Aryan Kumar
69a746c4ce
Add MiniMaxH3FlexRef2VAModel / MiniMaxH3FlexFL2VAModel for OmniRefPDDQADMethod: dense attention, Pika's shifted 1000..40 ladder, re-noise steps whose noise is a function of (row seed, forward, modality), NVFP4 linears only. Add an offline fp32 adapter merge script and QAD configs for both adapters. Let model profiles pick their attention backend and receive the prepared row in schedulers().
Training
Quick Start
Single-node
bash examples/train/run.sh <config.yaml> [--dotted.key value ...]
The script auto-detects available GPUs. Override with NUM_GPUS:
NUM_GPUS=4 bash examples/train/run.sh \
examples/train/configs/fine_tuning/wan/t2v.yaml
Multi-node (Slurm)
bash examples/train/run_slurm.sh <config.yaml> <num_nodes> [--dotted.key value ...]
bash examples/train/run_slurm.sh \
examples/train/configs/fine_tuning/wan/t2v.yaml 4 \
--training.distributed.hsdp_shard_dim 8 \
--training.distributed.hsdp_replicate_dim 4
Slurm environment variables:
| Variable | Default | Description |
|---|---|---|
PARTITION |
main |
Slurm partition |
NUM_GPUS |
8 |
GPUs per node |
CPUS_PER_TASK |
128 |
CPUs per task |
MEM |
1440G |
Memory per node |
EXCLUDE |
"" |
Nodes to exclude |
Overriding config values
Any config field can be overridden from the command line using dotted keys:
bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
--training.optimizer.learning_rate 1e-5 \
--training.loop.max_train_steps 1000 \
--training.checkpoint.output_dir outputs/my_experiment
Resuming from a checkpoint
bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
--training.checkpoint.resume_from_checkpoint outputs/my_experiment/checkpoint-500
W&B Logging
Training metrics and validation videos are logged to Weights & Biases. Set your API key before launching:
export WANDB_API_KEY=your_key_here
To disable W&B (e.g. for local debugging):
export WANDB_MODE=offline
The project name and run name are set in the config under training.tracker:
training:
tracker:
project_name: my_project
run_name: my_run
Directory Layout
examples/train/
├── configs/ # Single-step training configs (by method and model)
├── scenario/ # Multi-step end-to-end training pipelines
├── run.sh # Single-node launcher
└── run_slurm.sh # Multi-node Slurm launcher
See configs/README.md and scenario/README.md for details.
The featured QAD Wan2.1 MixKit scenario runs Attn-QAT supervised fine-tuning, checkpoint export, and three-step DMD2. See the Attn-QAT training guide.