Aryan Kumar
212f558aab
Add the OmniRef (Ref2VA, VSA tile 128) and V2/V2.1 Consumer (T2VA, VSA tile 64) profiles of one quantization-aware distillation recipe: NVFP4 W4A4 q/k/v/out and FFN linears in all 50 blocks, the FP4 sparse-attention emulator, frozen VSA gates and norms, output plus layer-reconstruction regression against a frozen bf16 teacher, and best-by-score checkpoints with audio eligibility guards. Training code: the PDD-QAD method, the two H3 model plugins, the NVFP4 emulated linear, and the tile-64/128 Triton emulator with its PyTorch numeric reference. The VSA attention impl gains a fine attention override hook and lets tile 128 run grad-capable forwards through it. The trainer gains opt-in tagged checkpoints and early stop hooks for the QAD guards. Also add two runnable configs, the training guide, and CPU/GPU tests.
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.