add-pipeline
GitHub用于在verl-omni中集成新模型或算法的流水线路由技能，指导用户分类任务并遵循官方指南完成适配。
Trigger Scenarios
Install
npx skills add verl-project/verl-omni --skill add-pipeline -g -y
SKILL.md
Frontmatter
{
"name": "add-pipeline",
"description": "Router for adding a diffusion or omni pipeline to verl-omni. Classifies the task (new architecture vs new algorithm; policy-gradient vs direct-preference) and points at the authoritative guide under docs\/contributing\/. Use when integrating a new model, or a new model+algorithm pair such as flow-GRPO \/ DPO \/ NFT \/ DanceGRPO."
}
Add a Pipeline
docs/contributing/ owns the steps and the final checklist for every case below.
This skill routes you to the right guide and adds only what those guides leave
out. Open the guide — do not work from a remembered procedure.
Step 0 — Classify, on two axes
Architecture and algorithm integration are orthogonal: one algorithm reaches any number of architectures through the adapter pair, and vice versa.
Adding an algorithm?
| Trains from | Family | Guide |
|---|---|---|
| reverse-trajectory logprob ratios + advantages (FlowGRPO, MixGRPO, DanceGRPO) | policy gradient | integrating_a_new_policy_gradient_algorithm_for_diffusion_model.md |
| final samples, rewards, or chosen/rejected pairs (offline DPO, DiffusionNFT) | direct preference | integrating_a_new_direct_preference_algorithm_for_diffusion_model.md |
Read ## Classify the Algorithm First at the top of the direct-preference guide
even if you land on the policy-gradient one: it also splits offline vs online and
maps each family to its trainer (PolicyGradientRayTrainer /
DirectPreferenceRayTrainer) and FSDP engine. That decides
algorithm.trainer_type and algorithm.sample_source, which nothing else in the
repo infers for you.
Adding an architecture?
| Model | Guide |
|---|---|
| diffusers text-to-image | integrating_a_diffusion_model.md — read first, the rest extend it |
| diffusers image-edit / I2I | integrating_an_i2i_diffusion_model.md |
a nn.Module that diffusers cannot load |
integrating_a_non_diffusers_model.md |
| multimodal autoregressive (omni) | integrating_an_omni_model.md |
| step execution on an adapter that already works | integrating_a_stepwise_continuous_batching_model.md |
Both at once? Architecture first — an algorithm can only be paired with an adapter that already exists.
Step 1 — Find the closest pair to copy
List the live registry rather than guessing at a template:
grep -rhoE '@[A-Za-z]+\.register\([^)]*\)' verl_omni/pipelines/*/[dv]*.py | sort -u
DiffusionModelBase rows are training adapters, VllmOmniPipelineBase rows are
rollout adapters; a pair with no rollout row reuses another package's pipeline.
Copy the closest package, then share rather than fork
(code-style).
Step 2 — Work the guide's checklist literally
Every guide but the omni one ends in a - [ ] checklist (the omni guide ends in its
own ## Common pitfalls instead). Work it item by item; between them the checklists
cover what is most often missed: the star-import in verl_omni/pipelines/__init__.py
(without it the adapter is invisible and nothing errors at import time), mirroring a
new pipeline config field in both diffusion_rollout.yaml and
diffusion_model.yaml, and wiring a smoke test into tests/gpu_smoke/.
Step 3 — When the run is wrong rather than broken
docs/contributing/common_pitfalls.md is symptom-first: fp32 latent/scheduler
precision loss, RoPE sequence-length mismatch, per-request vs per-GPU SDE seeding.
Check it before debugging a reward curve that trains but diverges from diffusers.
Step 4 — Test, then open the PR
Adapter-boundary CPU tests: run-cpu-tests. Title, trailers, duplicate-work checks: commit-and-pr.
Version History
- f92f3f0 Current 2026-08-16 07:00
