veomni-new-model

name: veomni-new-model description: "Use this skill when adding support for a new model to VeOmni. Covers the full lifecycle: analyzing the HuggingFace model, creating model patches, defining parallel plans, writing configs, integrating with the trainer, and testing. Trigger: 'add model', 'support new model', 'integrate ', 'new model support'."

Before You Start: Create Todos

Use TodoWrite to track all phases:

Phase 1: Analyze HF model             -> in_progress
Phase 2: Create model patch            -> pending
Phase 3: Define parallel plan          -> pending
Phase 4: Write training config         -> pending
Phase 5: Integrate with trainer        -> pending
Phase 6: Test                          -> pending

Phase 1: Analyze HuggingFace Model

1. Identify the model on HuggingFace. Read its config.json, modeling_*.py, and any processor configs.

2. Determine model category:

   • Text-only LLM -> veomni/models/transformers/<model_name>/
   • Vision-Language -> veomni/models/transformers/<model_name>/ + veomni/data/multimodal/
   • MoE model -> additional veomni/distributed/moe/ integration
   • Diffusion model -> veomni/models/diffusers/<model_name>/
   • Omni model -> veomni/models/seed_omni/

3. Check existing similar models: Find the closest existing model in veomni/models/transformers/ and use it as a reference. E.g., if adding a new Qwen variant, reference qwen3/ or qwen3_vl/.

4. Identify required patches: VeOmni uses a patchgen system (veomni/patchgen/) to auto-generate model patches from HuggingFace models. Check if a patch spec already exists or if one needs to be created.

Phase 2: Create Model Patch

1. Create the model directory: veomni/models/transformers/<model_name>/

2. Required files:

   • __init__.py — model registration (MODELING_REGISTRY / MODEL_CONFIG_REGISTRY / MODEL_PROCESSOR_REGISTRY)
   • <model_name>_gpu_patch_gen_config.py — declarative patchgen config (replace_class / override_method / replace_function / modify_init / add_post_import_block / drop_import_names) defining all VeOmni patches against the upstream HF modeling
   • <model_name>_npu_patch_gen_config.py — NPU patchgen config (often just imports the GPU config and applies NPU-specific overrides via name_map)
   • parallel_plan.py — FSDP / TP / EP sharding plan
   • generated/patched_modeling_<model_name>_{gpu,npu}.py — patchgen output (do NOT edit manually)

3. Patch patterns — follow existing models:

   • Sequence parallel: declare an OpSlot for attention/loss and override forward via patchgen
   • MoE: stack per-expert weights (gate_up_proj [E, 2*I, H] / down_proj [E, H, I]) and add a veomni_moe_experts_forward OpSlot
   • Cross-entropy: add a veomni_causal_lm_loss OpSlot and return CausalLMOutputWithLogProbs
   • Register the model class in the model package __init__.py (no entry in veomni/models/auto.py is needed for transformers models — registration happens via the per-model MODELING_REGISTRY decorators)

4. Run patchgen: make patchgen regenerates every generated/patched_modeling_*.py from the matching *_patch_gen_config.py.

Phase 3: Define Parallel Plan

1. Create parallel_plan.py in the model directory.

2. Define FSDP/FSDP2 sharding strategy:

   • Which layers to wrap (typically transformer blocks)
   • Activation checkpointing granularity
   • Parameter dtype policies

3. If the model is MoE, define expert parallelism plan in addition to FSDP.

4. Reference existing parallel plans for guidance (e.g., veomni/models/transformers/qwen3/parallel_plan.py).

Phase 4: Write Training Config

1. Model config: Create configs/model_configs/<model_family>/<ModelName>.json matching HuggingFace format.

2. Training config: Create YAML in the appropriate directory:

   • Text: configs/text/<model_name>.yaml
   • Multimodal: configs/multimodal/<model_name>/<model_name>.yaml
   • DiT: configs/dit/<model_name>.yaml

3. Config must include: model path, data config, optimizer settings, parallelism config, checkpoint settings.

4. Verify against existing configs — match the structure of similar model configs.

Phase 5: Integrate with Trainer

1. Verify the model works with the appropriate trainer:

   • Text -> TextTrainer (veomni/trainer/text_trainer.py)
   • VLM -> VLMTrainer (veomni/trainer/vlm_trainer.py)
   • DiT -> DitTrainer (veomni/trainer/dit_trainer.py)

2. If the model needs custom data preprocessing:

   • Add transform in veomni/data/data_transform.py or veomni/data/multimodal/
   • Register the transform for the model

3. If the model needs custom collator logic:

   • Extend veomni/data/data_collator.py

4. VLM only — multimodal metadata precompute: to keep the ViT forward free of host-device CUDA syncs, derive ViT cu_seqlens / max_seqlen in the collator rather than the forward. Follow the checklist in .agents/knowledge/multimodal_metadata.md ("Adding the hook to a new model"): a collate_multimodal_metadata patchgen helper + a get_metadata_collate_func override, the per-modality vit_metadata sub-dict threaded through Model.forward → ViT.forward (with a runtime fallback), and the model added to _MM_METADATA_WIRED_CASES in the sync gate test.

Phase 6: Test

1. Create toy config: Add tests/toy_config/<model_name>_toy/config.json with minimal parameters for fast testing.

2. Unit tests: Add tests in tests/models/ to verify:

   • Model loads correctly via veomni.models.auto
   • Forward pass produces correct output shape
   • Model patch applies without errors

3. E2e tests (if feasible): Test a short training run using the toy config.

4. Run make quality and pytest tests/models/.

5. Update documentation:

   • Add usage example to docs/ (training command, config reference).
   • Update .agents/knowledge/architecture.md if the model adds a new module or trainer path.
   • Update supported models table in project README.md if applicable.

Common Pitfalls

• Model registry: Registration must happen at import time in __init__.py. If the model's AutoConfig type is not registered, build_foundation_model() will fail.
• Generated files: Never edit files in generated/ directories — they are overwritten by patchgen. Edit the matching <model>_{gpu,npu}_patch_gen_config.py and re-run make patchgen instead.
• Tokenizer compatibility: Some models require specific tokenizer versions or custom chat templates — verify in veomni/data/chat_template.py.
• Transformers version: All modeling targets transformers==5.9.0 (pinned by the transformers-stable default dependency group). Models register through the patchgen-generated path under generated/; do not introduce legacy modeling_<m>.py files or apply_veomni_<m>_patch() helpers.