These speed-ups for the MiniMax-H3 pipelines keep the output bit-identical. Each
one has a switch that is off by default:
- FASTVIDEO_MINIMAX_H3_EXACT_KERNELS: Triton RoPE, AdaLN modulation, gated
residual and SwiGLU kernels that round to bf16 at the same points as the
eager ops.
- FASTVIDEO_H3_VSA_HEADS_FIRST_TILE: VSA-H3 tiles are written directly in the
heads-first layout of the kernel.
- FASTVIDEO_H3_VAE_TILE_PARALLEL: VAE spatial tiles are split across the
sequence-parallel ranks.
- FASTVIDEO_H3_REF2VA_MEMO_ENTRIES: repeat reference encodes are reused.
The example basic_fasth3_omniref_pdd.py --lossless-accel turns them all on.
On GB200 with 4 GPUs, MiniMax-H3 text to video with audio (124 frames,
1344x768) gives bit-identical frames and audio with the first three switches
on and off.
The HunyuanVideo and HunyuanVideo 1.5 transformers failed on the first forward
pass when the batch had more than one sample. The modulation inputs had the
shape [B, C], but the fused ops expect [B, 1, C], so they could not broadcast
over the sequence. The models now add the sequence axis, as the refiner and the
final layer already do. Results for batch size 1 do not change.
Ray sets CUDA_VISIBLE_DEVICES of each GPU actor to the GPU of that actor. The
import of fastvideo initializes CUDA, so the executor cannot change the device
list later. On a node with more than one GPU, each worker after the first worker
failed with "invalid device ordinal".
On NVIDIA GPUs, each worker actor now keeps the device list of its raylet
(RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1). Each worker uses the position of
its GPU in that list as its local rank. The safetensors loader now reads each
file on node-group rank 0, which is the rank that sends the broadcast.
Adds opt-in regional torch.compile to the YAML-driven modular Wan trainer (training.model.enable_torch_compile / torch_compile_kwargs) with FA4 and Wan modulation forward/backward as opaque custom ops, compiled repeated DiT blocks after FSDP setup, and activation checkpointing applied before FSDP for trainable Wan transformers.
Merged with main's #1716 (WanModel resolves the checkpointing type in __init__; the PR's pre-FSDP wrap uses it and the post-load wrap is gone) and #1757 (pre_fsdp_model_transform runs before this PR's pre_fsdp_transform). Maintainer review (Swarm order #776): AdaLN skip names are canonicalized through checkpoint-wrapper prefixes, test_inference_regional_compile.py joins the unit lane, the Wan modulation custom-op test moved to the transformer lane, regional_compile is classified in the schema inventory, and a 1/2-GPU training smoke covers fully_shard(CheckpointWrapper) in eager and regional-compile modes. Validated on GB200: the FA4 CuTe forward/backward parity and fullgraph-backward tests and all four smoke cases pass.
Also exempts MMAudio recipes, whose student builds its pipeline config at init, from the fine-tuning recipe construction check that #1648 left failing on main.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Adds the Cosmos Predict 2.5 distilled text-to-world sampler (sCM, per-frame timesteps) and the DFD video-to-world pipeline, with their schedulers, registry entries, presets and examples.
Maintainer review fixes (Swarm orders #718 and #748): the DiT receives one shared fps value (its RoPE table is batch-independent, so per-sample vectors crashed batch > 1; differing values now raise), non-finite rollouts raise FloatingPointError instead of being zeroed by nan_to_num, the DFD detector matches "dfd" only in the last path component, the distilled example defaults to 24 fps, and the distilled rollout keeps its state in FP64 through the scheduler step as the official driver does.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Retargeted onto fastvideo/models/wan/causal_transformer.py, where main moved the causal Wan implementation (fastvideo/models/dits/causal_wanvideo.py is now a compatibility shim), with the guarded flex-attention trim prepared during the maintainer review (Swarm order #657).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Kyle Hu