SolitaryThinker
d81f1f166b
- MiniMaxH3Model: allow per-role attention backends (TORCH_SDPA/FLASH_ATTN/ VIDEO_SPARSE_ATTN_H3); predict_noise routes attn_metadata_vsa for attn_kind='vsa' (dense roles keep None). - MiniMaxH3DMDModel.prepare_batch builds real VSA-H3 metadata (packed text|cond|audio prefix segments via _h3_vsa_prefix_segments, sparsity from training.vsa.sparsity) when the role runs VIDEO_SPARSE_ATTN_H3. - VSA-H3 tile(): fresh buffer for grad-tracking forwards; shared-buffer reuse trips autograd's in-place version check at student backward. - build_tracker: degrade to DummyTracker when wandb is unimportable or has no credentials (modular stack already logs per-step loss dicts via the wandb tracker; no new callback needed). - dmd2_vsa0_overfit.yaml: student VSA-H3 @ sparsity 0.0, teacher/critic FLASH_ATTN, 3-step DMD schedule, wandb project h3-dmd2-vsa. - preprocess_minimax_h3_overfit: --video/--prompt/--output-dir/--model-path CLI; overfit_vsa0.sh: preprocess + 4-GPU torchrun with FASTVIDEO_FA4=1. - CPU tests: VSA metadata build/routing, per-role backend override, wandb guard + loss-dict logging, tile-under-grad regression.
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.