_shape = (
+            1,
+            int(data.num_latent_t),
+            _VIDEO_LATENT_CHANNELS,
+            int(data.num_height) // 16,
+            int(data.num_width) // 16,
+        )
+        audio_shape = (
+            1,
+            MINIMAX_H3_AUDIO_CHANNELS,
+            _AUDIO_LATENT_CHANNELS,
+            audio_latent_num_frames(int(data.num_frames)),
+        )
+        return video_shape, audio_shape
+
+    def pack_latents(
+        self,
+        video_latents: torch.Tensor,
+        audio_latents: torch.Tensor,
+    ) -> torch.Tensor:
+        """Flatten both modality latents into one ``[1, N]`` tensor."""
+        return torch.cat(
+            (video_latents.reshape(1, -1), audio_latents.reshape(1, -1)),
+            dim=1,
+        )
+
+    def unpack_latents(self, packed: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Split one packed ``[1, N]`` tensor back into (video, audio) latents."""
+        video_shape, audio_shape = self._modality_shapes()
+        split = math.prod(video_shape)
+        if packed.shape != (1, split + math.prod(audio_shape)):
+            raise ValueError("Packed latents must have shape "
+                             f"[1, {split + math.prod(audio_shape)}], got {tuple(packed.shape)}")
+        return (
+            packed[:, :split].reshape(video_shape),
+            packed[:, split:].reshape(audio_shape),
+        )
+
+    def _noise_amounts(self, timestep: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Map one integer method timestep to both modality noise amounts."""
+        base = (timestep.reshape(-1)[:1].to(torch.float32) / _DMD_TIMESTEP_SCALE)
+        base = base.clamp(0.0, 1.0)
+        return (
+            shift_noise_amount(base, _VIDEO_SCHEDULER_SHIFT),
+            shift_noise_amount(base, _AUDIO_SCHEDULER_SHIFT),
+        )
+
+    # ------------------------------------------------------------------
+    # ModelBase overrides (packed convention)
+    # ------------------------------------------------------------------
+
+    def set_requires_negative_conditioning(self, requires: bool) -> None:
+        """Fail fast: H3 cannot encode negative prompts at training time."""
+        if requires:
+            raise ValueError("MiniMaxH3DMDModel has no negative-prompt encoder; set "
+                             "method.cfg_uncond={'text': 'zero'} for unconditional forwards")
+
+    def prepare_batch(
+        self,
+        raw_batch: dict[str, Any],
+        *,
+        generator: torch.Generator,
+        latents_source: Literal["data", "zeros"] = "data",
+    ) -> TrainingBatch:
+        """Prepare the T2VA batch, then expose clean latents in packed form."""
+        batch = super().prepare_batch(
+            raw_batch,
+            generator=generator,
+            latents_source=latents_source,
+        )
+        # DMD2 draws its own noise and timesteps per forward; only the packed
+        # clean latents matter here. The fine-tuning noisy fields are
+        # refreshed by predict_noise on every call.
+        batch.latents = self.pack_latents(batch.latents, batch.audio_latents)
+        return batch
+
+    def add_noise(
+        self,
+        clean_latents: torch.Tensor,
+        noise: torch.Tensor,
+        timestep: torch.Tensor,
+    ) -> torch.Tensor:
+        """Noise packed latents at one shared timestep, shifted per modality."""
+        sigma_video, sigma_audio = self._noise_amounts(timestep)
+        clean_video, clean_audio = self.unpack_latents(clean_latents)
+        noise_video, noise_audio = self.unpack_latents(noise)
+        return self.pack_latents(
+            self._mix(clean_video, noise_video, sigma_video),
+            self._mix(clean_audio, noise_audio, sigma_audio),
+        )
+
+    @staticmethod
+    def _mix(
+        clean: torch.Tensor,
+        noise: torch.Tensor,
+        sigma: torch.Tensor,
+    ) -> torch.Tensor:
+        sigma = sigma.to(device=clean.device, dtype=clean.dtype)
+        return (1.0 - sigma) * clean + sigma * noise
+
+    def predict_noise(
+        self,
+        noisy_latents: torch.Tensor,
+        timestep: torch.Tensor,
+        batch: TrainingBatch,
+        *,
+        conditional: bool,
+        cfg_uncond: dict[str, Any] | None = None,
+        attn_kind: Literal["dense", "vsa"] = "dense",
+    ) -> torch.Tensor:
+        """Run one packed joint forward at an explicit method timestep.
+
+        Both modality clean-time fields on ``batch`` are rewritten from
+        ``timestep`` so the packed-row timestep plan and the backward
+        forward-context stay coherent with this call.
+        """
+        sigma_video, sigma_audio = self._noise_amounts(timestep)
+        noisy_video, noisy_audio = self.unpack_latents(noisy_latents)
+        batch.timesteps = (1.0 - sigma_video).to(noisy_latents.device)
+        batch.audio_timesteps = (1.0 - sigma_audio).to(noisy_latents.device)
+        batch.audio_noisy_model_input = noisy_audio
+        video_pred, audio_pred = super().predict_noise(
+            noisy_video,
+            timestep,
+            batch,
+            conditional=conditional,
+            cfg_uncond=cfg_uncond,
+            attn_kind=attn_kind,
+        )
+        return self.pack_latents(video_pred, audio_pred)
+
+    def predict_x0(
+        self,
+        noisy_latents: torch.Tensor,
+        timestep: torch.Tensor,
+        batch: TrainingBatch,
+        *,
+        conditional: bool,
+        cfg_uncond: dict[str, Any] | None = None,
+        attn_kind: Literal["dense", "vsa"] = "dense",
+    ) -> torch.Tensor:
+        """Convert packed noise-minus-clean predictions to packed clean latents."""
+        pred_noise = self.predict_noise(
+            noisy_latents,
+            timestep,
+            batch,
+            conditional=conditional,
+            cfg_uncond=cfg_uncond,
+            attn_kind=attn_kind,
+        )
+        sigma_video, sigma_audio = self._noise_amounts(timestep)
+        noisy_video, noisy_audio = self.unpack_latents(noisy_latents)
+        pred_video, pred_audio = self.unpack_latents(pred_noise)
+        return self.pack_latents(
+            self._to_x0(noisy_video, pred_video, sigma_video),
+            self._to_x0(noisy_audio, pred_audio, sigma_audio),
+        )
+
+    @staticmethod
+    def _to_x0(
+        noisy: torch.Tensor,
+        pred_noise: torch.Tensor,
+        sigma: torch.Tensor,
+    ) -> torch.Tensor:
+        # noisy = (1 - sigma) * clean + sigma * noise and pred approximates
+        # noise - clean, so clean = noisy - sigma * pred.
+        sigma = sigma.to(device=noisy.device, dtype=noisy.dtype)
+        return noisy - sigma * pred_noise
+
+
+__all__ = ["MiniMaxH3DMDModel"]